Updated to deal with Gradio 4.xx versions

Update tts_server.py
Set API Local to use float as Transformers demands it
2024-05-14 21:01:38 +01:00 · 2024-05-14 18:19:29 +01:00 · 2024-05-12 14:00:10 +01:00 · 2024-05-06 23:40:39 +01:00 · 2024-05-06 01:39:01 +01:00 · 2024-05-06 01:38:04 +01:00
29 changed files with 2992 additions and 245 deletions
@@ -1,6 +1,6 @@
 ---
-name: Support request OR Bug report
-about: Create a report to help us improve
+name: Support Request/Issue
+about: Use this to create a support request or report a bug/issue
 title: ''
 labels: ''
 assignees: ''
@@ -1,6 +1,6 @@
 ---
 name: Feature request
-about: Suggest an idea for this project
+about: Use this to create a Feature request
 title: ''
 labels: ''
 assignees: ''
@@ -1,12 +1,19 @@
-FROM ubuntu:22.04
+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 libaio-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 . .
-RUN pip install --no-cache-dir -r requirements_other.txt
-EXPOSE 7851
+ENV CUDA_DOCKER_ARCH=all
+RUN pip install --no-cache-dir --no-deps -r system/requirements/requirements_docker.txt && \
+    pip install --no-cache-dir deepspeed
+
+EXPOSE 7851 7852
 RUN chmod +x launch.sh
 ENTRYPOINT ["sh", "-c", "./launch.sh"]
@@ -126,7 +126,7 @@ To perform a Standalone installation of AllTalk:

 > If you're unfamiliar with Python environments and wish to learn more, consider reviewing **Understanding Python Environments Simplified** in the Help section.

-> **Important**: Do not use dashes or spaces in your folder path (e.g. avoid `/my folder-is-this/alltalk_tts-main`) as this causes issues with Python.
+> **Important**: Do not use spaces in your folder path (e.g. avoid `/my folder-is-this/alltalk_tts-main`) as this causes issues with Python & Conda.

 </details>

@@ -177,11 +177,9 @@ If you're using a Mac or prefer a manual installation for any other reason, plea
   - `cd alltalk_tts`

 6. **Install Required Dependencies**:
- Install dependencies based on your machine type:
-  - For Nvidia graphics card machines:
-    - `pip install -r requirements_nvidia.txt`
-  - For other machines (mac, AMD, etc.):
-    - `pip install -r requirements_other.txt`
+   - Install dependencies for your machine type:
+     - For Windows: `pip install -r system\requirements\requirements_textgen.txt`
+     - For Linux/Mac: `pip install -r system/requirements/requirements_textgen.txt`

 7. **Optional DeepSpeed Installation**:
 - If you're using an Nvidia graphics card on Linux or Windows and wish to install **DeepSpeed**, follow the instructions [here](https://github.com/erew123/alltalk_tts?#-deepspeed-installation-options). 
@@ -240,7 +238,7 @@ Support for custom Python environments is limted. Please read **Custom Python en
 To run AllTalk as a standalone application with a custom Python environment, ensure you install AllTalk's requirements into the environment of your choice. The instructions provided are generalized due to the variety of potential Python environments.

 - **Python Compatibility**: The TTS engine requires Python **3.9.x** to **3.11.x**. AllTalk is tested with Python **3.11.x**. [See TTS Engine details](https://pypi.org/project/TTS/).
- **Path Names**: Avoid spaces and dashes `-` in path names as this can cause issues.
+- **Path Names**: Avoid spaces in path names as this can cause issues.
 - **Custom Python Environments**: If encountering issues potentially related to a custom environment, consider testing AllTalk with the quick setup standalone method that builds its own environment.

 #### Quick Overview of Python Environments
@@ -271,10 +269,8 @@ To run AllTalk as a standalone application with a custom Python environment, ens

 5. **Install Requirements**:
   - Install dependencies based on your machine type:
-     - For Nvidia graphics card machines:
-       - `pip install -r requirements_nvidia.txt`
-     - For other machines (mac, AMD, etc.):
-       - `pip install -r requirements_other.txt`
+     - For Windows: `pip install -r system\requirements\requirements_standalone.txt`
+     - For Linux/Mac: `pip install -r system/requirements/requirements_standalone.txt`

 6. **Start AllTalk**:
   - Run AllTalk with the following:
@@ -390,12 +386,18 @@ The update process closely mirrors the installation steps. Follow these to ensur
     - **For Linux/Mac**:
       - `pip install -r system/requirements/requirements_textgen.txt`

+5. **DeepSpeed Requirements**:
+   - If Text-gen-webui is using a new version of PyTorch, you may need to uninstall and update your DeepSpeed version.
+   - Use AllTalks diagnostics or start-up menu to identify your version of PyTorch.
+<br><br>
 </details>

 <details>
 <summary>UPDATING - Standalone Application</summary>
 <br>

+If you installed from a ZIP file, you cannot use a `git pull` to update, as noted in the Quick Setup instructions.
+
 For Standalone Application users, here's how to update your setup:

 1. **Open a Command Prompt/Terminal**:
@@ -421,7 +423,7 @@ For Standalone Application users, here's how to update your setup:
       - `pip install -r system\requirements\requirements_standalone.txt`
     - **For Linux/Mac**:
       - `pip install -r system/requirements/requirements_standalone.txt`
-
+<br><br>
 </details>

 ### 🟪 Resolving Update Issues
@@ -429,46 +431,26 @@ For Standalone Application users, here's how to update your setup:
 If you encounter problems during or after an update, following these steps can help resolve the issue by refreshing your installation while preserving your data:

 <details>
-<summary>RESOLVING - Text-Generation-webui</summary><br>
+<summary>RESOLVING - Updates</summary><br>

 The process involves renaming your existing `alltalk_tts` directory, setting up a fresh instance, and then migrating your data:

 1. **Rename Existing Directory**:
-   - First, rename your current `alltalk_tts` folder to back it up, e.g., `alltalk_tts.old`. This preserves any existing data.
+   - First, rename your current `alltalk_tts` folder to keep it safe e.g. `alltalk_tts.old`. This preserves any existing data.

-2. **Open a Console/Terminal**:
-   - Navigate to the Text-generation-webui directory and start the Python environment appropriate for your operating system:
-     - `cd text-generation-webui`
-
-   - Then use one of the following commands based on your OS:
-     - Windows: `cmd_windows.bat`
-     - Linux: `./cmd_linux.sh`
-     - macOS: `cmd_macos.sh`
-     - WSL (Windows Subsystem for Linux): `cmd_wsl.bat`<br><br>
+2. **Follow the Quick Setup instructions**:
+   - You will now follow the **Quick Setup** instructions, performing the `git clone https://github.com/erew123/alltalk_tts` to pull down a new copy of AllTalk and install the requirements.
     
     > If you're not familiar with Python environments, see **Understanding Python Environments Simplified** in the Help section for more info.

-3. **Clone the AllTalk TTS Repository**:
-   - Move into the `extensions` directory and clone a fresh copy of `alltalk_tts`:
-     - `cd extensions`
-     - `git clone https://github.com/erew123/alltalk_tts`
+3. **Migrate Your Data**:
+   - **Before** starting the AllTalk, transfer the `models`, `voices`, `outputs` folders and also `confignew.json` from `alltalk_tts.old` to the new `alltalk_tts` directory. This action preserves your voice history and prevents the need to re-download the model.

-4. **Install Requirements**:
-   - Navigate to the newly cloned `alltalk_tts` directory and install the necessary dependencies for your system:
-     - `cd alltalk_tts`
-   - Depending on your machine's OS, install the required dependencies using pip:
-     - **For Windows Machines**:
-       - `pip install -r system\requirements\requirements_textgen.txt`
-     - **For Linux/Mac**:
-       - `pip install -r system/requirements/requirements_textgen.txt`
-
-5. **Migrate Your Data**:
-   - Before starting the application, transfer the `models`, `voices`, and `outputs` folders from `alltalk_tts.old` to the new `alltalk_tts` directory. This action preserves your voice history and prevents the need to re-download the model.
-
-You're now ready to launch Text-generation-webui. Note that you may need to reapply any previously saved configuration changes through the configuration page.
+4) **Launch AllTalk**
+   - You're now ready to launch AllTalk and check it works correctly.

 6. **Final Step**:
- Once you've verified that everything is working as expected and you're satisfied with the setup, feel free to delete the `alltalk_tts.old` directory to free up space.
+    - Once you've verified that everything is working as expected and you're satisfied with the setup, feel free to delete the `alltalk_tts.old` directory to free up space.

 </details>

@@ -732,7 +714,7 @@ When a Python environment starts up, it changes the command prompt to show the P
 <details>
 	<summary>🟨 Standalone Install - start_{youros}.xx opens and closes instantly and AllTalk doesnt start</summary><br>

-This is more than likely caused by having a `-` in your folder path e.g. `c:\myfiles\alltalk_tts-main`. In this circumstance you would be best renaming the folder to remove the `-` from its name e.g. `c:\myfiles\alltalk_tts`, delete the `alltalk_environment` folder and `start_alltalk.bat` or `start_alltalk.sh` and then re-run `atsetup` to re-create the environment and startup files. 
+This is more than likely caused by having a space ` ` in your folder path e.g. `c:\program files\alltalk_tts`. In this circumstance you would be best moving the folder to a path without a space e.g. `c:\myfiles\alltalk_tts`. You would have to delete the `alltalk_environment` folder and `start_alltalk.bat` or `start_alltalk.sh` and then re-run `atsetup` to re-create the environment and startup files. 
 </details>
 <details>
 	<summary>🟨 I think AllTalks requirements file has installed something another extension doesn't like</summary><br>
@@ -960,6 +942,12 @@ Depending on which of your Nvidia GPU's is the more powerful one, you can change
 This is a long standing issue with Mozilla & Firefox and one I am unable to resolve as Mozilla have not resolved the issue with Firefox. The solution is to use another web browser if you want to use Streaming audio. For details of my prior invesitigation please look at this [ticket](https://github.com/erew123/alltalk_tts/issues/143)
 </details>

+<details>
+	<summary>🟨 Hindi Support - Not working or issues</summary><br>
+	
+Hindi support does not officially exist according to Coqui. Ive added a limited Hindi support at this time, however, It only works with API TTS method and Im sure there will be issues. [ticket](https://github.com/erew123/alltalk_tts/issues/178)
+</details>
+
 ### Application Specific Issues

 <details>
@@ -1013,7 +1001,7 @@ If you wish to try out the XTTS version 2.0.3 model and see if it works better,
 ### ⚫ Finetuning a model
 If you have a voice that the model doesnt quite reproduce correctly, or indeed you just want to improve the reproduced voice, then finetuning is a way to train your "XTTSv2 local" model **(stored in `/alltalk_tts/models/xxxxx/`)** on a specific voice. For this you will need:

- An Nvidia graphics card. (Please see the help section [note](https://github.com/erew123/alltalk_tts/edit/main/README.md#performance-and-compatibility-issues) if you have multiple Nvidia GPU's).
+- An Nvidia graphics card. (Please see the help section [note](https://github.com/erew123/alltalk_tts/edit/main/README.md#performance-and-compatibility-issues) if you have multiple Nvidia GPU's). Preferably 12GB+ VRAM on Windows. Minimum 16GB VRAM on Linux.
 - 18GB of disk space free (most of this is used temporarily)
 - At least 2 minutes of good quality speech from your chosen speaker in mp3, wav or flacc format, in one or more files (have tested as far as 20 minutes worth of audio).
 - As a side note, many people seem to think that the Whisper v2 model (used on Step 1) is giving better results at generating training datasets, so you may prefer to try that, as opposed to the Whisper 3 model.
@@ -1175,6 +1163,14 @@ At the end of the finetune process, you will have an option to `Compact and move

 If you've compacted and moved your model, its highly unlikely you would want to keep that data, however the choice is there to keep it if you wish. It will be between 5-10GB in size, so most people will want to delete it.

+#### ⚫ I have deeper questions about training the XTTS model, where can I find more information?
+
+If you have deeper questions about the XTTS model, its capabilites, the training process etc, anything thats not covered within the above text or the interface of `finetune.py`, please use the following links to research Coqui's documentation on the XTTS model. 
+
+- https://docs.coqui.ai/en/latest/models/xtts.html
+- https://github.com/coqui-ai/TTS
+- https://github.com/coqui-ai/TTS/discussions
+
 ---

 ### ⬜ AllTalk TTS Generator
@@ -1401,6 +1397,7 @@ Example:
 `en` English<br>
 `fr` French<br>
 `de` German<br>
+`hi` Hindi (Please see this re Hindi support, which is very limited https://github.com/erew123/alltalk_tts/issues/178) <br>
 `hu` Hungarian<br>
 `it` Italian<br>
 `ja` Japanese<br>
@@ -1,7 +1,7 @@
@echo off
 cd /D "%~dp0"
-
-set PATH=%PATH%;%SystemRoot%\system32
+setlocal enabledelayedexpansion
+@set "PATH=%PATH%;%SystemRoot%\system32"

 :: Generate the ESC character
 for /F %%a in ('echo prompt $E ^| cmd') do set "ESC=%%a"
@@ -25,6 +25,15 @@ set "L_CYAN=%ESC%[96m"
 set "L_WHITE=%ESC%[97m"
 set "RESET=%ESC%[0m"

+echo "%CD%"| findstr /C:" " >nul &&  echo. && echo    %L_BLUE%ALLTALK WINDOWS SETUP UTILITY%RESET% && echo. &&  echo. &&  echo    You are trying to install AllTalk in a folder that has a space in the folder path e.g. && echo. && echo       C:\%L_RED%program files%RESET%\alltalk_tts && echo.	&& echo    This causes errors with Conda and Python scripts. Please follow this link for reference: &&  echo. && echo      %L_CYAN%https://docs.anaconda.com/free/working-with-conda/reference/faq/#installing-anaconda%RESET% && echo. && echo    Please use a folder path that has no spaces in it e.g. && echo. && echo       C:\myfiles\alltalk_tts\ && echo. && echo. && pause && goto :end
+
+@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 Check if curl is available
 curl --version >nul 2>&1
 if "%ERRORLEVEL%" NEQ "0" (
@@ -120,7 +129,7 @@ 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
+if "%StandaloneOption%"=="9" goto EndStandalone
 goto StandaloneMenu

 :InstallNvidiaTextGen
@@ -128,6 +137,8 @@ pip install -r system\requirements\requirements_textgen.txt
 if %ERRORLEVEL% neq 0 (
    echo.
    echo    There was an error installing the requirements.
+    echo    Have you started your Text-gen-webui Python environment
+    echo    with cmd_{yourOS} before running atsetup.bat?
    echo    Press any key to return to the menu.
    echo.
    pause
@@ -383,7 +394,7 @@ 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 --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
+pip install torch==2.2.2+cu121 torchaudio>=2.2.2+cu121 --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
 echo Installing other requirements.
 echo.
 pip install -r system\requirements\requirements_standalone.txt
@@ -524,11 +535,11 @@ 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 --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
+pip install torch==2.2.2+cu121 torchaudio>=2.2.2+cu121 --upgrade --force-reinstall --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-14.0/deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
+C:/Windows/system32/curl.exe -LO https://github.com/erew123/alltalk_tts/releases/download/DeepSpeed-14.0/deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
 echo Installing DeepSpeed...
 pip install deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
 del deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
@@ -633,6 +644,16 @@ Echo.
 pause
 goto StandaloneMenu

+:EndStandalone
+echo Exiting AllTalk Setup Utility...
+echo.
+Echo    Remember, after installation you can....
+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.
+exit /b
+
 :End
 echo Exiting AllTalk Setup Utility...
 exit /b
@@ -28,6 +28,40 @@ check_curl() {
    fi
 }

+# Check if the current directory path contains a space
+containsSpace=false
+currentPath=$(pwd)
+if echo "$currentPath" | grep -q ' '; then
+    containsSpace=true
+fi
+
+if [ "$containsSpace" = true ]; then
+    echo
+    echo -e "    ${L_BLUE}ALLTALK LINUX SETUP UTILITY${NC}"
+    echo
+    echo
+    echo -e "    You are trying to install AllTalk in a folder that has a space in the"
+    echo -e "    folder path e.g."
+    echo 
+    echo -e "       /home/${L_RED}program files${NC}/alltalk_tts"
+    echo 
+    echo -e "    This causes errors with Conda and Python scripts. Please follow this"
+    echo -e "    link for reference:"
+    echo 
+    echo -e "      ${L_CYAN}https://docs.anaconda.com/free/working-with-conda/reference/faq/#installing-anaconda${NC}"
+    echo 
+    echo -e "    Please use a folder path that has no spaces in it e.g." 
+    echo 
+    echo -e "       /home/myfiles/alltalk_tts/"
+    echo 
+    echo
+    read -p "Press Enter to continue..." 
+    exit 1
+else
+    # Continue with the main menu
+    echo "Continue with the main menu."
+fi
+
 # Main Menu
 main_menu() {
    while true; do
@@ -291,7 +325,7 @@ install_custom_standalone() {
    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 --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu1
+    pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
    echo
    echo "    Installing additional requirements."
    echo
@@ -469,7 +503,7 @@ reapply_standalone() {
    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 --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu1
+    pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
    echo
    echo "    Installing additional requirements."
    echo
@@ -1,13 +1,21 @@
 version: '3.8'
 services:
  alltalk-tts:
-    image: erew123/alltalk_tts:cpu
+    image: alltalk:v1-9c
    restart: unless-stopped
    ports:
      - "7851:7851"
+      - "7852:7852"
    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 ]
@@ -291,12 +291,12 @@ 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, eval_split_number, gradio_progress=progress):
+def format_audio_list(target_language, whisper_model, out_path, eval_split_number, speaker_name_input, 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 = eval_split_number / 100.0
-    speaker_name="coqui"
+    speaker_name=speaker_name_input
    audio_total_size = 0
    os.makedirs(out_path, exist_ok=True)
    temp_folder = os.path.join(out_path, "temp")  # Update with your folder name
@@ -533,7 +533,7 @@ def basemodel_or_finetunedmodel_choice(value):
    elif value == "Existing finetuned model":
        basemodel_or_finetunedmodel = False

-def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path, max_audio_length=255995):
+def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, learning_rate, output_path, max_audio_length=255995):
    pfc_check_fail()
    #  Logging parameters
    RUN_NAME = "XTTS_FT"
@@ -657,7 +657,7 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
        optimizer="AdamW",
        optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
        optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2},
-        lr=5e-06,  # learning rate
+        lr=learning_rate,  # learning rate
        lr_scheduler="MultiStepLR",
        # it was adjusted accordly for the new step scheme
        lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1},
@@ -735,6 +735,9 @@ def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
    config.load_json(xtts_config)
    XTTS_MODEL = Xtts.init_from_config(config)
    print("[FINETUNE] \033[94mStarting Step 3\033[0m Loading XTTS model!")
+    print(xtts_checkpoint)
+    print(xtts_vocab)
+    print(xtts_speakers_pth)
    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()
@@ -773,18 +776,22 @@ def get_available_voices(minimum_size_kb=1200):
    ]
    return sorted([str(file) for file in voice_files])  # Return full path as string

+def find_best_models(directory):
+    """Find files named 'best_model.pth' in the given directory."""
+    return [str(file) for file in Path(directory).rglob("best_model.pth")]
+
 def find_models(directory, extension):
    """Find files with a specific extension in the given directory."""
-    return [file for file in Path(directory).rglob(f"*.{extension}")]
+    return [str(file) for file in Path(directory).rglob(f"*.{extension}")]

 def find_jsons(directory, filename):
    """Find files with a specific filename in the given directory."""
-    return list(Path(directory).rglob(filename))
+    return [str(file) for file in Path(directory).rglob(filename)]

 # Your main directory
 main_directory = Path(this_dir) / "finetune" / "tmp-trn"
-# XTTS checkpoint files (*.pth)
-xtts_checkpoint_files = find_models(main_directory, "pth")
+# XTTS checkpoint files (best_model.pth)
+xtts_checkpoint_files = find_best_models(main_directory)
 # XTTS config files (config.json)
 xtts_config_files = find_jsons(main_directory, "config.json")
 # XTTS vocab files (vocab.json)
@@ -800,48 +807,49 @@ def find_latest_best_model(folder_path):
    latest_file = max(files, key=os.path.getctime, default=None)
    return latest_file

-def compact_model():
+def compact_model(xtts_checkpoint_copy):
    this_dir = Path(__file__).parent.resolve()
-
-    best_model_path_str = find_latest_best_model(this_dir / "finetune" / "tmp-trn" / "training")
+    print("THIS DIR:", this_dir)
+    best_model_path_str = str(xtts_checkpoint_copy)  # Convert to string
+    print("best_model_path_str", best_model_path_str)
    
    # Check if the best model file exists
-    if best_model_path_str is None:
+    if not best_model_path_str:
        print("[FINETUNE] No trained model was found.")
        return "No trained model was found."
    
-    # Convert model_path_str to Path
-    best_model_path = Path(best_model_path_str)
+    print(f"[FINETUNE] Best model path: {best_model_path_str}")
    
    # Attempt to load the model
    try:
-        checkpoint = torch.load(best_model_path, map_location=torch.device("cpu"))
+        checkpoint = torch.load(best_model_path_str, map_location=torch.device("cpu"))
+        print(f"[FINETUNE] Checkpoint loaded: {best_model_path_str}")
    except Exception as e:
        print("[FINETUNE] Error loading checkpoint:", e)
        raise
    
-    del checkpoint["optimizer"]
-
-    for key in list(checkpoint["model"].keys()):
-        if "dvae" in key:
-            del checkpoint["model"][key]
-
    # Define the target directory
    target_dir = this_dir / "models" / "trainedmodel"
    
    # Create the target directory if it doesn't exist
    target_dir.mkdir(parents=True, exist_ok=True)

+    del checkpoint["optimizer"]
+
+    for key in list(checkpoint["model"].keys()):
+        if "dvae" in key:
+            del checkpoint["model"][key]
+    
    # Save the modified checkpoint in the target directory
-    torch.save(checkpoint, target_dir / "model.pth")
+    torch.save(checkpoint, str(target_dir / "model.pth"))  # Convert to string

    # Specify the files you want to copy
-    files_to_copy = ["vocab.json", "config.json", "dvae.pth", "mel_stats.pth","speakers_xtts.pth", ]
+    files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth", "dvae.pth"]
    
    for file_name in files_to_copy:
        src_path = this_dir / base_path / base_model_path / file_name
        dest_path = target_dir / file_name
-        shutil.copy(str(src_path), str(dest_path))
+        shutil.copy(str(src_path), str(dest_path))  # Convert to string
    
    source_wavs_dir = this_dir / "finetune" / "tmp-trn" / "wavs"
    target_wavs_dir = target_dir / "wavs"
@@ -852,16 +860,17 @@ def compact_model():
        # Check if it's a file and larger than 1000 KB
        if file_path.is_file() and file_path.stat().st_size > 1000 * 1024:
            # Copy the file to the target directory
-            shutil.copy(str(file_path), str(target_wavs_dir / file_path.name))
+            shutil.copy(str(file_path), str(target_wavs_dir / file_path.name))  # Convert to string
    
    print("[FINETUNE] Model copied to '/models/trainedmodel/'")
    return "Model copied to '/models/trainedmodel/'"


-def compact_lastfinetuned_model():
+def compact_lastfinetuned_model(xtts_checkpoint_copy):
    this_dir = Path(__file__).parent.resolve()

-    best_model_path_str = find_latest_best_model(this_dir / "finetune" / "tmp-trn" / "training")
+    best_model_path_str = xtts_checkpoint_copy
+    print(f"[FINETUNE] Best model path: {best_model_path_str}")

    # Check if the best model file exists
    if best_model_path_str is None:
@@ -880,21 +889,21 @@ def compact_lastfinetuned_model():

    del checkpoint["optimizer"]

-    for key in list(checkpoint["model"].keys()):
-        if "dvae" in key:
-            del checkpoint["model"][key]
-
    # Define the target directory
    target_dir = this_dir / "models" / "lastfinetuned"

    # Create the target directory if it doesn't exist
    target_dir.mkdir(parents=True, exist_ok=True)

+    for key in list(checkpoint["model"].keys()):
+        if "dvae" in key:
+            del checkpoint["model"][key]
+
    # Save the modified checkpoint in the target directory
    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", "speakers_xtts.pth",]
+    files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth", "dvae.pth",]

    for file_name in files_to_copy:
        src_path = this_dir / base_path / base_model_path / file_name
@@ -916,15 +925,16 @@ def compact_lastfinetuned_model():
    return "Model copied to '/models/lastfinetuned/'"


-def compact_legacy_model():
+def compact_custom_model(xtts_checkpoint_copy, folder_path):
    this_dir = Path(__file__).parent.resolve()

-    best_model_path_str = os.path.join(this_dir, "finetune", "best_model.pth")
+    best_model_path_str = xtts_checkpoint_copy
+    print(f"[FINETUNE] Best model path: {best_model_path_str}")

    # Check if the best model file exists
-    if not os.path.exists(best_model_path_str):
-        print("[FINETUNE] No model called best_model.pth was found in /finetune/")
-        return "No model called best_model.pth was found in /finetune/"
+    if best_model_path_str is None:
+        print("[FINETUNE] No trained model was found.")
+        return "No trained model was found."

    # Convert model_path_str to Path
    best_model_path = Path(best_model_path_str)
@@ -938,21 +948,41 @@ def compact_legacy_model():

    del checkpoint["optimizer"]

-    for key in list(checkpoint["model"].keys()):
-        if "dvae" in key:
-            del checkpoint["model"][key]
-
    # Define the target directory
-    target_dir = this_dir / "finetune"
+    target_dir = this_dir / "models" / folder_path

    # Create the target directory if it doesn't exist
    target_dir.mkdir(parents=True, exist_ok=True)

+    for key in list(checkpoint["model"].keys()):
+        if "dvae" in key:
+            del checkpoint["model"][key]
+
    # Save the modified checkpoint in the target directory
    torch.save(checkpoint, target_dir / "model.pth")

-    print("[FINETUNE] model.pth created in '/finetune/'")
-    return "model.pth created in '/finetune/'"
+    # Specify the files you want to copy
+    files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth", "dvae.pth",]
+
+    for file_name in files_to_copy:
+        src_path = this_dir / base_path / base_model_path / file_name
+        dest_path = target_dir / file_name
+        shutil.copy(str(src_path), str(dest_path))
+
+    source_wavs_dir = this_dir / "finetune" / "tmp-trn" / "wavs"
+    target_wavs_dir = target_dir / "wavs"
+    target_wavs_dir.mkdir(parents=True, exist_ok=True)
+
+    # Iterate through files in the source directory
+    for file_path in source_wavs_dir.iterdir():
+        # Check if it's a file and larger than 1000 KB
+        if file_path.is_file() and file_path.stat().st_size > 1000 * 1024:
+            # Copy the file to the target directory
+            shutil.copy(str(file_path), str(target_wavs_dir / file_path.name))
+    
+    print("[FINETUNE] Model copied to '/models/",folder_path,"/")
+    return f"Model copied to '/models/{folder_path}/'"
+

 def delete_training_data():
    # Define the folder to be deleted
@@ -1264,6 +1294,9 @@ if __name__ == "__main__":
                ### 🟥 <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>Important Note - Language support.</u>
+                ◽ Although I have done my best to help automate this step, the Whisper model is not great with all languages and may fail to build your training data correctly. The Large-v3 may be better at certain languages.<br>
+                ◽ You can find information about the Whisper model [here](https://github.com/openai/whisper?tab=readme-ov-file#available-models-and-languages) and you can find data about manually building training data [here](https://docs.coqui.ai/en/latest/formatting_your_dataset.html), as well as details below about the file structure this step performs.<br>
                ### 🟦 <u>What you need to do</u>
                ◽ 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>
                ◽ Place your audio files in <span style="color: #3366ff;">{str(audio_folder)}</span>          
@@ -1276,6 +1309,7 @@ if __name__ == "__main__":
                ◽ 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>
                ◽ 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/lang.txt</span> Contains a two digit langauage code e.g. `en`, `fr` etc.<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>
@@ -1334,6 +1368,11 @@ if __name__ == "__main__":
                    maximum=95,  # Maximum value
                    step=1,  # Increment step
                )
+                speaker_name_input = gr.Textbox(
+                label="The name of the speaker/person you are training",
+                value="personsname",
+                visible=True,
+            )
            progress_data = gr.Label(
                label="Progress:"
            )
@@ -1345,14 +1384,15 @@ if __name__ == "__main__":

            prompt_compute_btn = gr.Button(value="Step 1 - Create dataset")
        
-            def preprocess_dataset(language,  whisper_model, out_path, eval_split_number, progress=gr.Progress(track_tqdm=True)):
+            def preprocess_dataset(language, whisper_model, out_path, eval_split_number, speaker_name_input, 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, eval_split_number=eval_split_number, 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, speaker_name_input=speaker_name_input, gradio_progress=progress)
                    except:
                        traceback.print_exc()
                        error = traceback.format_exc()
@@ -1373,110 +1413,205 @@ if __name__ == "__main__":
 #### GRADIO STEP 2 ####
 #######################
        with gr.Tab("💻 Step 2 - Training"):
-
-            gr.Markdown(
-                f"""
-                ### 💻 <u>Training</u><br>                
-                ### 🟦 <u>What you need to do</u>
-                ◽ 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>
-                ◽ 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>
-                ◽ There are no absolute correct settings for training. It will vary based on:<br>
-                &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;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- How much audio you have (you may want less or more eval or epochs)<br>
-                ◽ 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>
-                ◽ **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>
-                ◽ **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():
-                train_csv = gr.Textbox(
-                    label="Train CSV:",
-                )
-                eval_csv = gr.Textbox(
-                    label="Eval CSV:",
-                )
-            with gr.Row():
-                num_epochs =  gr.Slider(
-                    label="Number of epochs:",
-                    minimum=1,
-                    maximum=100,
-                    step=1,
-                    value=args.num_epochs,
-                )
-                batch_size = gr.Slider(
-                    label="Batch size:",
-                    minimum=2,
-                    maximum=512,
-                    step=1,
-                    value=args.batch_size,
-                )
-                grad_acumm = gr.Slider(
-                    label="Grad accumulation steps:",
-                    minimum=2,
-                    maximum=128,
-                    step=1,
-                    value=args.grad_acumm,
-                )
-                max_audio_length = gr.Slider(
-                    label="Max permitted audio size in seconds:",
-                    minimum=2,
-                    maximum=20,
-                    step=1,
-                    value=args.max_audio_length,
-                )
-            with gr.Row():
-                model_to_train_choices = ["Base Model"]
-                if finetuned_model:
-                    model_to_train_choices.append("Existing finetuned model")
-                model_to_train = gr.Radio(
-                    choices=model_to_train_choices,
-                    label="Which model do you want to train?",
-                    value="Base Model"
-                )
+            with gr.Tab("Training"):
                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.
-                """
+                    f"""
+                    ### 💻 <u>Training</u><br>
+                    ### 🟥 <u>Important Note - Language support.</u>
+                    ◽ If this step is failing/erroring you may wish to check your training data was created correctly (Detailed in Step 1), confirming that wav files have been generated and your `metadata_train.csv` and `metadata_eval.csv` files have been populated.<br>             
+                    ### 🟦 <u>What you need to do</u>
+                    ◽ 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>
+                    ◽ 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>
+                    ◽ There are no absolute correct settings for training. It will vary based on:<br>
+                    &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;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- How much audio you have (you may want less or more eval or epochs)<br>
+                    ◽ 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>
+                    ### 🟨 <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():
+                    train_csv = gr.Textbox(
+                        label="Train CSV:",
+                        scale=2,
+                    )
+                    eval_csv = gr.Textbox(
+                        label="Eval CSV:",
+                        scale=2,
+                    )
+                    learning_rates = gr.Dropdown(
+                        value=5e-6,
+                        label="Learning Rate",
+                        choices=[
+                            ("1e-6", 1e-6),
+                            ("5e-6", 5e-6),
+                            ("1e-5", 1e-5),
+                            ("5e-5", 5e-5),
+                            ("1e-4", 1e-4),
+                            ("5e-4", 5e-4),
+                            ("1e-3", 1e-3),
+                        ],
+                        type="value",
+                        allow_custom_value=True,
+                        scale=0,
+                    )
+                with gr.Row():
+                    num_epochs =  gr.Slider(
+                        label="Number of epochs:",
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=args.num_epochs,
+                    )
+                    batch_size = gr.Slider(
+                        label="Batch size:",
+                        minimum=2,
+                        maximum=512,
+                        step=1,
+                        value=args.batch_size,
+                    )
+                    grad_acumm = gr.Slider(
+                        label="Grad accumulation steps:",
+                        minimum=1,
+                        maximum=32,
+                        step=1,
+                        value=args.grad_acumm,
+                    )
+                    max_audio_length = gr.Slider(
+                        label="Max permitted audio size in seconds:",
+                        minimum=2,
+                        maximum=20,
+                        step=1,
+                        value=args.max_audio_length,
+                    )
+                with gr.Row():
+                    model_to_train_choices = ["Base Model"]
+                    if finetuned_model:
+                        model_to_train_choices.append("Existing finetuned model")
+                    model_to_train = gr.Radio(
+                        choices=model_to_train_choices,
+                        label="Which model do you want to train?",
+                        value="Base Model"
+                    )
+                    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.
+                    """
+                    )
+
+                progress_train = gr.Label(
+                    label="Progress:"
+                )
+                logs_tts_train = gr.Textbox(
+                    label="Logs:",
+                    interactive=False,
+                )
+                demo.load(read_logs, None, logs_tts_train, every=1)
+                train_btn = gr.Button(value="Step 2 - Run the training")
+
+                def train_model(language, train_csv, eval_csv, learning_rates, num_epochs, batch_size, grad_acumm, output_path, max_audio_length):
+                    clear_gpu_cache()
+                    if not train_csv or not eval_csv:
+                        return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
+                    try:
+                        # convert seconds to waveform frames
+                        max_audio_length = int(max_audio_length * 22050)
+                        learning_rate = float(learning_rates)  # Convert the learning rate value to a float
+                        config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, learning_rate, output_path=str(output_path), max_audio_length=max_audio_length)
+                    except:
+                        traceback.print_exc()
+                        error = traceback.format_exc()
+                        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
+                    shutil.copy(config_path, 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")
+                    clear_gpu_cache()
+                    return "Model training done. Move to Step 3", config_path, vocab_file, ft_xtts_checkpoint, speaker_wav
+            with gr.Tab("Info - Grad Accumulation"):
+                                gr.Markdown(
+                    f"""
+                    ### 🟩 <u>Grad Accumulation</u><br>
+                    ◽ Gradient accumulation is a technique that allows you to simulate a larger batch size without actually increasing the memory consumption. Here's how it works:<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ Instead of updating the model's parameters after every batch, gradient accumulation enables you to process multiple batches and accumulate their gradients.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ By setting steps greater than 1, you can process multiple batches before a single optimization step e.g if steps is set to 4, the gradients will be accumulated over 4 batches before updating the model's parameters.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ This means that you can effectively use a larger batch size while keeping the actual batch size per iteration smaller, thereby reducing the memory footprint.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ Increasing the steps allows you to find a balance between memory consumption and computational efficiency. You can process more examples per optimization step without exceeding the available memory.<br><br>
+                    ◽ However, it's important to note that increasing the gradient accumulation steps does have an impact on the training process:<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ Since the model's parameters are updated less frequently (every grad_accum_steps batches), the training dynamics may be slightly different compared to updating the model after every batch.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ You may need to adjust the learning rate and other hyperparameters accordingly to compensate for the less frequent updates. Typically, you can increase the learning rate slightly when using gradient accumulation.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ The training progress will be slower in terms of the number of optimization steps per epoch, as the model updates occur less frequently.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ However, the overall training time may still be reduced compared to using a smaller batch size without gradient accumulation, as it allows for better utilization of GPU resources.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ To start, you can try setting steps to a value like 4 and see if it resolves the OOM error. If the error persists, you can experiment with higher values until you find a balance that works for your specific setup.
+                    """
+                )
+            with gr.Tab("Info - Batch Size"):
+                                gr.Markdown(
+                    f"""
+                    ### 🟩 <u>Batch Size</u><br>
+                    ◽ Batch size refers to the number of training examples processed in a single iteration (forward and backward pass) before updating the model's parameters.<br>
+                    ◽ A larger batch size allows for more efficient computation and utilization of hardware resources, especially when using GPUs.<br>
+                    ◽ However, increasing the batch size also increases the memory consumption during training, as more examples need to be stored in memory.<br>
+                    ◽ If the batch size is too large, it can lead to out-of-memory (OOM) errors, especially when training on GPUs with limited memory.<br>
+                    ◽ On the other hand, using a smaller batch size can result in slower training and may require more iterations to converge.<br>
+                    ◽ The optimal batch size depends on various factors, such as the model architecture, available hardware resources, and the specific problem being solved.<br>
+                    ◽ It's common to experiment with different batch sizes to find a balance between training speed and memory efficiency.<br>
+                    ◽ If you encounter OOM errors, try reducing the batch size until the training can proceed without memory issues.<br>
+                    ◽ Keep in mind that the batch size also affects the model's generalization and convergence behavior, so it's important to monitor the model's performance while adjusting the batch size.
+                    """
+                )
+            with gr.Tab("Info - Learning Rate"):
+                                gr.Markdown(
+                    f"""
+                    ### 🟩 <u>Learning Rate</u><br>
+                    ◽ 1e-6: Very small learning rate, slow but stable learning.<br>
+                    ◽ 5e-6: Small learning rate, slow but stable learning.<br>
+                    ◽ 1e-5: Moderate learning rate, balanced between stability and convergence speed.<br>
+                    ◽ 5e-5: Higher learning rate, faster convergence but potential instability.<br>
+                    ◽ 1e-4: High learning rate, faster convergence but increased risk of instability.<br>
+                    ◽ 5e-4: Very high learning rate, fast convergence but higher risk of instability.<br>
+                    ◽ 1e-3: Extremely high learning rate, very fast convergence but high risk of instability.<br><br>
+                    The optimal learning rate depends on the model architecture, dataset, and other hyperparameters.
+                    """
+                )
+            with gr.Tab("Info - Epochs"):
+                                gr.Markdown(
+                    f"""
+                    ### 🟩 <u>Epochs</u><br>
+                    ◽ An epoch represents a single pass through the entire training dataset during the training process.<br>
+                    ◽ In each epoch, the model sees and learns from all the training examples once.<br>
+                    ◽ The number of epochs determines how many times the model will iterate over the complete training dataset.<br>
+                    ◽ Increasing the number of epochs allows the model to learn more from the data and potentially improve its performance.<br>
+                    ◽ However, training for too many epochs can lead to overfitting, where the model becomes too specialized to the training data and fails to generalize well to unseen data.<br>
+                    ◽ The optimal number of epochs depends on factors such as the complexity of the problem, the size of the dataset, and the model's capacity.<br>
+                    ◽ It's common to use techniques like early stopping or validation monitoring to determine the appropriate number of epochs.<br>
+                    ◽ Early stopping involves monitoring the model's performance on a validation set and stopping the training when the performance starts to degrade, indicating potential overfitting.<br>
+                    ◽ Validation monitoring involves evaluating the model's performance on a separate validation set after each epoch and selecting the model checkpoint that performs best on the validation set.<br>
+                    ◽ It's recommended to start with a reasonable number of epochs and monitor the model's performance to determine if more epochs are needed or if early stopping should be applied
+                    """
+                )
+            with gr.Tab("Info - Max Audio"):
+                                gr.Markdown(
+                    f"""
+                    ### 🟩 <u>Max Permitted Audio Size (in seconds)</u><br>
+                    ◽ The max permitted audio size determines the maximum duration of audio files that can be used as input to the model during training and inference.<br>
+                    ◽ It is specified in seconds and represents the longest audio clip that the model can process.<br>
+                    ◽ Limiting the audio size helps to control the memory usage and computational requirements of the model.<br>
+                    ◽ If the audio files in your dataset vary in length, it's important to consider the max permitted audio size to ensure that all files can be processed effectively.<br>
+                    ◽ Audio files longer than the max permitted size will typically be truncated or split into smaller segments before being fed into the model.<br>
+                    ◽ The choice of max permitted audio size depends on the specific requirements of your application and the available hardware resources.<br>
+                    ◽ Increasing the max permitted audio size allows the model to handle longer audio clips but also increases the memory consumption and computational burden.<br>
+                    ◽ If you encounter memory issues or slow processing times, you may need to reduce the max permitted audio size to find a suitable balance.<br>
+                    ◽ It's important to consider the nature of your audio data and choose a max permitted audio size that captures the relevant information while being computationally feasible.
+                    """
                )

-            progress_train = gr.Label(
-                label="Progress:"
-            )
-            logs_tts_train = gr.Textbox(
-                label="Logs:",
-                interactive=False,
-            )
-            demo.load(read_logs, None, logs_tts_train, every=1)
-            train_btn = gr.Button(value="Step 2 - Run the training")
-
-            def train_model(language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path, max_audio_length):
-                clear_gpu_cache()
-                if not train_csv or not eval_csv:
-                    return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
-                try:
-                    # convert seconds to waveform frames
-                    max_audio_length = int(max_audio_length * 22050)
-                    config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path=str(output_path), max_audio_length=max_audio_length)
-                except:
-                    traceback.print_exc()
-                    error = traceback.format_exc()
-                    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
-                shutil.copy(config_path, 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")
-                clear_gpu_cache()
-                return "Model training done. Move to Step 3", config_path, vocab_file, ft_xtts_checkpoint, speaker_wav
                
 #######################
 #### GRADIO STEP 3 ####
@@ -1572,7 +1707,7 @@ if __name__ == "__main__":
                        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_best_models(main_directory), "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": ""},
@@ -1605,7 +1740,8 @@ if __name__ == "__main__":
                ◽ **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>
                ◽ **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>
+                ◽ **Currently** only Text-gen-webui and SillyTavern have loaders built for loading models in the <span style="color: #3366ff;">'/finetuned/'</span> folder. So if you wish to use your finetuned model as elsewhere in AllTalk please copy it over your base model e.g. copy your model over the top of <span style="color: #3366ff;">/models/xttsv2_2.0.2/</span><br>
+                ◽ 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 e.g copy it over <span style="color: #3366ff;">/models/xttsv2_2.0.2/</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>
                ◽ If you are not going to train anything again, you can delete the whisper model from inside of your huggingface cache (3GB approx) <br>
@@ -1617,6 +1753,21 @@ if __name__ == "__main__":
            final_progress_data = gr.Label(
                label="Progress:"
            )
+            with gr.Row():
+                xtts_checkpoint_copy = gr.Dropdown(
+                    [str(file) for file in xtts_checkpoint_files],
+                    label="XTTS checkpoint path (Select the model you want to copy over):",
+                    value="",
+                    allow_custom_value=True,
+                    scale=3,
+                )
+                # Create refresh button
+                refresh_button = create_refresh_button(
+                    [xtts_checkpoint_copy,],
+                    [
+                        lambda: {"choices": find_best_models(main_directory), "value": ""},
+                    ],
+                    elem_class="refresh-button-class")
            with gr.Row():
                compact_btn = gr.Button(value="OPTION A - Compact and move model to '/trainedmodel/'")
                gr.Markdown(
@@ -1631,6 +1782,9 @@ if __name__ == "__main__":
                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():
+                compact_custom_btn = gr.Button(value="OPTION C - Compact and move model to a folder name of your choosing")
+                folder_path = gr.Textbox(label="Enter a new folder name (will be sub the models folder)", lines=1, value="mycustomfolder")
            with gr.Row():
                delete_training_btn = gr.Button(value="Delete generated training data")
                gr.Markdown(
@@ -1653,6 +1807,7 @@ if __name__ == "__main__":
                    whisper_model,
                    out_path,
                    eval_split_number,
+                    speaker_name_input,
                ],
                outputs=[
                    progress_data,
@@ -1668,6 +1823,7 @@ if __name__ == "__main__":
                    lang,
                    train_csv,
                    eval_csv,
+                    learning_rates,
                    num_epochs,
                    batch_size,
                    grad_acumm,
@@ -1696,13 +1852,19 @@ if __name__ == "__main__":
                ],
                outputs=[progress_gen, tts_output_audio, reference_audio],
            )    
-            
            compact_btn.click(
                fn=compact_model,
+                inputs=[xtts_checkpoint_copy],
                outputs=[final_progress_data],
            )
            compact_lastfinetuned_btn.click(
                fn=compact_lastfinetuned_model,
+                inputs=[xtts_checkpoint_copy],
+                outputs=[final_progress_data],
+            )
+            compact_custom_btn.click(
+                fn=compact_custom_model,
+                inputs=[xtts_checkpoint_copy, folder_path],
                outputs=[final_progress_data],
            )
            delete_training_btn.click(
@@ -7,14 +7,16 @@ RUN apt-get update && apt-get upgrade -y \
    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
+
+RUN mkdir /app/pip_cache
+ENV PIP_CACHE_DIR=/app/pip_cache
+
 WORKDIR /app
 COPY . .
 ENV CUDA_DOCKER_ARCH=all
+RUN pip install --no-cache-dir --no-deps -r system/requirements/requirements_standalone.txt --cache-dir=/app/pip_cache && \
+    pip install --no-cache-dir deepspeed --cache-dir=/app/pip_cache

-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"]
@@ -174,7 +174,7 @@ def get_available_voices():
 ############################################
 def delete_old_files(folder_path, days_to_keep):
    current_time = datetime.now()
-    print(f"[{params['branding']}Startup] \033[92mWAV file deletion      :\033[93m {formatted_date}\033[0m",delete_output_wavs_setting,)
+    print(f"[{params['branding']}Startup] \033[92mWAV file deletion      :\033[93m", delete_output_wavs_setting,"\033[0m")
    for file_name in os.listdir(folder_path):
        file_path = os.path.join(folder_path, file_name)
        if os.path.isfile(file_path):
@@ -230,7 +230,7 @@ if finetuned_model:
        (trained_model_directory / file).exists() for file in required_files
    )
 if finetuned_model:
-    print(f"[{params['branding']}Startup] Finetuned model \033[93mDetected\033[0m")
+    print(f"[{params['branding']}Startup] \033[92mFinetuned model        :\033[93m Detected\033[0m")

 ####################################################
 #### SET GRADIO BUTTONS BASED ON confignew.json ####
@@ -698,9 +698,11 @@ def output_modifier(string, state):
                        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)
+                    cleaned_part = re.sub(r"([!?.\u3002\uFF1F\uFF01\uFF0C])\1+", r"\1", part)
+                    # Replace "Chinese ellipsis" with a single dot
+                    cleaned_part = re.sub(r"\u2026{1,2}", ". ", cleaned_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)
+                    cleaned_part = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', cleaned_part)
                    # Remove all newline characters (single or multiple)
                    cleaned_part = re.sub(r"\n+", " ", cleaned_part)

@@ -725,9 +727,11 @@ def output_modifier(string, state):
                # Decode HTML entities first
                cleaned_part = html.unescape(cleaned_string)
                # Replace multiple instances of certain punctuation marks with a single instance
-                cleaned_part = re.sub(r"([!?.])\1+", r"\1", cleaned_part)
+                cleaned_part = re.sub(r"([!?.\u3002\uFF1F\uFF01\uFF0C])\1+", r"\1", cleaned_part)
+                # Replace "Chinese ellipsis" with a single dot
+                cleaned_part = re.sub(r"\u2026{1,2}", ". ", cleaned_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)
+                cleaned_part = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', cleaned_part)
                # Remove all newline characters (single or multiple)
                cleaned_part = re.sub(r"\n+", " ", cleaned_part)
                # Process the part and give it a non-character name if being used vai API or standalone.
@@ -304,6 +304,7 @@
                <option value="nl">Dutch</option>
                <option value="fr">French</option>
                <option value="de">German</option>
+                <option value="hi">Hindi</option>
                <option value="hu">Hungarian</option>
                <option value="it">Italian</option>
                <option value="ja">Japanese</option>
@@ -2500,6 +2501,7 @@
        <code>en</code> English<br>
        <code>fr</code> French<br>
        <code>de</code> German<br>
+        <code>hi</code> Hindi<br>
        <code>hu</code> Hungarian<br>
        <code>it</code> Italian<br>
        <code>ja</code> Japanese<br>
@@ -124,6 +124,7 @@
            <option value="English" {% if data.language=='English' %}selected{% endif %}>English</option>
            <option value="French" {% if data.language=='French' %}selected{% endif %}>French</option>
            <option value="German" {% if data.language=='German' %}selected{% endif %}>German</option>
+            <option value="Hindi" {% if data.language=='Hindi' %}selected{% endif %}>Hindi</option>
            <option value="Hungarian" {% if data.language=='Hungarian' %}selected{% endif %}>Hungarian</option>
            <option value="Italian" {% if data.language=='Italian' %}selected{% endif %}>Italian</option>
            <option value="Japanese" {% if data.language=='Japanese' %}selected{% endif %}>Japanese</option>
@@ -6,6 +6,7 @@
  "English": "en",
  "French": "fr",
  "German": "de",
+  "Hindi": "hi",
  "Hungarian": "hu",
  "Italian": "it",
  "Japanese": "ja",
@@ -0,0 +1,173 @@
+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
+exceptiongroup
+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
@@ -22,3 +22,4 @@ nvidia-cublas-cu11>=11.11.3.6
 nvidia-cudnn-cu11>=9.0.0.312
 fuzzywuzzy>=0.18.0
 python-Levenshtein>=0.25.0
+spacy>=3.7.2
@@ -281,7 +281,7 @@ class AllTalkTtsProvider {
            if (narratorVoiceSelect) narratorVoiceSelect.disabled = !this.settings.narrator_enabled;
        }
        const modelSelect = document.getElementById('switch_model');
-        if (this.settings.finetuned_model === 'true') {
+        if (this.settings.finetuned_model === true) {
            const ftOption = document.createElement('option');
            ftOption.value = 'XTTSv2 FT';
            ftOption.textContent = 'XTTSv2 FT';
@@ -1,2 +1,2 @@
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truncated
@@ -532,6 +532,7 @@
                <option value="en" selected>English</option>
                <option value="fr">French</option>
                <option value="de">German</option>
+                <option value="hi">Hindi</option>
                <option value="hu">Hungarian</option>
                <option value="it">Italian</option>
                <option value="ja">Japanese</option>
@@ -921,18 +922,21 @@
            const playType = document.getElementById('playType').value; // Play type (chunk or stream)
            playModeValue = document.getElementById('playMode').value;

-            let cleanedText = textInput.replace(/ \- | \– /g, ' ');
+            let cleanedText = textInput.replace(/ \\- | \\– /g, ' ');
            cleanedText = cleanedText.replace(/%/g, " percent");
-            cleanedText = cleanedText.replace(/\.\s'/g, ".'");
-            cleanedText = cleanedText.replace(/(?<!\w)[']+(?!\w)/g, '');
+            cleanedText = cleanedText.replace(/\\.\\s'/g, ".'");
+            cleanedText = cleanedText.replace(/(?<!\\w)\['\]+(?!\\w)/g, '');
            cleanedText = cleanedText.replace(/"/g, "'");
-            cleanedText = cleanedText.replace(/[^a-zA-Z0-9\s\.,;:!?\-\'"À-ÿ\u0400-\u04FF\u0150\u0151\u0170\u0171]\$/g, '');
-            // Replace dash followed by space with comma and space
-            cleanedText = cleanedText.replace(/-\s/g, '; ');
-            // Replace double dash "--" with semicolon "; "
+            cleanedText = cleanedText.replace(/[^a-zA-Z0-9\s.,;:!?\-'"$À-ÿ\u0400-\u04FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u2026\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]/g, '');
+            cleanedText = cleanedText.replace(/-\\s/g, '; ');
            cleanedText = cleanedText.replace(/--/g, '; ');
-
-            let sentences = cleanedText.split(/(?<=[.!?:])\s+/);
+            // Split by Fullwidth/CJK punctuation regardless of if there are spaces behind them
+            // Please test if it works properly. In my test they it should be 
+            // (?<=[.!?:])\s+ instead of 
+            // (?<=\[.!?:\])\\s+ 
+            // so I don't know whats going on here
+            // let sentences = cleanedText.split(/(?<=[\[.!?:\]])\s+|(?<=[\u3002\uFF01\uFF1A\uFF1F])/);
+            let sentences = cleanedText.split(/(?<=[\[.!?:\]]['"\u2018\u2019\u201C\u201D]*)\s+|(?<=[\u3002\uFF01\uFF1A\uFF1F])/);

            if (!sentences) {
                console.error("No sentences found in the input text.");
@@ -476,6 +476,11 @@ async def deepspeed(request: Request, new_deepspeed_value: bool):
 ########################
 #### TTS GENERATION ####
 ########################
+debug_generate_audio = False
+tts_stop_generation = False # Called to stop generation of the current text at whatever stage its at. currently only set for streaming.
+tts_generation_lock = False # Tracks locking of the generaetion process.
+tts_narrator_generatingtts = False # Tracks if the current tts processes are narrator based, to avoid moving model on each chunk of text generated.
+# Not in use tts_queue_behaviour = False # True is to queue the current generation request. False is to cancel the current generation and start a new one.

 # TTS VOICE GENERATION METHODS (called from voice_preview and output_modifer)
 async def generate_audio(text, voice, language, temperature, repetition_penalty, output_file, streaming=False):
@@ -487,8 +492,10 @@ async def generate_audio(text, voice, language, temperature, repetition_penalty,
    async for _ in response:
        pass

+
 async def generate_audio_internal(text, voice, language, temperature, repetition_penalty, output_file, streaming):
-    global model
+    global model, tts_stop_generation, tts_generation_lock
+    tts_generation_lock = True
    if params["low_vram"] and device == "cpu":
        await switch_device()
    generate_start_time = time.time()  # Record the start time of generating TTS
@@ -539,6 +546,12 @@ async def generate_audio_internal(text, voice, language, temperature, repetition
            yield wav_buf.read()

            for i, chunk in enumerate(output):
+                if tts_stop_generation:
+                    print(f"[{params['branding']}TTSGen] Stopping audio generation.")
+                    file_chunks.clear()  # Clear the file_chunks list
+                    tts_stop_generation = False
+                    tts_generation_lock = False
+                    break
                file_chunks.append(chunk)
                if isinstance(chunk, list):
                    chunk = torch.cat(chunk, dim=0)
@@ -547,6 +560,7 @@ async def generate_audio_internal(text, voice, language, temperature, repetition
                chunk = np.clip(chunk, -1, 1)
                chunk = (chunk * 32767).astype(np.int16)
                yield chunk.tobytes()
+                print(f"[{params['branding']}Debug] Stream audio generation: Yielded audio chunk {i}.") if debug_generate_audio else None   
        else:
            # Non-streaming-specific operation
            torchaudio.save(output_file, torch.tensor(output["wav"]).unsqueeze(0), 24000)
@@ -565,9 +579,9 @@ async def generate_audio_internal(text, voice, language, temperature, repetition
            file_path=output_file,
            speaker_wav=[f"{this_dir}/voices/{voice}"],
            language=language,
-            temperature=temperature,
-            length_penalty=model.config.length_penalty,
-            repetition_penalty=repetition_penalty,
+            temperature=float(temperature),
+            length_penalty=float(model.config.length_penalty),
+            repetition_penalty=float(repetition_penalty),
            top_k=model.config.top_k,
            top_p=model.config.top_p,
        )
@@ -589,12 +603,11 @@ async def generate_audio_internal(text, voice, language, temperature, repetition
    # Print Generation time and settings
    generate_end_time = time.time()  # Record the end time to generate TTS
    generate_elapsed_time = generate_end_time - generate_start_time
-    print(
-        f"[{params['branding']}TTSGen] \033[93m{generate_elapsed_time:.2f} seconds. \033[94mLowVRAM: \033[33m{params['low_vram']} \033[94mDeepSpeed: \033[33m{params['deepspeed_activate']}\033[0m"
-    )
+    print(f"[{params['branding']}TTSGen] \033[93m{generate_elapsed_time:.2f} seconds. \033[94mLowVRAM: \033[33m{params['low_vram']} \033[94mDeepSpeed: \033[33m{params['deepspeed_activate']}\033[0m")
    # Move model back to cpu system ram if needed.
-    if params["low_vram"] and device == "cuda":
+    if params["low_vram"] and device == "cuda" and tts_narrator_generatingtts == False:
        await switch_device()
+    tts_generation_lock = False
    return


@@ -854,6 +867,13 @@ async def tts_generate_streaming(request: Request, text: str = Form(...), voice:
        print(f"An error occurred: {e}")
        return JSONResponse(content={"error": "An error occurred"}, status_code=500)

+@app.put("/api/stop-generation")
+async def stop_generation_endpoint():
+    global tts_stop_generation, tts_generation_lock
+    if tts_generation_lock and not tts_stop_generation:
+        tts_stop_generation = True
+    return {"message": "Generation stopped"}
+
 ##############################
 #### Standard Generation ####
 ##############################
@@ -890,7 +910,7 @@ class JSONInput(BaseModel):
    narrator_enabled: bool = Field(..., description="narrator_enabled needs to be true or false.")
    narrator_voice_gen: str = Field(..., pattern="^.*\.wav$", description="narrator_voice_gen needs to be the name of a wav file e.g. mysample.wav.")
    text_not_inside: str = Field(..., pattern="^(character|narrator)$", description="text_not_inside needs to be 'character' or 'narrator'.")
-    language: str = Field(..., pattern="^(ar|zh-cn|cs|nl|en|fr|de|hu|it|ja|ko|pl|pt|ru|es|tr)$", description="language needs to be one of the following ar|zh-cn|cs|nl|en|fr|de|hu|it|ja|ko|pl|pt|ru|es|tr.")
+    language: str = Field(..., pattern="^(ar|zh-cn|cs|nl|en|fr|de|hu|hi|it|ja|ko|pl|pt|ru|es|tr)$", description="language needs to be one of the following ar|zh-cn|cs|nl|en|fr|de|hu|hi|it|ja|ko|pl|pt|ru|es|tr.")
    output_file_name: str = Field(..., pattern="^[a-zA-Z0-9_]+$", description="output_file_name needs to be the name without any special characters or file extension e.g. 'filename'")
    output_file_timestamp: bool = Field(..., description="output_file_timestamp needs to be true or false.")
    autoplay: bool = Field(..., description="autoplay needs to be a true or false value.")
@@ -1029,12 +1049,17 @@ async def tts_generate(
            "autoplay_volume": autoplay_volume,
            "streaming": streaming,
        }
+        global tts_narrator_generatingtts
        JSONresult = TTSGenerator.validate_json_input(json_input_data)
        if JSONresult is None:
            pass
        else:
+            print(f"[{params['branding']}API] \033[91mError with API request:\033[0m", JSONresult)
            return JSONResponse(content={"error": JSONresult}, status_code=400)
        if narrator_enabled:
+            if params["low_vram"] and device == "cpu":
+                await switch_device()
+            tts_narrator_generatingtts = True
            processed_parts = process_text(text_input)
            audio_files_all_paragraphs = []
            for part_type, part in processed_parts:
@@ -1054,9 +1079,11 @@ async def tts_generate(
                    voice_description = "\033[36mCharacter (Text-not-inside)\033[0m" if text_not_inside == "character" 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)
+                cleaned_part = re.sub(r'([!?.\u3002\uFF1F\uFF01\uFF0C])\1+', r'\1', part)
+                # Replace "Chinese ellipsis" with a single dot
+                cleaned_part = re.sub(r"\u2026{1,2}", ". ", cleaned_part)
                # Further clean to remove any other unwanted characters
-                cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_part)
+                cleaned_part = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', cleaned_part)
                # Remove all newline characters (single or multiple)
                cleaned_part = re.sub(r'\n+', ' ', cleaned_part)
                output_file = this_dir / "outputs" / f"{output_file_name}_{uuid.uuid4()}_{int(time.time())}.wav"
@@ -1066,6 +1093,10 @@ async def tts_generate(
                audio_files_all_paragraphs.append(audio_path)
            # Combine audio files across paragraphs
            output_file_path, output_file_url, output_cache_url = combine(output_file_timestamp, output_file_name, audio_files_all_paragraphs)
+            tts_narrator_generatingtts = False
+            # Move model back to cpu system ram if needed.
+            if params["low_vram"] and device == "cuda":
+                await switch_device()
        else:
            if output_file_timestamp:
                timestamp = int(time.time())
@@ -1085,15 +1116,19 @@ async def tts_generate(
                output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}.wav'
            if text_filtering == "html":
                cleaned_string = html.unescape(standard_filtering(text_input))
-                cleaned_string = re.sub(r'([!?.])\1+', r'\1', text_input)
+                cleaned_string = re.sub(r'([!?.\u3002\uFF1F\uFF01\uFF0C])\1+', r'\1', text_input)
+                # Replace "Chinese ellipsis" with a single dot
+                cleaned_string = re.sub(r"\u2026{1,2}", ". ", cleaned_string)
                # Further clean to remove any other unwanted characters
-                cleaned_string = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_string)
+                cleaned_string = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', cleaned_string)
                # Remove all newline characters (single or multiple)
                cleaned_string = re.sub(r'\n+', ' ', cleaned_string)
            elif text_filtering == "standard":
-                cleaned_string = re.sub(r'([!?.])\1+', r'\1', text_input)
+                cleaned_string = re.sub(r'([!?.\u3002\uFF1F\uFF01\uFF0C])\1+', r'\1', text_input)
+                # Replace "Chinese ellipsis" with a single dot
+                cleaned_string = re.sub(r"\u2026{1,2}", ". ", cleaned_string)
                # Further clean to remove any other unwanted characters
-                cleaned_string = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_string)
+                cleaned_string = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', cleaned_string)
                # Remove all newline characters (single or multiple)
                cleaned_string = re.sub(r'\n+', ' ', cleaned_string)
            else:
Author	SHA1	Message	Date
erew123	7c7cb72c13	Updated to deal with Gradio 4.xx versions	2024-05-14 21:01:38 +01:00
erew123	3a105ce922	Update tts_server.py Set API Local to use float as Transformers demands it	2024-05-14 18:19:29 +01:00
erew123	690018c4ab	Test code rework to allow custom file names. Contains a box to add a custom file name. Files are generated in the format {custom-file-name}_{ID_number_XXXXX}_{uuid} This allows up to 99,999 files generated in 1x single session. The uuid needs to be applied to ensure files are not over-written and handle browser based caching of wav files.	2024-05-12 14:00:10 +01:00
erew123	6808439ca1	nailed text-gen-webui spacy version Added spacy>=3.7.2 to stop the requirements installation hunting around for Spacy versions so much. Tested this on a fresh install of Text-gen-webui and it ran through without issue.	2024-05-06 23:40:39 +01:00
erew123	8cf56faad2	run dos2unix to repair crlf to lf	2024-05-06 01:39:01 +01:00
erew123	cb7d486288	run dos2unix to repair crlf to lf	2024-05-06 01:38:04 +01:00
erew123	086999fc1b	Update atsetup.sh	2024-05-02 23:26:42 +01:00
erew123	a1b889108f	Faster narration generation w/low vram Moved the memory management of the TTS model into the narrator so that it doesnt shift the model backwards and forwards VRAM<>System ram on each portion of the generation. Only the beginning and end. Only benefits when Low VRAM is enabled, but should shave a few seconds of generation of your average generation.	2024-05-01 16:09:59 +01:00
erew123	9f18b24465	Changed how dvae weights are copied.	2024-05-01 02:38:24 +01:00
erew123	a3f157249c	Small change to splitting of text	2024-04-28 21:14:25 +01:00
erew123	eb08d31509	Added Streaming Stopping Currently this is in testing. curl -X POST http://localhost:7851/api/stop-generation Would stop the current streaming generation. This is more a v2 feature that is just getting tried out by someone else ATM.	2024-04-28 20:37:41 +01:00
erew123	1e1b7fdd8c	Merge pull request #190 from ytt246/main Edited text cleaning to support some commonly used fullwidth/CJK punctuations	2024-04-28 11:12:32 +01:00
IAUSHYJ	5aad7bcc45	tts generater split fix	2024-04-28 00:15:29 +04:00
erew123	dd52597f49	Grad acc steps min setting	2024-04-26 20:50:52 +01:00
erew123	5f836e08b8	Various updates additional documentation learning rate selectable custom folder paths for saving model selectable model on saving	2024-04-26 15:30:53 +01:00
IAUSHYJ	b89b6cdc1d	small fix	2024-04-26 13:23:23 +04:00
IAUSHYJ	558aed1888	stop cleaning common chinese punctuations	2024-04-26 03:40:20 +04:00
erew123	5eab3d28df	Add files via upload	2024-04-25 21:25:29 +01:00
erew123	fd76f496a0	re-jigged language support/filters	2024-04-25 21:24:44 +01:00
erew123	4d9b44d9a0	Update tts_generator.html	2024-04-25 18:06:49 +01:00
erew123	951df5a239	Update atsetup.bat	2024-04-24 18:40:38 +01:00
erew123	0a35ee2310	Update atsetup.bat	2024-04-24 18:38:32 +01:00
erew123	efeb2e67e0	Add files via upload	2024-04-20 14:54:33 +01:00
erew123	131f14de76	Add files via upload	2024-04-20 14:49:03 +01:00
erew123	4e67931fe9	bug fix	2024-04-20 10:28:18 +01:00
erew123	785525327d	Update README.md	2024-04-18 20:07:20 +01:00
erew123	d4647573cd	Add files via upload	2024-04-18 19:58:31 +01:00
erew123	c3895b602c	Hindi Support	2024-04-18 18:43:25 +01:00
erew123	f18eb9a4a0	Hindi Support	2024-04-18 18:39:10 +01:00
erew123	2834778e8f	Added Hindi finetune.py	2024-04-18 18:32:10 +01:00
erew123	2f42c69d25	Update README.md	2024-04-18 15:57:32 +01:00
erew123	8c65f026e6	ST AllTalk FT Model changed a value for detecting FT model.	2024-04-09 01:17:03 +01:00
erew123	fff01107c8	Update tts_server.py - API request console output	2024-04-06 11:45:32 +01:00
erew123	0bdc66f6e5	Add files via upload	2024-04-05 12:28:59 +01:00
erew123	1c3fc86085	Delete system/at_admin/textnotinside-min.jpg	2024-04-05 12:28:38 +01:00
erew123	d9eb0876a6	Delete system/at_admin/pythonrequirementswindows-min.jpg	2024-04-05 12:28:30 +01:00
erew123	9b4130a8eb	Delete system/at_admin/pythonenvironment-min.jpg	2024-04-05 12:28:22 +01:00
erew123	8fb5b4f695	Delete system/at_admin/deepspeedexample-min.jpg	2024-04-05 12:28:14 +01:00
erew123	65e306a198	Delete system/at_admin/atandsdplugin-min.jpg	2024-04-05 12:28:07 +01:00
erew123	3dabb63656	Delete system/at_admin/4abed1ca-min.jpg	2024-04-05 12:27:59 +01:00
erew123	241fa91473	Delete system/at_admin/3c6fde566d6a-min.jpg	2024-04-05 12:27:50 +01:00
erew123	2a5c8dd58a	Add files via upload	2024-04-05 12:17:28 +01:00
erew123	2bfa10d7be	Fix Windows 10 issue with path/environment.	2024-04-05 12:07:46 +01:00
erew123	3501e2e2f3	Fix crunker bug over networks Crunker had an update to resolve bad performance over networks. This would affect only the TTS Generator when used remotely from AllTalk.	2024-04-05 10:26:33 +01:00
erew123	679e3a0982	Update atsetup.bat encapsulate SET path	2024-04-04 19:11:59 +01:00
erew123	35d05c7689	Add files via upload	2024-04-04 11:07:21 +01:00
erew123	084cc2d2a2	Add files via upload	2024-04-04 11:02:59 +01:00
erew123	a96c82ff01	Add files via upload	2024-04-04 10:58:35 +01:00
erew123	aa7ff1940a	Add files via upload	2024-04-04 10:30:45 +01:00
erew123	58465ae14e	Update Dockerfile	2024-04-04 10:25:33 +01:00
erew123	2f49f61645	Add files via upload	2024-04-04 10:16:30 +01:00
erew123	58a70caebc	Update README.md	2024-04-04 01:58:04 +01:00
erew123	d0d470db0f	Update README.md	2024-04-04 01:57:02 +01:00
erew123	068fc72346	Update README.md	2024-04-04 01:55:10 +01:00
erew123	fc5c8c748f	Standalone reminder re scripts	2024-04-04 00:39:20 +01:00
erew123	24f86d08b7	Update README.md	2024-04-03 11:10:40 +01:00
erew123	99e402664c	Update feature_request.md	2024-04-02 16:57:21 +01:00
erew123	24cf9eae59	Update bug-report-support-request.md	2024-04-02 16:56:43 +01:00
erew123	18b1582d2a	Fix curl being funny on re-apply requirements	2024-04-02 16:16:10 +01:00
erew123	96d9df9438	Update README.md	2024-04-02 16:13:26 +01:00
erew123	4ca156a032	Waning/Documentation messages Added a routine to both atsetups to warn about paths with spaces in them. Added notes to finetune to make certain aspects clearer Changed a note in readme	2024-04-02 13:39:08 +01:00
erew123	5aecb5b7fd	Spelling/Colour correction	2024-04-01 15:24:20 +01:00
erew123	63c308ff05	Fix wav file deletion display	2024-04-01 14:52:31 +01:00
erew123	e6e4b0b47f	Merge pull request #156 from erew123/dev Dev to main	2024-04-01 13:55:59 +01:00
erew123	bedc8148ad	Update README.md	2024-04-01 13:52:32 +01:00