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Large Update
2024-03-28 21:51:42 +00:00 · 2024-03-28 21:51:11 +00:00 · 2024-03-28 21:49:33 +00:00 · 2024-03-28 21:38:03 +00:00 · 2024-03-28 21:37:52 +00:00 · 2024-03-28 21:37:45 +00:00
89 changed files with 12416 additions and 6739 deletions
@@ -0,0 +1 @@
 ko_fi: erew123
@@ -1,13 +1,14 @@
 ---
-name: Bug report/Support request
+name: Support request OR Bug report
 about: Create a report to help us improve
 title: ''
 labels: ''
 assignees: ''
 ---
 🔴 If you have installed AllTalk in a custom Python environment, I will only be able to provide limited assistance/support. AllTalk draws on a variety of scripts and libraries that are not written or managed by myself, and they may fail, error or give strange results in custom built python environments.
-Please generate a diagnostics report and upload the "diagnostics.log".
+🔴 Please generate a diagnostics report and upload the "diagnostics.log" as this helps me understand your configuration.
 https://github.com/erew123/alltalk_tts/tree/main?#-how-to-make-a-diagnostics-report-file
@@ -0,0 +1,67 @@
 name: Publish Docker image
 on:
  push:
    branches: [main]
  release:
    types: [published]
  workflow_dispatch:
 jobs:
  build_and_push:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        include:
          - dockerfile: "Dockerfile"
            platforms: "linux/amd64"
            tag_name: "cpu"
          - dockerfile: "nvidia.Dockerfile"
            platforms: "linux/amd64"
            tag_name: "cuda"
    steps:
      - name: Checkout code
        uses: actions/checkout@v4
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      - name: Lowercase Actor and Extract Repo Name
        run: |
          echo "ACTOR_LOWER=$(echo ${{ github.actor }} | tr '[:upper:]' '[:lower:]')" >> $GITHUB_ENV
          echo "REPO_NAME=$(echo ${{ github.repository }} | cut -d'/' -f 2)" >> $GITHUB_ENV
      - name: Login to GitHub Container Registry
        uses: docker/login-action@v3
        with:
          registry: ghcr.io
          username: ${{ env.ACTOR_LOWER }}
          password: ${{ secrets.GITHUB_TOKEN }}
      - name: Login to DockerHub
        uses: docker/login-action@v3
        with:
          username: ${{ vars.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_TOKEN }}
      - name: Build and push Docker image
        uses: docker/build-push-action@v5
        with:
          context: .
          file: ./${{ matrix.dockerfile }}
          platforms: ${{ matrix.platforms }}
          push: true
          tags: |
            ghcr.io/${{ env.ACTOR_LOWER }}/${{ env.REPO_NAME }}:${{ matrix.tag_name }}-${{ github.sha }}
            ghcr.io/${{ env.ACTOR_LOWER }}/${{ env.REPO_NAME }}:${{ matrix.tag_name }}
            ghcr.io/${{ env.ACTOR_LOWER }}/${{ env.REPO_NAME }}:latest
            ${{ vars.DOCKERHUB_USERNAME }}/${{ env.REPO_NAME }}:${{ matrix.tag_name }}-${{ github.sha }}
            ${{ vars.DOCKERHUB_USERNAME }}/${{ env.REPO_NAME }}:${{ matrix.tag_name }}
            ${{ vars.DOCKERHUB_USERNAME }}/${{ env.REPO_NAME }}:latest
      - name: Docker Hub Description
        uses: peter-evans/dockerhub-description@v3.4.2
        with:
          username: ${{ vars.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_TOKEN }}
          repository: ${{ vars.DOCKERHUB_USERNAME }}/${{ env.REPO_NAME }}
@@ -0,0 +1,9 @@
 {
    "files.associations": {
        "*.html": "jinja-html"
    },
    "emmet.includeLanguages": {
        "jinja-html": "html",
        "jinja2": "html"
    },
 }
@@ -1,285 +0,0 @@
 // Copyright (c) Microsoft Corporation.
 // SPDX-License-Identifier: Apache-2.0
 // DeepSpeed Team
 #include <ATen/cuda/CUDAContext.h>
 #include <torch/extension.h>
 #include <cassert>
 #include <vector>
 #include "quantization.h"
 template <typename T>
 at::Tensor ds_quantize(at::Tensor& vals, int groups, int bits)
 {
    auto t_size = vals.sizes();
    int size = 1;
    for (auto dim : t_size) size *= dim;
    if ((((size / groups) - 1) / 4096 + 1) <= 256) {
        launch_fake_quantize_kernel(
            (T*)vals.data_ptr(), size, groups, bits, at::cuda::getCurrentCUDAStream());
    }
    return vals;
 }
 template <typename T>
 at::Tensor ds_sr_quantize(at::Tensor& vals, int groups, int bits)
 {
    auto t_size = vals.sizes();
    int size = 1;
    for (auto dim : t_size) size *= dim;
    if (((size / groups) / 4 / 1024) <= 256) {
        launch_sr_fake_quantize_kernel(
            (T*)vals.data_ptr(), size, groups, bits, at::cuda::getCurrentCUDAStream());
    }
    return vals;
 }
 template <typename T>
 at::Tensor ds_quantize_asym(at::Tensor& vals, int groups, int bits)
 {
    auto t_size = vals.sizes();
    int size = 1;
    for (auto dim : t_size) size *= dim;
    if ((((size / groups) - 1) / 4096 + 1) <= 256) {
        launch_fake_quantize_kernel_asym(
            (T*)vals.data_ptr(), size, groups, bits, at::cuda::getCurrentCUDAStream());
    }
    return vals;
 }
 template <typename T>
 at::Tensor ds_sr_quantize_asym(at::Tensor& vals, int groups, int bits)
 {
    auto t_size = vals.sizes();
    int size = 1;
    for (auto dim : t_size) size *= dim;
    if (((size / groups) / 4 / 1024) <= 256) {
        launch_sr_fake_quantize_kernel_asym(
            (T*)vals.data_ptr(), size, groups, bits, at::cuda::getCurrentCUDAStream());
    }
    return vals;
 }
 std::vector<at::Tensor> quantize_kernel(at::Tensor& input_vals,
                                        int groups,
                                        int numBits,
                                        quantize::Type quantType)
 {
    auto dtype = at::kFloat;
    auto params_options = at::TensorOptions()
                              .dtype(dtype)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    const int param_elems = (quantize::requires_offset(quantType)) ? 2 : 1;
    auto params = torch::empty({groups, param_elems}, params_options);
    auto output_options = at::TensorOptions()
                              .dtype(at::kChar)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    auto output_sizes = input_vals.sizes().vec();
    output_sizes[output_sizes.size() - 1] /= numBits == 8 ? 1 : 2;
    auto output = torch::empty(output_sizes, output_options);
    const int elems_per_group = at::numel(input_vals) / groups;
    launch_quant((int8_t*)output.data_ptr(),
                 (float*)params.data_ptr(),
                 (__half*)input_vals.data_ptr(),
                 groups,
                 elems_per_group,
                 numBits,
                 quantType,
                 at::cuda::getCurrentCUDAStream());
    return {output, params};
 }
 template <typename T>
 at::Tensor dequantize(at::Tensor& quantized_data,
                      at::Tensor& params,
                      int groups,
                      int num_bits,
                      quantize::Type quant_type)
 {
    auto dtype = (std::is_same<T, float>::value) ? torch::kFloat32 : torch::kFloat16;
    auto output_options = at::TensorOptions()
                              .dtype(dtype)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    auto output_sizes = quantized_data.sizes().vec();
    output_sizes[output_sizes.size() - 1] *= num_bits == 8 ? 1 : 2;
    auto output = torch::empty(output_sizes, output_options);
    const int total_elems = at::numel(output);
    const int elems_per_group = total_elems / groups;
    launch_dequantize_kernel((T*)output.data_ptr(),
                             (const int8_t*)quantized_data.data_ptr(),
                             (const float*)params.data_ptr(),
                             quant_type,
                             num_bits,
                             elems_per_group,
                             total_elems,
                             at::cuda::getCurrentCUDAStream());
    return output;
 }
 at::Tensor dequantize_int4_to_half_experimental(at::Tensor& data_in,
                                                at::Tensor& scale_buffer,
                                                at::Tensor& min_val_buffer,
                                                int num_group,
                                                int group_size)
 {
    auto output_options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA);
    auto output = torch::empty({num_group, group_size}, output_options);
    launch_dequantize_int4_to_half_experimental((uint8_t*)data_in.data_ptr(),
                                                (half*)output.data_ptr(),
                                                (half*)scale_buffer.data_ptr(),
                                                (half*)min_val_buffer.data_ptr(),
                                                num_group,
                                                group_size,
                                                at::cuda::getCurrentCUDAStream());
    return output;
 }
 std::vector<at::Tensor> ds_swizzle_quant(at::Tensor& input_vals,
                                         int groups,
                                         int num_bits,
                                         quantize::Type quant_type,
                                         int pipeline_size,
                                         int nodes,
                                         int devices_per_node)
 {
    auto scales_options = at::TensorOptions()
                              .dtype(at::kFloat)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    const int scales_elems = (quantize::requires_offset(quant_type)) ? 2 : 1;
    auto scales = torch::empty({groups, scales_elems}, scales_options);
    auto output_options = at::TensorOptions()
                              .dtype(at::kChar)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    const int quantization_scalar = 8 / num_bits;
    const int compressed_vals = at::numel(input_vals) / quantization_scalar;
    auto output = torch::empty({compressed_vals}, output_options);
    const int elems_per_group = at::numel(input_vals) / groups;
    launch_swizzled_quant((int8_t*)output.data_ptr(),
                          (float*)scales.data_ptr(),
                          (__half*)input_vals.data_ptr(),
                          num_bits,
                          quant_type,
                          groups,
                          elems_per_group,
                          pipeline_size,
                          nodes,
                          devices_per_node,
                          at::cuda::getCurrentCUDAStream());
    return {output, scales};
 }
 std::vector<at::Tensor> quantized_reduction(at::Tensor& input_vals,
                                            at::Tensor& input_scales,
                                            int in_groups,
                                            int out_groups,
                                            int num_bits,
                                            quantize::Type quant_type,
                                            int devices_per_node)
 {
    auto scales_options = at::TensorOptions()
                              .dtype(at::kFloat)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    const int scales_elems = (quantize::requires_offset(quant_type)) ? 2 : 1;
    auto scales = torch::empty({out_groups, scales_elems}, scales_options);
    auto output_options = at::TensorOptions()
                              .dtype(at::kChar)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
     std::vector<int64_t> sz_vector(input_vals.sizes().begin(), input_vals.sizes().end());
     sz_vector[sz_vector.size() - 1] = sz_vector.back() / devices_per_node;  // num of GPU per nodes
     at::IntArrayRef sz(sz_vector);
     auto output = torch::empty(sz, output_options);
     const int elems_per_in_tensor = at::numel(input_vals) / devices_per_node;
     const int elems_per_in_group = elems_per_in_tensor / (in_groups / devices_per_node);
     const int elems_per_out_group = elems_per_in_tensor / out_groups;
 // old code:
 //    std::vector<long int> sz(input_vals.sizes().begin(), input_vals.sizes().end());
 //    sz[sz.size() - 1] = sz.back() / devices_per_node;  // num of GPU per nodes
 //    const int elems_per_in_tensor = at::numel(input_vals) / devices_per_node;
 //    auto output = torch::empty(sz, output_options);
 //    const int elems_per_in_group = elems_per_in_tensor / (in_groups / devices_per_node);
 //    const int elems_per_out_group = elems_per_in_tensor / out_groups;
    launch_dequant_reduce((int8_t*)output.data_ptr(),
                          (float*)scales.data_ptr(),
                          (const int8_t*)input_vals.data_ptr(),
                          (const float*)input_scales.data_ptr(),
                          devices_per_node,
                          num_bits,
                          quant_type,
                          out_groups,
                          elems_per_out_group,
                          elems_per_in_tensor,
                          in_groups / devices_per_node,
                          elems_per_in_group,
                          at::cuda::getCurrentCUDAStream());
    return {output, scales};
 }
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
 {
    m.def("ds_quantize_fp32", &ds_quantize<float>, "DeepSpeed Quantize with fp32 (CUDA)");
    m.def("ds_quantize_fp16", &ds_quantize<__half>, "DeepSpeed Quantize with fp16 (CUDA)");
    m.def("ds_sr_quantize_fp32", &ds_sr_quantize<float>, "DeepSpeed Quantize with fp32 (CUDA)");
    m.def("ds_sr_quantize_fp16", &ds_sr_quantize<__half>, "DeepSpeed Quantize with fp16 (CUDA)");
    m.def("ds_quantize_asym_fp32", &ds_quantize_asym<float>, "DeepSpeed Quantize with fp32 (CUDA)");
    m.def(
        "ds_quantize_asym_fp16", &ds_quantize_asym<__half>, "DeepSpeed Quantize with fp16 (CUDA)");
    m.def("ds_sr_quantize_asym_fp32",
          &ds_sr_quantize_asym<float>,
          "DeepSpeed Quantize with fp32 (CUDA)");
    m.def("ds_sr_quantize_asym_fp16",
          &ds_sr_quantize_asym<__half>,
          "DeepSpeed Quantize with fp16 (CUDA)");
    pybind11::enum_<quantize::Type>(m, "QuantizationType")
        .value("Symmetric", quantize::Type::Symmetric)
        .value("Asymmetric", quantize::Type::Asymmetric)
        .export_values();
    m.def("quantize", &quantize_kernel);
    m.def("dequantize", &dequantize<__half>);
    m.def("dequantize_fp32", &dequantize<float>);
    m.def("dequantize_int4_to_half_experimental",
          &dequantize_int4_to_half_experimental,
          "Dequantize int4 to half (experimental)");
    m.def("swizzle_quant", &ds_swizzle_quant);
    m.def("quantized_reduction", &quantized_reduction);
 }
@@ -1,160 +0,0 @@
 # Copyright (c) Microsoft Corporation.
 # SPDX-License-Identifier: Apache-2.0
 # DeepSpeed Team
 import os
 import torch
 import deepspeed
 import subprocess
 import argparse
 import psutil
 from .ops.op_builder.all_ops import ALL_OPS
 from .git_version_info import installed_ops, torch_info
 from deepspeed.accelerator import get_accelerator
 GREEN = '\033[92m'
 RED = '\033[91m'
 YELLOW = '\033[93m'
 END = '\033[0m'
 SUCCESS = f"{GREEN} [SUCCESS] {END}"
 OKAY = f"{GREEN}[OKAY]{END}"
 WARNING = f"{YELLOW}[WARNING]{END}"
 FAIL = f'{RED}[FAIL]{END}'
 INFO = '[INFO]'
 color_len = len(GREEN) + len(END)
 okay = f"{GREEN}[OKAY]{END}"
 warning = f"{YELLOW}[WARNING]{END}"
 def op_report(verbose=True):
    max_dots = 23
    max_dots2 = 11
    h = ["op name", "installed", "compatible"]
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
    print("DeepSpeed C++/CUDA extension op report")
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
    print("NOTE: Ops not installed will be just-in-time (JIT) compiled at\n"
          "      runtime if needed. Op compatibility means that your system\n"
          "      meet the required dependencies to JIT install the op.")
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
    print("JIT compiled ops requires ninja")
    ninja_status = OKAY if ninja_installed() else FAIL
    print('ninja', "." * (max_dots - 5), ninja_status)
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
    print(h[0], "." * (max_dots - len(h[0])), h[1], "." * (max_dots2 - len(h[1])), h[2])
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
    installed = f"{GREEN}[YES]{END}"
    no = f"{YELLOW}[NO]{END}"
    for op_name, builder in ALL_OPS.items():
        dots = "." * (max_dots - len(op_name))
        is_compatible = OKAY if builder.is_compatible(verbose) else no
        is_installed = installed if installed_ops.get(op_name, False) else no
        dots2 = '.' * ((len(h[1]) + (max_dots2 - len(h[1]))) - (len(is_installed) - color_len))
        print(op_name, dots, is_installed, dots2, is_compatible)
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
 def ninja_installed():
    try:
        import ninja  # noqa: F401 # type: ignore
    except ImportError:
        return False
    return True
 def nvcc_version():
    import torch.utils.cpp_extension
    cuda_home = torch.utils.cpp_extension.CUDA_HOME
    if cuda_home is None:
        return f"{RED} [FAIL] cannot find CUDA_HOME via torch.utils.cpp_extension.CUDA_HOME={torch.utils.cpp_extension.CUDA_HOME} {END}"
    try:
        output = subprocess.check_output([cuda_home + "/bin/nvcc", "-V"], universal_newlines=True)
    except FileNotFoundError:
        return f"{RED} [FAIL] nvcc missing {END}"
    output_split = output.split()
    release_idx = output_split.index("release")
    release = output_split[release_idx + 1].replace(',', '').split(".")
    return ".".join(release)
 def get_shm_size():
    try:
        temp_dir = os.getenv('TEMP') or os.getenv('TMP') or os.path.join(os.path.expanduser('~'), 'tmp')
        shm_stats = psutil.disk_usage(temp_dir)
        shm_size = shm_stats.total
        shm_hbytes = human_readable_size(shm_size)
        warn = []
        if shm_size < 512 * 1024**2:
            warn.append(
                f" {YELLOW} [WARNING] Shared memory size might be too small, consider increasing it. {END}"
            )
            # Add additional warnings specific to your use case if needed.
        return shm_hbytes, warn
    except Exception as e:
        return "UNKNOWN", [f"Error getting shared memory size: {e}"]
 def human_readable_size(size):
    units = ['B', 'KB', 'MB', 'GB', 'TB']
    i = 0
    while size >= 1024 and i < len(units) - 1:
        size /= 1024
        i += 1
    return f'{size:.2f} {units[i]}'
 def debug_report():
    max_dots = 33
    report = [("torch install path", torch.__path__), ("torch version", torch.__version__),
              ("deepspeed install path", deepspeed.__path__),
              ("deepspeed info", f"{deepspeed.__version__}, {deepspeed.__git_hash__}, {deepspeed.__git_branch__}")]
    if get_accelerator().device_name() == 'cuda':
        hip_version = getattr(torch.version, "hip", None)
        report.extend([("torch cuda version", torch.version.cuda), ("torch hip version", hip_version),
                       ("nvcc version", (None if hip_version else nvcc_version())),
                       ("deepspeed wheel compiled w.", f"torch {torch_info['version']}, " +
                        (f"hip {torch_info['hip_version']}" if hip_version else f"cuda {torch_info['cuda_version']}"))
                       ])
    else:
        report.extend([("deepspeed wheel compiled w.", f"torch {torch_info['version']} ")])
    report.append(("shared memory (/dev/shm) size", get_shm_size()))
    print("DeepSpeed general environment info:")
    for name, value in report:
        warns = []
        if isinstance(value, tuple):
            value, warns = value
        print(name, "." * (max_dots - len(name)), value)
        if warns:
            for warn in warns:
                print(warn)
 def parse_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('--hide_operator_status',
                        action='store_true',
                        help='Suppress display of installation and compatibility statuses of DeepSpeed operators. ')
    parser.add_argument('--hide_errors_and_warnings', action='store_true', help='Suppress warning and error messages.')
    args = parser.parse_args()
    return args
 def main(hide_operator_status=False, hide_errors_and_warnings=False):
    if not hide_operator_status:
        op_report(verbose=not hide_errors_and_warnings)
    debug_report()
 def cli_main():
    args = parse_arguments()
    main(hide_operator_status=args.hide_operator_status, hide_errors_and_warnings=args.hide_errors_and_warnings)
 if __name__ == "__main__":
    main()
@@ -1,299 +0,0 @@
 // Copyright (c) Microsoft Corporation.
 // SPDX-License-Identifier: Apache-2.0
 // DeepSpeed Team
 #include <ATen/cuda/CUDAContext.h>
 #include <torch/extension.h>
 #include <cassert>
 #include <vector>
 #include "quantization.h"
 template <typename T>
 at::Tensor ds_quantize(at::Tensor& vals, int groups, int bits)
 {
    auto t_size = vals.sizes();
    int size = 1;
    for (auto dim : t_size) size *= dim;
    if ((((size / groups) - 1) / 4096 + 1) <= 256) {
        launch_fake_quantize_kernel(
            (T*)vals.data_ptr(), size, groups, bits, at::cuda::getCurrentCUDAStream());
    }
    return vals;
 }
 template <typename T>
 at::Tensor ds_sr_quantize(at::Tensor& vals, int groups, int bits)
 {
    auto t_size = vals.sizes();
    int size = 1;
    for (auto dim : t_size) size *= dim;
    if (((size / groups) / 4 / 1024) <= 256) {
        launch_sr_fake_quantize_kernel(
            (T*)vals.data_ptr(), size, groups, bits, at::cuda::getCurrentCUDAStream());
    }
    return vals;
 }
 template <typename T>
 at::Tensor ds_quantize_asym(at::Tensor& vals, int groups, int bits)
 {
    auto t_size = vals.sizes();
    int size = 1;
    for (auto dim : t_size) size *= dim;
    if ((((size / groups) - 1) / 4096 + 1) <= 256) {
        launch_fake_quantize_kernel_asym(
            (T*)vals.data_ptr(), size, groups, bits, at::cuda::getCurrentCUDAStream());
    }
    return vals;
 }
 template <typename T>
 at::Tensor ds_sr_quantize_asym(at::Tensor& vals, int groups, int bits)
 {
    auto t_size = vals.sizes();
    int size = 1;
    for (auto dim : t_size) size *= dim;
    if (((size / groups) / 4 / 1024) <= 256) {
        launch_sr_fake_quantize_kernel_asym(
            (T*)vals.data_ptr(), size, groups, bits, at::cuda::getCurrentCUDAStream());
    }
    return vals;
 }
 std::vector<at::Tensor> quantize_kernel(at::Tensor& input_vals,
                                        int groups,
                                        int numBits,
                                        quantize::Type quantType)
 {
    auto dtype = at::kFloat;
    auto params_options = at::TensorOptions()
                              .dtype(dtype)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    const int param_elems = (quantize::requires_offset(quantType)) ? 2 : 1;
    auto params = torch::empty({groups, param_elems}, params_options);
    auto output_options = at::TensorOptions()
                              .dtype(at::kChar)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    auto output_sizes = input_vals.sizes().vec();
    output_sizes[output_sizes.size() - 1] /= numBits == 8 ? 1 : 2;
    auto output = torch::empty(output_sizes, output_options);
    const int elems_per_group = at::numel(input_vals) / groups;
    launch_quant((int8_t*)output.data_ptr(),
                 (float*)params.data_ptr(),
                 (__half*)input_vals.data_ptr(),
                 groups,
                 elems_per_group,
                 numBits,
                 quantType,
                 at::cuda::getCurrentCUDAStream());
    return {output, params};
 }
 template <typename T>
 at::Tensor dequantize(at::Tensor& quantized_data,
                      at::Tensor& params,
                      int groups,
                      int num_bits,
                      quantize::Type quant_type)
 {
    auto dtype = (std::is_same<T, float>::value) ? torch::kFloat32 : torch::kFloat16;
    auto output_options = at::TensorOptions()
                              .dtype(dtype)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    auto output_sizes = quantized_data.sizes().vec();
    output_sizes[output_sizes.size() - 1] *= num_bits == 8 ? 1 : 2;
    auto output = torch::empty(output_sizes, output_options);
    const int total_elems = at::numel(output);
    const int elems_per_group = total_elems / groups;
    launch_dequantize_kernel((T*)output.data_ptr(),
                             (const int8_t*)quantized_data.data_ptr(),
                             (const float*)params.data_ptr(),
                             quant_type,
                             num_bits,
                             elems_per_group,
                             total_elems,
                             at::cuda::getCurrentCUDAStream());
    return output;
 }
 at::Tensor dequantize_int4_to_half_experimental(at::Tensor& data_in,
                                                at::Tensor& scale_buffer,
                                                at::Tensor& min_val_buffer,
                                                int num_group,
                                                int group_size)
 {
    auto output_options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA);
    auto output = torch::empty({num_group, group_size}, output_options);
    launch_dequantize_int4_to_half_experimental((uint8_t*)data_in.data_ptr(),
                                                (half*)output.data_ptr(),
                                                (half*)scale_buffer.data_ptr(),
                                                (half*)min_val_buffer.data_ptr(),
                                                num_group,
                                                group_size,
                                                at::cuda::getCurrentCUDAStream());
    return output;
 }
 at::Tensor dequantize_int8_to_half_experimental(at::Tensor& data_in,
                                                at::Tensor& scale_buffer,
                                                at::Tensor& min_val_buffer,
                                                int num_group,
                                                int group_size)
 {
    auto output_options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA);
    auto output = torch::empty({num_group, group_size}, output_options);
    launch_dequantize_int8_to_half_experimental((uint8_t*)data_in.data_ptr(),
                                                (half*)output.data_ptr(),
                                                (half*)scale_buffer.data_ptr(),
                                                (half*)min_val_buffer.data_ptr(),
                                                num_group,
                                                group_size,
                                                at::cuda::getCurrentCUDAStream());
    return output;
 }
 std::vector<at::Tensor> ds_swizzle_quant(at::Tensor& input_vals,
                                         int groups,
                                         int num_bits,
                                         quantize::Type quant_type,
                                         int pipeline_size,
                                         int nodes,
                                         int devices_per_node)
 {
    auto scales_options = at::TensorOptions()
                              .dtype(at::kFloat)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    const int scales_elems = (quantize::requires_offset(quant_type)) ? 2 : 1;
    auto scales = torch::empty({groups, scales_elems}, scales_options);
    auto output_options = at::TensorOptions()
                              .dtype(at::kChar)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    const int quantization_scalar = 8 / num_bits;
    const int compressed_vals = at::numel(input_vals) / quantization_scalar;
    auto output = torch::empty({compressed_vals}, output_options);
    const int elems_per_group = at::numel(input_vals) / groups;
    launch_swizzled_quant((int8_t*)output.data_ptr(),
                          (float*)scales.data_ptr(),
                          (__half*)input_vals.data_ptr(),
                          num_bits,
                          quant_type,
                          groups,
                          elems_per_group,
                          pipeline_size,
                          nodes,
                          devices_per_node,
                          at::cuda::getCurrentCUDAStream());
    return {output, scales};
 }
 std::vector<at::Tensor> quantized_reduction(at::Tensor& input_vals,
                                            at::Tensor& input_scales,
                                            int in_groups,
                                            int out_groups,
                                            int num_bits,
                                            quantize::Type quant_type,
                                            int devices_per_node)
 {
    auto scales_options = at::TensorOptions()
                              .dtype(at::kFloat)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    const int scales_elems = (quantize::requires_offset(quant_type)) ? 2 : 1;
    auto scales = torch::empty({out_groups, scales_elems}, scales_options);
    auto output_options = at::TensorOptions()
                              .dtype(at::kChar)
                              .layout(at::kStrided)
                              .device(at::kCUDA)
                              .requires_grad(false);
    std::vector<int64_t> sz_vector(input_vals.sizes().begin(), input_vals.sizes().end());
    sz_vector[sz_vector.size() - 1] = sz_vector.back() / devices_per_node;  // num of GPU per nodes
    at::IntArrayRef sz(sz_vector);
    auto output = torch::empty(sz, output_options);
    const int elems_per_in_tensor = at::numel(input_vals) / devices_per_node;
    const int elems_per_in_group = elems_per_in_tensor / (in_groups / devices_per_node);
    const int elems_per_out_group = elems_per_in_tensor / out_groups;
    launch_dequant_reduce((int8_t*)output.data_ptr(),
                          (float*)scales.data_ptr(),
                          (const int8_t*)input_vals.data_ptr(),
                          (const float*)input_scales.data_ptr(),
                          devices_per_node,
                          num_bits,
                          quant_type,
                          out_groups,
                          elems_per_out_group,
                          elems_per_in_tensor,
                          in_groups / devices_per_node,
                          elems_per_in_group,
                          at::cuda::getCurrentCUDAStream());
    return {output, scales};
 }
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
 {
    m.def("ds_quantize_fp32", &ds_quantize<float>, "DeepSpeed Quantize with fp32 (CUDA)");
    m.def("ds_quantize_fp16", &ds_quantize<__half>, "DeepSpeed Quantize with fp16 (CUDA)");
    m.def("ds_sr_quantize_fp32", &ds_sr_quantize<float>, "DeepSpeed Quantize with fp32 (CUDA)");
    m.def("ds_sr_quantize_fp16", &ds_sr_quantize<__half>, "DeepSpeed Quantize with fp16 (CUDA)");
    m.def("ds_quantize_asym_fp32", &ds_quantize_asym<float>, "DeepSpeed Quantize with fp32 (CUDA)");
    m.def(
        "ds_quantize_asym_fp16", &ds_quantize_asym<__half>, "DeepSpeed Quantize with fp16 (CUDA)");
    m.def("ds_sr_quantize_asym_fp32",
          &ds_sr_quantize_asym<float>,
          "DeepSpeed Quantize with fp32 (CUDA)");
    m.def("ds_sr_quantize_asym_fp16",
          &ds_sr_quantize_asym<__half>,
          "DeepSpeed Quantize with fp16 (CUDA)");
    pybind11::enum_<quantize::Type>(m, "QuantizationType")
        .value("Symmetric", quantize::Type::Symmetric)
        .value("Asymmetric", quantize::Type::Asymmetric)
        .export_values();
    m.def("quantize", &quantize_kernel);
    m.def("dequantize", &dequantize<__half>);
    m.def("dequantize_fp32", &dequantize<float>);
    m.def("dequantize_int4_to_half_experimental",
          &dequantize_int4_to_half_experimental,
          "Dequantize int4 to half (experimental)");
    m.def("dequantize_int8_to_half_experimental",
          &dequantize_int8_to_half_experimental,
          "Dequantize int8 to half (experimental)");
    m.def("swizzle_quant", &ds_swizzle_quant);
    m.def("quantized_reduction", &quantized_reduction);
 }
@@ -1,161 +0,0 @@
 # Copyright (c) Microsoft Corporation.
 # SPDX-License-Identifier: Apache-2.0
 # DeepSpeed Team
 import os
 import torch
 import deepspeed
 import subprocess
 import argparse
 import psutil
 from .ops.op_builder.all_ops import ALL_OPS
 from .git_version_info import installed_ops, torch_info
 from deepspeed.accelerator import get_accelerator
 GREEN = '\033[92m'
 RED = '\033[91m'
 YELLOW = '\033[93m'
 END = '\033[0m'
 SUCCESS = f"{GREEN} [SUCCESS] {END}"
 OKAY = f"{GREEN}[OKAY]{END}"
 WARNING = f"{YELLOW}[WARNING]{END}"
 FAIL = f'{RED}[FAIL]{END}'
 INFO = '[INFO]'
 color_len = len(GREEN) + len(END)
 okay = f"{GREEN}[OKAY]{END}"
 warning = f"{YELLOW}[WARNING]{END}"
 def op_report(verbose=True):
    max_dots = 23
    max_dots2 = 11
    h = ["op name", "installed", "compatible"]
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
    print("DeepSpeed C++/CUDA extension op report")
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
    print("NOTE: Ops not installed will be just-in-time (JIT) compiled at\n"
          "      runtime if needed. Op compatibility means that your system\n"
          "      meet the required dependencies to JIT install the op.")
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
    print("JIT compiled ops requires ninja")
    ninja_status = OKAY if ninja_installed() else FAIL
    print('ninja', "." * (max_dots - 5), ninja_status)
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
    print(h[0], "." * (max_dots - len(h[0])), h[1], "." * (max_dots2 - len(h[1])), h[2])
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
    installed = f"{GREEN}[YES]{END}"
    no = f"{YELLOW}[NO]{END}"
    for op_name, builder in ALL_OPS.items():
        dots = "." * (max_dots - len(op_name))
        is_compatible = OKAY if builder.is_compatible(verbose) else no
        is_installed = installed if installed_ops.get(op_name, False) else no
        dots2 = '.' * ((len(h[1]) + (max_dots2 - len(h[1]))) - (len(is_installed) - color_len))
        print(op_name, dots, is_installed, dots2, is_compatible)
    print("-" * (max_dots + max_dots2 + len(h[0]) + len(h[1])))
 def ninja_installed():
    try:
        import ninja  # noqa: F401 # type: ignore
    except ImportError:
        return False
    return True
 def nvcc_version():
    import torch.utils.cpp_extension
    cuda_home = torch.utils.cpp_extension.CUDA_HOME
    if cuda_home is None:
        return f"{RED} [FAIL] cannot find CUDA_HOME via torch.utils.cpp_extension.CUDA_HOME={torch.utils.cpp_extension.CUDA_HOME} {END}"
    try:
        output = subprocess.check_output([cuda_home + "/bin/nvcc", "-V"], universal_newlines=True)
    except FileNotFoundError:
        return f"{RED} [FAIL] nvcc missing {END}"
    output_split = output.split()
    release_idx = output_split.index("release")
    release = output_split[release_idx + 1].replace(',', '').split(".")
    return ".".join(release)
 def get_shm_size():
    try:
        temp_dir = os.getenv('TEMP') or os.getenv('TMP') or os.path.join(os.path.expanduser('~'), 'tmp')
        shm_stats = psutil.disk_usage(temp_dir)
        shm_size = shm_stats.total
        shm_hbytes = human_readable_size(shm_size)
        warn = []
        if shm_size < 512 * 1024**2:
            warn.append(
                f" {YELLOW} [WARNING] Shared memory size might be too small, consider increasing it. {END}"
            )
            # Add additional warnings specific to your use case if needed.
        return shm_hbytes, warn
    except Exception as e:
        return "UNKNOWN", [f"Error getting shared memory size: {e}"]
 def human_readable_size(size):
    units = ['B', 'KB', 'MB', 'GB', 'TB']
    i = 0
    while size >= 1024 and i < len(units) - 1:
        size /= 1024
        i += 1
    return f'{size:.2f} {units[i]}'
 def debug_report():
    max_dots = 33
    report = [("torch install path", torch.__path__), ("torch version", torch.__version__),
              ("deepspeed install path", deepspeed.__path__),
              ("deepspeed info", f"{deepspeed.__version__}, {deepspeed.__git_hash__}, {deepspeed.__git_branch__}")]
    if get_accelerator().device_name() == 'cuda':
        hip_version = getattr(torch.version, "hip", None)
        report.extend([("torch cuda version", torch.version.cuda), ("torch hip version", hip_version),
                       ("nvcc version", (None if hip_version else nvcc_version())),
                       ("deepspeed wheel compiled w.", f"torch {torch_info['version']}, " +
                        (f"hip {torch_info['hip_version']}" if hip_version else f"cuda {torch_info['cuda_version']}"))
                       ])
    else:
        report.extend([("deepspeed wheel compiled w.", f"torch {torch_info['version']} ")])
    report.append(("shared memory (/dev/shm) size", get_shm_size()))
    print("DeepSpeed general environment info:")
    for name, value in report:
        warns = []
        if isinstance(value, tuple):
            value, warns = value
        print(name, "." * (max_dots - len(name)), value)
        if warns:
            for warn in warns:
                print(warn)
 def parse_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('--hide_operator_status',
                        action='store_true',
                        help='Suppress display of installation and compatibility statuses of DeepSpeed operators. ')
    parser.add_argument('--hide_errors_and_warnings', action='store_true', help='Suppress warning and error messages.')
    args = parser.parse_args()
    return args
 def main(hide_operator_status=False, hide_errors_and_warnings=False):
    if not hide_operator_status:
        op_report(verbose=not hide_errors_and_warnings)
    debug_report()
 def cli_main():
    args = parse_arguments()
    main(hide_operator_status=args.hide_operator_status, hide_errors_and_warnings=args.hide_errors_and_warnings)
 if __name__ == "__main__":
    main()
@@ -1,18 +0,0 @@
 These are the modified files you would need for a MANUAL build of DeepSpeed 11.1 or 11.2.
 They were created by a mix of instructions from erew123 and S95Sedan.
 - You still have to download the DeepSpeed v11.1 or v11.2 installation files from Microsoft to use these files.
 https://github.com/microsoft/DeepSpeed/releases/tag/v0.11.2
 - These files were provided by S95Sedan here: 
 https://github.com/S95Sedan/Deepspeed-Windows
 - They are related to the manual build of DeepSpeed for AllTalk, which is linked here: 
 https://github.com/erew123/alltalk_tts/tree/main?tab=readme-ov-file#-option-2---a-bit-more-complicated
@@ -0,0 +1,12 @@
 FROM ubuntu:22.04
 ENV HOST 0.0.0.0
 RUN apt-get update && apt-get upgrade -y \
    && apt-get install -y git build-essential portaudio19-dev \
    python3 python3-pip gcc wget \
    && ln -s /usr/bin/python3 /usr/bin/python
 WORKDIR /app
 COPY . .
 RUN pip install --no-cache-dir -r requirements_other.txt
 EXPOSE 7851
 RUN chmod +x launch.sh
 ENTRYPOINT ["sh", "-c", "./launch.sh"]
@@ -0,0 +1,638 @@
@echo off
 cd /D "%~dp0"
 set PATH=%PATH%;%SystemRoot%\system32
 :: Generate the ESC character
 for /F %%a in ('echo prompt $E ^| cmd') do set "ESC=%%a"
 :: Standard Colors
 set "BLACK=%ESC%[30m"
 set "RED=%ESC%[31m"
 set "GREEN=%ESC%[32m"
 set "YELLOW=%ESC%[33m"
 set "BLUE=%ESC%[34m"
 set "MAGENTA=%ESC%[35m"
 set "CYAN=%ESC%[36m"
 set "WHITE=%ESC%[37m"
 set "L_BLACK=%ESC%[90m"
 set "L_RED=%ESC%[91m"
 set "L_GREEN=%ESC%[92m"
 set "L_YELLOW=%ESC%[93m"
 set "L_BLUE=%ESC%[94m"
 set "L_MAGENTA=%ESC%[95m"
 set "L_CYAN=%ESC%[96m"
 set "L_WHITE=%ESC%[97m"
 set "RESET=%ESC%[0m"
@rem Check if curl is available
 curl --version >nul 2>&1
 if "%ERRORLEVEL%" NEQ "0" (
    echo curl is not available on this system. Please install curl then re-run the script https://curl.se/ 
 	echo or perform a manual installation of a Conda Python environment.
    goto end
 )
 :MainMenu
 cls
 echo.
 echo    %L_BLUE%ALLTALK WINDOWS SETUP UTILITY%RESET%
 echo.
 echo    INSTALLATION TYPE
 echo    1) I am using AllTalk as part of %L_GREEN%Text-generation-webui%RESET%
 echo    2) I am using AllTalk as a %L_GREEN%Standalone Application%RESET%
 echo.
 echo    9) %L_RED%Exit/Quit%RESET%
 echo.
 set /p UserOption="    Enter your choice: "
 if "%UserOption%"=="1" goto WebUIMenu
 if "%UserOption%"=="2" goto StandaloneMenu
 if "%UserOption%"=="9" goto End
 goto MainMenu
 :WebUIMenu
 cls
 echo.
 echo    %L_BLUE%TEXT-GENERATION-WEBUI SETUP%RESET%
 echo.
 echo    Please ensure you have started your Text-generation-webui Python 
 echo    environment. If you have NOT done this, please run %L_GREEN%cmd_windows.bat%RESET% 
 echo    in the %L_GREEN%text-generation-webui%RESET% folder and then re-run this script.
 echo.
 echo    BASE REQUIREMENTS
 echo    1) Apply/Re-Apply the requirements for %L_GREEN%Text-generation-webui%RESET%.
 echo.
 echo    OPTIONAL
 echo    2) Git Pull the latest AllTalk updates from Github
 echo.
 echo    DEEPSPEED FOR %L_YELLOW=%PyTorch 2.1.x%RESET%
 echo    4) Install DeepSpeed v11.2 for CUDA %L_GREEN%11.8%RESET% and Python-3.11.x and %L_YELLOW%PyTorch 2.1.x%RESET%.
 echo    5) Install DeepSpeed v11.2 for CUDA %L_GREEN%12.1%RESET% and Python-3.11.x and %L_YELLOW%PyTorch 2.1.x%RESET%.
 echo.
 echo    DEEPSPEED FOR %L_YELLOW=%PyTorch 2.2.x%RESET% (March 2024 builds of Text-gen-webui and later)
 echo    6) Install DeepSpeed v14.0 for CUDA %L_GREEN%12.1%RESET% and Python-3.11.x and %L_YELLOW%PyTorch 2.2.x%RESET%.
 echo    7) Install DeepSpeed v14.0 for CUDA %L_GREEN%11.8%RESET% and Python-3.11.x and %L_YELLOW%PyTorch 2.2.x%RESET%.
 echo.
 echo    U) Uninstall DeepSpeed.
 echo.
 echo.   OTHER
 echo    8) Generate a diagnostics file.
 echo.
 echo    9) %L_RED%Exit/Quit%RESET%
 echo.
 set /p WebUIOption="Enter your choice: "
 if "%WebUIOption%"=="1" goto InstallNvidiaTextGen
 if "%WebUIOption%"=="2" goto TGGitpull
 if "%WebUIOption%"=="4" goto InstallDeepSpeed118TextGen
 if "%WebUIOption%"=="5" goto InstallDeepSpeed121TextGen
 if "%WebUIOption%"=="6" goto InstallDeepSpeed121TextGenPytorch221
 if "%WebUIOption%"=="7" goto InstallDeepSpeed118TextGenPytorch221
 if "%WebUIOption%"=="U" goto UnInstallDeepSpeed
 if "%WebUIOption%"=="u" goto UnInstallDeepSpeed
 if "%WebUIOption%"=="8" goto GenerateDiagsTextGen
 if "%WebUIOption%"=="9" goto End
 goto WebUIMenu
 :StandaloneMenu
 cls
 echo.
 echo    %L_BLUE%ALLTALK STANDALONE APPLICATION SETUP%RESET%
 echo.
 echo    BASE REQUIREMENTS
 echo    1) Install AllTalk as a Standalone Application
 echo.
 echo    OPTIONAL
 echo    2) Git Pull the latest AllTalk updates from Github
 echo    3) Re-Apply/Update the requirements file
 echo    4) Delete AllTalk's custom Python environment
 echo    5) Purge the PIP cache
 echo.
 echo.   OTHER
 echo    8) Generate a diagnostics file
 echo.
 echo    9) %L_RED%Exit/Quit%RESET%
 echo.
 set /p StandaloneOption="Enter your choice: "
 if "%StandaloneOption%"=="1" goto InstallCustomStandalone
 if "%StandaloneOption%"=="2" goto STGitpull
 if "%StandaloneOption%"=="3" goto STReapplyrequirements
 if "%StandaloneOption%"=="4" goto STDeleteCustomStandalone
 if "%StandaloneOption%"=="5" goto STPurgepipcache
 if "%StandaloneOption%"=="8" goto GenerateDiagsStandalone
 if "%StandaloneOption%"=="9" goto End
 goto StandaloneMenu
 :InstallNvidiaTextGen
 pip install -r system\requirements\requirements_textgen.txt
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    There was an error installing the requirements.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto WebUIMenu
 )
 Echo.
 echo.
 Echo    Requirements installed successfully.
 Echo. 
 pause
 goto WebUIMenu
 :TGGitpull
 git pull
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    There was an error pulling from Github.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto WebUIMenu
 )
 Echo.
 echo     AllTalk Updated from Github. Please re-apply
 echo     the latest requirements file. Option 1
 Echo. 
 pause
 goto WebUIMenu
 :InstallDeepSpeed118TextGen
 echo Downloading DeepSpeed...
 curl -LO https://github.com/erew123/alltalk_tts/releases/download/deepspeed/deepspeed-0.11.2+cuda118-cp311-cp311-win_amd64.whl
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    Failed to download DeepSpeed wheel file.
    echo    Please check your internet connection or try again later.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto WebUIMenu
 )
 echo DeepSpeed wheel file downloaded successfully.
 echo Installing DeepSpeed...
 pip install deepspeed-0.11.2+cuda118-cp311-cp311-win_amd64.whl
 if %ERRORLEVEL% neq 0 (
    echo. 
    echo    Failed to install DeepSpeed.
    echo    Please check if the wheel file is compatible with your system.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto WebUIMenu
 )
 Echo.
 echo.
 Echo    DeepSpeed installed successfully.
 Echo. 
 del deepspeed-0.11.2+cuda118-cp311-cp311-win_amd64.whl
 pause
 goto WebUIMenu
 :InstallDeepSpeed121TextGen
 echo Downloading DeepSpeed...
 curl -LO https://github.com/erew123/alltalk_tts/releases/download/deepspeed/deepspeed-0.11.2+cuda121-cp311-cp311-win_amd64.whl
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    Failed to download DeepSpeed wheel file.
    echo    Please check your internet connection or try again later.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto WebUIMenu
 )
 echo    DeepSpeed wheel file downloaded successfully.
 echo    Installing DeepSpeed...
 pip install deepspeed-0.11.2+cuda121-cp311-cp311-win_amd64.whl
 if %ERRORLEVEL% neq 0 (
    echo. 
    echo    Failed to install DeepSpeed.
    echo    Please check if the wheel file is compatible with your system.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto WebUIMenu
 )
 Echo.
 echo.
 Echo    DeepSpeed installed successfully.
 Echo. 
 del deepspeed-0.11.2+cuda121-cp311-cp311-win_amd64.whl
 pause
 goto WebUIMenu
 :InstallDeepSpeed121TextGenPytorch221
 echo Downloading DeepSpeed...
 curl -LO https://github.com/erew123/alltalk_tts/releases/download/DeepSpeed-14.0/deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    Failed to download DeepSpeed wheel file.
    echo    Please check your internet connection or try again later.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto WebUIMenu
 )
 echo    DeepSpeed wheel file downloaded successfully.
 echo    Installing DeepSpeed...
 pip install deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
 if %ERRORLEVEL% neq 0 (
    echo. 
    echo    Failed to install DeepSpeed.
    echo    Please check if the wheel file is compatible with your system.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto WebUIMenu
 )
 Echo.
 echo.
 Echo    DeepSpeed installed successfully.
 Echo. 
 del deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
 pause
 goto WebUIMenu
 :InstallDeepSpeed118TextGenPytorch221
 echo Downloading DeepSpeed...
 curl -LO https://github.com/erew123/alltalk_tts/releases/download/DeepSpeed-14.0/deepspeed-0.14.0+cu118-cp311-cp311-win_amd64.whl
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    Failed to download DeepSpeed wheel file.
    echo    Please check your internet connection or try again later.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto WebUIMenu
 )
 echo    DeepSpeed wheel file downloaded successfully.
 echo    Installing DeepSpeed...
 pip install deepspeed-0.14.0+cu118-cp311-cp311-win_amd64.whl
 if %ERRORLEVEL% neq 0 (
    echo. 
    echo    Failed to install DeepSpeed.
    echo    Please check if the wheel file is compatible with your system.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto WebUIMenu
 )
 Echo.
 echo.
 Echo    DeepSpeed installed successfully.
 Echo. 
 del deepspeed-0.14.0+cu118-cp311-cp311-win_amd64.whl
 pause
 goto WebUIMenu
 :UnInstallDeepSpeed
 pip uninstall deepspeed
 if %ERRORLEVEL% neq 0 (
    echo. 
    echo    There was an error uninstalling DeepSpeed.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto WebUIMenu
 )
 echo.
 Echo    DeepSpeed uninstalled successfully.
 Echo. 
 pause
 goto WebUIMenu
 :GenerateDiagsTextGen
 Python diagnostics.py
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    There was an error running diagnostics. Have you correctly started your 
    echo    Text-generation-webui Python environment with %L_GREEN%cmd_windows.bat%RESET%?
    echo.
    pause
    goto WebUIMenu
 )
 Echo.
 echo.
 Echo    Diagnostics.log generated. Please scroll up to look over the log.
 Echo. 
 pause
 goto WebUIMenu
 :InstallCustomStandalone
 set PATH=%PATH%;%SystemRoot%\system32
@rem Check if curl is available
 curl --version >nul 2>&1
 if "%ERRORLEVEL%" NEQ "0" (
    echo curl is not available on this system. Please install curl then re-run the script https://curl.se/ 
 	echo or perform a manual installation of a Conda Python environment.
    goto end
 )
 echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. Please correct your folder names and re-try installation. && goto exit
@rem Check for special characters in installation path
 set "SPCHARMESSAGE=WARNING: Special characters were detected in the installation path! This can cause the installation to fail!"
 echo "%CD%"| findstr /R /C:"[!#\$%&()\*+,;<=>?@\[\]\^`{|}~]" >nul && (
    call :PrintBigMessage %SPCHARMESSAGE%
 )
 set SPCHARMESSAGE=
@rem fix failed install when installing to a separate drive
 set TMP=%cd%\alltalk_environment
 set TEMP=%cd%\alltalk_environment
@rem deactivate existing conda envs as needed to avoid conflicts
 (call conda deactivate && call conda deactivate && call conda deactivate) 2>nul
@rem config
 set INSTALL_DIR=%cd%\alltalk_environment
 set CONDA_ROOT_PREFIX=%cd%\alltalk_environment\conda
 set INSTALL_ENV_DIR=%cd%\alltalk_environment\env
 set MINICONDA_DOWNLOAD_URL=https://repo.anaconda.com/miniconda/Miniconda3-py310_23.3.1-0-Windows-x86_64.exe
 set conda_exists=F
@rem figure out whether git and conda need to be installed
 call "%CONDA_ROOT_PREFIX%\_conda.exe" --version >nul 2>&1
 if "%ERRORLEVEL%" EQU "0" (
    set conda_exists=T
    call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%"
    goto RunScript
 )
@rem download and install conda if not installed
 echo Downloading Miniconda from %MINICONDA_DOWNLOAD_URL% to %INSTALL_DIR%\miniconda_installer.exe
 mkdir "%INSTALL_DIR%"
 call curl -Lk "%MINICONDA_DOWNLOAD_URL%" > "%INSTALL_DIR%\miniconda_installer.exe" || ( echo. && echo Miniconda failed to download. && goto end )
 echo Installing Miniconda to %CONDA_ROOT_PREFIX%
 start /wait "" "%INSTALL_DIR%\miniconda_installer.exe" /InstallationType=JustMe /NoShortcuts=1 /AddToPath=0 /RegisterPython=0 /NoRegistry=1 /S /D=%CONDA_ROOT_PREFIX%
 echo Miniconda version:
 call "%CONDA_ROOT_PREFIX%\_conda.exe" --version || ( echo. && echo Miniconda not found. && goto end )
@rem create the installer env
 echo Packages to install: %PACKAGES_TO_INSTALL%
 call "%CONDA_ROOT_PREFIX%\_conda.exe" create --no-shortcuts -y -k --prefix "%INSTALL_ENV_DIR%" python=3.11 || ( echo. && echo Conda environment creation failed. && goto end )
@rem check if conda environment was actually created
 if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo Conda environment is empty. && goto end )
@rem activate installer env
 call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo Miniconda hook not found. && goto end )
 echo.
 echo     Downloading and installing PyTorch. This step can take a long time
 echo     depending on your internet connection and hard drive speed. Please
 echo     be patient.
 echo.
 pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121
 echo Installing other requirements.
 echo.
 pip install -r system\requirements\requirements_standalone.txt
 curl -LO https://github.com/erew123/alltalk_tts/releases/download/DeepSpeed-12.7/deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
 echo Installing DeepSpeed...
 pip install deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
 del deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
@rem Create start_environment.bat to run AllTalk environment
 echo @echo off > start_environment.bat
 echo cd /D "%~dp0" >> start_environment.bat
 echo set CONDA_ROOT_PREFIX=%cd%\alltalk_environment\conda >> start_environment.bat
 echo set INSTALL_ENV_DIR=%cd%\alltalk_environment\env >> start_environment.bat
 echo call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" >> start_environment.bat
@rem Create start_alltalk.bat to run AllTalk
 echo @echo off > start_alltalk.bat
 echo cd /D "%~dp0" >> start_alltalk.bat
 echo set CONDA_ROOT_PREFIX=%cd%\alltalk_environment\conda >> start_alltalk.bat
 echo set INSTALL_ENV_DIR=%cd%\alltalk_environment\env >> start_alltalk.bat
 echo call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" >> start_alltalk.bat
 echo call python script.py >> start_alltalk.bat
@rem Create start_finetune.bat to run AllTalk
 echo @echo off > start_finetune.bat
 echo cd /D "%~dp0" >> start_finetune.bat
 echo set CONDA_ROOT_PREFIX=%cd%\alltalk_environment\conda >> start_finetune.bat
 echo set INSTALL_ENV_DIR=%cd%\alltalk_environment\env >> start_finetune.bat
 echo call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" >> start_finetune.bat
 echo call python finetune.py >> start_finetune.bat
 Echo.
 Echo    Run %L_YELLOW%start_alltalk.bat%RESET% to start AllTalk.
 Echo    Run %L_YELLOW%start_finetune.bat%RESET% to start Finetuning.
 Echo    Run %L_YELLOW%start_environment.bat%RESET% to start the AllTalk Python environment.
 Echo.
 pause
 goto StandaloneMenu
 :STDeleteCustomStandalone
@rem Check if the alltalk_environment directory exists
 if not exist "%cd%\alltalk_environment\" (
    echo.
    echo    %L_GREEN%alltalk_environment%RESET% directory does not exist. No need to delete.
    echo.
    pause
    goto StandaloneMenu
 )
@rem Check if a Conda environment is active
 if not defined CONDA_PREFIX goto NoCondaEnvDeleteCustomStandalone
@rem Deactivate Conda environment if it's active
 Echo    Exiting the Conda Environment. Please run %L_GREEN%atsetup.bat%RESET% again and delete the environment.
 conda deactivate
 :NoCondaEnvDeleteCustomStandalone
 echo Deleting "alltalk_environment". Please wait.
 rd /s /q "alltalk_environment"
 del start_alltalk.bat
 del start_environment.bat
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    Failed to delete alltalk_environment folder.
    echo    Please make sure it is not in use and try again.
    echo.
    pause
    goto StandaloneMenu
 )
 echo.
 Echo.
 echo    Environment %L_GREEN%alltalk_environment%RESET% deleted. Please set up the environment again.
 Echo.
 pause
 goto StandaloneMenu
 :GenerateDiagsStandalone
 cd /D "%~dp0"
 set CONDA_ROOT_PREFIX=%cd%\alltalk_environment\conda
 set INSTALL_ENV_DIR=%cd%\alltalk_environment\env
@rem Check if the Conda environment exists
 if not exist "%INSTALL_ENV_DIR%\python.exe" (
    echo.
    echo    The Conda environment at "%INSTALL_ENV_DIR%" does not exist.
    echo    Please install the environment before proceeding.
    echo. 
    pause
    goto StandaloneMenu
 )
@rem Attempt to activate the Conda environment
 call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%"
 if errorlevel 1 (
    echo. 
    echo    Failed to activate the Conda environment.
    echo    Please check your installation and try again.
    echo.
    pause
    goto StandaloneMenu
 )
@rem Run diagnostics
 Python diagnostics.py
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    There was an error running diagnostics.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto StandaloneMenu
 )
 Echo.
 Echo.
 Echo    Diagnostics.log generated. Please scroll up to look over the log.
 Echo.
 pause
 goto StandaloneMenu
 :STReapplyrequirements
 cd /D "%~dp0"
 set CONDA_ROOT_PREFIX=%cd%\alltalk_environment\conda
 set INSTALL_ENV_DIR=%cd%\alltalk_environment\env
@rem Check if the Conda environment exists
 if not exist "%INSTALL_ENV_DIR%\python.exe" (
    echo.
    echo    The Conda environment at "%INSTALL_ENV_DIR%" does not exist.
    echo    Please install the environment before proceeding.
    echo. 
    pause
    goto StandaloneMenu
 )
@rem Attempt to activate the Conda environment
 call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%"
 if errorlevel 1 (
    echo. 
    echo    Failed to activate the Conda environment.
    echo    Please check your installation and try again.
    echo.
    pause
    goto StandaloneMenu
 )
@rem Run Reapply requirements
 echo.
 echo     Downloading and installing PyTorch. This step can take a long time
 echo     depending on your internet connection and hard drive speed. Please
 echo     be patient.
 echo.
 pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121
 echo Installing other requirements.
 echo.
 pip install -r system\requirements\requirements_standalone.txt
 curl -LO https://github.com/erew123/alltalk_tts/releases/download/DeepSpeed-12.7/deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
 echo Installing DeepSpeed...
 pip install deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
 del deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    There was an error.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto StandaloneMenu
 )
 Echo.
 Echo.
 Echo    Requirements have been re-applied/updated.
 Echo.
 pause
 goto StandaloneMenu
 :STPurgepipcache
 cd /D "%~dp0"
 set CONDA_ROOT_PREFIX=%cd%\alltalk_environment\conda
 set INSTALL_ENV_DIR=%cd%\alltalk_environment\env
@rem Check if the Conda environment exists
 if not exist "%INSTALL_ENV_DIR%\python.exe" (
    echo.
    echo    The Conda environment at "%INSTALL_ENV_DIR%" does not exist.
    echo    Please install the environment before proceeding.
    echo. 
    pause
    goto StandaloneMenu
 )
@rem Attempt to activate the Conda environment
 call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%"
 if errorlevel 1 (
    echo. 
    echo    Failed to activate the Conda environment.
    echo    Please check your installation and try again.
    echo.
    pause
    goto StandaloneMenu
 )
@rem Clear the PIP cache
 echo.
 echo     Purging the PIP cache of downloaded files.
 echo.
 pip cache purge
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    There was an error.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto StandaloneMenu
 )
 Echo.
 Echo    The PIP cache has been purged.
 Echo.
 pause
 goto StandaloneMenu
 :STGitpull
 cd /D "%~dp0"
 set CONDA_ROOT_PREFIX=%cd%\alltalk_environment\conda
 set INSTALL_ENV_DIR=%cd%\alltalk_environment\env
@rem Check if the Conda environment exists
 if not exist "%INSTALL_ENV_DIR%\python.exe" (
    echo.
    echo    The Conda environment at "%INSTALL_ENV_DIR%" does not exist.
    echo    Please install the environment before proceeding.
    echo. 
    pause
    goto StandaloneMenu
 )
@rem Attempt to activate the Conda environment
 call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%"
 if errorlevel 1 (
    echo. 
    echo    Failed to activate the Conda environment.
    echo    Please check your installation and try again.
    echo.
    pause
    goto StandaloneMenu
 )
@rem Pull from Github
 echo.
 echo     Pulling the latest updates. Please re-apply
 echo     the latest requirements file. Option 3
 echo.
 git pull
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    There was an error pulling from Github.
    echo    Press any key to return to the menu.
    echo.
    pause
    goto StandaloneMenu
 )
 Echo.
 echo     AllTalk Updated from Github. Please re-apply
 echo     the latest requirements file. Option 3
 Echo.
 pause
 goto StandaloneMenu
 :End
 echo Exiting AllTalk Setup Utility...
 exit /b
@@ -0,0 +1,486 @@
 #!/bin/bash
 # ANSI color codes
 RED='\033[1;31m'
 GREEN='\033[1;32m'
 YELLOW='\033[1;33m'
 BLUE='\033[1;34m'
 CYAN='\033[1;36m'
 MAGENTA='\033[1;35m'
 WHITE='\033[1;37m'
 L_RED='\033[1;91m'
 L_GREEN='\033[1;92m'
 L_YELLOW='\033[1;93m'
 L_BLUE='\033[1;94m'
 L_CYAN='\033[1;96m'
 L_MAGENTA='\033[1;95m'
 NC='\033[0m' # No Color
 # Navigate to the script's directory
 cd "$(dirname "$0")"
 # Function to check if curl is installed
 check_curl() {
    if ! command -v curl &> /dev/null; then
        echo "curl is not available on this system. Please install curl then re-run the script https://curl.se/"
        echo "or perform a manual installation of a Conda Python environment."
        exit 1
    fi
 }
 # Main Menu
 main_menu() {
    while true; do
        clear
        echo
        echo -e "    ${L_BLUE}ALLTALK LINUX SETUP UTILITY${NC}"
        echo
        echo "    INSTALLATION TYPE"
        echo -e "    1) I am using AllTalk as part of ${L_GREEN}Text-generation-webui${NC}"
        echo -e "    2) I am using AllTalk as a ${L_GREEN}Standalone Application${NC}"
        echo
        echo -e "    9)${L_RED} Exit/Quit${NC}"
        echo
        read -p "    Enter your choice: " user_option
        case $user_option in
            1) webui_menu ;;
            2) standalone_menu ;;
            9) exit 0 ;;
            *) echo "Invalid option"; sleep 2 ;;
        esac
    done
 }
 # Text-generation-webui Menu
 webui_menu() {
    while true; do
        clear
        echo
        echo -e "    ${L_BLUE}TEXT-GENERATION-WEBUI SETUP${NC}"
        echo
        echo -e "    Please ensure you have started your Text-generation-webui Python"
        echo -e "    environment. If you have NOT done this, please run ${L_GREEN}/cmd_linux.sh${NC}"
        echo -e "    in the ${L_GREEN}text-generation-webui${NC} folder and then re-run this script."
        echo
        echo "    BASE REQUIREMENTS"
        echo -e "    1) Apply/Re-Apply the requirements for an ${L_GREEN}Text-generation-webui${NC}"
        echo
        echo "    OPTIONAL"
        echo "    2) Git Pull the latest AllTalk updates from Github"
        echo
        echo "    DEEPSPEED"
        echo "    4) Install DeepSpeed."
        echo "    5) Uninstall DeepSpeed."
        echo
        echo "    OTHER"
        echo "    6) Generate a diagnostics file."
        echo
        echo -e "    9)${L_RED} Exit/Quit${NC}"
        echo
        read -p "    Enter your choice: " webui_option
        case $webui_option in
            1) install_nvidia_textgen ;;
            2) tg_gitpull ;;
            4) install_deepspeed ;;
            5) uninstall_deepspeed ;;
            6) generate_diagnostics_textgen ;;
            9) exit 0 ;;
            *) echo "Invalid option"; sleep 2 ;;
        esac
    done
 }
 install_nvidia_textgen() {
    local requirements_file="system/requirements/requirements_textgen.txt"
    echo "    Installing Requirements from $requirements_file..."
    if ! pip install -r "$requirements_file"; then
        echo
        echo "    There was an error pulling from Github."
        echo "    Please check the output for details."
        read -p "    Press any key to return to the menu. " -n 1
        echo
        return
    fi
    echo
    echo "    Requirements installed successfully."
    echo
    echo -e "    To install ${L_YELLOW}DeepSpeed${NC} on Linux, there are additional"
    echo -e "    steps required. Please see the Github or documentation on DeepSeed."
    echo
    read -p "    Press any key to continue. " -n 1
    echo
 }
 tg_gitpull() {
    echo
    if ! git pull; then
        echo
        echo "    There was an error installing the requirements."
        echo "    Please check the output for details."
        echo
        read -p "    Press any key to return to the menu. " -n 1
        echo
        return
    fi
    echo
    echo "    AllTalk Updated from Github. Please re-apply"
    echo "    the latest requirements file. (Option 1)"
    echo
    read -p "    Press any key to continue. " -n 1
    echo
 }
 # Function to install DeepSpeed
 install_deepspeed() {
    clear
    echo
    echo -e "    ${L_BLUE}DEEPSPEED INSTALLATION REQUIREMENTS${NC}"
    echo
    echo -e "    - NVIDIA CUDA Toolkit must be installed from your ${L_GREEN}Linux package manager${NC}"
    echo -e "      or ${L_GREEN}https://developer.nvidia.com/cuda-toolkit-archive${NC}"
    echo -e "    - The environment variable ${L_GREEN}CUDA_HOME${NC} must be set in your Python env"
    echo -e "      e.g. export CUDA_HOME=/usr/local/cuda."
    echo -e "    - ${L_GREEN}libaio-dev${NC} must be installed."
    echo
    echo -e "    See the following link for full instructions:"
    echo -e "    ${L_GREEN}https://github.com/erew123/alltalk_tts?#-deepspeed-installation-options${NC}"
    echo
    echo -e "    ${L_BLUE}Please ${L_RED}confirm${L_BLUE} that you have completed these steps before continuing" 
    echo -e "    with the DeepSpeed installation, or the install ${L_RED}will fail.${NC}"
    echo
    read -p "    Have you completed all the above steps? (y/n): " confirm
    if [ "$confirm" != "y" ]; then
        echo -e "    ${RED}DeepSpeed installation cannot proceed without completing the prerequisites.${NC}"
        return
    fi
    echo -e "\n    ${GREEN}Proceeding with DeepSpeed installation...${NC}"
    pip install deepspeed
    if [ $? -ne 0 ]; then
        echo -e "    ${RED}There was an error installing DeepSpeed.${NC}"
        return
    fi
    echo -e "    ${GREEN}DeepSpeed installed successfully.${NC}"
    read -p "    Press any key to continue. " -n 1
    echo
 }
 uninstall_deepspeed() {
    echo "Uninstalling DeepSpeed..."
    pip uninstall -y deepspeed
    if [ $? -ne 0 ]; then
        echo
        echo "    There was an error uninstalling DeepSpeed."
        echo
        echo "    Press any key to return to the menu."
        read -n 1
        return
    fi
    echo
    echo "    DeepSpeed uninstalled successfully."
    echo
    echo "    Press any key to continue."
    read -n 1
 }
 generate_diagnostics_textgen() {
    # Run diagnostics
    if ! python diagnostics.py; then
        echo
        echo "    There was an error running diagnostics."
        echo
        read -p "    Press any key to return to the menu. " -n 1
        echo
        return
    fi
    echo
    echo "    Diagnostics log file generated successfully."
    echo "    Please see diagnostics.log"
    echo
    read -p "    Press any key to continue. " -n 1
    echo
 }
 # Standalone Menu
 standalone_menu() {
    while true; do
        clear
        echo
        echo -e "    ${L_BLUE}ALLTALK STANDALONE APPLICATION SETUP${NC}"
        echo
        echo "    BASE REQUIREMENTS"
        echo "    1) Install AllTalk as a Standalone Application"
        echo
        echo "    OPTIONAL"
        echo "    2) Git Pull the latest AllTalk updates from Github"
        echo "    3) Re-Apply/Update the requirements file"
        echo "    4) Delete AllTalk's custom Python environment"
        echo "    5) Purge the PIP cache"
        echo
        echo "    DEEPSPEED"
        echo "    6) DeepSpeed Instructions/Install"
        echo
        echo "    OTHER"        
        echo "    8) Generate a diagnostics file"
        echo
        echo -e "    9)${L_RED} Exit/Quit${NC}"
        echo
        read -p "    Enter your choice: " standalone_option
        case $standalone_option in
            1) install_custom_standalone ;;
            2) gitpull_standalone ;;
            3) reapply_standalone ;;
            4) delete_custom_standalone ;;
            5) pippurge_standalone ;;
            6) install_deepspeed ;;
            8) generate_diagnostics_standalone ;;
            9) exit 0 ;;
            *) echo "Invalid option"; sleep 2 ;;
        esac
    done
 }
 install_custom_standalone() {
    cd "$(dirname "${BASH_SOURCE[0]}")"
    if [[ "$(pwd)" =~ " " ]]; then
        echo "This script relies on Miniconda which can not be silently installed under a path with spaces."
        exit
    fi
    # Deactivate existing conda envs as needed to avoid conflicts
    { conda deactivate && conda deactivate && conda deactivate; } 2> /dev/null
    OS_ARCH=$(uname -m)
    case "${OS_ARCH}" in
        x86_64*)    OS_ARCH="x86_64" ;;
        arm64* | aarch64*) OS_ARCH="aarch64" ;;
        *)          echo "Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64" && exit ;;
    esac
    # Config
    INSTALL_DIR="$(pwd)/alltalk_environment"
    CONDA_ROOT_PREFIX="${INSTALL_DIR}/conda"
    INSTALL_ENV_DIR="${INSTALL_DIR}/env"
    MINICONDA_DOWNLOAD_URL="https://repo.anaconda.com/miniconda/Miniconda3-py310_23.3.1-0-Linux-${OS_ARCH}.sh"
    if [ ! -x "${CONDA_ROOT_PREFIX}/bin/conda" ]; then
        echo "Downloading Miniconda from $MINICONDA_DOWNLOAD_URL to ${INSTALL_DIR}/miniconda_installer.sh"
        mkdir -p "${INSTALL_DIR}"
        curl -L "${MINICONDA_DOWNLOAD_URL}" -o "${INSTALL_DIR}/miniconda_installer.sh"
        chmod +x "${INSTALL_DIR}/miniconda_installer.sh"
        bash "${INSTALL_DIR}/miniconda_installer.sh" -b -p "${CONDA_ROOT_PREFIX}"
        echo "Miniconda installed."
    fi
    if [ ! -d "${INSTALL_ENV_DIR}" ]; then
        "${CONDA_ROOT_PREFIX}/bin/conda" create -y --prefix "${INSTALL_ENV_DIR}" python=3.11
        echo "Conda environment created at ${INSTALL_ENV_DIR}."
    fi
    # Activate the environment and install requirements
    source "${CONDA_ROOT_PREFIX}/etc/profile.d/conda.sh"
    conda activate "${INSTALL_ENV_DIR}"
    echo
    echo "    Downloading and installing PyTorch. This step can take a long time"
    echo "    depending on your internet connection and hard drive speed. Please"
    echo "    be patient."
    pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121
    echo
    echo "    Installing additional requirements."
    echo
    pip install -r system/requirements/requirements_standalone.txt
    # Create start_environment.sh to run AllTalk
    cat << EOF > start_environment.sh
 #!/bin/bash
 cd "$(dirname "${BASH_SOURCE[0]}")"
 if [[ "$(pwd)" =~ " " ]]; then echo This script relies on Miniconda which can not be silently installed under a path with spaces. && exit; fi
 # deactivate existing conda envs as needed to avoid conflicts
 { conda deactivate && conda deactivate && conda deactivate; } 2> /dev/null
 # config
 CONDA_ROOT_PREFIX="$(pwd)/alltalk_environment/conda"
 INSTALL_ENV_DIR="$(pwd)/alltalk_environment/env"
 # environment isolation
 export PYTHONNOUSERSITE=1
 unset PYTHONPATH
 unset PYTHONHOME
 export CUDA_PATH="$INSTALL_ENV_DIR"
 export CUDA_HOME="$CUDA_PATH"
 export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
 # activate env
 bash --init-file <(echo "source \"$CONDA_ROOT_PREFIX/etc/profile.d/conda.sh\" && conda activate \"$INSTALL_ENV_DIR\"")
 EOF
    # Create start_alltalk.sh to run AllTalk
    cat << EOF > start_alltalk.sh
 #!/bin/bash
 export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
 source "${CONDA_ROOT_PREFIX}/etc/profile.d/conda.sh"
 conda activate "${INSTALL_ENV_DIR}"
 python script.py
 EOF
    # Create start_finetune.sh to run AllTalk
    cat << EOF > start_finetune.sh
 #!/bin/bash
 export TRAINER_TELEMETRY=0
 export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
 source "${CONDA_ROOT_PREFIX}/etc/profile.d/conda.sh"
 conda activate "${INSTALL_ENV_DIR}"
 python finetune.py
 EOF
    chmod +x start_alltalk.sh
    chmod +x start_environment.sh
    chmod +x start_finetune.sh
    echo
    echo
    echo -e "    Run ${L_YELLOW}./start_alltalk.sh${NC} to start AllTalk."
    echo -e "    Run ${L_YELLOW}./start_finetune.sh${NC} to start Finetuning."
    echo -e "    Run ${L_YELLOW}./start_environment.sh${NC} to start the AllTalk Python environment."
    echo
    echo -e "    To install ${L_YELLOW}DeepSpeed${NC} on Linux, there are additional"
    echo -e "    steps required. Please see the Github or documentation on DeepSeed."
    echo
    read -p "    Press any key to continue. " -n 1
 }
 delete_custom_standalone() {
    local env_dir="$PWD/alltalk_environment"
    # Check if the alltalk_environment directory exists
    if [ ! -d "$env_dir" ]; then
        echo "    \"$env_dir\" directory does not exist. No need to delete."
        read -p "    Press any key to continue. " -n 1
        echo
        return
    fi
    # Check if a Conda environment is active and deactivate it
    if [ -n "$CONDA_PREFIX" ]; then
        echo "    Exiting the Conda environment. You may need to start ./atstart.sh again"
        conda deactivate
    fi
    echo "Deleting \"$env_dir\". Please wait."
    rm -rf "$env_dir"
    if [ -d "$env_dir" ]; then
        echo "    Failed to delete \"$env_dir\" folder."
        echo "    Please make sure it is not in use and try again."
    else
        echo "    Environment \"$env_dir\" deleted successfully."
    fi
    read -p "    Press any key to continue. " -n 1
    echo
 }
 generate_diagnostics_standalone() {
    local env_dir="$PWD/alltalk_environment"
    local conda_root_prefix="${env_dir}/conda"
    local install_env_dir="${env_dir}/env"
    if [ ! -d "${install_env_dir}" ]; then
        echo
        echo "    The Conda environment at '${install_env_dir}' does not exist."
        echo "    Please install the environment before proceeding."
        echo
        read -p "    Press any key to return to the menu. " -n 1
        echo
        return
    fi
    source "${conda_root_prefix}/etc/profile.d/conda.sh"
    conda activate "${install_env_dir}"
    if ! python diagnostics.py; then
        echo
        echo "    There was an error running diagnostics."
        echo
        read -p "    Press any key to return to the menu. " -n 1
        echo
        return
    fi
    echo
    echo "    Diagnostics completed successfully."
    read -p "    Press any key to continue. " -n 1
    echo
 }
 gitpull_standalone() {
    local env_dir="$PWD/alltalk_environment"
    local conda_root_prefix="${env_dir}/conda"
    local install_env_dir="${env_dir}/env"
    if [ ! -d "${install_env_dir}" ]; then
        echo
        echo "    The Conda environment at '${install_env_dir}' does not exist."
        echo "    Please install the environment before proceeding."
        echo
        read -p "    Press any key to return to the menu. " -n 1
        echo
        return
    fi
    source "${conda_root_prefix}/etc/profile.d/conda.sh"
    conda activate "${install_env_dir}"
    if ! git pull; then
        echo
        echo "    There was an error pulling from Github."
        echo
        read -p "    Press any key to return to the menu. " -n 1
        echo
        return
    fi
    echo
    echo "    AllTalk Updated from Github. Please re-apply."
    echo "    the latest requirements file. (Option 3)"
    echo
    read -p "    Press any key to continue. " -n 1
    echo
 }
 pippurge_standalone() {
    if ! pip cache purge; then
        echo
        echo "    There was an error."
        echo
        read -p "    Press any key to return to the menu. " -n 1
        echo
        return
    fi
    echo
    echo "    The PIP cache has been purged."
    echo
    read -p "    Press any key to continue. " -n 1
    echo
 }
 reapply_standalone() {
    local env_dir="$PWD/alltalk_environment"
    local conda_root_prefix="${env_dir}/conda"
    local install_env_dir="${env_dir}/env"
    if [ ! -d "${install_env_dir}" ]; then
        echo
        echo "    The Conda environment at '${install_env_dir}' does not exist."
        echo "    Please install the environment before proceeding."
        echo
        read -p "    Press any key to return to the menu. " -n 1
        echo
        return
    fi
    source "${conda_root_prefix}/etc/profile.d/conda.sh"
    conda activate "${install_env_dir}"
    echo
    echo "    Downloading and installing PyTorch. This step can take a long time"
    echo "    depending on your internet connection and hard drive speed. Please"
    echo "    be patient."
    pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121
    echo
    echo "    Installing additional requirements."
    echo
    pip install -r system/requirements/requirements_standalone.txt
    echo
    echo "    Requirements have been re-applied/updated."
    echo
    read -p "    Press any key to continue. " -n 1
    echo
 }
 # Start the main menu
 main_menu
@@ -1 +1,25 @@
-{"activate": true, "autoplay": true, "branding": "AllTalk ", "narrator_enabled": false, "deepspeed_activate": false, "delete_output_wavs": "Disabled", "ip_address": "127.0.0.1", "language": "English", "low_vram": false, "local_temperature": "0.7", "local_repetition_penalty": "10.0", "tts_model_loaded": true, "tts_model_name": "tts_models/multilingual/multi-dataset/xtts_v2", "narrator_voice": "female_01.wav", "output_folder_wav": "extensions/alltalk_tts/outputs/", "output_folder_wav_standalone": "outputs/", "port_number": "7851", "remove_trailing_dots": false, "show_text": true, "tts_method_api_local": false, "tts_method_api_tts": false, "tts_method_xtts_local": true, "voice": "female_01.wav"}
+{
    "activate": true,
    "autoplay": true,
    "branding": "AllTalk ",
    "narrator_enabled": false,
    "deepspeed_activate": false,
    "delete_output_wavs": "Disabled",
    "ip_address": "127.0.0.1",
    "language": "English",
    "low_vram": false,
    "local_temperature": "0.7",
    "local_repetition_penalty": "10.0",
    "tts_model_loaded": true,
    "tts_model_name": "tts_models/multilingual/multi-dataset/xtts_v2",
    "narrator_voice": "female_01.wav",
    "output_folder_wav": "extensions/alltalk_tts/outputs/",
    "output_folder_wav_standalone": "outputs/",
    "port_number": "7851",
    "remove_trailing_dots": false,
    "show_text": true,
    "tts_method_api_local": false,
    "tts_method_api_tts": false,
    "tts_method_xtts_local": true,
    "voice": "female_01.wav"
 }
@@ -8,12 +8,15 @@ try:
    import re
    import sys
    import glob
    import site
    import textwrap
    import torch
    import packaging.version
    import packaging.specifiers
    from packaging.specifiers import SpecifierSet
    from packaging.specifiers import SpecifierSet
    from packaging.version import parse as parse_version
    from pathlib import Path
 except ImportError as e:
    print(f"\033[91mError importing module: {e}\033[0m\n")
    print("\033[94mPlease ensure you started the Text-generation-webUI Python environment with either\033[0m")
@@ -21,6 +24,8 @@ except ImportError as e:
    print("\033[94mfrom the text-generation-webui directory, and then try running the diagnostics again.\033[0m")
    exit(1)
 this_dir = Path(__file__).parent
 try:
    import psutil
 except ImportError:
@@ -29,24 +34,30 @@ except ImportError:
    import psutil
 def get_requirements_file():
-    requirements_files = glob.glob("requirements*.txt")
+    this_dir = Path(__file__).parent  # Assuming 'this_dir' is defined as the script's directory
    requirements_dir = this_dir / 'system' / 'requirements'
    requirements_files = list(requirements_dir.glob('requirements*.txt'))  # Using pathlib for globbing
    if not requirements_files:
        print("\033[91mNo requirements files found.\033[0m")
        return None
    print("\033[94m\nSelect a requirements file to check against (or press Enter for default 'requirements.txt'):\033[0m\n")
-    for i, file in enumerate(requirements_files, start=1):
+    for i, file_path in enumerate(requirements_files, start=1):
-        print(f"{i}. {file}")
+        print(f"    {i}. {file_path.name}")  # Only print the filename
    choice = input("\nEnter the number of your choice: ")
    try:
        choice = int(choice) - 1
-        return requirements_files[choice]
+        return str(requirements_files[choice])  # Return the full path as a string
    except (ValueError, IndexError):
-        return "requirements.txt"
+        return str(requirements_dir / "requirements.txt")  # Return the default path as a string
 # Set up logging with filemode='w'
-logging.basicConfig(filename='diagnostics.log', filemode='w', level=logging.INFO)
+logging.basicConfig(filename='diagnostics.log',
                    filemode='w',
                    level=logging.INFO,
                    format='%(message)s')  # Custom format
 # Function to get GPU information using subprocess
 def get_gpu_info():
@@ -56,6 +67,22 @@ def get_gpu_info():
    except FileNotFoundError:
        return "NVIDIA GPU information not available"
 def get_cpu_info():
    cpu_info = {
        'physical_cores': psutil.cpu_count(logical=False),
        'total_cores': psutil.cpu_count(logical=True),
        'max_frequency': psutil.cpu_freq().max
    }
    return cpu_info
 def get_disk_info():
    disk_info = []
    partitions = psutil.disk_partitions()
    for p in partitions:
        usage = psutil.disk_usage(p.mountpoint)
        disk_info.append(f" Drive: {p.device} | Total: {usage.total / (1024 ** 3):.2f} GB | Used: {usage.used / (1024 ** 3):.2f} GB | Free: {usage.free / (1024 ** 3):.2f} GB | Type: {p.fstype}")
    return disk_info
 # Function to check if a port is in use
 def is_port_in_use(port):
    for conn in psutil.net_connections():
@@ -73,6 +100,38 @@ def satisfies_wildcard(installed_version, required_version):
        return True
    return False
 def test_cuda():
    cuda_available = torch.cuda.is_available()
    if cuda_available:
        try:
            # Attempt to create a tensor on GPU
            torch.tensor([1.0, 2.0]).cuda()
            return "Success - CUDA is available and working."
        except Exception as e:
            return f"Fail - CUDA is available but not working. Error: {e}"
    else:
        return "Fail - CUDA is not available."
 def find_files_in_path_with_wildcard(pattern):
    # Get the site-packages directory of the current Python environment
    site_packages_path = site.getsitepackages()
    found_paths = []
    # Adjust the sub-directory based on the operating system
    sub_directory = "nvidia/cublas"
    if platform.system() == "Linux":
        sub_directory = os.path.join(sub_directory, "lib")
    else:
        sub_directory = os.path.join(sub_directory, "bin")
    # Iterate over each site-packages directory (there can be more than one)
    for directory in site_packages_path:
        # Construct the search directory path
        search_directory = os.path.join(directory, sub_directory)
        # Use glob to find all files matching the pattern in this directory
        for file_path in glob.glob(os.path.join(search_directory, pattern)):
            if os.path.isfile(file_path):  # Ensure it's a file
                found_paths.append(file_path)
    return found_paths
 # Function to log and print system information
 def log_system_info():
    # System information
@@ -88,6 +147,9 @@ def log_system_info():
    # Torch version
    torch_version = torch.__version__
    cuda_test_result = test_cuda()
    cpu_info = get_cpu_info()
    disk_info = get_disk_info()
    # System RAM using psutil
    try:
@@ -123,6 +185,10 @@ def log_system_info():
    search_path = sys.path
    path_env = os.environ.get('PATH', 'N/A')
    # Check for cublas
    file_name = 'cublas64_11.*' if platform.system() == "Windows" else 'libcublas.so.11*'
    found_paths = find_files_in_path_with_wildcard(file_name)
    # Compare with requirements file
    requirements_file = get_requirements_file()
    if requirements_file:
@@ -146,12 +212,27 @@ def log_system_info():
            logging.info(f"NOTE {requirements_file} not found. Skipping version checks.")
    # Log and print information
    logging.info(f"OPERATING SYSTEM:")
    logging.info(f" OS Version: {os_version}")
    logging.info(f" Note: Windows 11 will list as build is 10.x.22xxx")
-    logging.info(f"Torch Version: {torch_version}")
+    logging.info(f"\nHARDWARE ENVIRONMENT:")
    logging.info(f" CPU: Physical Cores: {cpu_info['physical_cores']}, Total Cores: {cpu_info['total_cores']}, Max Frequency: {cpu_info['max_frequency']} MHz")
    logging.info(f" System RAM: {system_ram}")
-    logging.info(f"CUDA_HOME: {cuda_home}")
+    logging.info(f"\nGPU INFORMATION:{gpu_info}")
    logging.info("DISK INFORMATION:")
    for disk in disk_info:
        logging.info(disk)
    logging.info("\nNETWORK PORT:")
    logging.info(f" Port Status: {port_status}")
    logging.info("\nCUDA:")
    logging.info(f" CUDA Working: {cuda_test_result}")
    logging.info(f" CUDA_HOME: {cuda_home}")
    if found_paths:
        logging.info(f" Cublas64_11 Path: {', '.join(found_paths)}")
    else:
        logging.info(f" Cublas64_11 Path: Not found in any search path directories.")
    logging.info("\nPYTHON & PYTORCH:")
    logging.info(f" Torch Version: {torch_version}")
    logging.info(f" Python Version: {platform.python_version()}")
    logging.info(f" Python Version Info: {python_version_info}")
    logging.info(f" Python Executable: {python_executable}")
@@ -160,38 +241,43 @@ def log_system_info():
    logging.info("\nPython Search Path:")
    for path in search_path:
        logging.info(f"  {path}")
-    logging.info("\nOS PATH Environment Variable:")
+    logging.info("\nOS SEARCHPATH ENVIRONMENT:")
    for path in path_env.split(';'):
        logging.info(f"  {path}")
    if required_packages:  # Check if the dictionary is not empty
-        logging.info("\nPackage Versions:")
+        logging.info("\nPACKAGE VERSIONS vs REQUIREMENTS FILE:")
        max_package_length = max(len(package) for package in required_packages.keys())
        for package_name, (operator, required_version) in required_packages.items():
            installed_version = installed_packages.get(package_name, 'Not installed')
            logging.info(f" {package_name.ljust(max_package_length)}  Required: {operator} {required_version.ljust(12)}  Installed: {installed_version}")
-    logging.info(f"GPU Information:\n{gpu_info}")
+    logging.info("\nPYTHON PACKAGES:")
    logging.info("Package Versions:")
    for package, version in package_versions.items():
        logging.info(f" {package}>= {version}")
    # Print to screen
    print(f"\n\033[94mOS Version:\033[0m \033[92m{os_version}\033[0m")
    print(f"\033[94mOS Ver note:\033[0m \033[92m(Windows 11 will say build is 10.x.22xxx)\033[0m")
    print(f"\033[94mCUDA_HOME:\033[0m \033[92m{cuda_home}\033[0m")
    print(f"\033[94mSystem RAM:\033[0m \033[92m{system_ram}\033[0m")
-    print(f"\033[94mPort Status:\033[0m \033[92m{port_status}\033[0m")
+    for disk in disk_info:
-    print(f"\033[94mTorch Version:\033[0m \033[92m{torch_version}\033[0m")
+        print(f"\033[94mDisk:\033[0m \033[92m{disk}\033[0m")
    print(f"\033[94m\nGPU Information:\033[0m {gpu_info}")
    print(f"\033[94mPort Status:\033[0m \033[92m{port_status}\033[0m\n")
    if "Fail" in cuda_test_result:
        print(f"\033[91mCUDA Working:\033[0m \033[91m{cuda_test_result}\033[0m")
    else:
        print(f"\033[94mCUDA Working:\033[0m \033[92m{cuda_test_result}\033[0m")
    print(f"\033[94mCUDA_HOME:\033[0m \033[92m{cuda_home}\033[0m")
    if found_paths:
        print(f"\033[94mCublas64_11 Path:\033[0m \033[92m{', '.join(found_paths)}\033[0m")
    else:
        print(f"\033[94mCublas64_11 Path:\033[0m \033[91mNot found in any search path directories.\033[0m")    
    print(f"\033[94m\nTorch Version:\033[0m \033[92m{torch_version}\033[0m")
    print(f"\033[94mPython Version:\033[0m \033[92m{platform.python_version()}\033[0m")
    print(f"\033[94mPython Version Info:\033[0m \033[92m{python_version_info}\033[0m")
    print(f"\033[94mPython Executable:\033[0m \033[92m{python_executable}\033[0m")
    print(f"\033[94mPython Virtual Environment:\033[0m \033[92m{python_virtual_env}\033[0m (Should be N/A when in Text-generation-webui Conda Python environment)")
    print(f"\033[94mConda Environment:\033[0m \033[92m{conda_env}\033[0m")
    print(f"\n\033[94mPython Search Path:\033[0m")
    for path in search_path:
        print(f"  {path}")
    print(f"\n\033[94mOS Search PATH Environment Variable:\033[0m")
    for path in path_env.split(';'):
        print(f"  {path}")
    if required_packages:  # Check if the dictionary is not empty
        print("\033[94m\nRequirements file package comparison:\033[0m")
        max_package_length = max(len(package) for package in required_packages.keys())
@@ -215,6 +301,13 @@ def log_system_info():
            # Print colored output
            print(f"  {package_name.ljust(max_package_length)}  Required: {color_required}{operator} {required_version.ljust(12)}\033[0m  Installed: {color_installed}{installed_version}\033[0m")
        print("\nOn Nvidia Graphics cards machines, if your \033[92mInstalled\033[0m version of \033[92mTorch\033[0m and \033[92mTorchaudio\033[0m does")    
        print("not have \033[92m+cu118\033[0m (Cuda 11.8) or \033[92m+cu121\033[0m (Cuda 12.1) listed after them, you do not have CUDA")
        print("installed for Torch or Torchaudio in this Python environment. This will cause you problems")
        print("with \033[94mAllTalk\033[0m and \033[94mFinetuning.\033[0m You may have to 'pip install' a new version of torch and")
        print("torchaudio, using '\033[94m--upgrade --force-reinstall\033[0m' with the correct version of PyTorch for\033[0m")
        print("your Python environment.\033[0m")
        print("\033[94m\nRequirements file specifier meanings:\033[0m")
        explanation = textwrap.dedent("""
        == Exact version              != Any version except          < Less than               
@@ -224,9 +317,9 @@ def log_system_info():
        """)
        print(explanation.strip())
    print("")
-    print(f"GPU Information:{gpu_info}")
+    print(f"\033[94mDiagnostic log created:\033[0m \033[92mdiagnostics.log. \033[94mA brief summary of results is displayed above on\033[0m")
-    print(f"\033[94mDiagnostic log created:\033[0m \033[92mdiagnostics.log\033[0m")
+    print(f"\033[94mscreen. Please see the log file for more detail.\033[0m")
-    print(f"\033[94mPlease upload the log file with any support ticket.\033[0m")
+    print(f"\033[94m\nPlease upload the log file with any support ticket.\033[0m")
 if __name__ == "__main__":
    log_system_info()
@@ -0,0 +1,20 @@
 version: '3.8'
 services:
  alltalk-tts:
    image: erew123/alltalk_tts:cuda
    restart: unless-stopped
    ports:
      - "7851:7851"
    volumes:
      - ./outputs:/app/outputs/
      - ./models:/app/models/
      - ./voices:/app/voices/
      - ./finetune/put-voice-samples-in-here:/app/finetune/put-voice-samples-in-here
      - ./dockerconfig.json:/app/confignew.json
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [ gpu ]
@@ -0,0 +1,13 @@
 version: '3.8'
 services:
  alltalk-tts:
    image: erew123/alltalk_tts:cpu
    restart: unless-stopped
    ports:
      - "7851:7851"
    volumes:
      - ./outputs:/app/outputs/
      - ./models:/app/models/
      - ./voices:/app/voices/
      - ./finetune/put-voice-samples-in-here:/app/finetune/put-voice-samples-in-here
      - ./dockerconfig.json:/app/confignew.json
@@ -0,0 +1,25 @@
 {
    "activate": true,
    "autoplay": true,
    "branding": "AllTalk ",
    "narrator_enabled": false,
    "deepspeed_activate": true,
    "delete_output_wavs": "Disabled",
    "ip_address": "127.0.0.1",
    "language": "English",
    "low_vram": false,
    "local_temperature": "0.7",
    "local_repetition_penalty": "10.0",
    "tts_model_loaded": true,
    "tts_model_name": "tts_models/multilingual/multi-dataset/xtts_v2",
    "narrator_voice": "female_01.wav",
    "output_folder_wav": "extensions/alltalk_tts/outputs/",
    "output_folder_wav_standalone": "outputs/",
    "port_number": "7851",
    "remove_trailing_dots": false,
    "show_text": true,
    "tts_method_api_local": false,
    "tts_method_api_tts": false,
    "tts_method_xtts_local": true,
    "voice": "female_01.wav"
 }
@@ -1,6 +1,8 @@
 import argparse
 import os
 import sys
 import platform
 import site
 import tempfile
 import signal
 import gradio as gr
@@ -13,14 +15,18 @@ import random
 import gc
 import time
 import shutil
 import psutil
 import pandas
 import glob
 import json
 from pathlib import Path
 from tqdm import tqdm
 from faster_whisper import WhisperModel  
-from TTS.tts.layers.xtts.tokenizer import multilingual_cleaners
+# Use a local Tokenizer to resolve Japanese support
-
+# from TTS.tts.layers.xtts.tokenizer import multilingual_cleaners
 from system.ft_tokenizer.tokenizer import multilingual_cleaners
 import importlib.metadata as metadata
 from packaging import version
 # STARTUP VARIABLES 
 this_dir = Path(__file__).parent.resolve()
@@ -30,6 +36,7 @@ progress = 0
 theme = gr.themes.Default()
 refresh_symbol = '🔄'
 os.environ['TRAINER_TELEMETRY'] = '0'
 pfc_status = "pass"
 # Define the path to the modeldownload config file file
 modeldownload_config_file_path = this_dir / "modeldownload.json"
@@ -62,6 +69,215 @@ if finetuned_model:
    finetuned_model = all((trained_model_directory / file).exists() for file in required_files)
 basemodel_or_finetunedmodel = True
 #######################
 #### DIAGS for PFC ####
 #######################
 def check_disk_space():
    global pfc_status
    # Get the current working directory
    current_directory = os.getcwd()
    # Get the disk usage statistics for the current directory's disk
    disk_usage = shutil.disk_usage(current_directory)
    # Convert the free space to GB (1GB = 1 << 30 bytes)
    free_space_gb = disk_usage.free / (1 << 30)
    # Check if the free space is more than 18GB
    is_more_than_18gb = free_space_gb > 18
    disk_space_icon = "✅"
    if not is_more_than_18gb:
        disk_space_icon ="❌"
        pfc_status = "fail"  # Update global status if disk space check fails
    # Generating the markdown text for disk space check
    disk_space_markdown = f"""
    ### 🟩 <u>Disk Space Check</u>
    &nbsp;&nbsp;&nbsp;&nbsp; {disk_space_icon} **Disk Space (> 18 GB):** {'' if is_more_than_18gb else 'You have less than 18GB on this disk '} {free_space_gb:.2f} GB
    """
    return disk_space_markdown
 def test_cuda():
    global pfc_status
    cuda_home = os.environ.get('CUDA_HOME', 'N/A')
    cuda_available = torch.cuda.is_available()
    if cuda_available:
        try:
            # Attempt to create a tensor on GPU
            torch.tensor([1.0, 2.0]).cuda()
            cuda_status = "CUDA is available and working."
            cuda_icon = "✅"
        except Exception as e:
            cuda_status = f"CUDA is available but not working. Error: {e}"
            cuda_icon = "❌"
            pfc_status = "fail"  # Update global status
    else:
        cuda_status = "CUDA is not available."
        pfc_status = "fail"  # Update global status
    return cuda_status, cuda_icon, cuda_home 
 def find_files_in_path_with_wildcard(pattern):
    # Get the site-packages directory of the current Python environment
    site_packages_path = site.getsitepackages()
    found_paths = []
    # Adjust the sub-directory based on the operating system
    sub_directory = "nvidia/cublas"
    if platform.system() == "Linux":
        sub_directory = os.path.join(sub_directory, "lib")
    else:
        sub_directory = os.path.join(sub_directory, "bin")
    # Iterate over each site-packages directory (there can be more than one)
    for directory in site_packages_path:
        # Construct the search directory path
        search_directory = os.path.join(directory, sub_directory)
        # Use glob to find all files matching the pattern in this directory
        for file_path in glob.glob(os.path.join(search_directory, pattern)):
            if os.path.isfile(file_path):  # Ensure it's a file
                found_paths.append(file_path)
    return found_paths
 def generate_cuda_markdown():
    global pfc_status
    cuda_status, cuda_icon, cuda_home = test_cuda()
    file_name = 'cublas64_11.*' if platform.system() == "Windows" else 'libcublas.so.11*'
    found_paths = find_files_in_path_with_wildcard(file_name)
    if found_paths:
        found_paths_str = ' '.join(found_paths)
        found_path_icon = '✅'
    else:
        found_paths_str = "cublas64_11 is not accessible."
        found_path_icon = '❌'
        pfc_status = "fail"  # Update global status
        # Check if 'cu118' or 'cu121' is in the PyTorch version string
    pytorch_version = torch.__version__
    if 'cu118' in pytorch_version or 'cu121' in pytorch_version:
        pytorch_cuda_version_status = ''
        pytorch_icon = '✅'
    else:
        pytorch_cuda_version_status = 'Pytorch CUDA version problem '
        pytorch_icon = '❌'
        pfc_status = "fail"  # Update global status
    cuda_markdown = f"""
    ### 🟨 <u>CUDA Information</u><br>
    &nbsp;&nbsp;&nbsp;&nbsp; {found_path_icon} **Cublas64_11 found:** {found_paths_str}  
    &nbsp;&nbsp;&nbsp;&nbsp; {pytorch_icon} **CUDA_HOME path:** {cuda_home}
    """
    pytorch_markdown = f"""
    ### 🟦 <u>Python & Pytorch Information</u>  
    &nbsp;&nbsp;&nbsp;&nbsp; {pytorch_icon} **PyTorch Version:** {pytorch_cuda_version_status} {torch.__version__}  
    &nbsp;&nbsp;&nbsp;&nbsp; {cuda_icon} **CUDA is working:** {cuda_status}
    """
    return cuda_markdown, pytorch_markdown
 def get_system_ram_markdown():
    global pfc_status
    virtual_memory = psutil.virtual_memory()
    total_ram_gb = virtual_memory.total / (1024 ** 3)
    available_ram_gb = virtual_memory.available / (1024 ** 3)
    used_ram_percentage = virtual_memory.percent
    # Check if the available RAM is less than 8GB
    warning_if_low_ram = available_ram_gb < 8
    # Decide the message based on the available RAM
    ram_status_message = "Warning" if warning_if_low_ram else ""
    ram_status_icon = "⚠️" if warning_if_low_ram else "✅"
    if torch.cuda.is_available():
        gpu_device_id = torch.cuda.current_device()
        gpu_device_name = torch.cuda.get_device_name(gpu_device_id) 
        # Get the total and available memory in bytes, then convert to GB
        gpu_total_mem_gb = torch.cuda.get_device_properties(gpu_device_id).total_memory / (1024 ** 3)
        # gpu_available_mem_gb = (torch.cuda.get_device_properties(gpu_device_id).total_memory - torch.cuda.memory_allocated(gpu_device_id)) / (1024 ** 3)
        # gpu_available_mem_gb = (torch.cuda.get_device_properties(gpu_device_id).total_memory - torch.cuda.memory_reserved(gpu_device_id)) / (1024 ** 3)
        gpu_reserved_mem_gb = torch.cuda.memory_reserved(gpu_device_id) / (1024 ** 3)
        gpu_available_mem_gb = gpu_total_mem_gb - gpu_reserved_mem_gb
        # Check if total or available memory is less than 11 GB and set icons
        gpu_total_status_icon = "⚠️" if gpu_total_mem_gb < 12 else "✅"
        gpu_available_status_icon = "⚠️" if gpu_available_mem_gb < 12 else "✅"
        gpu_status_icon = "✅"
    else:
        gpu_status_icon = "⚠️"
        gpu_device_name = "Cannot detect a CUDA card"
        gpu_total_mem_gb = "Cannot detect a CUDA card"
        gpu_available_mem_gb = "Cannot detect a CUDA card"
        gpu_total_status_icon = gpu_status_icon
        gpu_available_status_icon = gpu_status_icon
    system_ram_markdown = f"""
    ### 🟪 <u>System RAM and VRAM Information</u>  <br>
    &nbsp;&nbsp;&nbsp;&nbsp; {ram_status_icon} **Total RAM:** {total_ram_gb:.2f} GB<br>
    &nbsp;&nbsp;&nbsp;&nbsp; {ram_status_icon} **Available RAM:** {ram_status_message + ' - Available RAM is less than 8 GB. You have ' if warning_if_low_ram else ''} {available_ram_gb:.2f} GB available ({used_ram_percentage:.2f}% used)<br><br>
    &nbsp;&nbsp;&nbsp;&nbsp; {gpu_status_icon} **GPU Name:** {gpu_device_name}<br>
    &nbsp;&nbsp;&nbsp;&nbsp; {gpu_total_status_icon} **GPU Total RAM:** {gpu_total_mem_gb:.2f} GB<br>
    &nbsp;&nbsp;&nbsp;&nbsp; {gpu_available_status_icon} **GPU Available RAM:** {gpu_available_mem_gb:.2f} GB<br>
    """
    return system_ram_markdown
 def check_base_model(base_model_path, files_to_download):
    global pfc_status
    # Assuming files_to_download is a dict with keys as filenames
    base_model_files = list(files_to_download.keys())
    missing_files = []
    # Check if all base model files exist
    for file in base_model_files:
        file_path = this_dir / base_path / model_path / file
        if not file_path.exists():
            missing_files.append(file)
            pfc_status = "fail"
    return len(missing_files) == 0
 # Assuming base_model_path and files_to_download are set from the JSON config as shown above
 base_model_detected = check_base_model(base_model_path, files_to_download)
 def generate_base_model_markdown(base_model_detected):
    global pfc_status
    base_model_status = 'Base model detected' if base_model_detected else 'Base model not detected'
    base_model_icon = '✅' if base_model_detected else '❌'
    base_model_markdown = f"""
    ### ⬛ <u>XTTS Base Model Detection</u>
    &nbsp;&nbsp;&nbsp;&nbsp; {base_model_icon} **Base XTTS Model Status:** {base_model_status}
    """
    return base_model_markdown
 def check_tts_version(required_version="0.22.0"):
    global pfc_status
    try:
        # Get the installed version of TTS
        installed_version = metadata.version("tts")
        # Check if the installed version meets the required version
        if version.parse(installed_version) >= version.parse(required_version):
            tts_status = f"TTS version {installed_version} is installed and meets the requirement."
            tts_status_icon = "✅"
        else:
            tts_status = f"❌ Fail - TTS version {installed_version} is installed but does not meet the required version {required_version}."
            tts_status_icon = "❌"
            pfc_status = "fail"  # Update global status
    except metadata.PackageNotFoundError:
        # If TTS is not installed
        tts_status = "TTS is not installed."
        pfc_status = "fail"  # Update global status
    tts_markdown = f"""
    ### 🟥 <u>TTS Information</u><br>
    &nbsp;&nbsp;&nbsp;&nbsp; {tts_status_icon} **TTS Version:** {tts_status}
    """
    return tts_markdown
 # Disk space check results to append to the Markdown
 disk_space_results = check_disk_space()
 cuda_results, pytorch_results = generate_cuda_markdown()
 system_ram_results = get_system_ram_markdown()
 base_model_results = generate_base_model_markdown(base_model_detected)
 tts_version_status = check_tts_version()
 def pfc_check_fail():
    global pfc_status
    if pfc_status == "fail":
        print("[FINETUNE]")
        print("[FINETUNE] \033[91m****** WARNING PRE-FLIGHT CHECKS FAILED ******* WARNING PRE-FLIGHT CHECKS FAILED *****\033[0m")
        print("[FINETUNE] \033[91m* Please refer to the \033[93mPre-flight check tab \033[91mand resolve any issues before continuing. *\033[0m")
        print("[FINETUNE] \033[91m*********** Expect errors and failures if you do not resolve these issues. ***********\033[0m")
        print("[FINETUNE]")
    return
 #####################
 #### STEP 1 BITS ####
 #####################
@@ -75,10 +291,11 @@ def create_temporary_file(folder, suffix=".wav"):
    unique_filename = f"custom_tempfile_{int(time.time())}_{random.randint(1, 1000)}{suffix}"
    return os.path.join(folder, unique_filename)
-def format_audio_list(target_language, whisper_model, out_path, gradio_progress=progress):
+def format_audio_list(target_language, whisper_model, out_path, eval_split_number, gradio_progress=progress):
    pfc_check_fail()
    audio_files = [os.path.join(audio_folder, file) for file in os.listdir(audio_folder) if file.endswith(('.mp3', '.flac', '.wav'))]
    buffer=0.2
-    eval_percentage=0.15
+    eval_percentage = eval_split_number / 100.0
    speaker_name="coqui"
    audio_total_size = 0
    os.makedirs(out_path, exist_ok=True)
@@ -87,6 +304,7 @@ def format_audio_list(target_language, whisper_model, out_path, gradio_progress=
    print("[FINETUNE] \033[94mPart of AllTalk\033[0m https://github.com/erew123/alltalk_tts/")
    print("[FINETUNE] \033[94mCoqui Public Model License\033[0m")
    print("[FINETUNE] \033[94mhttps://coqui.ai/cpml.txt\033[0m")
    print(f"[FINETUNE] \033[94mWhisper model: \033[92m{whisper_model} \033[94mLanguage: \033[92m{target_language} \033[94mEvaluation data percentage: \033[92m{eval_split_number}%\033[0m")
    print("[FINETUNE] \033[94mStarting Step 1\033[0m - Preparing Audio/Generating the dataset")
    # Write the target language to lang.txt in the output directory
    lang_file_path = os.path.join(out_path, "lang.txt")
@@ -109,7 +327,8 @@ def format_audio_list(target_language, whisper_model, out_path, gradio_progress=
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print("[FINETUNE] Loading Whisper Model:", whisper_model)
-    asr_model = WhisperModel(whisper_model, device=device, compute_type="float16")
+    print("[FINETUNE] Model will be downloaded if its not available, which will take a few minutes.")
    asr_model = WhisperModel(whisper_model, device=device, compute_type="float32")
    metadata = {"audio_file": [], "text": [], "speaker_name": []}
@@ -286,6 +505,8 @@ def format_audio_list(target_language, whisper_model, out_path, gradio_progress=
    # deallocate VRAM and RAM
    del asr_model, final_eval_set, final_training_set, new_data_df, existing_metadata
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    existing_train_df = None
    existing_eval_df = None
    print("[FINETUNE] Train CSV:", train_metadata_path)
@@ -313,6 +534,7 @@ def basemodel_or_finetunedmodel_choice(value):
        basemodel_or_finetunedmodel = False
 def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path, max_audio_length=255995):
    pfc_check_fail()
    #  Logging parameters
    RUN_NAME = "XTTS_FT"
    PROJECT_NAME = "XTTS_trainer"
@@ -322,6 +544,27 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
    # Set here the path that the checkpoints will be saved. Default: ./training/
    OUT_PATH = os.path.join(output_path, "training")
    print("[FINETUNE] \033[94mStarting Step 2\033[0m - Fine-tuning the XTTS Encoder")
    print(f"[FINETUNE] \033[94mLanguage: \033[92m{language} \033[94mEpochs: \033[92m{num_epochs} \033[94mBatch size: \033[92m{batch_size}\033[0m \033[94mGrad accumulation steps: \033[92m{grad_acumm}\033[0m")
    print(f"[FINETUNE] \033[94mTraining   : \033[92m{train_csv}\033[0m")
    print(f"[FINETUNE] \033[94mEvaluation : \033[92m{eval_csv}\033[0m")
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
 		# Get the current device ID
        gpu_device_id = torch.cuda.current_device()
        gpu_available_mem_gb = (torch.cuda.get_device_properties(gpu_device_id).total_memory - torch.cuda.memory_allocated(gpu_device_id)) / (1024 ** 3)
        print(f"[FINETUNE] \033[94mAvailable VRAM: \033[92m{gpu_available_mem_gb:.2f} GB\033[0m")
        if gpu_available_mem_gb < 12:
            print(f"[FINETUNE]")
            print(f"[FINETUNE] \033[91m****** WARNING PRE-FLIGHT CHECKS FAILED ******* WARNING PRE-FLIGHT CHECKS FAILED *****\033[0m")
            print(f"[FINETUNE] \033[94mAvailable VRAM: \033[92m{gpu_available_mem_gb:.2f} GB\033[0m")
            print(f"[FINETUNE] \033[94mIf you are running on a Linux system and you have 12GB's or less of VRAM, this step\033[0m")
            print(f"[FINETUNE] \033[94mmay fail, due to not enough GPU VRAM. Windows systems will use system RAM as extended\033[0m")
            print(f"[FINETUNE] \033[94mVRAM and so should work ok. However, Windows machines will need enough System RAM\033[0m")
            print(f"[FINETUNE] \033[94mavailable. Please read the PFC help section available on the first tab of the web\033[0m")
            print(f"[FINETUNE] \033[94minterface for more information.\033[0m")
            print(f"[FINETUNE] \033[91m****** WARNING PRE-FLIGHT CHECKS FAILED ******* WARNING PRE-FLIGHT CHECKS FAILED *****\033[0m")
            print(f"[FINETUNE]")
    # Create the directory
    os.makedirs(OUT_PATH, exist_ok=True)
@@ -346,7 +589,7 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
    if basemodel_or_finetunedmodel:
        # BASE XTTS model checkpoints for fine-tuning.
-        print("[FINETUNE] Starting finetuning on Base Model")
+        print("[FINETUNE] Starting finetuning on \033[92mBase Model\033[0m")
        TOKENIZER_FILE = str(this_dir / base_path / model_path / "vocab.json")
        XTTS_CHECKPOINT = str(this_dir / base_path / model_path / "model.pth")
        XTTS_CONFIG_FILE = str(this_dir / base_path / model_path / "config.json")
@@ -354,7 +597,7 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
        MEL_NORM_FILE = str(this_dir / base_path / model_path / "mel_stats.pth")
    else:
        # FINETUNED XTTS model checkpoints for fine-tuning.
-        print("[FINETUNE] Starting finetuning on Existing Finetuned Model")
+        print("[FINETUNE] Starting finetuning on \033[92mExisting Finetuned Model\033[0m")
        TOKENIZER_FILE = str(this_dir / base_path / "trainedmodel" / "vocab.json")
        XTTS_CHECKPOINT = str(this_dir / base_path / "trainedmodel" / "model.pth")
        XTTS_CONFIG_FILE = str(this_dir / base_path / "trainedmodel" / "config.json")
@@ -381,6 +624,11 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
    # define audio config
    audio_config = XttsAudioConfig(sample_rate=22050, dvae_sample_rate=22050, output_sample_rate=24000)
    # Resolve Japanese threading issue
    number_of_workers = 8
    if language == "ja":
        number_of_workers = 0
    # training parameters config
    config = GPTTrainerConfig(
        epochs=num_epochs,
@@ -395,7 +643,7 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
        batch_size=BATCH_SIZE,
        batch_group_size=48,
        eval_batch_size=BATCH_SIZE,
-        num_loader_workers=8,
+        num_loader_workers=number_of_workers,
        eval_split_max_size=256,
        print_step=50,
        plot_step=100,
@@ -469,17 +717,25 @@ def clear_gpu_cache():
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
 def find_a_speaker_file(folder_path):
    search_path = folder_path / "*" / "speakers_xtts.pth"
    files = glob.glob(str(search_path), recursive=True)
    latest_file = max(files, key=os.path.getctime, default=None)
    return latest_file
 XTTS_MODEL = None
 def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
    pfc_check_fail()
    global XTTS_MODEL
    clear_gpu_cache()
    if not xtts_checkpoint or not xtts_config or not xtts_vocab:
        return "You need to run the previous steps or manually set the `XTTS checkpoint path`, `XTTS config path`, and `XTTS vocab path` fields !!"
    xtts_speakers_pth = find_a_speaker_file(this_dir / "models")
    config = XttsConfig()
    config.load_json(xtts_config)
    XTTS_MODEL = Xtts.init_from_config(config)
    print("[FINETUNE] \033[94mStarting Step 3\033[0m Loading XTTS model!")
-    XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False)
+    XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False, speaker_file_path=xtts_speakers_pth)
    if torch.cuda.is_available():
        XTTS_MODEL.cuda()
@@ -510,7 +766,6 @@ def run_tts(lang, tts_text, speaker_audio_file):
    return "Speech generated !", out_path, speaker_audio_file
 def get_available_voices(minimum_size_kb=1200):
    voice_files = [
        voice for voice in Path(f"{this_dir}/finetune/tmp-trn/wavs").glob("*.wav")
@@ -581,7 +836,7 @@ def compact_model():
    torch.save(checkpoint, target_dir / "model.pth")
    # Specify the files you want to copy
-    files_to_copy = ["vocab.json", "config.json", "dvae.pth", "mel_stats.pth"]
+    files_to_copy = ["vocab.json", "config.json", "dvae.pth", "mel_stats.pth","speakers_xtts.pth", ]
    for file_name in files_to_copy:
        src_path = this_dir / base_path / base_model_path / file_name
@@ -639,7 +894,7 @@ def compact_lastfinetuned_model():
    torch.save(checkpoint, target_dir / "model.pth")
    # Specify the files you want to copy
-    files_to_copy = ["vocab.json", "config.json", "dvae.pth", "mel_stats.pth"]
+    files_to_copy = ["vocab.json", "config.json", "dvae.pth", "mel_stats.pth", "speakers_xtts.pth",]
    for file_name in files_to_copy:
        src_path = this_dir / base_path / base_model_path / file_name
@@ -699,7 +954,6 @@ def compact_legacy_model():
    print("[FINETUNE] model.pth created in '/finetune/'")
    return "model.pth created in '/finetune/'"
 def delete_training_data():
    # Define the folder to be deleted
    folder_to_delete = Path(this_dir / "finetune" / "tmp-trn")
@@ -724,7 +978,6 @@ def delete_training_data():
        print(f"[FINETUNE] Folder {folder_to_delete} does not exist.")
        return "Folder '/finetune/tmp-trn/' does not exist."
 def delete_voice_sample_contents():
    # Define the folder to be cleared
    folder_to_clear = Path(this_dir / "finetune" / "put-voice-samples-in-here")
@@ -747,7 +1000,6 @@ def delete_voice_sample_contents():
        print(f"[FINETUNE] Folder {folder_to_clear} does not exist.")
        return f"Folder 'put-voice-samples-in-here' does not exist."
 #######################
 #### OTHER Generic ####
 #######################
@@ -773,7 +1025,6 @@ class Logger:
 sys.stdout = Logger()
 sys.stderr = sys.stdout
 # logging.basicConfig(stream=sys.stdout, level=logging.INFO)
 import logging
 logging.basicConfig(
@@ -784,47 +1035,67 @@ logging.basicConfig(
    ]
 )
 def read_logs():
    sys.stdout.flush()
    with open(sys.stdout.log_file, "r") as f:
        return f.read()
 def cleanup_before_exit(signum, frame):
    print("[FINETUNE] Received interrupt signal. Cleaning up and exiting...")
    # Perform cleanup operations here if necessary
    sys.exit(0)
-
+def create_refresh_button(refresh_components, refresh_methods, elem_class, interactive=True):
 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_class, interactive=True):
    """
    Copied from https://github.com/AUTOMATIC1111/stable-diffusion-webui
    """
    def refresh():
-        refresh_method()
+        updates = {}
-        args = refreshed_args() if callable(refreshed_args) else refreshed_args
+        for component, method in zip(refresh_components, refresh_methods):
-
+            args = method() if callable(method) else method
            if args and 'choices' in args:
                # Select the most recent file (last in the sorted list)
                args['value'] = args['choices'][-1] if args['choices'] else ""
            for k, v in args.items():
-            setattr(refresh_component, k, v)
+                setattr(component, k, v)
            updates[component] = gr.update(**(args or {}))
        return updates
-        return gr.update(**(args or {}))
+    refresh_button = gr.Button("Refresh Dropdowns", elem_classes=elem_class, interactive=interactive)
    refresh_button = gr.Button(refresh_symbol, elem_classes=elem_class, interactive=interactive, scale=0)
    refresh_button.click(
        fn=refresh,
        inputs=[],
-        outputs=[refresh_component]
+        outputs=refresh_components
    )
    return refresh_button
 pfc_markdown = f"""
    ### 🚀 <u>Pre-flight Checklist for Fine-tuning</u><br>
    ◽  <strong>Ensure</strong> each criterion is marked with a green check mark ✅ and a Pass status. <strong>Finetuning will fail otherwise.</strong><br>
    ◽  The help tabs along the top will assist in resolving issues and you can also find additional help guides on the AllTalk [GitHub repository](https://github.com/erew123/alltalk_tts#-finetuning-a-model).<br>
    ◽  For an overview of fine-tuning procedures, please refer to the "General Finetuning info" tab or visit the AllTalk [GitHub repository](https://github.com/erew123/alltalk_tts#-finetuning-a-model).
    """
 custom_css = """
 body {
    font-size: 16px; /* Adjust the base font size as needed */
 }
 h1, h2, h3, h4, h5, h6 {
    font-size: 1.25em; /* Adjust heading sizes relative to the base size */
 }
 p {
    font-size: 1.1em; /* Paragraph font size, relative to the base size */
    margin-bottom: 10px; /* Adjust paragraph spacing */
 }
 .gradio_container {
    zoom: 1.1; /* Adjust the zoom to scale the entire container */
 }
 """
 if __name__ == "__main__":
    # Register the signal handler
    signal.signal(signal.SIGINT, cleanup_before_exit)
 ################
 #### GRADIO ####
 ################
@@ -868,61 +1139,150 @@ if __name__ == "__main__":
 #### GRADIO INFO ####
 #####################
-    with gr.Blocks(theme=theme) as demo:
+    with gr.Blocks(theme=theme, css=custom_css) as demo:
-        with gr.Tab("Information/Guide"):
+        with gr.Tab("🚀 PFC"):
            with gr.Tab("🚀 Pre-Flight Checklist"):
                gr.Markdown(
                f"""
                {pfc_markdown}       
                {disk_space_results}
                {system_ram_results}
                {cuda_results}
                {pytorch_results}
                {base_model_results}
                {tts_version_status}
                """
            )
            with gr.Tab("🟩 Disks Help"):
                gr.Markdown(
                f"""
                {disk_space_results}<br><br>
                ◽ During actual training (Step 2) Finetuning will require approximately 18GB's of free disk space while performing training and will fail or perform badly if there is any less disk space. The majority of this disk space is used temporarily and will be cleared when you reach Step 4 and move & compact the model then delete the training data.<br>
                ◽ Because lots of data is being copied around, <strong>mechanical hard disks</strong> will be slow.
                """
            )
            with gr.Tab("🟪 RAM & VRAM Help"):
                gr.Markdown(
                f"""
                {system_ram_results}<br>
                ◽ During actual training (Step 2) Finetuning will use around **14GB's** of VRAM. If your GPU doesnt have 14GB's of VRAM:<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - **Windows** systems will attempt to extend VRAM into your System RAM, and so should work.<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - **Linux** systems can only use the available VRAM, so may fail on 12GB VRAM or smaller GPU's.<br>
                ◽ For **Windows** users with 12GB or less, if you also have very low or slow System RAM you can expect bad performance or the training to fail.<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - 12GB cards may need 2GB System RAM.<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - 8GB cards may need 6GB System RAM.<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - 6GB cards may need 8GB System RAM.<br>
                ◽ Its hard to estimate what performance impact this could will have, due to different memory speeds, PCI speeds, GPU speeds etc.<br>
                ◽ If you have a low VRAM scenario and also are attempting to run on a mechanical hard drive, Finetuning could take ??? amount of time.<br>
                ◽ On Windows machines, please ensure you have **not** disabled System Memory Fallback for Stable Diffusion <a href="https://nvidia.custhelp.com/app/answers/detail/a_id/5490/~/system-memory-fallback-for-stable-diffusion" target="_blank">link here</a><br>
                """
            )
            with gr.Tab("🟨 CUDA & Cublas Help"):
                gr.Markdown(
                f"""         
                {cuda_results}<br><br>
                ◽ It DOESNT matter what version of CUDA you have installed within Python either, CUDA 11.8, CUDA 12.1 etc. The NVIDIA CUDA Development Toolkit is a completly different and seperate thing from Python/PyTorch.<br>
                ◽ Finetuning simply wants to access a tool within the CUDA Development Toolkit 11.8 called Cublas64_11.<br>
                ◽ If you dont have the toolkit installed, the idea is just to install the smallest bit possible and this will not affect or impact other things on your system.<br><br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ You will need to download the Nvidia Cuda Toolkit 11.8<span style="color: #3366ff;"> network install</span> from <a href="https://developer.nvidia.com/cuda-11-8-0-download-archive" target="_blank">link here</a><br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ 1) Run the installer and select <span style="color: #3366ff;">Custom Advanced</span> Uncheck <span style="color: #3366ff;">everything</span> at the top then expand <span style="color: #3366ff;">CUDA</span>, <span style="color: #3366ff;">Development</span> > <span style="color: #3366ff;">Compiler</span> > and select <span style="color: #3366ff;;">nvcc</span> then expand <span style="color: #3366ff;;">Libraries</span> and select <span style="color: #3366ff;;">CUBLAS</span>.<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ 2) Back at the top of <span style="color: #3366ff;">CUDA</span>, expand <span style="color: #3366ff;">Runtime</span> > <span style="color: #3366ff;">Libraries</span> and select <span style="color: #3366ff;">CUBLAS</span>. Click <span style="color: #3366ff;;">Next</span>, accept the default path (taking a note of its location) and let the install run. <br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ 3) You should be able to drop to your terminal or command prompt and type <span style="color: #3366ff;">nvcc --version</span> and have it report <span style="color: #00a000;">Cuda compilation tools, release 11.8</span>. If it does you are good to go. If it doesn't > Step 4.<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ 4) <strong>Linux users</strong>, you can temporarily add these paths on your current terminal window with (you may need to confirm these are correct for your flavour of Linux):<br><br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <span style="color: #3366ff;">export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64&colon;&dollar;&lbrace;LD_LIBRARY_PATH&colon;&plus;&colon;&dollar;&lbrace;LD_LIBRARY_PATH&rbrace;&rbrace;</span> (Add it to your ~/.bashrc if you want this to be permanent)<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <span style="color: #3366ff;">export LD_LIBRARY_PATH=/usr/local/cuda-11.8/bin</span><br><br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <strong>Windows users</strong> need the add the following to the PATH environment variable. Start menu and search for "Environment Variables" or "Edit the system environment variables.". <br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Find and select the "Path" variable, then click on the "Edit...". Click on the "New" button and add:<br><br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <span style="color: #3366ff;">C:&bsol;Program Files&bsol;NVIDIA GPU Computing Toolkit&bsol;CUDA&bsol;v11.8&bsol;bin.</span><br><br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ 5) Once you have these set correctly, you should be able to open a new command prompt/terminal and <span style="color: #3366ff;">nvcc --version</span> at the command prompt/terminal, resulting in <span style="color: #00a000;">Cuda compilation tools, release 11.8</span>.<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ 6) If the nvcc command doesn't work OR it reports a version different from 11.8, finetuning wont work, so you will to double check your environment variables and get them working correctly.<br>
                """
            )
            with gr.Tab("🟦 Python & PyTorch Help"):
                gr.Markdown(
                f"""         
                {pytorch_results}<br><br>
                ◽ On the PyTorch version the:<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- first few digits are the version of PyTorch e.g. 2.1.0 is PyTorch 2.1.0<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- last few digits refer to the CUDA version e.g. cu118 is Cuda 11.8. cu121 is Cuda 12.1.<br>
                ◽ Ensure you have started your Python envuronment before running finetuning otherwise you will have failures on the above checks.<br>
                ◽ If PyTorch does not show a CUDA version, then PyTorch will need reinstalling with CUDA. I would suggest running <span style="color: #3366ff;">pip cache purge</span> before installing PyTorch again.<br>
                ◽ It DOESNT matter what version of PyTorch and CUDA you have installed within Python, CUDA 11.8, CUDA 12.1 etc. The NVIDIA CUDA Development Toolkit is a completly different and seperate thing.<br>
                ◽ Finetuning simply wants to access a tool within the CUDA Development Toolkit called Cublas64_11.<br>
                ◽ If you dont have the toolkit installed, the idea is just to install the smallest bit possible and this will not affect or impact other things on your system.<br>
                """
            )
            with gr.Tab("⬛ XTTS Base Model Help"):
                gr.Markdown(
                f"""         
                {base_model_results}<br><br>
                ◽ If your basemodel is not being detected, please ensure that <span style="color: #3366ff;">finetune.py</span> is being run from the AllTalk main folder.<br>
                ◽ Ensure you have started AllTalk normally at least once. You can start it again and it will download any missing files.<br>
                ◽ Check that there is an XTTS model within the models folder e.g. <span style="color: #3366ff;">/models/xttsv2_2.0.2/</span><br>
                ◽ The files required are "model.pth", "vocab.json", "config.json", "dvae.pth", "mel_stats.pth", "speakers_xtts.pth".
                """
            )
            with gr.Tab("🟥 TTS Version Help"):
                gr.Markdown(
                f"""         
                {tts_version_status}<br><br>
                ◽ If your TTS version is showing as the incorrect version, please reinstall the Finetuning requirements at the command prompt/terminal.<br>
                ◽ <span style="color: #3366ff;">pip install -r requirements_finetune.txt</span><br>
                """
            )
        with gr.Tab("ℹ️ General Finetuning info"):
            gr.Markdown(
            f"""
                ## <u>Finetuning Information</u><br>
            ### 🟥 <u>Important Note</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - <span style="color: #3366ff;">finetune.py</span> needs to be run from the <span style="color: #3366ff;">/alltalk_tts/</span> folder. Don't move the location of this script.
+            ◽ <span style="color: #3366ff;">finetune.py</span> needs to be run from the <span style="color: #3366ff;">/alltalk_tts/</span> folder. Don't move the location of this script.
                #### &nbsp;&nbsp;&nbsp;&nbsp; - Have you run AllTalk at least once? It needs to have downloaded+updated the voice model, before we can finetune it.
            ### 🟦 <u>What you need to run finetuning</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - An Nvidia GPU. Tested on Windows with extended shared VRAM and training used about 16GB's total (which worked on a 12GB card).
+            ◽ An Nvidia GPU.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - If you have multiple Nvidia GPU's in your system, please see this [important note](https://github.com/erew123/alltalk_tts#-i-have-multiple-gpus-and-i-have-problems-running-finetuning).
+            ◽ If you have multiple Nvidia GPU's in your system, please see the Github Help section [Multiple GPU's](https://github.com/erew123/alltalk_tts#performance-and-compatibility-issues).<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - I have not been able to test this on a GPU with less than 12GB of VRAM, so cannot say if it will work or how that would affect performance.
+            ◽ Some decent quality audio, multiple files if you like. Minimum of 2 minutes and Ive tested up to 20 minutes of audio.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - <span style="color: red;">Version 11.8</span> of Nvidia cuBLAS and cuDNN (guide below). Only 11.8 of cuBLAS and cuDNN work for this process currently.
+            ◽ There is no major need to chop down your audio files into small slices as Step 1 will do that for you automatically and prepare the training set. But this can be helpful in cases where:<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Minimum <span style="color: red;">18GB</span> free disk space (most of it is used temporarily).
+            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - Whisper doesnt correctly detect and split down audio files.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Some decent quality audio, multiple files if you like. Minimum of 2 minutes and Ive tested up to 20 minutes of audio.
+            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - You do not get a lot of "Speaker Reference Audio" files at the end of training.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - There is no major need to chop down your audio files into small slices as Step 1 will do that for you automatically and prepare the training set. Ive been testing with 5 minute long clips.
+            ◽ This process will need access to all your GPU and VRAM, so close any other software that's using your GPU currently.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - This process will need access to all your GPU and VRAM, so close any other software that's using your GPU currently.
+            ### 🟨 <u>What do I do from here?</u><br>
-                ### 🟨 <u>Setting up cuBLAS and cuDNN <span style="color: red;">Version 11.8</span></u>
+            ◽ Proceed through Step 1, 2, 3 and onto "What to do next".<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - If you have the <span style="color: #3366ff;;">Nvidia CUDA Toolkit Version 11.8</span> installed and can type <span style="color: #3366ff;;">nvcc --version</span> at the command prompt/terminal and it reports <span style="color: #00a000;">Cuda compilation tools, release 11.8</span> you should be good to go. 
+            ### 🟩 <u>Additional Information</u><br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - If you dont have the toolkit installed, the idea is just to install the smallest bit possible and this will not affect or impact other things on your system.
+            ◽ Guidance is provided on each step of the process however, if you are after more detailed information please visit:<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - You will need to download the Nvidia Cuda Toolkit 11.8<span style="color: #3366ff;"> network</span> install from [here](https://developer.nvidia.com/cuda-11-8-0-download-archive) 
+            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ◽ [AllTalk Github Finetuning](https://github.com/erew123/alltalk_tts#-finetuning-a-model)<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1) Run the installer and select <span style="color: #3366ff;">Custom Advanced</span> Uncheck <span style="color: #3366ff;">everything</span> at the top then expand <span style="color: #3366ff;">CUDA</span>, <span style="color: #3366ff;">Development</span> > <span style="color: #3366ff;">Compiler</span> > and select <span style="color: #3366ff;;">nvcc</span> then expand <span style="color: #3366ff;;">Libraries</span> and select <span style="color: #3366ff;;">CUBLAS</span>.
+            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ◽ [Coqui XTTS Documentation](https://docs.coqui.ai/en/latest/index.html)<br>
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2) Back at the top of <span style="color: #3366ff;">CUDA</span>, expand <span style="color: #3366ff;">Runtime</span> > <span style="color: #3366ff;">Libraries</span> and select <span style="color: #3366ff;">CUBLAS</span>. Click <span style="color: #3366ff;;">Next</span>, accept the default path (taking a note of its location) and let the install run. 
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3) You should be able to drop to your terminal or command prompt and type <span style="color: #3366ff;">nvcc --version</span> and have it report <span style="color: #00a000;">Cuda compilation tools, release 11.8</span>. If it does you are good to go. If it doesn't > Step 4.
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4) Linux users, you can temporarily add these paths on your current terminal window with (you may need to confirm these are correct for your flavour of Linux):
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <span style="color: #3366ff;">export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64&colon;&dollar;&lbrace;LD_LIBRARY_PATH&colon;&plus;&colon;&dollar;&lbrace;LD_LIBRARY_PATH&rbrace;&rbrace;</span> (Add it to your ~/.bashrc if you want this to be permanent)
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <span style="color: #3366ff;">export LD_LIBRARY_PATH=/usr/local/cuda-11.8/bin</span>
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Windows users need the add the following to the PATH environment variable. Start menu and search for "Environment Variables" or "Edit the system environment variables.". 
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Find and select the "Path" variable, then click on the "Edit...". Click on the "New" button and add:
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <span style="color: #3366ff;">C:&bsol;Program Files&bsol;NVIDIA GPU Computing Toolkit&bsol;CUDA&bsol;v11.8&bsol;bin.</span>
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5) Once you have these set correctly, you should be able to open a new command prompt/terminal and <span style="color: #3366ff;">nvcc --version</span> at the command prompt/terminal, resulting in <span style="color: #00a000;">Cuda compilation tools, release 11.8</span>.
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 6) If the nvcc command doesn't work OR it reports a version different from 11.8, finetuning wont work, so you will to double check your environment variables and get them working correctly.
            """
        )
 #######################
 #### GRADIO STEP 1 ####
 #######################
-        with gr.Tab("Step 1 - Preparing Audio/Generating the dataset"):
+        with gr.Tab("📁 Step 1 - Generating the dataset"):
            gr.Markdown(
                f"""
-                ## <u>STEP 1 - Preparing Audio/Generating the dataset</u><br>
+                ### 📁 <u>Generating the dataset</u><br>
                ### 🟥 <u>Important Note - Windows - "UserWarning: huggingface_hub cache-system uses symlinks."</u>
                ◽ This error is caused by Huggingfaces download software downloading the Whisper model. If you get this error, please restart your Windows command prompt with "Run as Administrator" and restart finetuning.<br>
                ◽ This should only occur the 1st time it downloads the Whisper model. [Huggingface Reference here](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache#limitations)
                ### 🟦 <u>What you need to do</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Please read Coqui's guide on what makes a good dataset [here](https://docs.coqui.ai/en/latest/what_makes_a_good_dataset.html#what-makes-a-good-dataset)
+                ◽ Please read Coqui's guide on what makes a good dataset [here](https://docs.coqui.ai/en/latest/what_makes_a_good_dataset.html#what-makes-a-good-dataset)<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Place your audio files in <span style="color: #3366ff;">{str(audio_folder)}</span>                
+                ◽ Place your audio files in <span style="color: #3366ff;">{str(audio_folder)}</span>          
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Your audio samples can be in the format <span style="color: #3366ff;">mp3, wav,</span> or <span style="color: #3366ff;">flac.</span>
+                ◽ Your audio samples can be in the format <span style="color: #3366ff;">mp3, wav,</span> or <span style="color: #3366ff;">flac.</span><br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - You will need a minimum of <span style="color: #3366ff;">2 minutes</span> of audio in either one or multiple audio files. Very small sample files cause errors, so I would suggest 30 seconds and longer samples. 
+                ◽ You will need a minimum of <span style="color: #3366ff;">2 minutes</span> of audio in either one or multiple audio files. 5 to 10 minutes of audio would probably be better, allowing for more varied sample data to be generated.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - When you have completed Steps 1, 2, and 3, you are welcome to delete your samples from "put-voice-samples-in-here".<br>
+                ◽ Very small sample files cause errors, so I would recommend that the samples are at least 30 seconds and longer.<br>
-                ### 🟨 <u>What this step is doing</u>
+                ◽ FYI Anecdotal evidence suggests that the Whisper 2 model may yield superior results in audio splitting and dataset creation.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - With step one, we are going to be stripping your audio file(s) into smaller files, using Whisper to find spoken words/sentences, compile that into excel sheets of training data, ready for finetuning the model on Step 2.
+                ### 🟨 <u>What this step is doing</u><br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Whilst you can choose multiple Whisper models, its best only to use the 1 model as each one is about 3GB in size and will download to your local huggingface cache on first-time use. If and when you have completed training, you wish to delete this 3GB model from your system, you are welcome to do so.
+                ◽ With step one, we are going to be stripping your audio file(s) into smaller files, using Whisper to find spoken words/sentences, compile that into excel sheets of training data, ready for Step 2.<br>
-                ### 🟩 <u>How long will this take?</u>
+                ◽ Whisper is making a best effort to find spoken audio and break it down into smaller audio files. The content of these audio files is then transcribed into CSV fles (which you can edit in Excel or similar).<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - First time, it needs to download the Whisper model which is 3GB. After that a few minutes on an average 3-4 year old system.
+                ◽ These files (audio and CSV) are used at the next step to train the model "this is what the audio sounds like and these are the words being spoken".<br>
                ◽ If you wish to manually look at the CSV files before running the next step, you are welcome to do so and edit them as necessary. The greater the accuracy of the text the better the training will be.<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ◽ <span style="color: #3366ff;">/alltalk_tts/finetune/tmp-trn/metadata_train.csv</span> Contains the list of wavs to text that will be used. This is what the model is trained with.<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ◽ <span style="color: #3366ff;">/alltalk_tts/finetune/tmp-trn/metadata_eval.csv</span> Lists the evaluation wav to text, used to generate TTS while training and evaluate the quality of generated TTS to the original sample.<br>
                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ◽ <span style="color: #3366ff;">/alltalk_tts/finetune/tmp-trn/wavs/</span> These are the wavs split out from your original training samples and match the contents of the CSV files.<br>
                ◽ Whilst you can choose multiple Whisper models, its best only to use the 1 model as each one is about 3GB in size and will download to your local huggingface cache on first-time use. <br>
                ◽ If and when you have completed training, should you wish to delete this 3GB Whisper model from your system, you are welcome to do so.<br>
                ### 🟩 <u>How long will this take?</u><br>
                ◽ First time, it needs to download the Whisper model which is 3GB. After that a few minutes on an average 3-4 year old system.<br>
                """
            )
@@ -934,7 +1294,7 @@ if __name__ == "__main__":
            with gr.Row():
                whisper_model = gr.Dropdown(
                    label="Whisper Model",
-                    value="large-v3",
+                    value="large-v2",
                    choices=[
                        "large-v3",
                        "large-v2",
@@ -966,6 +1326,14 @@ if __name__ == "__main__":
                        "ja"
                    ],
                )
                eval_split_number = gr.Number(
                    label="Evaluation data Split (the % to use for Evaluation data)",
                    value=15,  # Default value
                    minimum=5,  # Minimum value
                    maximum=95,  # Maximum value
                    step=1,  # Increment step
                )
            progress_data = gr.Label(
                label="Progress:"
            )
@@ -977,14 +1345,14 @@ if __name__ == "__main__":
            prompt_compute_btn = gr.Button(value="Step 1 - Create dataset")
-            def preprocess_dataset(language,  whisper_model, out_path, progress=gr.Progress(track_tqdm=True)):
+            def preprocess_dataset(language,  whisper_model, out_path, eval_split_number, progress=gr.Progress(track_tqdm=True)):
                clear_gpu_cache()
                test_for_audio_files = [file for file in os.listdir(audio_folder) if any(file.lower().endswith(ext) for ext in ['.wav', '.mp3', '.flac'])]
                if not test_for_audio_files:
                    return "I cannot find any mp3, wav or flac files in the folder called 'put-voice-samples-in-here'", "", ""
                else:
                    try:
-                        train_meta, eval_meta, audio_total_size = format_audio_list(target_language=language, whisper_model=whisper_model ,out_path=out_path, gradio_progress=progress)
+                        train_meta, eval_meta, audio_total_size = format_audio_list(target_language=language, whisper_model=whisper_model, out_path=out_path, eval_split_number=eval_split_number, gradio_progress=progress)
                    except:
                        traceback.print_exc()
                        error = traceback.format_exc()
@@ -1004,20 +1372,27 @@ if __name__ == "__main__":
 #######################
 #### GRADIO STEP 2 ####
 #######################
-        with gr.Tab("Step 2 - Fine-tuning the XTTS Encoder"):
+        with gr.Tab("💻 Step 2 - Training"):
            gr.Markdown(
                f"""
-                ## <u>STEP 2 - Fine-tuning the XTTS Encoder</u><br>
+                ### 💻 <u>Training</u><br>                
                ### 🟦 <u>What you need to do</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - The <span style="color: #3366ff;">Train CSV</span> and <span style="color: #3366ff;">Eval CSV</span> should already be populated. If not, just go back to Step 1 and click the button again.             
+                ◽ The <span style="color: #3366ff;">Train CSV</span> and <span style="color: #3366ff;">Eval CSV</span> should already be populated. If not, just go back to Step 1 and click "Create Dataset" again.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - If for any reason it crashed, close the app and re-start the finetuning and kick it off again, clicking through setp 1 (it doesnt need to regenerate the data)
+                ◽ The default settings below are the suggested settings for most purposes, however you may choose to alter them depending on your specific use case.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - The settings below are factory and should be ok.<br>
+                ◽ There are no absolute correct settings for training. It will vary based on:<br>
-                ### 🟨 <u>What this step is doing</u>
+                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- What you are training (A human voice, A cartoon voice, A new language entirely etc)<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Very simply put, it's taking all our wav files generated in Step 1, along with our recorded speech in out excel documents and its training the model on that voice.
+                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- How much audio you have (you may want less or more eval or epochs)<br>
-                ### 🟩 <u>How long will this take?</u>
+                ◽ The key indicator of sufficient model training is whether it sounds right to you. Coqui suggests beginning with the base settings for training. If the resulting audio doesn't meet your expectations, additional training sessions may be necessary.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - On a RTX 4070 with factory settings (as below) and 20 minutes of audio, this took 20 minutes. Again, it will vary by system and how much audio you are throwing at it. You have time to go make a coffee.
+                ◽ **Number of epochs:** Defines how many times the entire dataset is passed through the neural network; more epochs can improve learning but increase the risk of memorizing the training data.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Look at your command prompt/terminal window if you want to see what it is doing.
+                ◽ **Batch size:** Sets how many training examples are used in one iteration. Larger sizes speed up training but require more computational memory.<br>
                ◽ **Grad(ient) accumulation steps:** Allows larger batches by spreading the computation across multiple steps, enabling training with limited hardware resources.<br>
                ◽ **Max permitted audio size in seconds:** Limits the length of audio that the model processes. Any audio exceeding this duration will be truncated to fit the model's input constraints.<br>
                ### 🟨 <u>What this step is doing</u><br>
                ◽ Very simply put, it's taking all our wav files generated in Step 1, along with our recorded speech in out excel documents and its training the model on that voice e.g. listen to this audio file and this is what is being said in it, so learn to reproduce it.<br>
                ### 🟩 <u>How long will this take?</u><br>
                ◽ On a RTX 4070 with factory settings (as below) and 20 minutes of audio, this took 20 minutes. Again, it will vary by system and how much audio you are throwing at it. You have time to go make a coffee.<br>
                ◽ Look at your command prompt/terminal window if you want to see what it is doing.<br>
                """
            )
            with gr.Row():
@@ -1067,7 +1442,7 @@ if __name__ == "__main__":
                )
                gr.Markdown(
                f"""
-                ##### If you have an existing finetuned model in <span style="color: #3366ff;">/models/trainedmodel/</span> you will have an option to select <span style="color: #3366ff;">Existing finetuned model</span>, allowing you to further train it. Otherwise, you will only have Base Model as an option.
+                If you have an existing finetuned model in <span style="color: #3366ff;">/models/trainedmodel/</span> you will have an option to select <span style="color: #3366ff;">Existing finetuned model</span>, allowing you to further train it. Otherwise, you will only have Base Model as an option.
                """
                )
@@ -1095,8 +1470,8 @@ if __name__ == "__main__":
                    return f"The training was interrupted due an error !! Please check the console to check the full error message! \n Error summary: {error}", "", "", "", ""
                # copy original files to avoid parameters changes issues
-                os.system(f"cp {config_path} {exp_path}")
+                shutil.copy(config_path, exp_path)
-                os.system(f"cp {vocab_file} {exp_path}")
+                shutil.copy(vocab_file, exp_path)
                ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
                print("[FINETUNE] Model training done. Move to Step 3")
@@ -1107,21 +1482,25 @@ if __name__ == "__main__":
 #### GRADIO STEP 3 ####
 #######################
-        with gr.Tab("Step 3 - Inference/Testing"):
+        with gr.Tab("✅ Step 3 - Testing"):
            gr.Markdown(
                f"""
-                ## <u>STEP 3 -  Inference/Testing</u><br>
+                ### ✅ <u>Testing</u><br>
                ### 🟥 <u>Important Note</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - This step will error if you are using TTS version 0.22.0. Please re-run  <span style="color: #3366ff;">pip install -r requirements_finetune.txt</span> if it errors loading the model.
+                ◽ If you dont believe the dropdown lists are populated correctly, you can use the "Refresh Dropdowns" button to search the finetuning folder path and update the lists. You can also manually edit the path to the listed files.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - If you dont see multiple speaker reference files and you used more than 3 minutes of speech, try refreshing the page as it may not have loaded them correctly.
+                ◽ You can use any config.json or vocab.json, as long as they match the model version you trained on. Only the XTTS checkpoint matters.<br>
                ◽ Upon successful processing of your speech data, expect to find multiple speaker reference files if your input consisted of over 3 minutes of speech.<br>
                ◽ Audio clips shorter than 7 seconds are automatically excluded from the reference list, as they generally do not provide sufficient length for effective TTS generation.<br>
                ◽ Should you find a lower number of reference files than anticipated, it may be due to the initial segmentation performed by Whisper (Step 1), which can occasionally result in clips that are too brief for our criteria or indeed too long. In such cases, consider manually segmenting your audio to give Whisper a better chance at generating training data and repeating the process.<br>
                ### 🟦 <u>What you need to do</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - The model is now trained and you are at the testing stage. Hopefully all the dropdowns should be pre-populated now.
+                ◽ **Click** the **Refresh Dropdowns** button to correctly populate the **Speaker Reference Audio** with all the available WAV samples.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - You need to <span style="color: #3366ff;">Load Fine-tuned XTTS model</span> and then select your <span style="color: #3366ff;">Speaker Reference Audio</span>. You can choose various <span style="color: #3366ff;">Speaker Reference Audios</span> to see which works best.
+                ◽ The model is now trained and you are at the testing stage. Hopefully all the dropdowns should be pre-populated now.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - All the <span style="color: #3366ff;">Speaker Reference Audios</span> in the dropdown are ones that are <span style="color: #3366ff;">8 seconds</span> long or more. You can use one of these later for your voice sample in All Talk, so remember the one you like.
+                ◽ You need to <span style="color: #3366ff;">Load Fine-tuned XTTS model</span> and then select your <span style="color: #3366ff;">Speaker Reference Audio</span>. You can choose various <span style="color: #3366ff;">Speaker Reference Audios</span> to see which works best.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Some <span style="color: #3366ff;">Speaker Reference Audios</span> are a bit long and may cause issues generating the TTS, so try different ones.
+                ◽ All the <span style="color: #3366ff;">Speaker Reference Audios</span> in the dropdown are ones that are <span style="color: #3366ff;">8 seconds</span> long or more. You can use one of these later for your voice sample in All Talk, so remember the one you like.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Once you are happy, move to the next tab for instruction on cleaning out the training data and how to copy your newly trained model over your current model.
+                ◽ Some <span style="color: #3366ff;">Speaker Reference Audios</span> are a bit long and may cause issues generating the TTS, so try different ones.<br>
                ◽ Once you are happy, move to the next tab for instruction on cleaning out the training data and how to copy your newly trained model over your current model.<br>
                ### 🟨 <u>What this step is doing</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Its loading the finetuned model into memory, loading a voice sample and generating TTS, so that you can test out how well fine tuning worked.
+                ◽ Its loading the finetuned model into memory, loading a voice sample and generating TTS, so that you can test out how well fine tuning worked.
                """
            )
@@ -1129,21 +1508,21 @@ if __name__ == "__main__":
                with gr.Column() as col1:
                    xtts_checkpoint = gr.Dropdown(
                        [str(file) for file in xtts_checkpoint_files],
-                        label="XTTS checkpoint path:",
+                        label="XTTS checkpoint path (best_model.pth):",
                        value="",
                        allow_custom_value=True,
                    )
                    xtts_config = gr.Dropdown(
                        [str(file) for file in xtts_config_files],
-                        label="XTTS config path:",
+                        label="XTTS config path (config.json):",
                        value="",
                        allow_custom_value=True,
                    )
                    xtts_vocab = gr.Dropdown(
                        [str(file) for file in xtts_vocab_files],
-                        label="XTTS vocab path:",
+                        label="XTTS vocab path (vocab.json):",
                        value="",
                        allow_custom_value=True,
                    )
@@ -1165,14 +1544,8 @@ if __name__ == "__main__":
                            allow_custom_value=True,  # Allow custom values
                            scale=1,
                        )
                        # Create refresh button
                        refresh_button = create_refresh_button(
                            speaker_reference_audio,
                            lambda: None,  # Specify your refresh logic if needed
                            lambda: {"choices": get_available_voices(), "value": ""},  # Update choices and value
                            "refresh-button",
                        )
                    with gr.Row():
                        tts_language = gr.Dropdown(
                            label="Language",
                            value="en",
@@ -1195,6 +1568,17 @@ if __name__ == "__main__":
                                "ja",
                            ]
                        )
                        # Create refresh button
                        refresh_button = create_refresh_button(
                            [xtts_checkpoint, xtts_config, xtts_vocab, speaker_reference_audio],
                            [
                                lambda: {"choices": find_models(main_directory, "pth"), "value": ""},
                                lambda: {"choices": find_jsons(main_directory, "config.json"), "value": ""},
                                lambda: {"choices": find_jsons(main_directory, "vocab.json"), "value": ""},
                                lambda: {"choices": get_available_voices(), "value": ""},
                            ],
                            elem_class="refresh-button-class"
                        )
                    tts_text = gr.Textbox(
                        label="Input Text:",
                        value="I've just fine tuned a text to speech language model and this is how it sounds. If it doesn't sound right, I will try a different Speaker Reference Audio file.",
@@ -1209,22 +1593,25 @@ if __name__ == "__main__":
                    tts_output_audio = gr.Audio(label="TTS Generated Audio.")
                    reference_audio = gr.Audio(label="Speaker Reference Audio Used.")
-        with gr.Tab("What to do next"):
+        with gr.Tab("🔜 What to do next"):
            gr.Markdown(
                f"""
-                ## <u>What to do next</u><br>
+                ### 🔜 <u>What to do next</u><br>
                ### 🟦 <u>What you need to do</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - You have a few options below:
+                You have a few options below:<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 🔹 Compact &amp; move model. This will compress the raw finetuned model and move it, along with any large enough wav files created to one of two locations (please read text next to the button).<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; You can use the wav files in that location as sample voices with your model. Models moved to <span style="color: #3366ff;">/models/trainedmodel/</span> will be loadable within Text-gen-webui on the <span style="color: #3366ff;">XTTSv2 FT</span> loader.
+                ◽ **Compact &amp; move model:** This will compress the raw finetuned model and move it, along with any large enough wav files created to one of two locations (please read text next to the button). You can use the wav files in that location as sample voices with your model. Models moved to <span style="color: #3366ff;">/models/trainedmodel/</span> will be loadable within Text-gen-webui on the <span style="color: #3366ff;">XTTSv2 FT</span> loader.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 🔹 Delete Generated Training data. This will delete the finetuned model and any other training data generated during this process. You will more than likely want to do this after you have Compacted &amp; moved the model.
+                ◽ **Delete Generated Training data:** This will delete the finetuned model and any other training data generated during this process. You will more than likely want to do this after you have Compacted &amp; moved the model.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 🔹 Delete original voice samples. This will delete the voice samples you placed in put-voice-samples-in-here (if you no longer have a use for them).
+                ◽ **Delete original voice samples:** This will delete the voice samples you placed in put-voice-samples-in-here (if you no longer have a use for them).<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 🔹 Compress a legacy finetuned model. For any models that were generated pre the current version of finetuning, you can still compact them down.
+                ◽ **Note:** Sample wav files will be copied into the model folder. You will need to MANUALLY copy/move the WAV file(s) you want to use to the AllTalk voices folder, to make them available within the interface.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - Instructions on using the finetuned model in Text-generation-webui and also further tuning this model you just created are on <span style="color: #3366ff;">OPTION A - Compact and move model to '/trainedmodel/'</span>.
+                ◽ **Note:** Text-generation-webui users, <span style="color: #3366ff;">OPTION A - Compact and move model to '/trainedmodel/'</span>. will make the model available for use within Text-gen-webui.<br>
                ### 🟩 <u>Using your Finetuned model as you main model</u>
                ◽ If you wish to use the model you have just fintuned as your main model, you will need to MANUALLY copy it over the base model in <span style="color: #3366ff;">/models/trainedmodel/</span> after you have used Option A or B.<br>
                ◽ Should you ever wish to revert your model back to the base model, you can delete the files in <span style="color: #3366ff;">/models/xttsv2_2.0.2/</span> and new copies will be re-downloaded when AllTalk starts up.<br>
                ### 🟨 <u>Clearing up disk space</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - If you are not going to train anything again, you can delete the whisper model from inside of your huggingface cache (3GB approx) 
+                ◽ If you are not going to train anything again, you can delete the whisper model from inside of your huggingface cache (3GB approx) <br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Linux <span style="color: #3366ff;">~/.cache/huggingface/hub/(folder-here)</span>
+                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ◽ **Linux:** <span style="color: #3366ff;">~/.cache/huggingface/hub/(folder-here)</span><br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Windows <span style="color: #3366ff;">C:&bsol;users&lpar;your-username&rpar;&bsol;.cache&bsol;huggingface&bsol;hub&bsol;(folder-here)</span>.
+                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ◽ **Windows:** <span style="color: #3366ff;">C:&bsol;users&lpar;your-username&rpar;&bsol;.cache&bsol;huggingface&bsol;hub&bsol;(folder-here)</span>.<br>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; - You can also uninstall the Nvidia CUDA 11.8 Toolkit if you wish and remove your environment variable entries.
+                ◽ You can also uninstall the Nvidia CUDA 11.8 Toolkit if you wish and remove your environment variable entries.<br>
                """
            )
            final_progress_data = gr.Label(
@@ -1234,35 +1621,28 @@ if __name__ == "__main__":
                compact_btn = gr.Button(value="OPTION A - Compact and move model to '/trainedmodel/'")
                gr.Markdown(
                f"""
-                ##### This will compact the model and move it, along with the reference wav's to <span style="color: #3366ff;">/models/trainedmodel/</span> and will <span style="color: red;">OVERWRITE</span> any model in that location. This model will become available to load in Text-gen-webui with the <span style="color: #3366ff;">XTTSv2 FT</span> loader. It will also be detected by Finetune as a model that can be further trained (Reload finetune.py).
+                This will compact the model and move it, along with the reference wav's to <span style="color: #3366ff;">/models/trainedmodel/</span> and will <span style="color: red;">OVERWRITE</span> any model in that location. This model will become available to load in Text-gen-webui with the <span style="color: #3366ff;">XTTSv2 FT</span> loader. It will also be detected by Finetune as a model that can be further trained (Reload finetune.py).
                """
                )
            with gr.Row():
                compact_lastfinetuned_btn = gr.Button(value="OPTION B - Compact and move model to '/lastfinetuned/'")
                gr.Markdown(
                f"""
-                ##### This will compact the model and move it, along with the reference wav's to <span style="color: #3366ff;">/models/lastfinetuned/</span> and will <span style="color: red;">OVERWRITE</span> any model in that location. This will just leave you a finetuned model that you can do with as you please. It will <span style="color: red;">NOT</span> be available in Text-gen-webui unless you copy it over one of the other model loader locations.
+                This will compact the model and move it, along with the reference wav's to <span style="color: #3366ff;">/models/lastfinetuned/</span> and will <span style="color: red;">OVERWRITE</span> any model in that location. This will just leave you a finetuned model that you can do with as you please. It will <span style="color: red;">NOT</span> be available in Text-gen-webui unless you copy it over one of the other model loader locations.
                """
                )
            with gr.Row():
                delete_training_btn = gr.Button(value="Delete generated training data")
                gr.Markdown(
                f"""
-                ##### This will <span style="color: red;">DELETE</span> your training data and the raw finetuned model from <span style="color: #3366ff;">/finetune/tmp-trn/</span>.
+                This will <span style="color: red;">DELETE</span> your training data and the raw finetuned model from <span style="color: #3366ff;">/finetune/tmp-trn/</span>.
                """
                )
            with gr.Row():
                delete_voicesamples_btn = gr.Button(value="Delete original voice samples")
                gr.Markdown(
                f"""
-                ##### This will <span style="color: red;">DELETE</span> your original voice samples from <span style="color: #3366ff;">/finetune/put-voice-samples-in-here/</span>.
+                This will <span style="color: red;">DELETE</span> your original voice samples from <span style="color: #3366ff;">/finetune/put-voice-samples-in-here/</span>.
                """
                )
            with gr.Row():
                compact_legacy_btn = gr.Button(value="Compact a legacy finetuned model")
                gr.Markdown(
                f"""
                ##### This will compact your old finetuned models from 5GB to 1.9GB. Place your 5GB <span style="color: #3366ff;">base_model.pth</span> file in <span style="color: #3366ff;">/finetune/</span> (rename your file to <span style="color: #3366ff;">base_model.pth</span> if necessary). This will generate you a <span style="color: #3366ff;">model.pth</span> file.
                """
                )
@@ -1272,6 +1652,7 @@ if __name__ == "__main__":
                    lang,
                    whisper_model,
                    out_path,
                    eval_split_number,
                ],
                outputs=[
                    progress_data,
@@ -1332,10 +1713,6 @@ if __name__ == "__main__":
                fn=delete_voice_sample_contents,
                outputs=[final_progress_data],
            )
            compact_legacy_btn.click(
                fn=compact_legacy_model,
                outputs=[final_progress_data],
            )
            model_to_train.change(basemodel_or_finetunedmodel_choice, model_to_train, None)
    demo.queue().launch(
@@ -1,26 +0,0 @@
 import torch
 import os
 MODEL_PATH = "best_model.pth"
 # Check if best_model.pth exists
 if not os.path.exists(MODEL_PATH):
    print(f"\nCannot find \033[94m{MODEL_PATH}\033[0m in the current folder.")
    # Check if model.pth exists as an alternative
    ALTERNATIVE_PATH = "model.pth"
    if os.path.exists(ALTERNATIVE_PATH):
        print(f"\nIf your model is currently called \033[93m{ALTERNATIVE_PATH}\033[0m please rename it to \033[94mbest_model.pth\033[0m.")
    else:
        print(f"Please put \033[91mbest_model.pth\033[0m in the current folder or rename your model file to \033[94mbest_model.pth\033[0m.")
    exit(1)
 # Attempt to load the model
 try:
    checkpoint = torch.load(MODEL_PATH, map_location=torch.device("cpu"))
 except Exception as e:
    print("Error loading checkpoint:", e)
    raise
 del checkpoint["optimizer"]
 for key in list(checkpoint["model"].keys()):
    if "dvae" in key:
        del checkpoint["model"][key]
 torch.save(checkpoint, "model.pth")
 print(f"\nYour compacted model is created \033[93mmodel.pth\033[0m.")
@@ -0,0 +1,5 @@
 #!/bin/sh
 python modeldownload.py
 uvicorn tts_server:app --host 0.0.0.0 --port 7851 --workers 1 --proxy-headers &
 sleep 5
 python script.py
@@ -45,18 +45,18 @@ else:
    )
 # Read the version specifier from requirements_nvidia.txt
-with open(this_dir / "requirements_nvidia.txt", "r") as req_file:
+with open(this_dir / "system" / "requirements" / "requirements_standalone.txt", "r") as req_file:
    requirements = req_file.readlines()
 tts_version_required = None
 for req in requirements:
-    if req.startswith("TTS>="):
+    if req.startswith("TTS=="):
-        tts_version_required = req.strip().split(">=")[1]
+        tts_version_required = req.strip().split("==")[1]
        break
 if tts_version_required is None:
    raise ValueError(
-        f"[{params['branding']}Startup] \033[91mWarning\033[0m Could not find TTS version specifier in requirements_nvidia.txt"
+        f"[{params['branding']}Startup] \033[91mWarning\033[0m Could not find TTS version specifier in requirements file"
    )
@@ -0,0 +1,20 @@
 FROM nvidia/cuda:12.1.1-devel-ubuntu22.04
 ENV HOST 0.0.0.0
 RUN apt-get update && apt-get upgrade -y \
    && apt-get install -y git build-essential portaudio19-dev \
    python3 python3-pip gcc wget \
    ocl-icd-opencl-dev opencl-headers clinfo \
    libclblast-dev libopenblas-dev \
    && mkdir -p /etc/OpenCL/vendors && echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd \
    && ln -s /usr/bin/python3 /usr/bin/python
 WORKDIR /app
 COPY . .
 ENV CUDA_DOCKER_ARCH=all
 RUN pip install --no-cache-dir -r requirements_nvidia.txt && \
    pip install --no-cache-dir -r requirements_finetune.txt && \
    pip install --no-cache-dir -r requirements_other.txt && \
    pip install --no-cache-dir deepspeed
 EXPOSE 7851
 RUN chmod +x launch.sh
 ENTRYPOINT ["sh", "-c", "./launch.sh"]
@@ -1,7 +0,0 @@
 gradio>=3.50.2
 torch>=2.1.0+cu118
 torchaudio>=2.1.0+cu118
 TTS==0.21.3
 faster-whisper>=0.10.0
 tqdm>=4.66.0
 pandas>=1.5.0
@@ -1,15 +0,0 @@
 numpy>=1.22.0
 soundfile>=0.12.1
 uvicorn>=0.24.0.post1
 TTS>=0.21.3
 torch>=2.1.0
 torchaudio>=2.1.0
 fastapi>=0.104.1
 Jinja2>=3.1.2
 requests>=2.31.0
 tqdm>=4.66.1
 importlib-metadata>=4.8.1
 packaging>=23.2
 pydantic>=1.10.13
 sounddevice>=0.4.6
 python-multipart>=0.0.6
@@ -16,18 +16,28 @@ import numpy as np
 import soundfile as sf
 import uuid
 ######################################
 #### ALLTALK ALLOWED STARTUP TIME ####
 ######################################
 startup_wait_time = 120
 # You can change the above setting to a larger number to allow AllTAlk more time to start up. The default setting is 120 seconds (2 minutes).
 # On some older systems you may need to allow AllTalk more time. So you could set it to 240 (4 minutes) which will give AllTalk more to load.
 #################################################################
 #### LOAD PARAMS FROM confignew.json - REQUIRED FOR BRANDING ####
 #################################################################
 # STARTUP VARIABLE - Create "this_dir" variable as the current script directory
 this_dir = Path(__file__).parent.resolve()
 # load config file in and get settings
 def load_config(file_path):
    with open(file_path, "r") as config_file:
        config = json.load(config_file)
    return config
 config_file_path = this_dir / "confignew.json"
 # Load the params dictionary from the confignew.json file
 params = load_config(config_file_path)
@@ -48,6 +58,7 @@ except ModuleNotFoundError:
 # IMPORT - Attempt Importing DeepSpeed (required for displaying Deepspeed checkbox in gradio)
 try:
    import deepspeed
    deepspeed_installed = True
 except ImportError:
    deepspeed_installed = False
@@ -60,6 +71,7 @@ try:
    from modules.logging_colors import logger
    from modules.ui import create_refresh_button
    from modules.utils import gradio
    # This is set to check if the script is being run within text generation webui or as a standalone script. False is running as part of text gen web ui or a gradio interface
    running_in_standalone = False
    output_folder_wav = params["output_folder_wav"]
@@ -73,17 +85,19 @@ except ModuleNotFoundError:
 #### STARTUP VARIABLES ####
 ###########################
 # STARTUP VARIABLE - Import languges file for Gradio to be able to display them in the interface
-with open(this_dir / "languages.json", encoding="utf8") as f:
+with open(this_dir / "system" / "config" / "languages.json", encoding="utf8") as f:
    languages = json.load(f)
 # Create a global lock
 process_lock = threading.Lock()
 # Base setting for a possible FineTuned model existing and the loader being available
 tts_method_xtts_ft = False
 # Gather the voice files
 def get_available_voices():
    return sorted([voice.name for voice in Path(f"{this_dir}/voices").glob("*.wav")])
 #########################
 #### LICENSE DISPLAY ####
 #########################
@@ -141,6 +155,7 @@ def check_required_files():
    subprocess.run(["python", str(download_script_path)])
    print(f"[{params['branding']}Startup] All required files are present.")
 # STARTUP Call Check routine
 check_required_files()
@@ -155,7 +170,9 @@ finetuned_model = trained_model_directory.exists()
 # If true, this will add a extra option in the Gradio interface for loading Xttsv2 FT
 if finetuned_model:
    required_files = ["model.pth", "config.json", "vocab.json"]
-    finetuned_model = all((trained_model_directory / file).exists() for file in required_files)
+    finetuned_model = all(
        (trained_model_directory / file).exists() for file in required_files
    )
 if finetuned_model:
    print(f"[{params['branding']}Startup] Finetuned model \033[93mDetected\033[0m")
@@ -173,6 +190,7 @@ elif params["tts_method_xtts_local"] == True:
 # Set the default for Narrated text without asterisk or quotes to be Narrator
 non_quoted_text_is = True
 ######################
 #### GRADIO STUFF ####
 ######################
@@ -230,17 +248,23 @@ def signal_handler(sig, frame):
 # Attach the signal handler to the SIGINT signal (Ctrl+C)
 signal.signal(signal.SIGINT, signal_handler)
-
+# Check if we're running in docker
 if os.path.isfile("/.dockerenv"):
    print(
        f"[{params['branding']}Startup] \033[94mRunning in Docker. Please wait.\033[0m"
    )
 else:
    # Start the subprocess
    process = subprocess.Popen(["python", script_path])
    # Check if the subprocess has started successfully
    if process.poll() is None:
        print(f"[{params['branding']}Startup] TTS Subprocess starting")
        print(f"[{params['branding']}Startup]")
        print(
-        f"[{params['branding']}Startup] Readme available here:",
+            f"[{params['branding']}Startup] \033[94m {params['branding']}Settings & Documentation:\033[00m",
-        f"http://{params['ip_address']}:{params['port_number']}",
+            f"\033[92mhttp://{params['ip_address']}:{params['port_number']}\033[00m",
        )
        print(f"[{params['branding']}Startup]")
    else:
        print(
            f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess Webserver failing to start process"
@@ -264,7 +288,7 @@ else:
        # Cleanly kill off this script, but allow text-generation-webui to keep running, albeit without this alltalk_tts
        sys.exit(1)
-timeout = 120  # Adjust the timeout as needed
+    timeout = startup_wait_time  # Gather timeout setting from startup_wait_time
    # Introduce a delay before starting the check loop
    time.sleep(26)  # Wait 26 secs before checking if the tts_server.py has started up.
@@ -277,12 +301,24 @@ while time.time() - start_time < timeout:
        except requests.RequestException as e:
            # Print the exception for debugging purposes
            print(
-            f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess has NOT started up yet, Will keep trying for 120 seconds maximum. Please wait."
+                f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess has NOT started up yet, Will keep trying for {timeout} seconds maximum. Please wait."
            )
-    time.sleep(4)
+        time.sleep(5)
    else:
        print(
-        f"[{params['branding']}Startup] Startup timed out. Check the server logs for more information."
+            f"\n[{params['branding']}Startup] Startup timed out. Full help available here \033[92mhttps://github.com/erew123/alltalk_tts#-help-with-problems\033[0m"
        )
        print(
            f"[{params['branding']}Startup] On older system you may wish to open and edit \033[94mscript.py\033[0m with a text editor and changing the"
        )
        print(
            f"[{params['branding']}Startup] \033[94mstartup_wait_time = 120\033[0m setting to something like \033[94mstartup_wait_time = 240\033[0m as this will allow"
        )
        print(
            f"[{params['branding']}Startup] AllTalk more time to try load the model into your VRAM. Otherise please visit the Github for"
        )
        print(
            f"[{params['branding']}Startup] a list of other possible troubleshooting options."
        )
        # Cleanly kill off this script, but allow text-generation-webui to keep running, albeit without this alltalk_tts
        sys.exit(1)
@@ -311,26 +347,26 @@ def send_reload_request(tts_method):
                params["tts_method_xtts_local"] = False
                params["tts_method_api_tts"] = True
                params["deepspeed_activate"] = False
-                audio_path = this_dir / "templates" / "apitts.wav"
+                audio_path = this_dir / "system" / "at_sounds" / "apitts.wav"
                tts_method_xtts_ft = False
            elif tts_method == "API Local":
                params["tts_method_api_tts"] = False
                params["tts_method_xtts_local"] = False
                params["tts_method_api_local"] = True
                params["deepspeed_activate"] = False
-                audio_path = this_dir / "templates" / "apilocal.wav"
+                audio_path = this_dir / "system" / "at_sounds" / "apilocal.wav"
                tts_method_xtts_ft = False
            elif tts_method == "XTTSv2 Local":
                params["tts_method_api_tts"] = False
                params["tts_method_api_local"] = False
                params["tts_method_xtts_local"] = True
-                audio_path = this_dir / "templates" / "xttslocal.wav"
+                audio_path = this_dir / "system" / "at_sounds" / "xttslocal.wav"
                tts_method_xtts_ft = False
            elif tts_method == "XTTSv2 FT":
                params["tts_method_api_tts"] = False
                params["tts_method_api_local"] = False
                params["tts_method_xtts_local"] = False
-                audio_path = this_dir / "templates" / "xttsfinetuned.wav"
+                audio_path = this_dir / "system" / "at_sounds" / "xttsfinetuned.wav"
                tts_method_xtts_ft = True
        return f'<audio src="file/{audio_path}" controls autoplay></audio>'
    except requests.exceptions.RequestException as e:
@@ -340,6 +376,7 @@ def send_reload_request(tts_method):
        )
        return {"status": "error", "message": str(e)}
 ##################
 #### LOW VRAM ####
 ##################
@@ -348,9 +385,9 @@ def send_lowvram_request(low_vram):
    try:
        params["tts_model_loaded"] = False
        if low_vram:
-            audio_path = this_dir / "templates" / "lowvramenabled.wav"
+            audio_path = this_dir / "system" / "at_sounds" / "lowvramenabled.wav"
        else:
-            audio_path = this_dir / "templates" / "lowvramdisabled.wav"
+            audio_path = this_dir / "system" / "at_sounds" / "lowvramdisabled.wav"
        url = f"{base_url}/api/lowvramsetting?new_low_vram_value={low_vram}"
        headers = {"Content-Type": "application/json"}
        response = requests.post(url, headers=headers)
@@ -377,9 +414,9 @@ def send_deepspeed_request(deepspeed_param):
    try:
        params["tts_model_loaded"] = False
        if deepspeed_param:
-            audio_path = this_dir / "templates" / "deepspeedenabled.wav"
+            audio_path = this_dir / "system" / "at_sounds" / "deepspeedenabled.wav"
        else:
-            audio_path = this_dir / "templates" / "deepspeeddisabled.wav"
+            audio_path = this_dir / "system" / "at_sounds" / "deepspeeddisabled.wav"
        url = f"{base_url}/api/deepspeed?new_deepspeed_value={deepspeed_param}"
        headers = {"Content-Type": "application/json"}
@@ -454,9 +491,13 @@ def combine(audio_files, output_folder, state):
    # Save the combined audio to a file with a specified sample rate
    if "character_menu" in state:
-        output_file_path = os.path.join(output_folder, f'{state["character_menu"]}_{int(time.time())}_combined.wav')
+        output_file_path = os.path.join(
            output_folder, f'{state["character_menu"]}_{int(time.time())}_combined.wav'
        )
    else:
-        output_file_path = os.path.join(output_folder, f'TTSOUT_{int(time.time())}_combined.wav')
+        output_file_path = os.path.join(
            output_folder, f"TTSOUT_{int(time.time())}_combined.wav"
        )
    sf.write(output_file_path, audio, samplerate=sample_rate)
    # Clean up unnecessary files
    for audio_file in audio_files:
@@ -470,7 +511,7 @@ def combine(audio_files, output_folder, state):
 ################################
 # PREVIEW VOICE - Generate Random Sentence if Voice Preview box is empty
 def random_sentence():
-    with open(this_dir / "harvard_sentences.txt") as f:
+    with open(this_dir / "system" / "config" / "harvard_sentences.txt") as f:
        return random.choice(list(f))
@@ -487,12 +528,19 @@ def voice_preview(string):
    output_file = Path(params["output_folder_wav"]) / "voice_preview.wav"
    # Generate the audio
    language_code = languages.get(params["language"])
    temperature = params["local_temperature"]
    repetition_penalty = params["local_repetition_penalty"]
    # Convert the WindowsPath object to a string before using it in JSON payload
    output_file_str = output_file.as_posix()
    # Lock before making the generate request
    with process_lock:
        generate_response = send_generate_request(
-            string, params["voice"], language_code, output_file_str
+            string,
            params["voice"],
            language_code,
            temperature,
            repetition_penalty,
            output_file_str,
        )
    # Check if lock is already acquired
    if process_lock.locked():
@@ -509,8 +557,82 @@ def voice_preview(string):
        return f"[{params['branding']}Server] Audio generation failed. Status: {generate_response.get('status')}"
-def replace_asterisk(match):
+#######################
-    return match.group(0) + '&quot;<*'
+#### TEXT CLEANING ####
 #######################
 def process_text(text):
    # Normalize HTML encoded quotes
    text = html.unescape(text)
    # Replace ellipsis with a single dot
    text = re.sub(r"\.{3,}", ".", text)
    # Pattern to identify combined narrator and character speech
    combined_pattern = r'(\*[^*"]+\*|"[^"*]+")'
    # List to hold parts of speech along with their type
    ordered_parts = []
    # Track the start of the next segment
    start = 0
    # Find all matches
    for match in re.finditer(combined_pattern, text):
        # Add the text before the match, if any, as ambiguous
        if start < match.start():
            ambiguous_text = text[start : match.start()].strip()
            if ambiguous_text:
                ordered_parts.append(("ambiguous", ambiguous_text))
        # Add the matched part as either narrator or character
        matched_text = match.group(0)
        if matched_text.startswith("*") and matched_text.endswith("*"):
            ordered_parts.append(("narrator", matched_text.strip("*").strip()))
        elif matched_text.startswith('"') and matched_text.endswith('"'):
            ordered_parts.append(("character", matched_text.strip('"').strip()))
        else:
            # In case of mixed or improperly formatted parts
            if "*" in matched_text:
                ordered_parts.append(("narrator", matched_text.strip("*").strip('"')))
            else:
                ordered_parts.append(("character", matched_text.strip('"').strip("*")))
        # Update the start of the next segment
        start = match.end()
    # Add any remaining text after the last match as ambiguous
    if start < len(text):
        ambiguous_text = text[start:].strip()
        if ambiguous_text:
            ordered_parts.append(("ambiguous", ambiguous_text))
    return ordered_parts
 ########################
 #### IMAGE CLEANING ####
 ########################
 img_pattern = r'<img[^>]*src\s*=\s*["\'][^"\'>]+["\'][^>]*>'
 def extract_and_remove_images(text):
    """
    Extracts all image data from the text and removes it for clean TTS processing.
    Returns the cleaned text and the extracted image data.
    """
    img_matches = re.findall(img_pattern, text)
    img_info = "\n".join(img_matches)  # Store extracted image data
    cleaned_text = re.sub(img_pattern, '', text)  # Remove images from text
    return cleaned_text, img_info
 def reinsert_images(text, img_info):
    """
    Reinserts the previously extracted image data back into the text.
    """
    if img_info:  # Check if there are images to reinsert
        text += f"\n\n{img_info}"
    return text
 #################################
 #### TTS STANDARD GENERATION ####
@@ -519,102 +641,104 @@ def replace_asterisk(match):
 def output_modifier(string, state):
    if not params["activate"]:
        return string
-    original_string = string
+    img_info = ""
-    cleaned_string = before_audio_generation(string, params)
+    cleaned_text, img_info = extract_and_remove_images(string)
    # print("Cleaned STRING IS:", cleaned_text)
    cleaned_string = before_audio_generation(cleaned_text, params)
    if cleaned_string is None:
        return
    string = cleaned_string
    language_code = languages.get(params["language"])
    temperature = params["local_temperature"]
    repetition_penalty = params["local_repetition_penalty"]
    # Create a list to store generated audio paths
    audio_files = []
    if process_lock.acquire(blocking=False):
        try:
            if params["narrator_enabled"]:
-                # Do Some basic stripping and remove any double CR's
+                processed_parts = process_text(cleaned_string)
-                processed_string = (
+
                    original_string
                    .replace("***", "*")
                    .replace("**", "*")
                    .replace("\n\n", "\n")
                    .replace("\n", "")
                )
                processed_string = re.sub(r'\.\*\n\*', '. ', processed_string)
                processed_string = (processed_string .replace('&quot;\n\n*', '&quot;<*'))
                processed_string = (processed_string .replace('&quot;\n', '< '))
                processed_string = (processed_string .replace("&#x27;", "'"))
                processed_string = (processed_string .replace("\n", " "))
                processed_string = (processed_string .replace('&quot;', '&quot;<'))
                processed_string = (processed_string .replace('"', '&quot;<'))
                #pattern = re.compile(r'(?<=[a-zA-Z!])&quot;<(?=[a-zA-Z])')
                pattern = re.compile(r'(?<=[a-zA-Z!?\.])&quot;<(?=[a-zA-Z])')
                processed_string = pattern.sub('&quot; ', processed_string)
                pattern = re.compile(r'(?<=[a-zA-Z])\*(?=[a-zA-Z])')
                processed_string = pattern.sub('*&quot;', processed_string)
                processed_string = processed_string.replace('&quot;<. *', '&quot;< *"')
                processed_string = processed_string.replace('< *"', '< *')
                processed_string = processed_string.replace('. *', '< *')
                processed_string = processed_string.replace('! *', '&quot;< *')
                processed_string = processed_string.replace('? *', '< *')
                processed_string = processed_string.replace('* ', '* &quot;<')
                processed_string = processed_string.replace('*. ', '* &quot;<')
                processed_string = re.sub(r'\*(?=[a-zA-Z])', replace_asterisk, processed_string)
                # Set up a tracking of the individual wav files.
                audio_files_all_paragraphs = []
-                # Split the line using &quot; and ".* " (so end of sentences, leaving special characters added to the start of all OTHER sentences, bar possibly the first one if its starting with a *
+                for part_type, part in processed_parts:
                parts = re.split(r'&quot;|\.\*', processed_string)
                audio_files_paragraph = []
                for i, part in enumerate(parts):
                    # Skip parts that are too short
                    if len(part.strip()) <= 3:
                        continue
-                    # Figure out which type of line it is, then replace characters as necessary to avoid TTS trying to pronunce them, htmlunescape after. 
+
-                    # Character will always be a < with a letter immediately after it
+                    # Determine the voice to use based on the part type
-                    if '<' in part and '< *' not in part and '<*' not in part and '<  *' not in part and '< ' not in part and '<  ' not in part:
+                    if part_type == "narrator":
                        cleaned_part = html.unescape(part.replace('<', ''))
                        voice_to_use = params["voice"]
                        #Narrator will always be an * or < with an * a position or two after it.
                    elif '<*' in part or '< *' in part or '<  *' in part or '*' in part:
                        cleaned_part = html.unescape(part.replace('<*', '').replace('< *', '').replace('<  *', '').replace('*', ''))
                        voice_to_use = params["narrator_voice"]
-                        #If the other two dont capture it, aka, the AI gave no * or &quot; on the line, use non_quoted_text_is aka user interface, user can choose Char or Narrator
+                        print(
-                    elif non_quoted_text_is:
+                            f"[{params['branding']}TTSGen] \033[92mNarrator\033[0m"
-                        cleaned_part = html.unescape(part.replace('< ', '').replace('<  ', '').replace('<  ', ''))
+                        )  # Green
                    elif part_type == "character":
                        voice_to_use = params["voice"]
                        print(
                            f"[{params['branding']}TTSGen] \033[36mCharacter\033[0m"
                        )  # Yellow
                    else:
-                        cleaned_part = html.unescape(part.replace('< ', '').replace('<  ', '').replace('<  ', ''))
+                        # Handle ambiguous parts based on user preference
-                        voice_to_use = params["narrator_voice"]
+                        voice_to_use = (
-                    # Generate that TTS and output to a file
+                            params["voice"]
                            if non_quoted_text_is
                            else params["narrator_voice"]
                        )
                        voice_description = (
                            "\033[36mCharacter (Text-not-inside)\033[0m"
                            if non_quoted_text_is
                            else "\033[92mNarrator (Text-not-inside)\033[0m"
                        )
                        print(f"[{params['branding']}TTSGen] {voice_description}")
                    # Replace multiple exclamation marks, question marks, or other punctuation with a single instance
                    cleaned_part = re.sub(r"([!?.])\1+", r"\1", part)
                    # Further clean to remove any other unwanted characters
                    cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0100-\u017F\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\u4E00-\u9FFF\u3400-\u4DBF\u20000-\u2A6DF\u3000-\u303F\$]', '', cleaned_part)
                    # Remove all newline characters (single or multiple)
                    cleaned_part = re.sub(r"\n+", " ", cleaned_part)
                    # Generate TTS and output to a file
                    output_filename = get_output_filename(state)
                    generate_response = send_generate_request(
-                        cleaned_part, voice_to_use, language_code, output_filename
+                        cleaned_part,
                        voice_to_use,
                        language_code,
                        temperature,
                        repetition_penalty,
                        output_filename,
                    )
                    audio_path = generate_response.get("data", {}).get("audio_path")
-                    audio_files_paragraph.append(audio_path)
+                    audio_files_all_paragraphs.append(audio_path)
-                # Accumulate audio files within the paragraph
+
                audio_files_all_paragraphs.extend(audio_files_paragraph)
                # Combine audio files across paragraphs
                final_output_file = combine(
                    audio_files_all_paragraphs, params["output_folder_wav"], state
                )
            else:
-                processed_string = (
+                # Decode HTML entities first
-                    original_string
+                cleaned_part = html.unescape(cleaned_string)
-                    .replace("***", "")
+                # Replace multiple instances of certain punctuation marks with a single instance
-                    .replace("**", "")
+                cleaned_part = re.sub(r"([!?.])\1+", r"\1", cleaned_part)
-                    .replace("*", "")
+                # Further clean to remove any other unwanted characters
-                    .replace("\n\n", "\n")
+                cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0100-\u017F\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\u4E00-\u9FFF\u3400-\u4DBF\u20000-\u2A6DF\u3000-\u303F\$]', '', cleaned_part)
-                    .replace("&#x27;", "'")
+                # Remove all newline characters (single or multiple)
-                )
+                cleaned_part = re.sub(r"\n+", " ", cleaned_part)
                cleaned_part = html.unescape(processed_string)                
                # Process the part and give it a non-character name if being used vai API or standalone.
                if "character_menu" in state:
-                    output_file = Path(f'{params["output_folder_wav"]}/{state["character_menu"]}_{int(time.time())}.wav')
+                    output_file = Path(
                        f'{params["output_folder_wav"]}/{state["character_menu"]}_{int(time.time())}.wav'
                    )
                else:
-                    output_file = Path(f'{params["output_folder_wav"]}/TTSOUT_{int(time.time())}.wav')
+                    output_file = Path(
                        f'{params["output_folder_wav"]}/TTSOUT_{int(time.time())}.wav'
                    )
                output_file_str = output_file.as_posix()
                output_file = get_output_filename(state)
                generate_response = send_generate_request(
-                    cleaned_part, params["voice"], language_code, output_file_str
+                    cleaned_part,
                    params["voice"],
                    language_code,
                    temperature,
                    repetition_penalty,
                    output_file_str,
                )
                audio_path = generate_response.get("data", {}).get("audio_path")
                final_output_file = audio_path
@@ -633,42 +757,40 @@ def output_modifier(string, state):
        if audio_path:
            # Handle Gradio and playback
            autoplay = "autoplay" if params["autoplay"] else ""
-            string = (
+            string = (f'<audio src="file/{final_output_file}" controls {autoplay}></audio>')
                f'<audio src="file/{final_output_file}" controls {autoplay}></audio>'
            )
            if params["show_text"]:
-                string += f"\n\n{original_string}"
+                string += reinsert_images(cleaned_string, img_info)
                shared.processing_message = "*Is typing...*"
            return string
        else:
-            print(
+            print(f"[{params['branding']}Server] \033[91mWarning\033[0m No audio path in the response.")
                f"[{params['branding']}Server] \033[91mWarning\033[0m No audio path in the response."
            )
    else:
-        print(
+        print(f"[{params['branding']}Server] \033[91mWarning\033[0m Audio generation failed. Status:", generate_response.get("message"),)
            f"[{params['branding']}Server] \033[91mWarning\033[0m Audio generation failed. Status:",
            generate_response.get("message"),
        )
 def get_output_filename(state):
    if "character_menu" in state:
-        return Path(f'{params["output_folder_wav"]}/{state["character_menu"]}_{str(uuid.uuid4())[:8]}.wav').as_posix()
+        return Path(
            f'{params["output_folder_wav"]}/{state["character_menu"]}_{str(uuid.uuid4())[:8]}.wav'
        ).as_posix()
    else:
-        return Path(f'{params["output_folder_wav"]}/TTSOUT_{str(uuid.uuid4())[:8]}.wav').as_posix()
+        return Path(
            f'{params["output_folder_wav"]}/TTSOUT_{str(uuid.uuid4())[:8]}.wav'
        ).as_posix()
 ###############################################
 #### SEND GENERATION REQUEST TO TTS ENGINE ####
 ###############################################
-def send_generate_request(text, voice, language, output_file):
+def send_generate_request(
    text, voice, language, temperature, repetition_penalty, output_file
 ):
    url = f"{base_url}/api/generate"
    payload = {
        "text": text,
        "voice": voice,
        "language": language,
        "temperature": temperature,
        "repetition_penalty": repetition_penalty,
        "output_file": output_file,
    }
    headers = {"Content-Type": "application/json"}
@@ -693,12 +815,14 @@ def state_modifier(state):
    state["stream"] = False
    return state
 def update_narrator_enabled(value):
    if value == "Enabled":
        params["narrator_enabled"] = True
    elif value == "Disabled":
        params["narrator_enabled"] = False
 def update_non_quoted_text_is(value):
    global non_quoted_text_is
    if value == "Narrator":
@@ -706,6 +830,7 @@ def update_non_quoted_text_is(value):
    elif value == "Char":
        non_quoted_text_is = True
 def input_modifier(string, state):
    if not params["activate"]:
        return string
@@ -713,42 +838,56 @@ def input_modifier(string, state):
    shared.processing_message = "*Is recording a voice message...*"
    return string
 def custom_css():
    path_to_css = Path(f"{this_dir}/style.css")
    return open(path_to_css, "r").read()
 def ui():
    with gr.Accordion(params["branding"] + " TTS (XTTSv2)"):
        # Activate alltalk_tts, Enable autoplay, Show text
        with gr.Row():
-            activate = gr.Checkbox(value=params["activate"], label="Activate TTS")
+            activate = gr.Checkbox(value=params["activate"], label="Enable TTS")
-            autoplay = gr.Checkbox(
+            autoplay = gr.Checkbox(value=params["autoplay"], label="Autoplay TTS")
-                value=params["autoplay"], label="Play TTS automatically"
+            show_text = gr.Checkbox(value=params["show_text"], label="Show Text")
            )
        # Low vram enable, Deepspeed enable, Remove trailing dots
        with gr.Row():
-            show_text = gr.Checkbox(
+            low_vram = gr.Checkbox(
-                value=params["show_text"], label="Show message text under audio player"
+                value=params["low_vram"], label="Enable Low VRAM Mode"
            )
            low_vram_play = gr.HTML(visible=False)
            deepspeed_checkbox = gr.Checkbox(
                value=params["deepspeed_activate"],
                label="Enable DeepSpeed",
                visible=deepspeed_installed,
            )
            deepspeed_checkbox_play = gr.HTML(visible=False)
            remove_trailing_dots = gr.Checkbox(
-                value=params["remove_trailing_dots"],
+                value=params["remove_trailing_dots"], label='Remove trailing "."'
                label='Remove trailing "." from text segments before generation',
            )
        # TTS method, Character voice selection
        with gr.Row():
            model_loader_choices = ["API TTS", "API Local", "XTTSv2 Local"]
            if finetuned_model:
                model_loader_choices.append("XTTSv2 FT")
            tts_radio_buttons = gr.Radio(
                choices=model_loader_choices,
                label="TTS Method (Each method sounds slightly different)",
                value=gr_modelchoice,  # Set the default value
            )
            tts_radio_buttons_play = gr.HTML(visible=False)
            with gr.Row():
                available_voices = get_available_voices()
-                default_voice = params["voice"]
+                default_voice = params[
-                # Check if the default voice is in the list of available voices
+                    "voice"
                ]  # Check if the default voice is in the list of available voices
                if default_voice not in available_voices:
                    # Handle the case where the default voice is not in the list (choose a default value or update it)
                    default_voice = available_voices[
                        0
                    ]  # Choose the first available voice as the default
                # Add allow_custom_value=True to the Dropdown
                voice = gr.Dropdown(
                    available_voices,
-                    label="Default Voice",
+                    label="Character Voice",
                    value=default_voice,
                    allow_custom_value=True,
                )
@@ -762,14 +901,11 @@ def ui():
                    "refresh-button",
                )
-            language = gr.Dropdown(
+        # Language, Narrator voice
                languages.keys(),
                label="Language",
                allow_custom_value=True,
                value=params["language"],
            )
        with gr.Row():
            language = gr.Dropdown(
                languages.keys(), label="Language", value=params["language"]
            )
            with gr.Row():
                narrator_voice_gr = gr.Dropdown(
                    get_available_voices(),
@@ -786,43 +922,42 @@ def ui():
                    },
                    "refresh-button",
                )
        # Temperature, Repetition Penalty
        with gr.Row():
            local_temperature_gr = gr.Slider(
                minimum=0.05,
                maximum=1,
                step=0.05,
                label="Temperature",
                value=params["local_temperature"],
            )
            local_repetition_penalty_gr = gr.Slider(
                minimum=0.5,
                maximum=20,
                step=0.5,
                label="Repetition Penalty",
                value=params["local_repetition_penalty"],
            )
        # Narrator enable, Non quoted text, Explanation text
        with gr.Row():
            with gr.Row():
                narrator_enabled_gr = gr.Radio(
                    choices={"Enabled": "true", "Disabled": "false"},
-                    label="Narrator Activation",
+                    label="Narrator",
                    value="Enabled" if params.get("narrator_enabled") else "Disabled",
                )
                non_quoted_text_is_gr = gr.Radio(
-                    choices={"Char": "true", "Narrator": "false"},
+                    choices={"Character": "true", "Narrator": "false"},
-                    label='Text NOT inside of * or " is',
+                    label='Unmarked text NOT inside of * or " is',
-                    value="Char" if non_quoted_text_is else "Narrator",
+                    value="Character" if non_quoted_text_is else "Narrator",
                )
        with gr.Row():
            low_vram = gr.Checkbox(
                value=params["low_vram"], label="Low VRAM mode (Read NOTE)"
            )
            low_vram_play = gr.HTML(visible=False)
            deepspeed_checkbox = gr.Checkbox(
                value=params["deepspeed_activate"],
                label="Activate DeepSpeed (Read NOTE)",
                visible=deepspeed_installed,
            )
            deepspeed_checkbox_play = gr.HTML(visible=False)
        with gr.Row():
            model_loader_choices=["API TTS", "API Local", "XTTSv2 Local"]
            if finetuned_model:
                model_loader_choices.append("XTTSv2 FT")
            tts_radio_buttons = gr.Radio(
                choices=model_loader_choices,
                label="Select TTS Generation Method (Read NOTE)",
                value=gr_modelchoice,  # Set the default value
            )
            tts_radio_buttons_play = gr.HTML(visible=False)
                explanation_text = gr.HTML(
-                f"<p>⚙️ <a href='http://{params['ip_address']}:{params['port_number']}'>Click here for the Settings page, help & instructions</a><a href='http://{params['ip_address']}:{params['port_number']}'></a>⚙️<br>NOTE: Switching Model Type, Low VRAM & DeepSpeed, each takes 15 seconds. Each TTS generation method has a slightly different sound. DeepSpeed checkbox is only visible if DeepSpeed is installed on your system."
+                    f"<p>⚙️ <a href='http://{params['ip_address']}:{params['port_number']}'>Settings and Documentation Page</a><a href='http://{params['ip_address']}:{params['port_number']}'></a>⚙️<br>- Low VRAM Mode and Deepspeed take 15 seconds to be enabled or disabled.<br>- The DeepSpeed checkbox is only visible if DeepSpeed is present.</p>"
                )
        # Preview speech
        with gr.Row():
            preview_text = gr.Text(
                show_label=False,
@@ -892,18 +1027,34 @@ def ui():
    autoplay.change(lambda x: params.update({"autoplay": x}), autoplay, None)
    low_vram.change(lambda x: params.update({"low_vram": x}), low_vram, None)
    low_vram.change(lambda x: send_lowvram_request(x), low_vram, low_vram_play, None)
-    tts_radio_buttons.change(send_reload_request, tts_radio_buttons, tts_radio_buttons_play, None)
+    tts_radio_buttons.change(
-    deepspeed_checkbox.change(send_deepspeed_request, deepspeed_checkbox, deepspeed_checkbox_play, None)
+        send_reload_request, tts_radio_buttons, tts_radio_buttons_play, None
    )
    deepspeed_checkbox.change(
        send_deepspeed_request, deepspeed_checkbox, deepspeed_checkbox_play, None
    )
    remove_trailing_dots.change(
        lambda x: params.update({"remove_trailing_dots": x}), remove_trailing_dots, None
    )
    voice.change(lambda x: params.update({"voice": x}), voice, None)
    language.change(lambda x: params.update({"language": x}), language, None)
    # TSS Settings
    local_temperature_gr.change(
        lambda x: params.update({"local_temperature": x}), local_temperature_gr, None
    )
    local_repetition_penalty_gr.change(
        lambda x: params.update({"local_repetition_penalty": x}),
        local_repetition_penalty_gr,
        None,
    )
    # Narrator selection actions
    narrator_enabled_gr.change(update_narrator_enabled, narrator_enabled_gr, None)
    non_quoted_text_is_gr.change(update_non_quoted_text_is, non_quoted_text_is_gr, None)
-    narrator_voice_gr.change(lambda x: params.update({"narrator_voice": x}), narrator_voice_gr, None)
+    narrator_voice_gr.change(
        lambda x: params.update({"narrator_voice": x}), narrator_voice_gr, None
    )
    # Play preview
    preview_text.submit(voice_preview, preview_text, preview_audio)
@@ -1,8 +0,0 @@
 .SDAP .hires_opts input[type="number"] {
    width: 6em !important;
 }
 /* silero_tts preview */
 .form:has(> #silero_preview_text) {
    min-width: 75%
 }
@@ -89,25 +89,25 @@
    <h3>AllTalk Startup Settings</h3><br>
    <form action="/update-settings" method="post">
-        <label for="activate" style="color: #33cc33;">Activate TTS:</label>
+        <label for="activate" style="color: #b273fa;">TG-webui Activate TTS:</label>
        <select id="activate" name="activate" required="">
            <option {% if data.activate %}selected{% endif %} value="true">True</option>
            <option {% if not data.activate %}selected{% endif %} value="false">False</option>
        </select>
-        <label for="autoplay" style="color: #33cc33;">&nbsp; &nbsp; Autoplay TTS output:</label>
+        <label for="autoplay" style="color: #b273fa;">&nbsp; &nbsp; TG-webui Autoplay TTS:</label>
        <select id="autoplay" name="autoplay" required="">
            <option {% if data.autoplay %}selected{% endif %} value="true">True</option>
            <option {% if not data.autoplay %}selected{% endif %} value="false">False</option>
        </select>
-        <label for="show_text" style="color: #33cc33;"> Show Text on generation:</label>
+        <label for="show_text" style="color: #b273fa;">TG-webui Show Text:</label>
        <select id="show_text" name="show_text" required="">
            <option {% if data.show_text %}selected{% endif %} value="true">True</option>
            <option {% if not data.show_text %}selected{% endif %} value="false">False</option>
        </select>
-        <label for="voice" style="color: #33cc33;">&nbsp; &nbsp; Default voice:</label>
+        <label for="voice" style="color: #b273fa;">&nbsp; &nbsp;  TG-webui Default voice:</label>
        <select id="voice" name="voice" required="">
            {% for file in wav_files %}
            <option value="{{ file }}" {% if data.voice==file %}selected{% endif %}>{{ file }}</option>
@@ -115,7 +115,7 @@
        </select>
-        <label for="language" style="color: #33cc33;">Default Language:</label>
+        <label for="language" style="color: #b273fa;">TG-webui Default Language:</label>
        <select id="language" name="language" required="">
            <option value="Arabic" {% if data.language=='Arabic' %}selected{% endif %}>Arabic</option>
            <option value="Chinese" {% if data.language=='Chinese' %}selected{% endif %}>Chinese</option>
@@ -135,20 +135,20 @@
            <option value="Turkish" {% if data.language=='Turkish' %}selected{% endif %}>Turkish</option>
        </select>
-        <label for="narrator_voice" style="color: #33cc33;">&nbsp; &nbsp; Narrator voice:</label>
+        <label for="narrator_voice" style="color: #b273fa;">&nbsp; &nbsp; TG-webui Narrator voice:</label>
        <select id="narrator_voice" name="narrator_voice" required="">
            {% for file in wav_files %}
            <option value="{{ file }}" {% if data.narrator_voice==file %}selected{% endif %}>{{ file }}</option>
            {% endfor %}
        </select>
-        <label for="remove_trailing_dots" style="color: #33cc33;"> Remove Trailing Dots:</label>
+        <label for="remove_trailing_dots" style="color: #b273fa;">TG-webui Remove Trailing Dots:</label>
        <select id="remove_trailing_dots" name="remove_trailing_dots" required="">
            <option {% if data.remove_trailing_dots %}selected{% endif %} value="true">True</option>
            <option {% if not data.remove_trailing_dots %}selected{% endif %} value="false">False</option>
        </select>
-        <label for="narrator_enabled" style="color: #33cc33;">&nbsp; &nbsp; Narrator enabled:</label>
+        <label for="narrator_enabled" style="color: #b273fa;">&nbsp; &nbsp; TG-webui Narrator enabled:</label>
        <select id="narrator_enabled" name="narrator_enabled" required="">
            <option {% if data.narrator_enabled %}selected{% endif %} value="true">True</option>
            <option {% if not data.narrator_enabled %}selected{% endif %} value="false">False</option>
@@ -0,0 +1,869 @@
 import os
 import re
 import textwrap
 from functools import cached_property
 import pypinyin
 import torch
 from hangul_romanize import Transliter
 from hangul_romanize.rule import academic
 from num2words import num2words
 from spacy.lang.ar import Arabic
 from spacy.lang.en import English
 from spacy.lang.es import Spanish
 from spacy.lang.ja import Japanese
 from spacy.lang.zh import Chinese
 from tokenizers import Tokenizer
 from TTS.tts.layers.xtts.zh_num2words import TextNorm as zh_num2words
 def get_spacy_lang(lang):
    if lang == "zh":
        return Chinese()
    elif lang == "ja":
        return Japanese()
    elif lang == "ar":
        return Arabic()
    elif lang == "es":
        return Spanish()
    else:
        # For most languages, Enlish does the job
        return English()
 def split_sentence(text, lang, text_split_length=250):
    """Preprocess the input text"""
    text_splits = []
    if text_split_length is not None and len(text) >= text_split_length:
        text_splits.append("")
        nlp = get_spacy_lang(lang)
        nlp.add_pipe("sentencizer")
        doc = nlp(text)
        for sentence in doc.sents:
            if len(text_splits[-1]) + len(str(sentence)) <= text_split_length:
                # if the last sentence + the current sentence is less than the text_split_length
                # then add the current sentence to the last sentence
                text_splits[-1] += " " + str(sentence)
                text_splits[-1] = text_splits[-1].lstrip()
            elif len(str(sentence)) > text_split_length:
                # if the current sentence is greater than the text_split_length
                for line in textwrap.wrap(
                    str(sentence),
                    width=text_split_length,
                    drop_whitespace=True,
                    break_on_hyphens=False,
                    tabsize=1,
                ):
                    text_splits.append(str(line))
            else:
                text_splits.append(str(sentence))
        if len(text_splits) > 1:
            if text_splits[0] == "":
                del text_splits[0]
    else:
        text_splits = [text.lstrip()]
    return text_splits
 _whitespace_re = re.compile(r"\s+")
 # List of (regular expression, replacement) pairs for abbreviations:
 _abbreviations = {
    "en": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            ("mrs", "misess"),
            ("mr", "mister"),
            ("dr", "doctor"),
            ("st", "saint"),
            ("co", "company"),
            ("jr", "junior"),
            ("maj", "major"),
            ("gen", "general"),
            ("drs", "doctors"),
            ("rev", "reverend"),
            ("lt", "lieutenant"),
            ("hon", "honorable"),
            ("sgt", "sergeant"),
            ("capt", "captain"),
            ("esq", "esquire"),
            ("ltd", "limited"),
            ("col", "colonel"),
            ("ft", "fort"),
        ]
    ],
    "es": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            ("sra", "señora"),
            ("sr", "señor"),
            ("dr", "doctor"),
            ("dra", "doctora"),
            ("st", "santo"),
            ("co", "compañía"),
            ("jr", "junior"),
            ("ltd", "limitada"),
        ]
    ],
    "fr": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            ("mme", "madame"),
            ("mr", "monsieur"),
            ("dr", "docteur"),
            ("st", "saint"),
            ("co", "compagnie"),
            ("jr", "junior"),
            ("ltd", "limitée"),
        ]
    ],
    "de": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            ("fr", "frau"),
            ("dr", "doktor"),
            ("st", "sankt"),
            ("co", "firma"),
            ("jr", "junior"),
        ]
    ],
    "pt": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            ("sra", "senhora"),
            ("sr", "senhor"),
            ("dr", "doutor"),
            ("dra", "doutora"),
            ("st", "santo"),
            ("co", "companhia"),
            ("jr", "júnior"),
            ("ltd", "limitada"),
        ]
    ],
    "it": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            # ("sig.ra", "signora"),
            ("sig", "signore"),
            ("dr", "dottore"),
            ("st", "santo"),
            ("co", "compagnia"),
            ("jr", "junior"),
            ("ltd", "limitata"),
        ]
    ],
    "pl": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            ("p", "pani"),
            ("m", "pan"),
            ("dr", "doktor"),
            ("sw", "święty"),
            ("jr", "junior"),
        ]
    ],
    "ar": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            # There are not many common abbreviations in Arabic as in English.
        ]
    ],
    "zh": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            # Chinese doesn't typically use abbreviations in the same way as Latin-based scripts.
        ]
    ],
    "cs": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            ("dr", "doktor"),  # doctor
            ("ing", "inženýr"),  # engineer
            ("p", "pan"),  # Could also map to pani for woman but no easy way to do it
            # Other abbreviations would be specialized and not as common.
        ]
    ],
    "ru": [
        (re.compile("\\b%s\\b" % x[0], re.IGNORECASE), x[1])
        for x in [
            ("г-жа", "госпожа"),  # Mrs.
            ("г-н", "господин"),  # Mr.
            ("д-р", "доктор"),  # doctor
            # Other abbreviations are less common or specialized.
        ]
    ],
    "nl": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            ("dhr", "de heer"),  # Mr.
            ("mevr", "mevrouw"),  # Mrs.
            ("dr", "dokter"),  # doctor
            ("jhr", "jonkheer"),  # young lord or nobleman
            # Dutch uses more abbreviations, but these are the most common ones.
        ]
    ],
    "tr": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            ("b", "bay"),  # Mr.
            ("byk", "büyük"),  # büyük
            ("dr", "doktor"),  # doctor
            # Add other Turkish abbreviations here if needed.
        ]
    ],
    "hu": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            ("dr", "doktor"),  # doctor
            ("b", "bácsi"),  # Mr.
            ("nőv", "nővér"),  # nurse
            # Add other Hungarian abbreviations here if needed.
        ]
    ],
    "ko": [
        (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
        for x in [
            # Korean doesn't typically use abbreviations in the same way as Latin-based scripts.
        ]
    ],
    "ja": [
        (re.compile("\\b%s\\b" % x[0]), x[1])
        for x in [
            ("氏", "さん"),  # Mr.
            ("夫人", "おんなのひと"),  # Mrs.
            ("博士", "はかせ"),  # Doctor or PhD
            ("株", "株式会社"),  # Corporation
            ("有", "有限会社"),  # Limited company
            ("大学", "だいがく"),   # University
            ("先生", "せんせい"),   # Teacher/Professor/Master
            ("君", "くん")   # Used at the end of boys' names to express familiarity or affection.
        ]
    ],
 }
 def expand_abbreviations_multilingual(text, lang="en"):
    for regex, replacement in _abbreviations[lang]:
        text = re.sub(regex, replacement, text)
    return text
 _symbols_multilingual = {
    "en": [
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " and "),
            ("@", " at "),
            ("%", " percent "),
            ("#", " hash "),
            ("$", " dollar "),
            ("£", " pound "),
            ("°", " degree "),
        ]
    ],
    "es": [
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " y "),
            ("@", " arroba "),
            ("%", " por ciento "),
            ("#", " numeral "),
            ("$", " dolar "),
            ("£", " libra "),
            ("°", " grados "),
        ]
    ],
    "fr": [
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " et "),
            ("@", " arobase "),
            ("%", " pour cent "),
            ("#", " dièse "),
            ("$", " dollar "),
            ("£", " livre "),
            ("°", " degrés "),
        ]
    ],
    "de": [
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " und "),
            ("@", " at "),
            ("%", " prozent "),
            ("#", " raute "),
            ("$", " dollar "),
            ("£", " pfund "),
            ("°", " grad "),
        ]
    ],
    "pt": [
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " e "),
            ("@", " arroba "),
            ("%", " por cento "),
            ("#", " cardinal "),
            ("$", " dólar "),
            ("£", " libra "),
            ("°", " graus "),
        ]
    ],
    "it": [
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " e "),
            ("@", " chiocciola "),
            ("%", " per cento "),
            ("#", " cancelletto "),
            ("$", " dollaro "),
            ("£", " sterlina "),
            ("°", " gradi "),
        ]
    ],
    "pl": [
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " i "),
            ("@", " małpa "),
            ("%", " procent "),
            ("#", " krzyżyk "),
            ("$", " dolar "),
            ("£", " funt "),
            ("°", " stopnie "),
        ]
    ],
    "ar": [
        # Arabic
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " و "),
            ("@", " على "),
            ("%", " في المئة "),
            ("#", " رقم "),
            ("$", " دولار "),
            ("£", " جنيه "),
            ("°", " درجة "),
        ]
    ],
    "zh": [
        # Chinese
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " 和 "),
            ("@", " 在 "),
            ("%", " 百分之 "),
            ("#", " 号 "),
            ("$", " 美元 "),
            ("£", " 英镑 "),
            ("°", " 度 "),
        ]
    ],
    "cs": [
        # Czech
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " a "),
            ("@", " na "),
            ("%", " procento "),
            ("#", " křížek "),
            ("$", " dolar "),
            ("£", " libra "),
            ("°", " stupně "),
        ]
    ],
    "ru": [
        # Russian
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " и "),
            ("@", " собака "),
            ("%", " процентов "),
            ("#", " номер "),
            ("$", " доллар "),
            ("£", " фунт "),
            ("°", " градус "),
        ]
    ],
    "nl": [
        # Dutch
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " en "),
            ("@", " bij "),
            ("%", " procent "),
            ("#", " hekje "),
            ("$", " dollar "),
            ("£", " pond "),
            ("°", " graden "),
        ]
    ],
    "tr": [
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " ve "),
            ("@", " at "),
            ("%", " yüzde "),
            ("#", " diyez "),
            ("$", " dolar "),
            ("£", " sterlin "),
            ("°", " derece "),
        ]
    ],
    "hu": [
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " és "),
            ("@", " kukac "),
            ("%", " százalék "),
            ("#", " kettőskereszt "),
            ("$", " dollár "),
            ("£", " font "),
            ("°", " fok "),
        ]
    ],
    "ko": [
        # Korean
        (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
        for x in [
            ("&", " 그리고 "),
            ("@", " 에 "),
            ("%", " 퍼센트 "),
            ("#", " 번호 "),
            ("$", " 달러 "),
            ("£", " 파운드 "),
            ("°", " 도 "),
        ]
    ],
    "ja": [
        (re.compile(r"%s" % re.escape(x[0])), x[1])
        for x in [
            ("&", " と "),
            ("@", " アットマーク "),
            ("%", " パーセント "),
            ("#", " ナンバー "),
            ("$", " ドル "),
            ("£", " ポンド "),
            ("°", " 度"),
            ]
    ],
 }
 def expand_symbols_multilingual(text, lang="en"):
    for regex, replacement in _symbols_multilingual[lang]:
        text = re.sub(regex, replacement, text)
        text = text.replace("  ", " ")  # Ensure there are no double spaces
    return text.strip()
 _ordinal_re = {
    "en": re.compile(r"([0-9]+)(st|nd|rd|th)"),
    "es": re.compile(r"([0-9]+)(º|ª|er|o|a|os|as)"),
    "fr": re.compile(r"([0-9]+)(º|ª|er|re|e|ème)"),
    "de": re.compile(r"([0-9]+)(st|nd|rd|th|º|ª|\.(?=\s|$))"),
    "pt": re.compile(r"([0-9]+)(º|ª|o|a|os|as)"),
    "it": re.compile(r"([0-9]+)(º|°|ª|o|a|i|e)"),
    "pl": re.compile(r"([0-9]+)(º|ª|st|nd|rd|th)"),
    "ar": re.compile(r"([0-9]+)(ون|ين|ث|ر|ى)"),
    "cs": re.compile(r"([0-9]+)\.(?=\s|$)"),  # In Czech, a dot is often used after the number to indicate ordinals.
    "ru": re.compile(r"([0-9]+)(-й|-я|-е|-ое|-ье|-го)"),
    "nl": re.compile(r"([0-9]+)(de|ste|e)"),
    "tr": re.compile(r"([0-9]+)(\.|inci|nci|uncu|üncü|\.)"),
    "hu": re.compile(r"([0-9]+)(\.|adik|edik|odik|edik|ödik|ödike|ik)"),
    "ko": re.compile(r"([0-9]+)(번째|번|차|째)"),
    "ja": re.compile(r"([0-9]+)(番|回|つ|目|等|位)")
 }
 _number_re = re.compile(r"[0-9]+")
 _currency_re = {
    "USD": re.compile(r"((\$[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+\$))"),
    "GBP": re.compile(r"((£[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+£))"),
    "EUR": re.compile(r"(([0-9\.\,]*[0-9]+€)|((€[0-9\.\,]*[0-9]+)))"),
 }
 _comma_number_re = re.compile(r"\b\d{1,3}(,\d{3})*(\.\d+)?\b")
 _dot_number_re = re.compile(r"\b\d{1,3}(.\d{3})*(\,\d+)?\b")
 _decimal_number_re = re.compile(r"([0-9]+[.,][0-9]+)")
 def _remove_commas(m):
    text = m.group(0)
    if "," in text:
        text = text.replace(",", "")
    return text
 def _remove_dots(m):
    text = m.group(0)
    if "." in text:
        text = text.replace(".", "")
    return text
 def _expand_decimal_point(m, lang="en"):
    amount = m.group(1).replace(",", ".")
    return num2words(float(amount), lang=lang if lang != "cs" else "cz")
 def _expand_currency(m, lang="en", currency="USD"):
    amount = float((re.sub(r"[^\d.]", "", m.group(0).replace(",", "."))))
    full_amount = num2words(amount, to="currency", currency=currency, lang=lang if lang != "cs" else "cz")
    and_equivalents = {
        "en": ", ",
        "es": " con ",
        "fr": " et ",
        "de": " und ",
        "pt": " e ",
        "it": " e ",
        "pl": ", ",
        "cs": ", ",
        "ru": ", ",
        "nl": ", ",
        "ar": ", ",
        "tr": ", ",
        "hu": ", ",
        "ko": ", ",
    }
    if amount.is_integer():
        last_and = full_amount.rfind(and_equivalents[lang])
        if last_and != -1:
            full_amount = full_amount[:last_and]
    return full_amount
 def _expand_ordinal(m, lang="en"):
    return num2words(int(m.group(1)), ordinal=True, lang=lang if lang != "cs" else "cz")
 def _expand_number(m, lang="en"):
    return num2words(int(m.group(0)), lang=lang if lang != "cs" else "cz")
 def expand_numbers_multilingual(text, lang="en"):
    if lang == "zh":
        text = zh_num2words()(text)
    else:
        if lang in ["en", "ru"]:
            text = re.sub(_comma_number_re, _remove_commas, text)
        else:
            text = re.sub(_dot_number_re, _remove_dots, text)
        try:
            text = re.sub(_currency_re["GBP"], lambda m: _expand_currency(m, lang, "GBP"), text)
            text = re.sub(_currency_re["USD"], lambda m: _expand_currency(m, lang, "USD"), text)
            text = re.sub(_currency_re["EUR"], lambda m: _expand_currency(m, lang, "EUR"), text)
        except:
            pass
        if lang != "tr":
            text = re.sub(_decimal_number_re, lambda m: _expand_decimal_point(m, lang), text)
        text = re.sub(_ordinal_re[lang], lambda m: _expand_ordinal(m, lang), text)
        text = re.sub(_number_re, lambda m: _expand_number(m, lang), text)
    return text
 def lowercase(text):
    return text.lower()
 def collapse_whitespace(text):
    return re.sub(_whitespace_re, " ", text)
 def multilingual_cleaners(text, lang):
    text = text.replace('"', "")
    if lang == "tr":
        text = text.replace("İ", "i")
        text = text.replace("Ö", "ö")
        text = text.replace("Ü", "ü")
    text = lowercase(text)
    text = expand_numbers_multilingual(text, lang)
    text = expand_abbreviations_multilingual(text, lang)
    text = expand_symbols_multilingual(text, lang=lang)
    text = collapse_whitespace(text)
    return text
 def basic_cleaners(text):
    """Basic pipeline that lowercases and collapses whitespace without transliteration."""
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text
 def chinese_transliterate(text):
    return "".join(
        [p[0] for p in pypinyin.pinyin(text, style=pypinyin.Style.TONE3, heteronym=False, neutral_tone_with_five=True)]
    )
 def japanese_cleaners(text, katsu):
    text = katsu.romaji(text)
    text = lowercase(text)
    return text
 def korean_transliterate(text):
    r = Transliter(academic)
    return r.translit(text)
 DEFAULT_VOCAB_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), "../data/tokenizer.json")
 class VoiceBpeTokenizer:
    def __init__(self, vocab_file=None):
        self.tokenizer = None
        if vocab_file is not None:
            self.tokenizer = Tokenizer.from_file(vocab_file)
        self.char_limits = {
            "en": 250,
            "de": 253,
            "fr": 273,
            "es": 239,
            "it": 213,
            "pt": 203,
            "pl": 224,
            "zh": 82,
            "ar": 166,
            "cs": 186,
            "ru": 182,
            "nl": 251,
            "tr": 226,
            "ja": 71,
            "hu": 224,
            "ko": 95,
        }
    @cached_property
    def katsu(self):
        import cutlet
        return cutlet.Cutlet()
    def check_input_length(self, txt, lang):
        lang = lang.split("-")[0]  # remove the region
        limit = self.char_limits.get(lang, 250)
        if len(txt) > limit:
            print(
                f"[!] Warning: The text length exceeds the character limit of {limit} for language '{lang}', this might cause truncated audio."
            )
    def preprocess_text(self, txt, lang):
        if lang in {"ar", "cs", "de", "en", "es", "fr", "hu", "it", "nl", "pl", "pt", "ru", "tr", "zh", "ko"}:
            txt = multilingual_cleaners(txt, lang)
            if lang == "zh":
                txt = chinese_transliterate(txt)
            if lang == "ko":
                txt = korean_transliterate(txt)
        elif lang == "ja":
            txt = japanese_cleaners(txt, self.katsu)
        elif lang == "hi":
            # @manmay will implement this
            txt = basic_cleaners(txt)
        else:
            raise NotImplementedError(f"Language '{lang}' is not supported.")
        return txt
    def encode(self, txt, lang):
        lang = lang.split("-")[0]  # remove the region
        self.check_input_length(txt, lang)
        txt = self.preprocess_text(txt, lang)
        lang = "zh-cn" if lang == "zh" else lang
        txt = f"[{lang}]{txt}"
        txt = txt.replace(" ", "[SPACE]")
        return self.tokenizer.encode(txt).ids
    def decode(self, seq):
        if isinstance(seq, torch.Tensor):
            seq = seq.cpu().numpy()
        txt = self.tokenizer.decode(seq, skip_special_tokens=False).replace(" ", "")
        txt = txt.replace("[SPACE]", " ")
        txt = txt.replace("[STOP]", "")
        txt = txt.replace("[UNK]", "")
        return txt
    def __len__(self):
        return self.tokenizer.get_vocab_size()
    def get_number_tokens(self):
        return max(self.tokenizer.get_vocab().values()) + 1
 def test_expand_numbers_multilingual():
    test_cases = [
        # English
        ("In 12.5 seconds.", "In twelve point five seconds.", "en"),
        ("There were 50 soldiers.", "There were fifty soldiers.", "en"),
        ("This is a 1st test", "This is a first test", "en"),
        ("That will be $20 sir.", "That will be twenty dollars sir.", "en"),
        ("That will be 20€ sir.", "That will be twenty euro sir.", "en"),
        ("That will be 20.15€ sir.", "That will be twenty euro, fifteen cents sir.", "en"),
        ("That's 100,000.5.", "That's one hundred thousand point five.", "en"),
        # French
        ("En 12,5 secondes.", "En douze virgule cinq secondes.", "fr"),
        ("Il y avait 50 soldats.", "Il y avait cinquante soldats.", "fr"),
        ("Ceci est un 1er test", "Ceci est un premier test", "fr"),
        ("Cela vous fera $20 monsieur.", "Cela vous fera vingt dollars monsieur.", "fr"),
        ("Cela vous fera 20€ monsieur.", "Cela vous fera vingt euros monsieur.", "fr"),
        ("Cela vous fera 20,15€ monsieur.", "Cela vous fera vingt euros et quinze centimes monsieur.", "fr"),
        ("Ce sera 100.000,5.", "Ce sera cent mille virgule cinq.", "fr"),
        # German
        ("In 12,5 Sekunden.", "In zwölf Komma fünf Sekunden.", "de"),
        ("Es gab 50 Soldaten.", "Es gab fünfzig Soldaten.", "de"),
        ("Dies ist ein 1. Test", "Dies ist ein erste Test", "de"),  # Issue with gender
        ("Das macht $20 Herr.", "Das macht zwanzig Dollar Herr.", "de"),
        ("Das macht 20€ Herr.", "Das macht zwanzig Euro Herr.", "de"),
        ("Das macht 20,15€ Herr.", "Das macht zwanzig Euro und fünfzehn Cent Herr.", "de"),
        # Spanish
        ("En 12,5 segundos.", "En doce punto cinco segundos.", "es"),
        ("Había 50 soldados.", "Había cincuenta soldados.", "es"),
        ("Este es un 1er test", "Este es un primero test", "es"),
        ("Eso le costará $20 señor.", "Eso le costará veinte dólares señor.", "es"),
        ("Eso le costará 20€ señor.", "Eso le costará veinte euros señor.", "es"),
        ("Eso le costará 20,15€ señor.", "Eso le costará veinte euros con quince céntimos señor.", "es"),
        # Italian
        ("In 12,5 secondi.", "In dodici virgola cinque secondi.", "it"),
        ("C'erano 50 soldati.", "C'erano cinquanta soldati.", "it"),
        ("Questo è un 1° test", "Questo è un primo test", "it"),
        ("Ti costerà $20 signore.", "Ti costerà venti dollari signore.", "it"),
        ("Ti costerà 20€ signore.", "Ti costerà venti euro signore.", "it"),
        ("Ti costerà 20,15€ signore.", "Ti costerà venti euro e quindici centesimi signore.", "it"),
        # Portuguese
        ("Em 12,5 segundos.", "Em doze vírgula cinco segundos.", "pt"),
        ("Havia 50 soldados.", "Havia cinquenta soldados.", "pt"),
        ("Este é um 1º teste", "Este é um primeiro teste", "pt"),
        ("Isso custará $20 senhor.", "Isso custará vinte dólares senhor.", "pt"),
        ("Isso custará 20€ senhor.", "Isso custará vinte euros senhor.", "pt"),
        (
            "Isso custará 20,15€ senhor.",
            "Isso custará vinte euros e quinze cêntimos senhor.",
            "pt",
        ),  # "cêntimos" should be "centavos" num2words issue
        # Polish
        ("W 12,5 sekundy.", "W dwanaście przecinek pięć sekundy.", "pl"),
        ("Było 50 żołnierzy.", "Było pięćdziesiąt żołnierzy.", "pl"),
        ("To będzie kosztować 20€ panie.", "To będzie kosztować dwadzieścia euro panie.", "pl"),
        ("To będzie kosztować 20,15€ panie.", "To będzie kosztować dwadzieścia euro, piętnaście centów panie.", "pl"),
        # Arabic
        ("في الـ 12,5 ثانية.", "في الـ اثنا عشر  , خمسون ثانية.", "ar"),
        ("كان هناك 50 جنديًا.", "كان هناك خمسون جنديًا.", "ar"),
        # ("ستكون النتيجة $20 يا سيد.", 'ستكون النتيجة عشرون دولار يا سيد.', 'ar'), # $ and € are mising from num2words
        # ("ستكون النتيجة 20€ يا سيد.", 'ستكون النتيجة عشرون يورو يا سيد.', 'ar'),
        # Czech
        ("Za 12,5 vteřiny.", "Za dvanáct celá pět vteřiny.", "cs"),
        ("Bylo tam 50 vojáků.", "Bylo tam padesát vojáků.", "cs"),
        ("To bude stát 20€ pane.", "To bude stát dvacet euro pane.", "cs"),
        ("To bude 20.15€ pane.", "To bude dvacet euro, patnáct centů pane.", "cs"),
        # Russian
        ("Через 12.5 секунды.", "Через двенадцать запятая пять секунды.", "ru"),
        ("Там было 50 солдат.", "Там было пятьдесят солдат.", "ru"),
        ("Это будет 20.15€ сэр.", "Это будет двадцать евро, пятнадцать центов сэр.", "ru"),
        ("Это будет стоить 20€ господин.", "Это будет стоить двадцать евро господин.", "ru"),
        # Dutch
        ("In 12,5 seconden.", "In twaalf komma vijf seconden.", "nl"),
        ("Er waren 50 soldaten.", "Er waren vijftig soldaten.", "nl"),
        ("Dat wordt dan $20 meneer.", "Dat wordt dan twintig dollar meneer.", "nl"),
        ("Dat wordt dan 20€ meneer.", "Dat wordt dan twintig euro meneer.", "nl"),
        # Chinese (Simplified)
        ("在12.5秒内", "在十二点五秒内", "zh"),
        ("有50名士兵", "有五十名士兵", "zh"),
        # ("那将是$20先生", '那将是二十美元先生', 'zh'), currency doesn't work
        # ("那将是20€先生", '那将是二十欧元先生', 'zh'),
        # Turkish
        # ("12,5 saniye içinde.", 'On iki virgül beş saniye içinde.', 'tr'), # decimal doesn't work for TR
        ("50 asker vardı.", "elli asker vardı.", "tr"),
        ("Bu 1. test", "Bu birinci test", "tr"),
        # ("Bu 100.000,5.", 'Bu yüz bin virgül beş.', 'tr'),
        # Hungarian
        ("12,5 másodperc alatt.", "tizenkettő egész öt tized másodperc alatt.", "hu"),
        ("50 katona volt.", "ötven katona volt.", "hu"),
        ("Ez az 1. teszt", "Ez az első teszt", "hu"),
        # Korean
        ("12.5 초 안에.", "십이 점 다섯 초 안에.", "ko"),
        ("50 명의 병사가 있었다.", "오십 명의 병사가 있었다.", "ko"),
        ("이것은 1 번째 테스트입니다", "이것은 첫 번째 테스트입니다", "ko"),
    ]
    for a, b, lang in test_cases:
        out = expand_numbers_multilingual(a, lang=lang)
        assert out == b, f"'{out}' vs '{b}'"
 def test_abbreviations_multilingual():
    test_cases = [
        # English
        ("Hello Mr. Smith.", "Hello mister Smith.", "en"),
        ("Dr. Jones is here.", "doctor Jones is here.", "en"),
        # Spanish
        ("Hola Sr. Garcia.", "Hola señor Garcia.", "es"),
        ("La Dra. Martinez es muy buena.", "La doctora Martinez es muy buena.", "es"),
        # French
        ("Bonjour Mr. Dupond.", "Bonjour monsieur Dupond.", "fr"),
        ("Mme. Moreau est absente aujourd'hui.", "madame Moreau est absente aujourd'hui.", "fr"),
        # German
        ("Frau Dr. Müller ist sehr klug.", "Frau doktor Müller ist sehr klug.", "de"),
        # Portuguese
        ("Olá Sr. Silva.", "Olá senhor Silva.", "pt"),
        ("Dra. Costa, você está disponível?", "doutora Costa, você está disponível?", "pt"),
        # Italian
        ("Buongiorno, Sig. Rossi.", "Buongiorno, signore Rossi.", "it"),
        # ("Sig.ra Bianchi, posso aiutarti?", 'signora Bianchi, posso aiutarti?', 'it'), # Issue with matching that pattern
        # Polish
        ("Dzień dobry, P. Kowalski.", "Dzień dobry, pani Kowalski.", "pl"),
        ("M. Nowak, czy mogę zadać pytanie?", "pan Nowak, czy mogę zadać pytanie?", "pl"),
        # Czech
        ("P. Novák", "pan Novák", "cs"),
        ("Dr. Vojtěch", "doktor Vojtěch", "cs"),
        # Dutch
        ("Dhr. Jansen", "de heer Jansen", "nl"),
        ("Mevr. de Vries", "mevrouw de Vries", "nl"),
        # Russian
        ("Здравствуйте Г-н Иванов.", "Здравствуйте господин Иванов.", "ru"),
        ("Д-р Смирнов здесь, чтобы увидеть вас.", "доктор Смирнов здесь, чтобы увидеть вас.", "ru"),
        # Turkish
        ("Merhaba B. Yılmaz.", "Merhaba bay Yılmaz.", "tr"),
        ("Dr. Ayşe burada.", "doktor Ayşe burada.", "tr"),
        # Hungarian
        ("Dr. Szabó itt van.", "doktor Szabó itt van.", "hu"),
    ]
    for a, b, lang in test_cases:
        out = expand_abbreviations_multilingual(a, lang=lang)
        assert out == b, f"'{out}' vs '{b}'"
 def test_symbols_multilingual():
    test_cases = [
        ("I have 14% battery", "I have 14 percent battery", "en"),
        ("Te veo @ la fiesta", "Te veo arroba la fiesta", "es"),
        ("J'ai 14° de fièvre", "J'ai 14 degrés de fièvre", "fr"),
        ("Die Rechnung beträgt £ 20", "Die Rechnung beträgt pfund 20", "de"),
        ("O meu email é ana&joao@gmail.com", "O meu email é ana e joao arroba gmail.com", "pt"),
        ("linguaggio di programmazione C#", "linguaggio di programmazione C cancelletto", "it"),
        ("Moja temperatura to 36.6°", "Moja temperatura to 36.6 stopnie", "pl"),
        ("Mám 14% baterie", "Mám 14 procento baterie", "cs"),
        ("Těším se na tebe @ party", "Těším se na tebe na party", "cs"),
        ("У меня 14% заряда", "У меня 14 процентов заряда", "ru"),
        ("Я буду @ дома", "Я буду собака дома", "ru"),
        ("Ik heb 14% batterij", "Ik heb 14 procent batterij", "nl"),
        ("Ik zie je @ het feest", "Ik zie je bij het feest", "nl"),
        ("لدي 14% في البطارية", "لدي 14 في المئة في البطارية", "ar"),
        ("我的电量为 14%", "我的电量为 14 百分之", "zh"),
        ("Pilim %14 dolu.", "Pilim yüzde 14 dolu.", "tr"),
        ("Az akkumulátorom töltöttsége 14%", "Az akkumulátorom töltöttsége 14 százalék", "hu"),
        ("배터리 잔량이 14%입니다.", "배터리 잔량이 14 퍼센트입니다.", "ko"),
    ]
    for a, b, lang in test_cases:
        out = expand_symbols_multilingual(a, lang=lang)
        assert out == b, f"'{out}' vs '{b}'"
 if __name__ == "__main__":
    test_expand_numbers_multilingual()
    test_abbreviations_multilingual()
    test_symbols_multilingual()
@@ -0,0 +1,172 @@
 absl-py==2.1.0
 aiofiles==23.2.1
 aiohttp==3.9.3
 aiosignal==1.3.1
 altair==5.2.0
 annotated-types==0.6.0
 anyascii==0.3.2
 anyio==4.3.0
 attrs==23.2.0
 audioread==3.0.1
 av==11.0.0
 Babel==2.14.0
 bangla==0.0.2
 blinker==1.7.0
 blis==0.7.11
 bnnumerizer==0.0.2
 bnunicodenormalizer==0.1.6
 catalogue==2.0.10
 certifi==2024.2.2
 cffi==1.16.0
 charset-normalizer==3.3.2
 click==8.1.7
 cloudpathlib==0.16.0
 colorama==0.4.6
 coloredlogs==15.0.1
 confection==0.1.4
 contourpy==1.2.0
 coqpit==0.0.17
 ctranslate2==4.1.0
 cutlet==0.4.0
 cycler==0.12.1
 cymem==2.0.8
 Cython==3.0.9
 dateparser==1.1.8
 decorator==5.1.1
 docopt==0.6.2
 einops==0.7.0
 encodec==0.1.1
 fastapi==0.110.0
 faster-whisper==1.0.1
 ffmpy==0.3.2
 filelock==3.13.3
 Flask==3.0.2
 flatbuffers==24.3.25
 fonttools==4.50.0
 frozenlist==1.4.1
 fsspec==2024.3.1
 fugashi==1.3.1
 fuzzywuzzy>=0.18.0
 g2pkk==0.1.2
 gradio==3.50.2
 gradio_client==0.6.1
 grpcio==1.62.1
 gruut==2.2.3
 gruut-ipa==0.13.0
 gruut-lang-de==2.0.0
 gruut-lang-en==2.0.0
 gruut-lang-es==2.0.0
 gruut-lang-fr==2.0.2
 h11==0.14.0
 hangul-romanize==0.1.0
 httpcore==1.0.4
 httpx==0.27.0
 huggingface-hub==0.22.1
 humanfriendly==10.0
 idna==3.6
 importlib_metadata==7.1.0
 importlib_resources==6.4.0
 inflect==7.0.0
 itsdangerous==2.1.2
 jaconv==0.3.4
 jamo==0.4.1
 jieba==0.42.1
 Jinja2==3.1.3
 joblib==1.3.2
 jsonlines==1.2.0
 jsonschema==4.21.1
 jsonschema-specifications==2023.12.1
 kiwisolver==1.4.5
 langcodes==3.3.0
 lazy_loader==0.3
 librosa==0.10.1
 llvmlite==0.42.0
 Markdown==3.6
 MarkupSafe==2.1.5
 matplotlib==3.8.3
 mojimoji==0.0.13
 mpmath==1.3.0
 msgpack==1.0.8
 multidict==6.0.5
 murmurhash==1.0.10
 networkx==2.8.8
 nltk==3.8.1
 num2words==0.5.13
 numba==0.59.1
 numpy==1.26.4
 nvidia-cublas-cu11>=11.11.3.6
 nvidia-cudnn-cu11>=9.0.0.312
 onnxruntime==1.17.1
 orjson==3.9.15
 packaging==24.0
 pandas==1.5.3
 pillow==10.2.0
 platformdirs==4.2.0
 pooch==1.8.1
 preshed==3.0.9
 protobuf==5.26.0
 psutil==5.9.8
 pycparser==2.21
 pydantic==2.6.4
 pydantic_core==2.16.3
 pydub==0.25.1
 pynndescent==0.5.11
 pyparsing==3.1.2
 pypinyin==0.51.0
 pyreadline3==3.4.1
 pysbd==0.3.4
 python-crfsuite==0.9.10
 python-dateutil==2.9.0.post0
 python-Levenshtein>=0.25.0
 python-multipart==0.0.9
 pytz==2024.1
 PyYAML==6.0.1
 referencing==0.34.0
 regex==2023.12.25
 requests==2.31.0
 rpds-py==0.18.0
 safetensors==0.4.2
 scikit-learn==1.4.1.post1
 scipy==1.12.0
 semantic-version==2.10.0
 six==1.16.0
 smart-open==6.4.0
 sniffio==1.3.1
 sounddevice==0.4.6
 soundfile==0.12.1
 soxr==0.3.7
 spacy==3.7.4
 spacy-legacy==3.0.12
 spacy-loggers==1.0.5
 srsly==2.4.8
 starlette==0.36.3
 SudachiDict-core==20240109
 SudachiPy==0.6.8
 sympy==1.12
 tensorboard==2.16.2
 tensorboard-data-server==0.7.2
 thinc==8.2.3
 threadpoolctl==3.4.0
 tokenizers==0.15.2
 toolz==0.12.1
 torch>=2.2.0
 torchaudio>=2.2.0
 tqdm==4.66.2
 trainer==0.0.36
 transformers==4.39.1
 TTS==0.22.0
 typer==0.9.4
 typing_extensions==4.10.0
 tzdata==2024.1
 tzlocal==5.2
 umap-learn==0.5.5
 Unidecode==1.3.8
 unidic-lite==1.0.8
 urllib3==2.2.1
 uvicorn==0.29.0
 wasabi==1.1.2
 weasel==0.3.4
 websockets==11.0.3
 Werkzeug==3.0.1
 yarl==1.9.4
 zipp==3.18.1
@@ -1,7 +1,8 @@
 numpy>=1.22.0
 soundfile>=0.12.1
 uvicorn>=0.24.0.post1
-TTS>=0.21.3
+transformers>=4.37.1
 TTS>=0.22.0
 torch>=2.1.0+cu118
 torchaudio>=2.1.0+cu118
 fastapi>=0.104.1
@@ -13,3 +14,11 @@ packaging>=23.2
 pydantic>=1.10.13
 sounddevice>=0.4.6
 python-multipart>=0.0.6
 cutlet>=0.3.0
 unidic-lite>=1.0.8
 faster-whisper>=0.10.0
 pandas>=1.5.0
 nvidia-cublas-cu11>=11.11.3.6
 nvidia-cudnn-cu11>=9.0.0.312
 fuzzywuzzy>=0.18.0
 python-Levenshtein>=0.25.0
@@ -0,0 +1,833 @@
 import { doExtrasFetch, getApiUrl, modules } from '../../extensions.js';
 import { saveTtsProviderSettings } from './index.js';
 export { AllTalkTtsProvider };
 class AllTalkTtsProvider {
    //########//
    // Config //
    //########//
    settings = {};
    constructor() {
        // Initialize with default settings if they are not already set
        this.settings = {
            provider_endpoint: this.settings.provider_endpoint || 'http://localhost:7851',
            language: this.settings.language || 'en',
            voiceMap: this.settings.voiceMap || {},
            at_generation_method: this.settings.at_generation_method || 'standard_generation',
            narrator_enabled: this.settings.narrator_enabled || 'false',
            at_narrator_text_not_inside: this.settings.at_narrator_text_not_inside || 'narrator',
            narrator_voice_gen: this.settings.narrator_voice_gen || 'female_01.wav',
            finetuned_model: this.settings.finetuned_model || 'false'
        };
        // Separate property for dynamically updated settings from the server
        this.dynamicSettings = {
            modelsAvailable: [],
            currentModel: '',
            deepspeed_available: false,
            deepSpeedEnabled: false,
            lowVramEnabled: false,
        };
    }
    ready = false;
    voices = [];
    separator = '. ';
    audioElement = document.createElement('audio');
    languageLabels = {
        'Arabic': 'ar',
        'Brazilian Portuguese': 'pt',
        'Chinese': 'zh-cn',
        'Czech': 'cs',
        'Dutch': 'nl',
        'English': 'en',
        'French': 'fr',
        'German': 'de',
        'Italian': 'it',
        'Polish': 'pl',
        'Russian': 'ru',
        'Spanish': 'es',
        'Turkish': 'tr',
        'Japanese': 'ja',
        'Korean': 'ko',
        'Hungarian': 'hu',
        'Hindi': 'hi',
    };
    get settingsHtml() {
        let html = `<div class="at-settings-separator">AllTalk Settings</div>`;
        html += `<div class="at-settings-row">
        <div class="at-settings-option">
            <label for="at_generation_method">AllTalk TTS Generation Method</label>
                <select id="at_generation_method">
                <option value="standard_generation">Standard Audio Generation (AT Narrator - Optional)</option>
                <option value="streaming_enabled">Streaming Audio Generation (AT Narrator - Disabled)</option>
        </select>
        </div>
        </div>`;
        html += `<div class="at-settings-row">
        <div class="at-settings-option">
            <label for="at_narrator_enabled">AT Narrator</label>
                <select id="at_narrator_enabled">
                <option value="true">Enabled</option>
                <option value="false">Disabled</option>
        </select>
        </div>
        <div class="at-settings-option">
            <label for="at_narrator_text_not_inside">Text Not Inside * or " is</label>
                <select id="at_narrator_text_not_inside">
                <option value="narrator">Narrator</option>
                <option value="character">Character</option>
        </select>
        </div>
    </div>`;
        html += `<div class="at-settings-row">
        <div class="at-settings-option">
            <label for="narrator_voice">Narrator Voice</label>
            <select id="narrator_voice">`;
        if (this.voices) {
            for (let voice of this.voices) {
                html += `<option value="${voice.voice_id}">${voice.name}</option>`;
            }
        }
        html += `</select>
        </div>
        <div class="at-settings-option">
            <label for="language_options">Language</label>
            <select id="language_options">`;
        for (let language in this.languageLabels) {
            html += `<option value="${this.languageLabels[language]}" ${this.languageLabels[language] === this.settings?.language ? 'selected="selected"' : ''}>${language}</option>`;
        }
        html += `</select>
        </div>
    </div>`;
        html += `<div class="at-model-endpoint-row">
    <div class="at-model-option">
            <label for="switch_model">Switch Model</label>
            <select id="switch_model">
            <option value="api_tts">API TTS</option>
            <option value="api_local">API Local</option>
            <option value="xttsv2_local">XTTSv2 Local</option>
        </select>
        </div>
        <div class="at-endpoint-option">
            <label for="at_server">AllTalk Endpoint:</label>
            <input id="at_server" type="text" class="text_pole" maxlength="80" value="${this.settings.provider_endpoint}"/>
        </div>
   </div>`;
        html += `<div class="at-model-endpoint-row">
    <div class="at-settings-option">
        <label for="low_vram">Low VRAM</label>
        <input id="low_vram" type="checkbox"/>
    </div>
    <div class="at-settings-option">
        <label for="deepspeed">DeepSpeed</label>
        <input id="deepspeed" type="checkbox"/>
    </div>
    <div class="at-settings-option status-option">
        <span>Status: <span id="status_info">Ready</span></span>
    </div>
    <div class="at-settings-option empty-option">
        <!-- This div remains empty for spacing -->
    </div>
 </div>`;
        html += `<div class="at-website-row">
        <div class="at-website-option">
        <span>AllTalk <a target="_blank" href="${this.settings.provider_endpoint}">Config & Docs</a>.</span>
    </div>
    <div class="at-website-option">
        <span>AllTalk <a target="_blank" href="https://github.com/erew123/alltalk_tts/">Website</a>.</span>
    </div>
 </div>`;
        html += `<div class="at-website-row">
 <div class="at-website-option">
 <span><strong>Text-generation-webui</strong> users - Uncheck <strong>Enable TTS</strong> in Text-generation-webui.</span>
 </div>
 </div>`;
        return html;
    }
    //#################//
    // Startup ST & AT //
    //#################//
    async loadSettings(settings) {
        updateStatus('Offline');
        if (Object.keys(settings).length === 0) {
            console.info('Using default AllTalk TTS Provider settings');
        } else {
            // Populate settings with provided values, ignoring server-provided settings
            for (const key in settings) {
                if (key in this.settings) {
                    this.settings[key] = settings[key];
                } else {
                    console.debug(`Ignoring non-user-configurable setting: ${key}`);
                }
            }
        }
        // Update UI elements to reflect the loaded settings
        $('#at_server').val(this.settings.provider_endpoint);
        $('#language_options').val(this.settings.language);
        //$('#voicemap').val(this.settings.voiceMap);
        $('#at_generation_method').val(this.settings.at_generation_method);
        $('#at_narrator_enabled').val(this.settings.narrator_enabled);
        $('#at_narrator_text_not_inside').val(this.settings.at_narrator_text_not_inside);
        $('#narrator_voice').val(this.settings.narrator_voice_gen);
        console.debug('AllTalkTTS: Settings loaded');
        try {
            // Check if TTS provider is ready
            await this.checkReady();
            await this.updateSettingsFromServer(); // Fetch dynamic settings from the TTS server
            await this.fetchTtsVoiceObjects(); // Fetch voices only if service is ready
            this.updateNarratorVoicesDropdown();
            this.updateLanguageDropdown();
            this.setupEventListeners();
            this.applySettingsToHTML();
            updateStatus('Ready');
        } catch (error) {
            console.error("Error loading settings:", error);
            updateStatus('Offline');
        }
    }
    applySettingsToHTML() {
        // Apply loaded settings or use defaults
        const narratorVoiceSelect = document.getElementById('narrator_voice');
        const atNarratorSelect = document.getElementById('at_narrator_enabled');
        const textNotInsideSelect = document.getElementById('at_narrator_text_not_inside');
        const generationMethodSelect = document.getElementById('at_generation_method');
        this.settings.narrator_voice = this.settings.narrator_voice_gen;
        // Apply settings to Narrator Voice dropdown
        if (narratorVoiceSelect && this.settings.narrator_voice) {
            narratorVoiceSelect.value = this.settings.narrator_voice.replace('.wav', '');
        }
        // Apply settings to AT Narrator Enabled dropdown
        if (atNarratorSelect) {
            // Sync the state with the checkbox in index.js
            const ttsPassAsterisksCheckbox = document.getElementById('tts_pass_asterisks'); // Access the checkbox from index.js
            const ttsNarrateQuotedCheckbox = document.getElementById('tts_narrate_quoted'); // Access the checkbox from index.js
            const ttsNarrateDialoguesCheckbox = document.getElementById('tts_narrate_dialogues'); // Access the checkbox from index.js
            // Sync the state with the checkbox in index.js
            if (this.settings.narrator_enabled) {
                ttsPassAsterisksCheckbox.checked = false;
                $('#tts_pass_asterisks').click(); // Simulate a click event
                $('#tts_pass_asterisks').trigger('change');
            }
            if (!this.settings.narrator_enabled) {
                ttsPassAsterisksCheckbox.checked = true;
                $('#tts_pass_asterisks').click(); // Simulate a click event
                $('#tts_pass_asterisks').trigger('change');
            }
            // Uncheck and set tts_narrate_quoted to false if narrator is enabled
            if (this.settings.narrator_enabledd) {
                ttsNarrateQuotedCheckbox.checked = true;
                ttsNarrateDialoguesCheckbox.checked = true;
                // Trigger click events instead of change events
                $('#tts_narrate_quoted').click();
                $('#tts_narrate_quoted').trigger('change');
                $('#tts_narrate_dialogues').click();
                $('#tts_narrate_dialogues').trigger('change');
            }
            atNarratorSelect.value = this.settings.narrator_enabled.toString();
            this.settings.narrator_enabled = this.settings.narrator_enabled.toString();
        }
        // Apply settings to the Language dropdown
        const languageSelect = document.getElementById('language_options');
        if (languageSelect && this.settings.language) {
            languageSelect.value = this.settings.language;
        }
        // Apply settings to Text Not Inside dropdown
        if (textNotInsideSelect && this.settings.text_not_inside) {
            textNotInsideSelect.value = this.settings.text_not_inside;
            this.settings.at_narrator_text_not_inside = this.settings.text_not_inside;
        }
        // Apply settings to Generation Method dropdown
        if (generationMethodSelect && this.settings.at_generation_method) {
            generationMethodSelect.value = this.settings.at_generation_method;
        }
        // Additional logic to disable/enable dropdowns based on the selected generation method
        const isStreamingEnabled = this.settings.at_generation_method === 'streaming_enabled';
        if (isStreamingEnabled) {
            // Disable certain dropdowns when streaming is enabled
            if (atNarratorSelect) atNarratorSelect.disabled = true;
            if (textNotInsideSelect) textNotInsideSelect.disabled = true;
            if (narratorVoiceSelect) narratorVoiceSelect.disabled = true;
        } else {
            // Enable dropdowns for standard generation
            if (atNarratorSelect) atNarratorSelect.disabled = false;
            if (textNotInsideSelect) textNotInsideSelect.disabled = !this.settings.narrator_enabled;
            if (narratorVoiceSelect) narratorVoiceSelect.disabled = !this.settings.narrator_enabled;
        }
        const modelSelect = document.getElementById('switch_model');
        if (this.settings.finetuned_model === 'true') {
            const ftOption = document.createElement('option');
            ftOption.value = 'XTTSv2 FT';
            ftOption.textContent = 'XTTSv2 FT';
            modelSelect.appendChild(ftOption);
        }
    }
    //##############################//
    // Check AT Server is Available //
    //##############################//
    async checkReady() {
        try {
            const response = await fetch(`${this.settings.provider_endpoint}/api/ready`);
            // Check if the HTTP request was successful
            if (!response.ok) {
                throw new Error(`HTTP Error Response: ${response.status} ${response.statusText}`);
            }
            const statusText = await response.text();
            // Check if the response is 'Ready'
            if (statusText === 'Ready') {
                this.ready = true; // Set the ready flag to true
                console.log('TTS service is ready.');
            } else {
                this.ready = false;
                console.log('TTS service is not ready.');
            }
        } catch (error) {
            console.error('Error checking TTS service readiness:', error);
            this.ready = false; // Ensure ready flag is set to false in case of error
        }
    }
    //######################//
    // Get Available Voices //
    //######################//
    async fetchTtsVoiceObjects() {
        const response = await fetch(`${this.settings.provider_endpoint}/api/voices`);
        if (!response.ok) {
            const errorText = await response.text();
            throw new Error(`HTTP ${response.status}: ${errorText}`);
        }
        const data = await response.json();
        const voices = data.voices.map(filename => {
            const voiceName = filename.replace('.wav', '');
            return {
                name: voiceName,
                voice_id: voiceName,
                preview_url: null, // Preview URL will be dynamically generated
                lang: 'en' // Default language
            };
        });
        this.voices = voices; // Assign to the class property
        return voices; // Also return this list
    }
    //##########################################//
    // Get Current AT Server Config & Update ST //
    //##########################################//
    async updateSettingsFromServer() {
        try {
            // Fetch current settings
            const response = await fetch(`${this.settings.provider_endpoint}/api/currentsettings`);
            if (!response.ok) {
                throw new Error(`Failed to fetch current settings: ${response.statusText}`);
            }
            const currentSettings = await response.json();
            // Update internal settings
            this.settings.modelsAvailable = currentSettings.models_available;
            this.settings.currentModel = currentSettings.current_model_loaded;
            this.settings.deepspeed_available = currentSettings.deepspeed_available;
            this.settings.deepSpeedEnabled = currentSettings.deepspeed_status;
            this.settings.lowVramEnabled = currentSettings.low_vram_status;
            this.settings.finetuned_model = currentSettings.finetuned_model
            // Update HTML elements
            this.updateModelDropdown();
            this.updateCheckboxes();
        } catch (error) {
            console.error(`Error updating settings from server: ${error}`);
        }
    }
    //###################################################//
    // Get Current AT Server Config & Update ST (Models) //
    //###################################################//
    updateModelDropdown() {
        const modelSelect = document.getElementById('switch_model');
        if (modelSelect) {
            modelSelect.innerHTML = ''; // Clear existing options
            this.settings.modelsAvailable.forEach(model => {
                const option = document.createElement('option');
                option.value = model.model_name;
                option.textContent = model.model_name; // Use model_name instead of name
                option.selected = model.model_name === this.settings.currentModel;
                modelSelect.appendChild(option);
            });
        }
    }
    //#######################################################//
    // Get Current AT Server Config & Update ST (DS and LVR) //
    //#######################################################//
    updateCheckboxes() {
        const deepspeedCheckbox = document.getElementById('deepspeed');
        const lowVramCheckbox = document.getElementById('low_vram');
        if (lowVramCheckbox) lowVramCheckbox.checked = this.settings.lowVramEnabled;
        if (deepspeedCheckbox) {
            deepspeedCheckbox.checked = this.settings.deepSpeedEnabled;
            deepspeedCheckbox.disabled = !this.settings.deepspeed_available; // Disable checkbox if deepspeed is not available
        }
    }
    //###############################################################//
    // Get Current AT Server Config & Update ST (AT Narrator Voices) //
    //###############################################################//
    updateNarratorVoicesDropdown() {
        const narratorVoiceSelect = document.getElementById('narrator_voice');
        if (narratorVoiceSelect && this.voices) {
            // Clear existing options
            narratorVoiceSelect.innerHTML = '';
            // Add new options
            for (let voice of this.voices) {
                const option = document.createElement('option');
                option.value = voice.voice_id;
                option.textContent = voice.name;
                narratorVoiceSelect.appendChild(option);
            }
        }
    }
    //######################################################//
    // Get Current AT Server Config & Update ST (Languages) //
    //######################################################//
    updateLanguageDropdown() {
        const languageSelect = document.getElementById('language_options');
        if (languageSelect) {
            // Ensure default language is set
            this.settings.language = this.settings.language;
            languageSelect.innerHTML = '';
            for (let language in this.languageLabels) {
                const option = document.createElement('option');
                option.value = this.languageLabels[language];
                option.textContent = language;
                if (this.languageLabels[language] === this.settings.language) {
                    option.selected = true;
                }
                languageSelect.appendChild(option);
            }
        }
    }
    //########################################//
    // Start AT TTS extenstion page listeners //
    //########################################//
    setupEventListeners() {
        let debounceTimeout;
        const debounceDelay = 500; // Milliseconds
        // Define the event handler function
        const onModelSelectChange = async (event) => {
            console.log("Model select change event triggered"); // Debugging statement
            const selectedModel = event.target.value;
            console.log(`Selected model: ${selectedModel}`); // Debugging statement 
            // Set status to Processing
            updateStatus('Processing');
            try {
                const response = await fetch(`${this.settings.provider_endpoint}/api/reload?tts_method=${encodeURIComponent(selectedModel)}`, {
                    method: 'POST'
                });
                if (!response.ok) {
                    throw new Error(`HTTP Error: ${response.status}`);
                }
                const data = await response.json();
                console.log("POST response data:", data); // Debugging statement 
                // Set status to Ready if successful
                updateStatus('Ready');
            } catch (error) {
                console.error("POST request error:", error); // Debugging statement
                // Set status to Error in case of failure
                updateStatus('Error');
            }
            // Handle response or error
        };
        const debouncedModelSelectChange = (event) => {
            clearTimeout(debounceTimeout);
            debounceTimeout = setTimeout(() => {
                onModelSelectChange(event);
            }, debounceDelay);
        };
        // Switch Model Listener
        const modelSelect = document.getElementById('switch_model');
        if (modelSelect) {
            // Remove the event listener if it was previously added
            modelSelect.removeEventListener('change', debouncedModelSelectChange);
            // Add the debounced event listener
            modelSelect.addEventListener('change', debouncedModelSelectChange);
        }
        // DeepSpeed Listener
        const deepspeedCheckbox = document.getElementById('deepspeed');
        if (deepspeedCheckbox) {
            deepspeedCheckbox.addEventListener('change', async (event) => {
                const deepSpeedValue = event.target.checked ? 'True' : 'False';
                // Set status to Processing
                updateStatus('Processing');
                try {
                    const response = await fetch(`${this.settings.provider_endpoint}/api/deepspeed?new_deepspeed_value=${deepSpeedValue}`, {
                        method: 'POST'
                    });
                    if (!response.ok) {
                        throw new Error(`HTTP Error: ${response.status}`);
                    }
                    const data = await response.json();
                    console.log("POST response data:", data); // Debugging statement
                    // Set status to Ready if successful
                    updateStatus('Ready');
                } catch (error) {
                    console.error("POST request error:", error); // Debugging statement
                    // Set status to Error in case of failure
                    updateStatus('Error');
                }
            });
        }
        // Low VRAM Listener
        const lowVramCheckbox = document.getElementById('low_vram');
        if (lowVramCheckbox) {
            lowVramCheckbox.addEventListener('change', async (event) => {
                const lowVramValue = event.target.checked ? 'True' : 'False';
                // Set status to Processing
                updateStatus('Processing');
                try {
                    const response = await fetch(`${this.settings.provider_endpoint}/api/lowvramsetting?new_low_vram_value=${lowVramValue}`, {
                        method: 'POST'
                    });
                    if (!response.ok) {
                        throw new Error(`HTTP Error: ${response.status}`);
                    }
                    const data = await response.json();
                    console.log("POST response data:", data); // Debugging statement
                    // Set status to Ready if successful
                    updateStatus('Ready');
                } catch (error) {
                    console.error("POST request error:", error); // Debugging statement
                    // Set status to Error in case of failure
                    updateStatus('Error');
                }
            });
        }
        // Narrator Voice Dropdown Listener
        const narratorVoiceSelect = document.getElementById('narrator_voice');
        if (narratorVoiceSelect) {
            narratorVoiceSelect.addEventListener('change', (event) => {
                this.settings.narrator_voice_gen = `${event.target.value}.wav`;
                this.onSettingsChange(); // Save the settings after change
            });
        }
        const textNotInsideSelect = document.getElementById('at_narrator_text_not_inside');
        if (textNotInsideSelect) {
            textNotInsideSelect.addEventListener('change', (event) => {
                this.settings.text_not_inside = event.target.value;
                this.onSettingsChange(); // Save the settings after change
            });
        }
        // AT Narrator Dropdown Listener
        const atNarratorSelect = document.getElementById('at_narrator_enabled');
        const ttsPassAsterisksCheckbox = document.getElementById('tts_pass_asterisks'); // Access the checkbox from index.js
        const ttsNarrateQuotedCheckbox = document.getElementById('tts_narrate_quoted'); // Access the checkbox from index.js
        const ttsNarrateDialoguesCheckbox = document.getElementById('tts_narrate_dialogues'); // Access the checkbox from index.js
        if (atNarratorSelect && textNotInsideSelect && narratorVoiceSelect) {
            atNarratorSelect.addEventListener('change', (event) => {
                const isNarratorEnabled = event.target.value === 'true';
                this.settings.narrator_enabled = isNarratorEnabled; // Update the setting here
                textNotInsideSelect.disabled = !isNarratorEnabled;
                narratorVoiceSelect.disabled = !isNarratorEnabled;
                // Sync the state with the checkbox in index.js
                if (isNarratorEnabled) {
                    ttsPassAsterisksCheckbox.checked = false;
                    $('#tts_pass_asterisks').click(); // Simulate a click event
                    $('#tts_pass_asterisks').trigger('change');
                }
                if (!isNarratorEnabled) {
                    ttsPassAsterisksCheckbox.checked = true;
                    $('#tts_pass_asterisks').click(); // Simulate a click event
                    $('#tts_pass_asterisks').trigger('change');
                }
                // Uncheck and set tts_narrate_quoted to false if narrator is enabled
                if (isNarratorEnabled) {
                    ttsNarrateQuotedCheckbox.checked = true;
                    ttsNarrateDialoguesCheckbox.checked = true;
                    // Trigger click events instead of change events
                    $('#tts_narrate_quoted').click();
                    $('#tts_narrate_quoted').trigger('change');
                    $('#tts_narrate_dialogues').click();
                    $('#tts_narrate_dialogues').trigger('change');
                }
                this.onSettingsChange(); // Save the settings after change
            });
        }
        // Event Listener for AT Generation Method Dropdown
        const atGenerationMethodSelect = document.getElementById('at_generation_method');
        const atNarratorEnabledSelect = document.getElementById('at_narrator_enabled');
        if (atGenerationMethodSelect) {
            atGenerationMethodSelect.addEventListener('change', (event) => {
                const selectedMethod = event.target.value;
                if (selectedMethod === 'streaming_enabled') {
                    // Disable and unselect AT Narrator
                    atNarratorEnabledSelect.disabled = true;
                    atNarratorEnabledSelect.value = 'false';
                    textNotInsideSelect.disabled = true;
                    narratorVoiceSelect.disabled = true;
                } else if (selectedMethod === 'standard_generation') {
                    // Enable AT Narrator
                    atNarratorEnabledSelect.disabled = false;
                }
                this.settings.at_generation_method = selectedMethod; // Update the setting here
                this.onSettingsChange(); // Save the settings after change
            });
        }
        // Listener for Language Dropdown
        const languageSelect = document.getElementById('language_options');
        if (languageSelect) {
            languageSelect.addEventListener('change', (event) => {
                this.settings.language = event.target.value;
                this.onSettingsChange(); // Save the settings after change
            });
        }
        // Listener for AllTalk Endpoint Input
        const atServerInput = document.getElementById('at_server');
        if (atServerInput) {
            atServerInput.addEventListener('input', (event) => {
                this.settings.provider_endpoint = event.target.value;
                this.onSettingsChange(); // Save the settings after change
            });
        }
    }
    //#############################//
    // Store ST interface settings //
    //#############################//
    onSettingsChange() {
        // Update settings based on the UI elements
        //this.settings.provider_endpoint = $('#at_server').val();
        this.settings.language = $('#language_options').val();
        //this.settings.voiceMap = $('#voicemap').val();
        this.settings.at_generation_method = $('#at_generation_method').val();
        this.settings.narrator_enabled = $('#at_narrator_enabled').val();
        this.settings.at_narrator_text_not_inside = $('#at_narrator_text_not_inside').val();
        this.settings.narrator_voice_gen = $('#narrator_voice').val();
        // Save the updated settings
        saveTtsProviderSettings();
    }
    //#########################//
    // ST Handle Reload button //
    //#########################//
    async onRefreshClick() {
        await this.checkReady(); // Check if the TTS provider is ready
        await this.loadSettings(this.settings); // Reload the settings
        // Additional actions as needed
    }
    //##################//
    // Preview AT Voice //
    //##################//
    async previewTtsVoice(voiceName) {
        try {
            // Prepare data for POST request
            const postData = new URLSearchParams();
            postData.append("voice", `${voiceName}.wav`);
            // Making the POST request
            const response = await fetch(`${this.settings.provider_endpoint}/api/previewvoice/`, {
                method: "POST",
                headers: {
                    'Content-Type': 'application/x-www-form-urlencoded'
                },
                body: postData,
            });
            if (!response.ok) {
                const errorText = await response.text();
                console.error(`[previewTtsVoice] Error Response Text:`, errorText);
                throw new Error(`HTTP ${response.status}: ${errorText}`);
            }
            // Assuming the server returns a URL to the .wav file
            const data = await response.json();
            if (data.output_file_url) {
                // Use an audio element to play the .wav file
                const audioElement = new Audio(data.output_file_url);
                audioElement.play().catch(e => console.error("Error playing audio:", e));
            } else {
                console.warn("[previewTtsVoice] No output file URL received in the response");
                throw new Error("No output file URL received in the response");
            }
        } catch (error) {
            console.error("[previewTtsVoice] Exception caught during preview generation:", error);
            throw error;
        }
    }
    //#####################//
    //  Populate ST voices //
    //#####################//
    async getVoice(voiceName, generatePreview = false) {
        // Ensure this.voices is populated
        if (this.voices.length === 0) {
            // Fetch voice objects logic
        }
        // Find the object where the name matches voiceName
        const match = this.voices.find(voice => voice.name === voiceName);
        if (!match) {
            // Error handling
        }
        // Generate preview URL only if requested
        if (!match.preview_url && generatePreview) {
            // Generate preview logic
        }
        return match; // Return the found voice object
    }
    //##########################################//
    //  Generate TTS Streaming or call Standard //
    //##########################################//
    async generateTts(inputText, voiceId) {
        try {
            if (this.settings.at_generation_method === 'streaming_enabled') {
                // Construct the streaming URL
                const streamingUrl = `${this.settings.provider_endpoint}/api/tts-generate-streaming?text=${encodeURIComponent(inputText)}&voice=${encodeURIComponent(voiceId)}.wav&language=${encodeURIComponent(this.settings.language)}&output_file=stream_output.wav`;
                console.log("Streaming URL:", streamingUrl);
                // Return the streaming URL directly
                return streamingUrl; 
            } else {
                // For standard method
                const outputUrl = await this.fetchTtsGeneration(inputText, voiceId);
                const audioResponse = await fetch(outputUrl);
                if (!audioResponse.ok) {
                    throw new Error(`HTTP ${audioResponse.status}: Failed to fetch audio data`);
                }
                return audioResponse; // Return the fetch response directly
            }
        } catch (error) {
            console.error("Error in generateTts:", error);
            throw error;
        }
    }
    //####################//
    //  Generate Standard //
    //####################//
    async fetchTtsGeneration(inputText, voiceId) {
        // Prepare the request payload
        const requestBody = new URLSearchParams({
            'text_input': inputText,
            'text_filtering': "standard",
            'character_voice_gen': voiceId + ".wav",
            'narrator_enabled': this.settings.narrator_enabled,
            'narrator_voice_gen': this.settings.narrator_voice_gen + ".wav",
            'text_not_inside': this.settings.at_narrator_text_not_inside,
            'language': this.settings.language,
            'output_file_name': "st_output",
            'output_file_timestamp': "true",
            'autoplay': "false",
            'autoplay_volume': "0.8"
        }).toString();
        try {
            const response = await doExtrasFetch(
                `${this.settings.provider_endpoint}/api/tts-generate`,
                {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/x-www-form-urlencoded',
                        'Cache-Control': 'no-cache',
                    },
                    body: requestBody
                }
            );
            if (!response.ok) {
                const errorText = await response.text();
                console.error(`[fetchTtsGeneration] Error Response Text:`, errorText);
                // toastr.error(response.statusText, 'TTS Generation Failed');
                throw new Error(`HTTP ${response.status}: ${errorText}`);
            }
            const data = await response.json();
            const outputUrl = data.output_file_url;
            return outputUrl; // Return only the output_file_url
        } catch (error) {
            console.error("[fetchTtsGeneration] Exception caught:", error);
            throw error; // Rethrow the error for further handling
        }
    }
 }
 //#########################//
 //  Update Status Messages //
 //#########################//
 function updateStatus(message) {
    const statusElement = document.getElementById('status_info');
    if (statusElement) {
        statusElement.textContent = message;
        switch (message) {
            case 'Offline':
                statusElement.style.color = 'red';
                break;
            case 'Ready':
                statusElement.style.color = 'lightgreen';
                break;
            case 'Processing':
                statusElement.style.color = 'blue';
                break;
            case 'Error':
                statusElement.style.color = 'red';
                break;
        }
    }
 }
@@ -0,0 +1,159 @@
 #tts_media_control {
    /* order: 100; */
    /*     width: 40px;
    height: 40px;
    margin: 0;
    padding: 1px; */
    outline: none;
    border: none;
    cursor: pointer;
    /*     transition: 0.3s;
    opacity: 0.7; */
    display: flex;
    align-items: center;
    /* justify-content: center; */
 }
 #ttsExtensionMenuItem {
    transition: 0.3s;
    opacity: 0.7;
 }
 #ttsExtensionMenuItem:hover {
    opacity: 1;
    filter: brightness(1.2);
 }
 #tts_media_control:hover {
    opacity: 1;
    filter: brightness(1.2);
 }
 .voice_preview {
    margin: 0.25rem 0.5rem;
    display: flex;
    justify-content: space-between;
    align-items: center;
    gap: 0.5rem;
 }
 .voice_preview .voice_name {
    text-align: left;
    flex: 1;
 }
 .voice_preview .voice_lang {
    width: 4rem;
    text-align: left;
 }
 .voice_preview .fa-play {
    cursor: pointer;
 }
 .tts-button {
    margin: 0;
    outline: none;
    border: none;
    cursor: pointer;
    transition: 0.3s;
    opacity: 0.7;
    align-items: center;
    justify-content: center;
 }
 .tts-button:hover {
    opacity: 1;
 }
 .tts_block {
    display: flex;
    align-items: baseline;
    column-gap: 5px;
    flex-wrap: wrap;
 }
 .tts_custom_voices {
    display: flex;
    align-items: baseline;
    gap: 5px;
 }
 .novel_tts_hints {
    font-size: calc(0.9 * var(--mainFontSize));
    display: flex;
    flex-direction: column;
    gap: 5px;
    margin-bottom: 5px;
 }
 .at-settings-row {
    display: flex;
    justify-content: space-between;
    align-items: center;
    width: 100%;
 }
 .at-settings-option {
    flex: 1;
    margin: 0 10px;
 }
 .at-endpoint-option {
    flex: 1; 
    margin: 0 10px;
    margin-right: 25px;
    width: 38%;
 }
 .at-website-row {
    display: flex;
    justify-content: start;
    align-items: center;
    margin-top: 10px; 
    margin-bottom: 10px;
 }
 .at-website-option {
    flex: 1; 
    margin-right: 10px; 
    margin-left: 10px; 
 }
 .at-settings-separator {
    margin-top: 10px;
    margin-bottom: 10px;
    padding: 18x;
    font-weight: bold;
    border-top: 1px solid #e1e1e1; /* Grey line */
    border-bottom: 1px solid #e1e1e1; /* Grey line */
    text-align: center;
 }
 .at-status-message {
    flex: 1;
    margin: 0 10px;
 }
 .at-model-endpoint-row {
    display: flex;
    justify-content: space-between;
    align-items: center;
    width: 100%;
 }
 .at-model-option, .endpoint-option {
    flex: 1;
    margin: 0 10px;
    margin-left: 10px; 
 }
 .at-endpoint-option {
    width: 38%;
 }
 #at-status_info {
    color: lightgreen; 
 }
@@ -0,0 +1,269 @@
 import json
 import argparse
 from pathlib import Path
 import os
 import glob
 import platform
 import site
 import subprocess
 import sys
 # version 0.8
 def find_files_in_path_with_wildcard(pattern):
    # Get the site-packages directory of the current Python environment
    site_packages_path = site.getsitepackages()
    found_paths = []
    # Adjust the sub-directory based on the operating system
    sub_directory = "nvidia/cublas"
    if platform.system() == "Linux":
        sub_directory = os.path.join(sub_directory, "lib")
    else:
        sub_directory = os.path.join(sub_directory, "bin")
    # Iterate over each site-packages directory (there can be more than one)
    for directory in site_packages_path:
        # Construct the search directory path
        search_directory = os.path.join(directory, sub_directory)
        # Use glob to find all files matching the pattern in this directory
        for file_path in glob.glob(os.path.join(search_directory, pattern)):
            if os.path.isfile(file_path):  # Ensure it's a file
                found_paths.append(file_path)
    return found_paths
 def detect_cublas():
    # Use different file patterns based on the operating system
    file_name_pattern = 'cublas64_11.*' if platform.system() == "Windows" else 'libcublas.so.11*'
    found_paths = find_files_in_path_with_wildcard(file_name_pattern)
    if found_paths:
        print("[AllTalk TTSDiff] \033[94mCublas:\033[0m \033[92mDetected\033[0m", found_paths)
    else:
        print("[AllTalk TTSDiff] \033[94mCublas:\033[0m \033[91mERROR Not Detected\033[0m")
        print("[AllTalk TTSDiff] \033[94mPlease install Cublas from the Nvidia CUDA Toolkit \033[0mhttps://developer.nvidia.com/cuda-downloads")
        exit(1)
 parser = argparse.ArgumentParser(description="Compare TTS output with the original text using detailed comparison.")
 parser.add_argument("--threshold", type=int, default=98, help="Similarity threshold for considering a match (default: 98)")
 parser.add_argument("--ttslistpath", help="Path to the ttsList.json file")
 parser.add_argument("--wavfilespath", help="Path to the wav outputs folder")
 args = parser.parse_args()
 if not args.ttslistpath:
    parser.error("[AllTalk TTSDiff] Please specify the path to the ttslist.json file using the --ttslistpath argument.")
 if not args.wavfilespath:
    parser.error("[AllTalk TTSDiff] Please specify the path to the wavs output folder file using the --wavfilespath argument.")
 json_file_path = Path(args.ttslistpath).resolve()
 wav_file_path = Path(args.wavfilespath).resolve()
 accuracy_threshold = args.threshold
 def disclaimer_text():
    print(f"\nDESCRIPTION: ")
    print(f"  This script is designed to assist in identifying bad TTS generation ID's from the TTS Generator.\n")
    print(f"  This will download the Whisper Large-v2 model (if not already downloaded), and use it to compare your text to")
    print(f"  to the generated TTS. If it finds an issue, it will flag the ID number that has the issue. This does NOT 100%")
    print(f"  ensure that all TTS generated is perfect, sounds good etc, but it should flag up glaring issues and help with")
    print(f"  the process of identifying bad TTS generations.")
    print(f"\nNOTES ON USE: ")
    print(f"  - Ensure AllTalk is running in its own command prompt/terminal.")
    print(f"  - This script should be running in its own command prompt/terminal window in the same Python Environment that")
    print(f"    AllTalk is running in.")
    print(f"  - After generating your text in the TTS Generator, you need to `Export List to JSON` and save the file in the")
    print(f"    same folder/directory this script is running from.")
    print(f"  - You can use `--threshold` with a numerical value from 1 to 100 to set accuracy detection. For example it may")
    print(f"    detect/transcribe `their` as `there`, which of course, both words sound the same. If you want 100% accuracy")
    print(f"    you will set 100 as the threshold. You will more than likely want a number in the high 90's. Default is set")
    print(f"    at 98, which gives a little flexibility.")
    print(f"  - The JSON file must be named \033[93mttsList.json\033[0m")
    print(f"  - When you have your ID list, go back into the TTS Generator, correct any lines and regenerate them. If you")
    print(f"    want to re-test everthing again after re-generating, you will need to export your list again and re-run this")
    print(f"    script again, against your newly exported JSON list.")
    print(f"  - This requires access to \033[93mcublas64_11\033[0m, the same as Finetuning.")
    print(f"    https://github.com/erew123/alltalk_tts/tree/main?#-important-requirements-cuda-118\n")
    return
 try:
    from faster_whisper import WhisperModel
    import re
    import string
    import spacy
    import torch
    from fuzzywuzzy import fuzz
 except ImportError as e:
    print(f"[AllTalk TTSDiff] ERROR STARTING SCRIPT:")
    print("[AllTalk TTSDiff] An error occurred importing one or more required libraries.")
    print("[AllTalk TTSDiff] Please ensure you have \033[94mactivated your Python environment \033[0mand \033[94minstalled all requirements.\033[0m")
    print("[AllTalk TTSDiff] \033[94mMissing library:\033[93m", str(e), "\033[0m")
    exit(1)
 # Attempt to load the spaCy model
 try:
    # Load a medium-sized spaCy model, adjust to "en_core_web_md" or "en_core_web_lg" as needed
    nlp = spacy.load("en_core_web_md")
 except Exception as e:
    print(f"[AllTalk TTSDiff] ERROR STARTING SCRIPT:")
    print("[AllTalk TTSDiff] Failed to load the spaCy language model.")
    # Attempt to download the spaCy model automatically
    try:
        print("[AllTalk TTSDiff] Attempting to automatically download the spaCy language model...")
        subprocess.run([sys.executable, "-m", "spacy", "download", "en_core_web_md"], check=True)
        # Try to load the model again after downloading
        nlp = spacy.load("en_core_web_md")
        print("[AllTalk TTSDiff] Model downloaded and loaded successfully.")
    except Exception as download_exception:
        print("[AllTalk TTSDiff] Automatic download failed.")
        print("[AllTalk TTSDiff] Please manually install the spaCy language model by running:")
        print("[AllTalk TTSDiff] \033[93mpython -m spacy download en_core_web_md\033[0m")
        exit(1)
 def texts_are_similar(text1, text2, threshold=0.8):
    # Preliminary check for short texts
    if len(text1) < 10 or len(text2) < 10:
        ratio = fuzz.ratio(text1, text2) / 100.0  # Use FuzzyWuzzy for short texts
        return ratio > threshold
    # Process the texts through the NLP model for longer texts
    doc1 = nlp(text1)
    doc2 = nlp(text2)
    # Compute semantic similarity
    similarity = doc1.similarity(doc2)
    return similarity >= threshold
 def normalize_text(text):
    # Normalize or remove CRLF and other non-standard whitespaces
    text = text.replace("\r\n", " ").replace("\n", " ").replace("\r", " ")
    # Convert to lowercase
    text = text.lower()
    # Standardize and then remove quotation marks
    text = text.replace("“", '"').replace("”", '"').replace("‘", "'").replace("’", "'")
    text = text.translate(str.maketrans('', '', '"\''))
    # Remove all other punctuation except hyphens to preserve compound words
    text = text.translate(str.maketrans('', '', string.punctuation.replace("-", "")))
    # Collapse any sequence of whitespace (including spaces) into a single space
    text = re.sub(r'\s+', ' ', text)
    return text.strip()
 spoken_punctuation_mapping = {
    "dot": ".",
    "comma": ",",
    # Add more mappings as needed
 }
 def contains_spoken_punctuation(transcribed_text):
    for spoken, punctuation in spoken_punctuation_mapping.items():
        if spoken in transcribed_text:
            return True
    return False
 def detailed_comparison(original_text, transcribed_text, threshold=98):
    original_clean = normalize_text(original_text)
    transcribed_clean = normalize_text(transcribed_text)
    ratio = fuzz.ratio(original_clean, transcribed_clean)
    return ratio > threshold
 def access_local_file(url):
    # Extract the file name from the URL
    file_name = url.split('/')[-1]
    # Construct the full path to the file on disk
    file_path = wav_file_path / file_name
    # Check if the file exists
    if not file_path.exists():
        print(f"[AllTalk TTSDiff] File does not exist: {file_path}")
        return None
    return file_path
 def transcribe_and_compare(file_url, original_text, model, item_id, flagged_ids):
    audio_file_path = access_local_file(file_url)
    if audio_file_path is None:
        print(f"[AllTalk TTSDiff] Could not access local file for URL: {file_url}")
        return  # Skip this file if it can't be accessed
    try:
        segments, info = model.transcribe(str(audio_file_path), beam_size=5)
        transcribed_text = " ".join([segment.text for segment in segments])
    except Exception as e:
        print(f"[AllTalk TTSDiff] Error transcribing file {audio_file_path}: {e}")
        return  # Skip this file if transcription fails
    segments, info = model.transcribe(str(audio_file_path), beam_size=5)
    transcribed_text = " ".join([segment.text for segment in segments])
    # Normalize texts for comparison
    original_text_normalized = normalize_text(original_text)
    transcribed_text_normalized = normalize_text(transcribed_text)
    # Enhanced comparison using detailed fuzzy matching
    is_detailed_match = detailed_comparison(original_text_normalized, transcribed_text_normalized, args.threshold)
    has_spoken_punctuation = contains_spoken_punctuation(transcribed_text_normalized) and not contains_spoken_punctuation(original_text_normalized)
    # Adjust is_match based on detailed comparison and spoken punctuation check
    is_match = is_detailed_match and not has_spoken_punctuation
    # Only log and flag IDs for review if there's a mismatch or detected issues
    if not is_match:
        #print(f"\033[93mMismatch Detected - ID:\033[0m {item_id}")
        print(f"[AllTalk TTSDiff] \033[93mText:\033[0m {original_text}")
        print(f"[AllTalk TTSDiff] \033[91mTTS :\033[0m{transcribed_text}")
        if has_spoken_punctuation:
            print(f"[AllTalk TTSDiff] Note: Potential incorrect spoken punctuation detected.")
        flagged_ids.append(item_id)  # Track the ID for review
 def main():
    print(f"[AllTalk TTSDiff]")
    print(f"[AllTalk TTSDiff]")
    print("[AllTalk TTSDiff] \033[92mStarting Compare of Text vs TTS:\033[0m")
    print("[AllTalk TTSDiff]")
    print("[AllTalk TTSDiff] \033[94mJSON file  :\033[92m", json_file_path, "\033[0m")
    print("[AllTalk TTSDiff] \033[94mWAV files  :\033[92m", wav_file_path, "\033[0m")
    print("[AllTalk TTSDiff] \033[94mAccuracy   :\033[92m", accuracy_threshold, "%", "\033[0m")
    detect_cublas()
    model_size = "large-v2"
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model = WhisperModel(model_size, device=device, compute_type="float32")
    flagged_ids = []  # Initialize the list to track IDs needing review
    try:
        with open(json_file_path, "r", encoding="utf-8") as f:
            tts_list = json.load(f)
    except FileNotFoundError:
        print(f"[AllTalk TTSDiff] ERROR STARTING SCRIPT:")
        print("[AllTalk TTSDiff] Error: \033[93mttsList.json\033[0m file not found.")
        print("[AllTalk TTSDiff] Please ensure you follow these steps:")
        print("[AllTalk TTSDiff] After generating your text in the TTS Generator, you need to `Export List to JSON` and save the file in the")
        print("[AllTalk TTSDiff] same folder/directory this script is running from.")
        print("[AllTalk TTSDiff] The JSON file must be named \033[93mttsList.json\033[0m")
        exit(1)
    print(f"[AllTalk TTSDiff]")
    for item in tts_list:
        print(f"[AllTalk TTSDiff] Processing ID: {item['id']}")
        transcribe_and_compare(item['fileUrl'], item['text'], model, item['id'], flagged_ids)
    print(f"[AllTalk TTSDiff]")
    # Print summary information at the end
    if flagged_ids:
        print(f"[AllTalk TTSDiff] \033[94mIDs needing review:\033[0m", ', '.join(map(str, flagged_ids)))
        print(f"[AllTalk TTSDiff]")
        print(f"[AllTalk TTSDiff] Review ID's and correct any lines by editing & regenerating as necessary.")
    else:
        print("[AllTalk TTSDiff] \033[94mIDs needing review:\033[0m No issues detected.")
    summary_data = {
        "flagged_ids": flagged_ids
    }
    with open(wav_file_path / "analysis_summary.json", "w") as summary_file:
        json.dump(summary_data, summary_file)
    print("[AllTalk TTSDiff]")
 if __name__ == "__main__":
    main()
@@ -0,0 +1,179 @@
 export interface CrunkerConstructorOptions {
    /**
     * Sample rate for Crunker's internal audio context.
     *
     * @default 44100
     */
    sampleRate: number;
 }
 export type CrunkerInputTypes = string | File | Blob;
 /**
 * An exported Crunker audio object.
 */
 export interface ExportedCrunkerAudio {
    blob: Blob;
    url: string;
    element: HTMLAudioElement;
 }
 /**
 * Crunker is the simple way to merge, concatenate, play, export and download audio files using the Web Audio API.
 */
 export default class Crunker {
    private readonly _sampleRate;
    private readonly _context;
    /**
     * Creates a new instance of Crunker with the provided options.
     *
     * If `sampleRate` is not defined, it will auto-select an appropriate sample rate
     * for the device being used.
     */
    constructor({ sampleRate }?: Partial<CrunkerConstructorOptions>);
    /**
     * Creates Crunker's internal AudioContext.
     *
     * @internal
     */
    private _createContext;
    /**
     *
     * The internal AudioContext used by Crunker.
     */
    get context(): AudioContext;
    /**
     * Asynchronously fetches multiple audio files and returns an array of AudioBuffers.
     */
    fetchAudio(...filepaths: CrunkerInputTypes[]): Promise<AudioBuffer[]>;
    /**
     * Merges (layers) multiple AudioBuffers into a single AudioBuffer.
     *
     * **Visual representation:**
     *
     * ![](https://user-images.githubusercontent.com/12958674/88806278-968f0680-d186-11ea-9cb5-8ef2606ffcc7.png)
     */
    mergeAudio(buffers: AudioBuffer[]): AudioBuffer;
    /**
     * Concatenates multiple AudioBuffers into a single AudioBuffer.
     *
     * **Visual representation:**
     *
     * ![](https://user-images.githubusercontent.com/12958674/88806297-9d1d7e00-d186-11ea-8cd2-c64cb0324845.png)
     */
    concatAudio(buffers: AudioBuffer[]): AudioBuffer;
    /**
     * Pads a specified AudioBuffer with silence from a specified start time,
     * for a specified length of time.
     *
     * Accepts float values as well as whole integers.
     *
     * @param buffer AudioBuffer to pad
     * @param padStart Time to start padding (in seconds)
     * @param seconds Duration to pad for (in seconds)
     */
    padAudio(buffer: AudioBuffer, padStart?: number, seconds?: number): AudioBuffer;
    /**
     * Slices an AudioBuffer from the specified start time to the end time, with optional fade in and out.
     *
     * @param buffer AudioBuffer to slice
     * @param start Start time (in seconds)
     * @param end End time (in seconds)
     * @param fadeIn Fade in duration (in seconds, default is 0)
     * @param fadeOut Fade out duration (in seconds, default is 0)
     */
    sliceAudio(buffer: AudioBuffer, start: number, end: number, fadeIn?: number, fadeOut?: number): AudioBuffer;
    /**
     * Plays the provided AudioBuffer in an AudioBufferSourceNode.
     */
    play(buffer: AudioBuffer): AudioBufferSourceNode;
    /**
     * Exports the specified AudioBuffer to a Blob, Object URI and HTMLAudioElement.
     *
     * Note that changing the MIME type does not change the actual file format. The
     * file format will **always** be a WAVE file due to how audio is stored in the
     * browser.
     *
     * @param buffer Buffer to export
     * @param type MIME type (default: `audio/wav`)
     */
    export(buffer: AudioBuffer, type?: string): ExportedCrunkerAudio;
    /**
     * Downloads the provided Blob.
     *
     * @param blob Blob to download
     * @param filename An optional file name to use for the download (default: `crunker`)
     */
    download(blob: Blob, filename?: string): HTMLAnchorElement;
    /**
     * Executes a callback if the browser does not support the Web Audio API.
     *
     * Returns the result of the callback, or `undefined` if the Web Audio API is supported.
     *
     * @param callback callback to run if the browser does not support the Web Audio API
     */
    notSupported<T>(callback: () => T): T | undefined;
    /**
     * Closes Crunker's internal AudioContext.
     */
    close(): this;
    /**
     * Returns the largest duration of the longest AudioBuffer.
     *
     * @internal
     */
    private _maxDuration;
    /**
     * Returns the largest number of channels in an array of AudioBuffers.
     *
     * @internal
     */
    private _maxNumberOfChannels;
    /**
     * Returns the sum of the lengths of an array of AudioBuffers.
     *
     * @internal
     */
    private _totalLength;
    /**
     * Returns whether the browser supports the Web Audio API.
     *
     * @internal
     */
    private _isSupported;
    /**
     * Writes the WAV headers for the specified Float32Array.
     *
     * Returns a DataView containing the WAV headers and file content.
     *
     * @internal
     */
    private _writeHeaders;
    /**
     * Converts a Float32Array to 16-bit PCM.
     *
     * @internal
     */
    private _floatTo16BitPCM;
    /**
     * Writes a string to a DataView at the specified offset.
     *
     * @internal
     */
    private _writeString;
    /**
     * Converts an AudioBuffer to a Float32Array.
     *
     * @internal
     */
    private _interleave;
    /**
     * Creates an HTMLAudioElement whose source is the specified Blob.
     *
     * @internal
     */
    private _renderAudioElement;
    /**
     * Creates an Object URL for the specified Blob.
     *
     * @internal
     */
    private _renderURL;
 }
@@ -0,0 +1,2 @@
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We'll try and continue anyway. (file: \"").concat(t,'")')),e.arrayBuffer()})),await e._context.decodeAudioData(n)})))}},{key:"mergeAudio",value:function(e){var t=this._context.createBuffer(this._maxNumberOfChannels(e),this._sampleRate*this._maxDuration(e),this._sampleRate);return e.forEach((function(e){for(var n=0;n<e.numberOfChannels;n++){for(var r=t.getChannelData(n),a=e.getChannelData(n),o=e.getChannelData(n).length-1;o>=0;o--)r[o]+=a[o];t.getChannelData(n).set(r)}})),t}},{key:"concatAudio",value:function(e){var t=this._context.createBuffer(this._maxNumberOfChannels(e),this._totalLength(e),this._sampleRate),n=0;return e.forEach((function(e){for(var r=0;r<e.numberOfChannels;r++)t.getChannelData(r).set(e.getChannelData(r),n);n+=e.length})),t}},{key:"padAudio",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0;if(0===n)return e;if(t<0)throw new Error('Crunker: Parameter "padStart" in padAudio must be positive');if(n<0)throw new Error('Crunker: Parameter "seconds" in padAudio must be positive');for(var r=this._context.createBuffer(e.numberOfChannels,Math.ceil(e.length+n*e.sampleRate),e.sampleRate),a=0;a<e.numberOfChannels;a++){var o=e.getChannelData(a);r.getChannelData(a).set(o.subarray(0,Math.ceil(t*e.sampleRate)+1),0),r.getChannelData(a).set(o.subarray(Math.ceil(t*e.sampleRate)+2,r.length+1),Math.ceil((t+n)*e.sampleRate))}return r}},{key:"sliceAudio",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:0,a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:0;if(t>=n)throw new Error('Crunker: "start" time should be less than "end" time in sliceAudio method');for(var o=Math.round((n-t)*this._sampleRate),i=Math.round(t*this._sampleRate),u=this._context.createBuffer(e.numberOfChannels,o,this._sampleRate),l=0;l<e.numberOfChannels;l++)for(var s=e.getChannelData(l),c=u.getChannelData(l),f=0;f<o;f++)c[f]=s[i+f],f<r*this._sampleRate&&(c[f]*=f/(r*this._sampleRate)),f>o-a*this._sampleRate&&(c[f]*=(o-f)/(a*this._sampleRate));return u}},{key:"play",value:function(e){var t=this._context.createBufferSource();return t.buffer=e,t.connect(this._context.destination),t.start(),Line truncated
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@@ -0,0 +1,4 @@
 /*! howler.js v2.2.4 | (c) 2013-2020, James Simpson of GoldFire Studios | MIT License | howlerjs.com */
 !function(){"use strict";var e=function(){this.init()};e.prototype={init:function(){var e=this||n;return e._counter=1e3,e._html5AudioPool=[],e.html5PoolSize=10,e._codecs={},e._howls=[],e._muted=!1,e._volume=1,e._canPlayEvent="canplaythrough",e._navigator="undefined"!=typeof window&&window.navigator?window.navigator:null,e.masterGain=null,e.noAudio=!1,e.usingWebAudio=!0,e.autoSuspend=!0,e.ctx=null,e.autoUnlock=!0,e._setup(),e},volume:function(e){var o=this||n;if(e=parseFloat(e),o.ctx||_(),void 0!==e&&e>=0&&e<=1){if(o._volume=e,o._muted)return o;o.usingWebAudio&&o.masterGain.gain.setValueAtTime(e,n.ctx.currentTime);for(var t=0;t<o._howls.length;t++)if(!o._howls[t]._webAudio)for(var r=o._howls[t]._getSoundIds(),a=0;a<r.length;a++){var u=o._howls[t]._soundById(r[a]);u&&u._node&&(u._node.volume=u._volume*e)}return o}return o._volume},mute:function(e){var o=this||n;o.ctx||_(),o._muted=e,o.usingWebAudio&&o.masterGain.gain.setValueAtTime(e?0:o._volume,n.ctx.currentTime);for(var t=0;t<o._howls.length;t++)if(!o._howls[t]._webAudio)for(var r=o._howls[t]._getSoundIds(),a=0;a<r.length;a++){var u=o._howls[t]._soundById(r[a]);u&&u._node&&(u._node.muted=!!e||u._muted)}return o},stop:function(){for(var e=this||n,o=0;o<e._howls.length;o++)e._howls[o].stop();return e},unload:function(){for(var e=this||n,o=e._howls.length-1;o>=0;o--)e._howls[o].unload();return e.usingWebAudio&&e.ctx&&void 0!==e.ctx.close&&(e.ctx.close(),e.ctx=null,_()),e},codecs:function(e){return(this||n)._codecs[e.replace(/^x-/,"")]},_setup:function(){var e=this||n;if(e.state=e.ctx?e.ctx.state||"suspended":"suspended",e._autoSuspend(),!e.usingWebAudio)if("undefined"!=typeof Audio)try{var o=new Audio;void 0===o.oncanplaythrough&&(e._canPlayEvent="canplay")}catch(n){e.noAudio=!0}else e.noAudio=!0;try{var o=new Audio;o.muted&&(e.noAudio=!0)}catch(e){}return e.noAudio||e._setupCodecs(),e},_setupCodecs:function(){var e=this||n,o=null;try{o="undefined"!=typeof Audio?new Audio:null}catch(n){return e}if(!o||"function"!=typeof o.canPlayType)return e;var t=o.canPlayType("audio/mpeg;").replace(/^no$/,""),r=e._navigator?e._navigator.userAgent:"",a=r.match(/OPR\/(\d+)/g),u=a&&parseInt(a[0].split("/")[1],10)<33,d=-1!==r.indexOf("Safari")&&-1===r.indexOf("Chrome"),i=r.match(/Version\/(.*?) /),_=d&&i&&parseInt(i[1],10)<15;return e._codecs={mp3:!(u||!t&&!o.canPlayType("audio/mp3;").replace(/^no$/,"")),mpeg:!!t,opus:!!o.canPlayType('audio/ogg; codecs="opus"').replace(/^no$/,""),ogg:!!o.canPlayType('audio/ogg; codecs="vorbis"').replace(/^no$/,""),oga:!!o.canPlayType('audio/ogg; codecs="vorbis"').replace(/^no$/,""),wav:!!(o.canPlayType('audio/wav; codecs="1"')||o.canPlayType("audio/wav")).replace(/^no$/,""),aac:!!o.canPlayType("audio/aac;").replace(/^no$/,""),caf:!!o.canPlayType("audio/x-caf;").replace(/^no$/,""),m4a:!!(o.canPlayType("audio/x-m4a;")||o.canPlayType("audio/m4a;")||o.canPlayType("audio/aac;")).replace(/^no$/,""),m4b:!!(o.canPlayType("audio/x-m4b;")||o.canPlayType("audio/m4b;")||o.canPlayType("audio/aac;")).replace(/^no$/,""),mp4:!!(o.canPlayType("audio/x-mp4;")||o.canPlayType("audio/mp4;")||o.canPlayType("audio/aac;")).replace(/^no$/,""),weba:!(_||!o.canPlayType('audio/webm; codecs="vorbis"').replace(/^no$/,"")),webm:!(_||!o.canPlayType('audio/webm; codecs="vorbis"').replace(/^no$/,"")),dolby:!!o.canPlayType('audio/mp4; codecs="ec-3"').replace(/^no$/,""),flac:!!(o.canPlayType("audio/x-flac;")||o.canPlayType("audio/flac;")).replace(/^no$/,"")},e},_unlockAudio:function(){var e=this||n;if(!e._audioUnlocked&&e.ctx){e._audioUnlocked=!1,e.autoUnlock=!1,e._mobileUnloaded||44100===e.ctx.sampleRate||(e._mobileUnloaded=!0,e.unload()),e._scratchBuffer=e.ctx.createBuffer(1,1,22050);var o=function(n){for(;e._html5AudioPool.length<e.html5PoolSize;)try{var t=new Audio;t._unlocked=!0,e._releaseHtml5Audio(t)}catch(n){e.noAudio=!0;break}for(var r=0;r<e._howls.length;r++)if(!e._howls[r]._webAudio)for(var a=e._howls[r]._getSoundIds(),u=0;u<a.length;u++){var d=e._howls[r]._soundById(a[u]);d&&d._node&&!d._node._unlocked&&(d._node._unlocked=!0,d._node.load())}e._autoResume();var i=e.ctx.createBufferSource();i.buffer=e._scratchBuffer,i.connect(e.ctx.destination),void 0===i.start?i.noteOn(0):i.start(0),"function"==typeof e.ctx.resume&&e.ctx.resume(),i.onended=function(){i.disconnect(0),e._audioUnlocked=!0,document.removeEventListener("touchstart",o,!0),document.removeEventListener("touchend",o,!0),document.removeEventListener("click",o,!0),document.removeEventListener("keydown",o,!0);for(var n=0;n<e._howls.length;n++)e._howls[n]._emit("unlock")}};return document.addEventListener("touchstart",o,!0),document.addEventListener("touchend",o,!0),document.addEventListener("click",o,!0),document.addEventListener("keydown",o,!0),e}},_obtainHtml5Audio:function(){var e=this||n;if(e._html5AudioPool.length)return e._html5AudioPool.pop();var o=(new Audio).play();return o&&"undefined"!=typeof Promise&&(o instanceof Promise||"function"==typeof o.then)&&o.catch(function(){console.warn("HTML5 Audio pool eLine truncated
 /*! Spatial Plugin */
 !function(){"use strict";HowlerGlobal.prototype._pos=[0,0,0],HowlerGlobal.prototype._orientation=[0,0,-1,0,1,0],HowlerGlobal.prototype.stereo=function(e){var n=this;if(!n.ctx||!n.ctx.listener)return n;for(var t=n._howls.length-1;t>=0;t--)n._howls[t].stereo(e);return n},HowlerGlobal.prototype.pos=function(e,n,t){var r=this;return r.ctx&&r.ctx.listener?(n="number"!=typeof n?r._pos[1]:n,t="number"!=typeof t?r._pos[2]:t,"number"!=typeof e?r._pos:(r._pos=[e,n,t],void 0!==r.ctx.listener.positionX?(r.ctx.listener.positionX.setTargetAtTime(r._pos[0],Howler.ctx.currentTime,.1),r.ctx.listener.positionY.setTargetAtTime(r._pos[1],Howler.ctx.currentTime,.1),r.ctx.listener.positionZ.setTargetAtTime(r._pos[2],Howler.ctx.currentTime,.1)):r.ctx.listener.setPosition(r._pos[0],r._pos[1],r._pos[2]),r)):r},HowlerGlobal.prototype.orientation=function(e,n,t,r,o,i){var a=this;if(!a.ctx||!a.ctx.listener)return a;var p=a._orientation;return n="number"!=typeof n?p[1]:n,t="number"!=typeof t?p[2]:t,r="number"!=typeof r?p[3]:r,o="number"!=typeof o?p[4]:o,i="number"!=typeof i?p[5]:i,"number"!=typeof e?p:(a._orientation=[e,n,t,r,o,i],void 0!==a.ctx.listener.forwardX?(a.ctx.listener.forwardX.setTargetAtTime(e,Howler.ctx.currentTime,.1),a.ctx.listener.forwardY.setTargetAtTime(n,Howler.ctx.currentTime,.1),a.ctx.listener.forwardZ.setTargetAtTime(t,Howler.ctx.currentTime,.1),a.ctx.listener.upX.setTargetAtTime(r,Howler.ctx.currentTime,.1),a.ctx.listener.upY.setTargetAtTime(o,Howler.ctx.currentTime,.1),a.ctx.listener.upZ.setTargetAtTime(i,Howler.ctx.currentTime,.1)):a.ctx.listener.setOrientation(e,n,t,r,o,i),a)},Howl.prototype.init=function(e){return function(n){var t=this;return t._orientation=n.orientation||[1,0,0],t._stereo=n.stereo||null,t._pos=n.pos||null,t._pannerAttr={coneInnerAngle:void 0!==n.coneInnerAngle?n.coneInnerAngle:360,coneOuterAngle:void 0!==n.coneOuterAngle?n.coneOuterAngle:360,coneOuterGain:void 0!==n.coneOuterGain?n.coneOuterGain:0,distanceModel:void 0!==n.distanceModel?n.distanceModel:"inverse",maxDistance:void 0!==n.maxDistance?n.maxDistance:1e4,panningModel:void 0!==n.panningModel?n.panningModel:"HRTF",refDistance:void 0!==n.refDistance?n.refDistance:1,rolloffFactor:void 0!==n.rolloffFactor?n.rolloffFactor:1},t._onstereo=n.onstereo?[{fn:n.onstereo}]:[],t._onpos=n.onpos?[{fn:n.onpos}]:[],t._onorientation=n.onorientation?[{fn:n.onorientation}]:[],e.call(this,n)}}(Howl.prototype.init),Howl.prototype.stereo=function(n,t){var r=this;if(!r._webAudio)return r;if("loaded"!==r._state)return r._queue.push({event:"stereo",action:function(){r.stereo(n,t)}}),r;var o=void 0===Howler.ctx.createStereoPanner?"spatial":"stereo";if(void 0===t){if("number"!=typeof n)return r._stereo;r._stereo=n,r._pos=[n,0,0]}for(var i=r._getSoundIds(t),a=0;a<i.length;a++){var p=r._soundById(i[a]);if(p){if("number"!=typeof n)return p._stereo;p._stereo=n,p._pos=[n,0,0],p._node&&(p._pannerAttr.panningModel="equalpower",p._panner&&p._panner.pan||e(p,o),"spatial"===o?void 0!==p._panner.positionX?(p._panner.positionX.setValueAtTime(n,Howler.ctx.currentTime),p._panner.positionY.setValueAtTime(0,Howler.ctx.currentTime),p._panner.positionZ.setValueAtTime(0,Howler.ctx.currentTime)):p._panner.setPosition(n,0,0):p._panner.pan.setValueAtTime(n,Howler.ctx.currentTime)),r._emit("stereo",p._id)}}return r},Howl.prototype.pos=function(n,t,r,o){var i=this;if(!i._webAudio)return i;if("loaded"!==i._state)return i._queue.push({event:"pos",action:function(){i.pos(n,t,r,o)}}),i;if(t="number"!=typeof t?0:t,r="number"!=typeof r?-.5:r,void 0===o){if("number"!=typeof n)return i._pos;i._pos=[n,t,r]}for(var a=i._getSoundIds(o),p=0;p<a.length;p++){var s=i._soundById(a[p]);if(s){if("number"!=typeof n)return s._pos;s._pos=[n,t,r],s._node&&(s._panner&&!s._panner.pan||e(s,"spatial"),void 0!==s._panner.positionX?(s._panner.positionX.setValueAtTime(n,Howler.ctx.currentTime),s._panner.positionY.setValueAtTime(t,Howler.ctx.currentTime),s._panner.positionZ.setValueAtTime(r,Howler.ctx.currentTime)):s._panner.setPosition(n,t,r)),i._emit("pos",s._id)}}return i},Howl.prototype.orientation=function(n,t,r,o){var i=this;if(!i._webAudio)return i;if("loaded"!==i._state)return i._queue.push({event:"orientation",action:function(){i.orientation(n,t,r,o)}}),i;if(t="number"!=typeof t?i._orientation[1]:t,r="number"!=typeof r?i._orientation[2]:r,void 0===o){if("number"!=typeof n)return i._orientation;i._orientation=[n,t,r]}for(var a=i._getSoundIds(o),p=0;p<a.length;p++){var s=i._soundById(a[p]);if(s){if("number"!=typeof n)return s._orientation;s._orientation=[n,t,r],s._node&&(s._panner||(s._pos||(s._pos=i._pos||[0,0,-.5]),e(s,"spatial")),void 0!==s._panner.orientationX?(s._panner.orientationX.setValueAtTime(n,Howler.ctx.currentTime),s._panner.orientationY.setValueAtTime(t,Howler.ctx.currentTime),s._panner.orientationZ.setValueAtTime(r,Howler.ctx.currentTime)):s._panner.setOrientation(n,t,r)),i._emit("orientation",s._id)}}return i},Howl.prototype.pannerAttr=function(){var n,t,r,o=this,i=arguments;if(!o._webAudioLine truncated
@@ -0,0 +1,77 @@
 import json
 import argparse
 import soundfile as sf
 from pathlib import Path
 parser = argparse.ArgumentParser(description="Compare TTS output with the original text using detailed comparison.")
 parser.add_argument("--ttslistpath", help="Path to the ttsList.json file")
 parser.add_argument("--wavfilespath", help="Path to the wav outputs folder")
 args = parser.parse_args()
 if not args.ttslistpath:
    parser.error("[AllTalk TTSSRT] Please specify the path to the ttslist.json file using the --ttslistpath argument.")
 if not args.wavfilespath:
    parser.error("[AllTalk TTSSRT] Please specify the path to the wavs output folder file using the --wavfilespath argument.")
 json_file_path = Path(args.ttslistpath).resolve()
 audio_files_base_path = Path(args.wavfilespath).resolve()
 print(f"[AllTalk TTSSRT]")
 print("[AllTalk TTSSRT] \033[92mStarting SRT Subtitle generation:\033[0m")
 print("[AllTalk TTSSRT]")
 print("[AllTalk TTSSRT] \033[94mJSON file  :\033[92m", json_file_path, "\033[0m")
 print("[AllTalk TTSSRT] \033[94mWAV files  :\033[92m", audio_files_base_path, "\033[0m")
 print("[AllTalk TTSSRT]")
 # Function to convert time in seconds to SRT time format
 def format_srt_time(seconds):
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    seconds = seconds % 60
    milliseconds = int((seconds - int(seconds)) * 1000)
    return f"{hours:02}:{minutes:02}:{int(seconds):02},{milliseconds:03}"
 # Load the JSON data
 with open(json_file_path, 'r') as json_file:
    data = json.load(json_file)
 # Initialize start time and SRT lines
 start_time = 0.0
 srt_lines = []
 # Loop through the JSON data to build SRT entries
 for entry in data:
    file_name = Path(entry['fileUrl']).name  # Get the file name from the URL
    file_path = audio_files_base_path / file_name
    print(f"[AllTalk TTSSRT] Processing file: {file_name}")
    if not file_path.exists():
        print(f"[AllTalk TTSSRT] Audio file does not exist: {file_path}")
        continue
    try:
        # Use soundfile to read the duration of the WAV file
        with sf.SoundFile(str(file_path), 'r') as f:
            duration = len(f) / f.samplerate
            end_time = start_time + duration
            # Format the SRT entry
            start_timestamp = format_srt_time(start_time)
            end_timestamp = format_srt_time(end_time)
            srt_lines.append(f"{len(srt_lines)+1}\n{start_timestamp} --> {end_timestamp}\n{entry['text']}\n")
            # Update the start time for the next entry
            start_time = end_time
    except RuntimeError as e:
        print(f"[AllTalk TTSSRT] Error reading {file_path}: {e}")
 # Join all SRT lines into the final SRT content
 srt_content = "\n".join(srt_lines)
 # Save the SRT content to a file
 srt_file_path = audio_files_base_path / "subtitles.srt"
 with open(srt_file_path, "w") as srt_file:
    srt_file.write(srt_content)
 print("[AllTalk TTSSRT]")
 print(f"[AllTalk TTSSRT] \033[94mSRT file created at \033[0m{srt_file_path}")
@@ -0,0 +1,2 @@
 /*! For license information please see 1995fcd6d39cade7d871.js.LICENSE.txt */
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@@ -0,0 +1 @@
 /*! regenerator-runtime -- Copyright (c) 2014-present, Facebook, Inc. -- license (MIT): https://github.com/facebook/regenerator/blob/main/LICENSE */
@@ -0,0 +1,80 @@
 /* 
 * Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT license.
 * See LICENSE in the project root for license information.
 */
 html,
 body {
     width: 100%;
     height: 100%;
     margin: 0;
     padding: 0;
 }
 ul {
     margin: 0;
     padding: 0;
 }
 .ms-welcome__header {
    padding: 20px;
    padding-bottom: 30px;
    padding-top: 100px;
    display: -webkit-flex;
    display: flex;
    -webkit-flex-direction: column;
    flex-direction: column;
    align-items: center;
 }
 .ms-welcome__main {
    display: -webkit-flex;
    display: flex;
    -webkit-flex-direction: column;
    flex-direction: column;
    -webkit-flex-wrap: nowrap;
    flex-wrap: nowrap;
    -webkit-align-items: center;
    align-items: center;
    -webkit-flex: 1 0 0;
    flex: 1 0 0;
    padding: 10px 20px;
 }
 .ms-welcome__main > h2 {
     width: 100%;
     text-align: center;
 }
 .ms-welcome__features {
     list-style-type: none;
     margin-top: 20px;
 }
 .ms-welcome__features.ms-List .ms-ListItem {
     padding-bottom: 20px;
     display: -webkit-flex;
     display: flex;
 }
 .ms-welcome__features.ms-List .ms-ListItem > .ms-Icon {
     margin-right: 10px;
 }
 .ms-welcome__action.ms-Button--hero {
     margin-top: 30px;
 }
 .ms-Button.ms-Button--hero .ms-Button-label {
  color: #0078d7;
 }
 .ms-Button.ms-Button--hero:hover .ms-Button-label,
 .ms-Button.ms-Button--hero:focus .ms-Button-label{
  color: #005a9e;
  cursor: pointer;
 }
 b {
    font-weight: bold;
 }
@@ -0,0 +1 @@
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      <bt:Urls>
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      </bt:Urls>
      <bt:ShortStrings>
        <bt:String id="AllTalkGroup.Label" DefaultValue="AllTalk Controls"/>
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      </bt:ShortStrings>
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        <bt:String id="AllTalkTaskpaneButton.Tooltip" DefaultValue="Open the AllTalk task pane to start using the add-in."/>
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 <html>
 <head>
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    <title>AllTalk TTS Add-in</title>
    <script src="https://appsforoffice.microsoft.com/lib/1.1/hosted/office.js"></script>
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            <option value="ru">Russian</option>
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            <div class="input-container"><label for="portNumber">Port Number:</label> <input type="number"
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        </div>
    </main>
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truncated
@@ -9,4 +9,6 @@ Author	BBC
 This file is licensed under the Creative Commons Attribution 3.0 Unported license.
 https://creativecommons.org/licenses/by/3.0/deed.en
-Downsampled to 22050hz, mono and cut shorter
+Downsampled to 22050hz, mono and cut shorter.
 This license applies to both the original file ( «Clint_Eastwood CC3.wav» ) and it's derivative, the enhanced file ( «Clint_Eastwood (enhanced)CC3.wav» ).
		`@@ -0,0 +1,2 @@`
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		`@@ -0,0 +1 @@`
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truncated