import argparse
import os
import sys
import platform
import site
import tempfile
import signal
import gradio as gr
import torch
import torchaudio
import traceback
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
import random
import gc
import time
import shutil
import psutil
import pandas as pd
import glob
import json
from pathlib import Path
from tqdm import tqdm
from faster_whisper import WhisperModel
# Use a local Tokenizer to resolve Japanese support
# from TTS.tts.layers.xtts.tokenizer import multilingual_cleaners
from system.ft_tokenizer.tokenizer import multilingual_cleaners
import importlib.metadata as metadata
from packaging import version
# STARTUP VARIABLES
this_dir = Path(__file__).parent.resolve()
audio_folder = this_dir / "finetune" / "put-voice-samples-in-here"
out_path = this_dir / "finetune" / "tmp-trn"
progress = 0
theme = gr.themes.Default()
refresh_symbol = '🔄'
os.environ['TRAINER_TELEMETRY'] = '0'
pfc_status = "pass"
base_path = this_dir / "models" / "xtts"
# Set the Gradio temporary directory
gradio_temp_dir = this_dir / "finetune" / "gradio_temp"
os.environ["GRADIO_TEMP_DIR"] = str(gradio_temp_dir)
########################
#### Find All Model ####
########################
base_model_detected = False
def scan_models_folder():
global base_model_detected
models_folder = base_path
available_models = {}
required_files = ["config.json", "model.pth", "mel_stats.pth", "speakers_xtts.pth", "vocab.json", "dvae.pth"]
try:
for subfolder in models_folder.iterdir():
if subfolder.is_dir():
model_name = subfolder.name
if all(subfolder.joinpath(file).exists() for file in required_files):
available_models[model_name] = True
base_model_detected = True
else:
print(f"[FINETUNE] \033[91mWarning\033[0m: Model folder '{model_name}' is missing required files")
if not available_models:
available_models["No Model Available"] = False
base_model_detected = False
except FileNotFoundError:
print("\n[FINETUNE] \033[91mError\033[0m: No XTTS models folder found. You have not yet downloaded any models or no XTTS")
print("[FINETUNE] \033[91mError\033[0m: models can be found. Please run AllTalk and download an XTTS model that can be")
print("[FINETUNE] \033[91mError\033[0m: used for training. Or place a full model in the following location")
print("[FINETUNE] \033[91mError\033[0m: \033[93m\\models\\xtts\\{modelfolderhere}\033[0m")
sys.exit(1) # Exit the script with an error status
return available_models
# Get available models
available_models = scan_models_folder()
#######################
#### DIAGS for PFC ####
#######################
def check_disk_space():
global pfc_status
# Get the current working directory
current_directory = os.getcwd()
# Get the disk usage statistics for the current directory's disk
disk_usage = shutil.disk_usage(current_directory)
# Convert the free space to GB (1GB = 1 << 30 bytes)
free_space_gb = disk_usage.free / (1 << 30)
# Check if the free space is more than 18GB
is_more_than_18gb = free_space_gb > 18
disk_space_icon = "✅"
if not is_more_than_18gb:
disk_space_icon ="❌"
pfc_status = "fail" # Update global status if disk space check fails
# Generating the markdown text for disk space check
disk_space_markdown = f"""
### 🟩 Disk Space Check
{disk_space_icon} **Disk Space (> 18 GB):** {'' if is_more_than_18gb else 'You have less than 18GB on this disk '} {free_space_gb:.2f} GB
"""
return disk_space_markdown
def test_cuda():
global pfc_status
cuda_home = os.environ.get('CUDA_HOME', 'N/A')
cuda_available = torch.cuda.is_available()
if cuda_available:
try:
# Attempt to create a tensor on GPU
torch.tensor([1.0, 2.0]).cuda()
cuda_status = "CUDA is available and working."
cuda_icon = "✅"
except Exception as e:
cuda_status = f"CUDA is available but not working. Error: {e}"
cuda_icon = "❌"
pfc_status = "fail" # Update global status
else:
cuda_status = "CUDA is not available."
pfc_status = "fail" # Update global status
return cuda_status, cuda_icon, cuda_home
def find_files_in_path_with_wildcard(pattern):
# Get the site-packages directory of the current Python environment
site_packages_path = site.getsitepackages()
found_paths = []
# Adjust the sub-directory based on the operating system
sub_directory = "nvidia/cublas"
if platform.system() == "Linux":
sub_directory = os.path.join(sub_directory, "lib")
else:
sub_directory = os.path.join(sub_directory, "bin")
# Iterate over each site-packages directory (there can be more than one)
for directory in site_packages_path:
# Construct the search directory path
search_directory = os.path.join(directory, sub_directory)
# Use glob to find all files matching the pattern in this directory
for file_path in glob.glob(os.path.join(search_directory, pattern)):
if os.path.isfile(file_path): # Ensure it's a file
found_paths.append(file_path)
return found_paths
def test_cuda():
if torch.cuda.is_available():
pytorch_cuda_version = torch.version.cuda
return f'CUDA {pytorch_cuda_version} is available in this Python environment', '✅', torch.cuda.get_device_name(0)
else:
return 'CUDA is not available in this Python environment', '❌', 'None'
def generate_cuda_markdown():
global pfc_status
pfc_status = "pass" # Initialize global status as pass
# Check CUDA availability and get CUDA home path
cuda_status, cuda_icon, cuda_home = test_cuda()
# Check for specific CUDA library files
file_name = 'cublas64_11.*' if platform.system() == "Windows" else 'libcublas.so.11*'
found_paths = find_files_in_path_with_wildcard(file_name)
if found_paths:
found_paths_str = ' '.join(str(path) for path in found_paths)
found_path_icon = '✅'
else:
found_paths_str = "cublas64_11 is not accessible."
found_path_icon = '❌'
pfc_status = "fail" # Update global status
# Check PyTorch version and CUDA version
pytorch_version = torch.__version__
pytorch_cuda_version = torch.version.cuda
if pytorch_cuda_version in ['11.8', '12.1']:
pytorch_cuda_version_status = ''
pytorch_icon = '✅'
else:
pytorch_cuda_version_status = 'Pytorch CUDA version problem '
pytorch_icon = '❌'
pfc_status = "fail" # Update global status
cuda_markdown = f"""
### 🟨 CUDA Information
{found_path_icon} **Cublas64_11 found:** {found_paths_str}
{pytorch_icon} **CUDA_HOME path:** {cuda_home} **(Will list your GPU if no specifc path set, which is ok)**
"""
pytorch_markdown = f"""
### 🟦 Python & Pytorch Information
{pytorch_icon} **PyTorch Version:** {pytorch_cuda_version_status} {torch.__version__}
{cuda_icon} **CUDA is working:** {cuda_status}
"""
return cuda_markdown, pytorch_markdown
def get_system_ram_markdown():
global pfc_status
virtual_memory = psutil.virtual_memory()
total_ram_gb = virtual_memory.total / (1024 ** 3)
available_ram_gb = virtual_memory.available / (1024 ** 3)
used_ram_percentage = virtual_memory.percent
# Check if the available RAM is less than 8GB
warning_if_low_ram = available_ram_gb < 8
# Decide the message based on the available RAM
ram_status_message = "Warning" if warning_if_low_ram else ""
ram_status_icon = "⚠️" if warning_if_low_ram else "✅"
if torch.cuda.is_available():
gpu_device_id = torch.cuda.current_device()
gpu_device_name = torch.cuda.get_device_name(gpu_device_id)
# Get the total and available memory in bytes, then convert to GB
gpu_total_mem_gb = torch.cuda.get_device_properties(gpu_device_id).total_memory / (1024 ** 3)
# gpu_available_mem_gb = (torch.cuda.get_device_properties(gpu_device_id).total_memory - torch.cuda.memory_allocated(gpu_device_id)) / (1024 ** 3)
# gpu_available_mem_gb = (torch.cuda.get_device_properties(gpu_device_id).total_memory - torch.cuda.memory_reserved(gpu_device_id)) / (1024 ** 3)
gpu_reserved_mem_gb = torch.cuda.memory_reserved(gpu_device_id) / (1024 ** 3)
gpu_available_mem_gb = gpu_total_mem_gb - gpu_reserved_mem_gb
# Check if total or available memory is less than 11 GB and set icons
gpu_total_status_icon = "⚠️" if gpu_total_mem_gb < 12 else "✅"
gpu_available_status_icon = "⚠️" if gpu_available_mem_gb < 12 else "✅"
gpu_status_icon = "✅"
else:
gpu_status_icon = "⚠️"
gpu_device_name = "Cannot detect a CUDA card"
gpu_total_mem_gb = "Cannot detect a CUDA card"
gpu_available_mem_gb = "Cannot detect a CUDA card"
gpu_total_status_icon = gpu_status_icon
gpu_available_status_icon = gpu_status_icon
system_ram_markdown = f"""
### 🟪 System RAM and VRAM Information
{ram_status_icon} **Total RAM:** {total_ram_gb:.2f} GB
{ram_status_icon} **Available RAM:** {ram_status_message + ' - Available RAM is less than 8 GB. You have ' if warning_if_low_ram else ''} {available_ram_gb:.2f} GB available ({used_ram_percentage:.2f}% used)
{gpu_status_icon} **GPU Name:** {gpu_device_name}
{gpu_total_status_icon} **GPU Total RAM:** {gpu_total_mem_gb:.2f} GB
{gpu_available_status_icon} **GPU Available RAM:** {gpu_available_mem_gb:.2f} GB
"""
return system_ram_markdown
def generate_base_model_markdown(base_model_detected):
global pfc_status
base_model_status = 'Base model detected' if base_model_detected else 'Base model not detected'
base_model_icon = '✅' if base_model_detected else '❌'
base_model_markdown = f"""
### ⬛ XTTS Base Model Detection
{base_model_icon} **Base XTTS Model Status:** {base_model_status}
"""
return base_model_markdown
def check_tts_version(required_version="0.24.0"):
global pfc_status
try:
# Get the installed version of TTS
installed_version = metadata.version("coqui-tts")
# Check if the installed version meets the required version
if version.parse(installed_version) >= version.parse(required_version):
tts_status = f"TTS version {installed_version} is installed and meets the requirement."
tts_status_icon = "✅"
else:
tts_status = f"❌ Fail - TTS version {installed_version} is installed but does not meet the required version {required_version}."
tts_status_icon = "❌"
pfc_status = "fail" # Update global status
except metadata.PackageNotFoundError:
# If TTS is not installed
tts_status = "TTS is not installed."
pfc_status = "fail" # Update global status
tts_markdown = f"""
### 🟥 TTS Information
{tts_status_icon} **TTS Version:** {tts_status}
"""
return tts_markdown
# Disk space check results to append to the Markdown
disk_space_results = check_disk_space()
cuda_results, pytorch_results = generate_cuda_markdown()
system_ram_results = get_system_ram_markdown()
base_model_results = generate_base_model_markdown(base_model_detected)
tts_version_status = check_tts_version()
def pfc_check_fail():
global pfc_status
if pfc_status == "fail":
print("[FINETUNE]")
print("[FINETUNE] \033[91m****** WARNING PRE-FLIGHT CHECKS FAILED ******* WARNING PRE-FLIGHT CHECKS FAILED *****\033[0m")
print("[FINETUNE] \033[91m* Please refer to the \033[93mPre-flight check tab \033[91mand resolve any issues before continuing. *\033[0m")
print("[FINETUNE] \033[91m*********** Expect errors and failures if you do not resolve these issues. ***********\033[0m")
print("[FINETUNE]")
return
#####################
#### STEP 1 BITS ####
#####################
def create_temp_folder():
temp_folder = os.path.join(os.path.dirname(__file__), 'temp_files')
os.makedirs(temp_folder, exist_ok=True)
return temp_folder
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, max_sample_length, eval_split_number, speaker_name_input, gradio_progress=None):
buffer = 0.2
max_duration = float(max_sample_length) # Ensure max_duration is a float
eval_percentage = eval_split_number / 100.0
speaker_name = speaker_name_input
audio_total_size = 0
os.makedirs(out_path, exist_ok=True)
temp_folder = os.path.join(out_path, "temp")
os.makedirs(temp_folder, exist_ok=True)
audio_folder = os.path.join(out_path, "wavs")
os.makedirs(audio_folder, exist_ok=True)
original_samples_folder = os.path.join(out_path, "..", "put-voice-samples-in-here")
print("[FINETUNE] Preparing Audio/Generating the dataset")
# Write the target language to lang.txt in the output directory
lang_file_path = os.path.join(out_path, "lang.txt")
current_language = None
if os.path.exists(lang_file_path):
with open(lang_file_path, 'r', encoding='utf-8') as existing_lang_file:
current_language = existing_lang_file.read().strip()
if current_language != target_language:
with open(lang_file_path, 'w', encoding='utf-8') as lang_file:
lang_file.write(target_language + '\n')
print("[FINETUNE] Updated lang.txt with the target language.")
else:
print("[FINETUNE] The existing language matches the target language")
device = "cuda" if torch.cuda.is_available() else "cpu"
asr_model = WhisperModel(whisper_model, device=device, compute_type="float32")
metadata = {"audio_file": [], "text": [], "speaker_name": []}
existing_metadata = {'train': None, 'eval': None}
train_metadata_path = os.path.join(out_path, "metadata_train.csv")
eval_metadata_path = os.path.join(out_path, "metadata_eval.csv")
if os.path.exists(train_metadata_path):
existing_metadata['train'] = pd.read_csv(train_metadata_path, sep="|")
print("[FINETUNE] Existing training metadata found and loaded.")
if os.path.exists(eval_metadata_path):
existing_metadata['eval'] = pd.read_csv(eval_metadata_path, sep="|")
print("[FINETUNE] Existing evaluation metadata found and loaded.")
# Process original voice samples to create WAV files
original_audio_files = [os.path.join(original_samples_folder, file) for file in os.listdir(original_samples_folder) if file.endswith(('.mp3', '.flac', '.wav'))]
if not original_audio_files:
print(f"[FINETUNE] No audio files found in {original_samples_folder}. Skipping processing.")
return None, None, 0
for idx, audio_path in tqdm(enumerate(original_audio_files)):
audio_file_name_without_ext, _ = os.path.splitext(os.path.basename(audio_path))
temp_audio_path = os.path.join(temp_folder, f"{audio_file_name_without_ext}.wav")
try:
shutil.copy2(audio_path, temp_audio_path)
except Exception as e:
print(f"[FINETUNE] Error copying file: {e}")
continue
wav, sr = torchaudio.load(temp_audio_path, format="wav")
wav = torch.as_tensor(wav).clone().detach().t().to(torch.float32), sr
prefix_check = f"wavs/{audio_file_name_without_ext}_"
skip_processing = False
for key in ['train', 'eval']:
if existing_metadata[key] is not None:
mask = existing_metadata[key]['audio_file'].str.startswith(prefix_check)
if mask.any():
print(f"[FINETUNE] Segments from {audio_file_name_without_ext} have been previously processed; skipping...")
skip_processing = True
audio_total_size = 121
break
if skip_processing:
continue
wav, sr = torchaudio.load(audio_path)
if wav.size(0) != 1:
wav = torch.mean(wav, dim=0, keepdim=True)
wav = wav.squeeze()
audio_total_size += (wav.size(-1) / sr)
segments, _ = asr_model.transcribe(audio_path, vad_filter=True, word_timestamps=True, language=target_language)
segments = list(segments)
i = 0
sentence = ""
sentence_start = None
first_word = True
words_list = []
for _, segment in enumerate(segments):
words = list(segment.words)
words_list.extend(words)
for word_idx, word in enumerate(words_list):
if first_word:
sentence_start = word.start
if word_idx == 0:
sentence_start = max(sentence_start - buffer, 0)
else:
previous_word_end = words_list[word_idx - 1].end
sentence_start = max(sentence_start - buffer, (previous_word_end + sentence_start) / 2)
sentence = word.word
first_word = False
else:
sentence += " " + word.word
# Split segment if it ends with punctuation or exceeds max_duration
if word.word[-1] in ["!", ".", "?"] or (word.end - sentence_start) > max_duration:
sentence = sentence.strip()
# Clean and normalize the sentence
sentence = multilingual_cleaners(sentence, target_language)
audio_file_name, _ = os.path.splitext(os.path.basename(audio_path))
audio_file = f"{audio_file_name}_{str(i).zfill(8)}.wav"
if word_idx + 1 < len(words_list):
next_word_start = words_list[word_idx + 1].start
else:
next_word_start = (wav.shape[0] - 1) / sr
word_end = min((word.end + next_word_start) / 2, word.end + buffer)
absolute_path = os.path.join(audio_folder, audio_file) # Save to the audio_folder
os.makedirs(os.path.dirname(absolute_path), exist_ok=True)
i += 1
first_word = True
audio = wav[int(sr * sentence_start):int(sr * word_end)].unsqueeze(0)
# If the audio is too long, split it into smaller chunks
while audio.size(-1) > max_duration * sr:
split_audio = audio[:, :int(max_duration * sr)]
audio = audio[:, int(max_duration * sr):]
# Normalize the file path
split_file_name = f"{audio_file_name}_{str(i).zfill(8)}.wav"
split_relative_path = os.path.join('wavs', split_file_name)
split_absolute_path = os.path.normpath(os.path.join(audio_folder, split_relative_path))
# Ensure the directory exists
os.makedirs(os.path.dirname(split_absolute_path), exist_ok=True)
# Save the split audio
torchaudio.save(split_absolute_path, split_audio, sr)
# Update metadata
metadata["audio_file"].append(split_relative_path)
metadata["text"].append(sentence)
metadata["speaker_name"].append(speaker_name)
i += 1
if audio.size(-1) >= sr / 3: # if the remaining audio is at least 0.33 seconds
torchaudio.save(absolute_path, audio, sr)
else:
continue
metadata["audio_file"].append(f"wavs/{audio_file_name}_{str(i).zfill(8)}.wav")
metadata["text"].append(sentence)
metadata["speaker_name"].append(speaker_name)
os.remove(temp_audio_path)
# Check if the WAV files folder contains files after processing
audio_files = [os.path.join(audio_folder, file) for file in os.listdir(audio_folder) if file.endswith('.wav')]
if not audio_files:
print(f"[FINETUNE] No processed audio files found in {audio_folder}. Skipping processing.")
return None, None, 0
if os.path.exists(train_metadata_path) and os.path.exists(eval_metadata_path):
existing_train_df = existing_metadata['train']
existing_eval_df = existing_metadata['eval']
else:
existing_train_df = pd.DataFrame(columns=["audio_file", "text", "speaker_name"])
existing_eval_df = pd.DataFrame(columns=["audio_file", "text", "speaker_name"])
new_data_df = pd.DataFrame(metadata)
combined_train_df = pd.concat([existing_train_df, new_data_df], ignore_index=True).drop_duplicates().reset_index(drop=True)
combined_train_df_shuffled = combined_train_df.sample(frac=1)
num_val_samples = int(len(combined_train_df_shuffled) * eval_percentage)
final_eval_set = combined_train_df_shuffled[:num_val_samples]
final_training_set = combined_train_df_shuffled[num_val_samples:]
final_training_set.sort_values('audio_file').to_csv(train_metadata_path, sep='|', index=False)
final_eval_set.sort_values('audio_file').to_csv(eval_metadata_path, sep='|', index=False)
del asr_model, final_eval_set, final_training_set, new_data_df, existing_metadata
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
return train_metadata_path, eval_metadata_path, audio_total_size
######################
#### STEP 2 BITS #####
######################
from trainer import Trainer, TrainerArgs
from TTS.config.shared_configs import BaseDatasetConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
from TTS.utils.manage import ModelManager
def basemodel_or_finetunedmodel_choice(value):
global basemodel_or_finetunedmodel
if value == "Base Model":
basemodel_or_finetunedmodel = True
elif value == "Existing finetuned model":
basemodel_or_finetunedmodel = False
def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, learning_rate, model_to_train, output_path, max_audio_length=255995, progress=gr.Progress()):
pfc_check_fail()
if "No Models Available" in eval_csv:
print(f"[FINETUNE] \033[91mError\033[0m: Cannot train. You have not selected a model. Please download a model.")
print(f"[FINETUNE] \033[91mError\033[0m: into a sub folder within the correct models folder.")
return
# Logging parameters
RUN_NAME = "XTTS_FT"
PROJECT_NAME = "XTTS_trainer"
DASHBOARD_LOGGER = "tensorboard"
LOGGER_URI = None
model_path = this_dir / "models" / "xtts" / model_to_train
# Set here the path that the checkpoints will be saved. Default: ./training/
OUT_PATH = os.path.join(output_path, "training")
print("[FINETUNE] \033[94mStarting Step 2\033[0m - Fine-tuning the XTTS Encoder")
print(f"[FINETUNE] \033[94mLanguage: \033[92m{language} \033[94mEpochs: \033[92m{num_epochs} \033[94mBatch size: \033[92m{batch_size}\033[0m \033[94mGrad accumulation steps: \033[92m{grad_acumm}\033[0m")
print(f"[FINETUNE] \033[94mTraining : \033[92m{train_csv}\033[0m")
print(f"[FINETUNE] \033[94mEvaluation : \033[92m{eval_csv}\033[0m")
print(f"[FINETUNE] \033[94mModel used : \033[92m{model_path}\033[0m")
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Get the current device ID
gpu_device_id = torch.cuda.current_device()
gpu_available_mem_gb = (torch.cuda.get_device_properties(gpu_device_id).total_memory - torch.cuda.memory_allocated(gpu_device_id)) / (1024 ** 3)
print(f"[FINETUNE] \033[94mAvailable VRAM: \033[92m{gpu_available_mem_gb:.2f} GB\033[0m")
if gpu_available_mem_gb < 12:
print(f"[FINETUNE]")
print(f"[FINETUNE] \033[91m****** WARNING PRE-FLIGHT CHECKS FAILED ******* WARNING PRE-FLIGHT CHECKS FAILED *****\033[0m")
print(f"[FINETUNE] \033[94mAvailable VRAM: \033[92m{gpu_available_mem_gb:.2f} GB\033[0m")
print(f"[FINETUNE] \033[94mIf you are running on a Linux system and you have 12GB's or less of VRAM, this step\033[0m")
print(f"[FINETUNE] \033[94mmay fail, due to not enough GPU VRAM. Windows systems will use system RAM as extended\033[0m")
print(f"[FINETUNE] \033[94mVRAM and so should work ok. However, Windows machines will need enough System RAM\033[0m")
print(f"[FINETUNE] \033[94mavailable. Please read the PFC help section available on the first tab of the web\033[0m")
print(f"[FINETUNE] \033[94minterface for more information.\033[0m")
print(f"[FINETUNE] \033[91m****** WARNING PRE-FLIGHT CHECKS FAILED ******* WARNING PRE-FLIGHT CHECKS FAILED *****\033[0m")
print(f"[FINETUNE]")
# Create the directory
os.makedirs(OUT_PATH, exist_ok=True)
# Training Parameters
OPTIMIZER_WD_ONLY_ON_WEIGHTS = True # for multi-gpu training please make it False
START_WITH_EVAL = False # if True it will star with evaluation
BATCH_SIZE = batch_size # set here the batch size
GRAD_ACUMM_STEPS = grad_acumm # set here the grad accumulation steps
# Define here the dataset that you want to use for the fine-tuning on.
config_dataset = BaseDatasetConfig(
formatter="coqui",
dataset_name="ft_dataset",
path=os.path.dirname(train_csv),
meta_file_train=train_csv,
meta_file_val=eval_csv,
language=language,
)
# Add here the configs of the datasets
DATASETS_CONFIG_LIST = [config_dataset]
TOKENIZER_FILE = str(model_path / "vocab.json")
XTTS_CHECKPOINT = str(model_path / "model.pth")
XTTS_CONFIG_FILE = str(model_path / "config.json")
DVAE_CHECKPOINT = model_path / "dvae.pth"
MEL_NORM_FILE = model_path / "mel_stats.pth"
SPEAKERS_FILE = model_path / "speakers_xtts.pth"
# Copy the supporting files
destination_dir = this_dir / "finetune" / "tmp-trn" / "chkptandnorm"
destination_dir.mkdir(parents=True, exist_ok=True)
shutil.copy2(DVAE_CHECKPOINT, destination_dir / DVAE_CHECKPOINT.name)
shutil.copy2(MEL_NORM_FILE, destination_dir / MEL_NORM_FILE.name)
shutil.copy2(SPEAKERS_FILE, destination_dir / SPEAKERS_FILE.name)
# init args and config
model_args = GPTArgs(
max_conditioning_length=132300, # 6 secs
min_conditioning_length=66150, # 3 secs
debug_loading_failures=False,
max_wav_length=max_audio_length, # ~11.6 seconds
max_text_length=200,
mel_norm_file=MEL_NORM_FILE,
dvae_checkpoint=DVAE_CHECKPOINT,
xtts_checkpoint=XTTS_CHECKPOINT, # checkpoint path of the model that you want to fine-tune
tokenizer_file=TOKENIZER_FILE,
gpt_num_audio_tokens=1026,
gpt_start_audio_token=1024,
gpt_stop_audio_token=1025,
gpt_use_masking_gt_prompt_approach=True,
gpt_use_perceiver_resampler=True,
)
# define audio config
audio_config = XttsAudioConfig(sample_rate=22050, dvae_sample_rate=22050, output_sample_rate=24000)
# Resolve Japanese threading issue
number_of_workers = 8
if language == "ja":
number_of_workers = 0
# training parameters config
config = GPTTrainerConfig(
epochs=num_epochs,
output_path=OUT_PATH,
model_args=model_args,
run_name=RUN_NAME,
project_name=PROJECT_NAME,
run_description="GPT XTTS training",
dashboard_logger=DASHBOARD_LOGGER,
logger_uri=LOGGER_URI,
audio=audio_config,
batch_size=BATCH_SIZE,
batch_group_size=48,
eval_batch_size=BATCH_SIZE,
num_loader_workers=number_of_workers,
eval_split_max_size=256,
print_step=50,
plot_step=100,
log_model_step=100,
save_step=1000,
save_n_checkpoints=1,
save_checkpoints=True,
# target_loss="loss",
print_eval=False,
# Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
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=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},
test_sentences=[],
)
progress(0, desc="Model is currently training. See console for more information")
# init the model from config
model = GPTTrainer.init_from_config(config)
# load training samples
train_samples, eval_samples = load_tts_samples(
DATASETS_CONFIG_LIST,
eval_split=True,
eval_split_max_size=config.eval_split_max_size,
eval_split_size=config.eval_split_size,
)
# init the trainer
trainer = Trainer(
TrainerArgs(
restore_path=None, # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter
skip_train_epoch=False,
start_with_eval=START_WITH_EVAL,
grad_accum_steps=GRAD_ACUMM_STEPS,
),
config,
output_path=OUT_PATH,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
)
trainer.fit()
# get the longest text audio file to use as speaker reference
samples_len = [len(item["text"].split(" ")) for item in train_samples]
longest_text_idx = samples_len.index(max(samples_len))
speaker_ref = train_samples[longest_text_idx]["audio_file"]
trainer_out_path = trainer.output_path
# deallocate VRAM and RAM
del model, trainer, train_samples, eval_samples, config, model_args, config_dataset
gc.collect()
train_samples = None
eval_samples = None
config_dataset = None
trainer = None
model = None
model_args = None
try:
return XTTS_CONFIG_FILE, XTTS_CHECKPOINT, TOKENIZER_FILE, trainer_out_path, speaker_ref
except Exception as e:
print(f"Error returning values: {e}")
return "Error", "Error", "Error", "Error", "Error"
##########################
#### STEP 3 AND OTHER ####
##########################
def clear_gpu_cache():
# clear the GPU cache
if torch.cuda.is_available():
torch.cuda.empty_cache()
def find_a_speaker_file(folder_path):
search_path = folder_path / "*" / "speakers_xtts.pth"
files = glob.glob(str(search_path), recursive=True)
latest_file = max(files, key=os.path.getctime, default=None)
return latest_file
XTTS_MODEL = None
def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
pfc_check_fail()
global XTTS_MODEL
clear_gpu_cache()
if not xtts_checkpoint or not xtts_config or not xtts_vocab:
return "No Models were selected. Click the Refresh Dropdowns button and try again."
xtts_speakers_pth = find_a_speaker_file(this_dir / "models")
config = XttsConfig()
config.load_json(xtts_config)
XTTS_MODEL = Xtts.init_from_config(config)
print("[FINETUNE] \033[94mStarting Step 3\033[0m Loading XTTS model!")
XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False, speaker_file_path=xtts_speakers_pth)
if torch.cuda.is_available():
XTTS_MODEL.cuda()
print("[FINETUNE] Model Loaded!")
return "Model Loaded!"
def run_tts(lang, tts_text, speaker_audio_file):
if XTTS_MODEL is None or not speaker_audio_file:
return "You need to run the previous step to load the model !!", None, None
speaker_audio_file = str(speaker_audio_file)
gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(audio_path=speaker_audio_file, gpt_cond_len=XTTS_MODEL.config.gpt_cond_len, max_ref_length=XTTS_MODEL.config.max_ref_len, sound_norm_refs=XTTS_MODEL.config.sound_norm_refs)
out = XTTS_MODEL.inference(
text=tts_text,
language=lang,
gpt_cond_latent=gpt_cond_latent,
speaker_embedding=speaker_embedding,
temperature=XTTS_MODEL.config.temperature, # Add custom parameters here
length_penalty=XTTS_MODEL.config.length_penalty,
repetition_penalty=XTTS_MODEL.config.repetition_penalty,
top_k=XTTS_MODEL.config.top_k,
top_p=XTTS_MODEL.config.top_p,
)
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
out["wav"] = torch.tensor(out["wav"]).unsqueeze(0)
out_path = fp.name
torchaudio.save(out_path, out["wav"], 24000)
return "Speech generated !", out_path, speaker_audio_file
def get_available_voices(minimum_size_kb=1200):
voice_files = [
voice for voice in Path(f"{this_dir}/finetune/tmp-trn/wavs").glob("*.wav")
if voice.stat().st_size > minimum_size_kb * 1200 # Convert KB to bytes
]
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 [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 [str(file) for file in Path(directory).rglob(filename)]
# Your main directory
main_directory = Path(this_dir) / "finetune" / "tmp-trn"
# 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)
xtts_vocab_files = find_jsons(main_directory, "vocab.json")
##########################
#### STEP 4 AND OTHER ####
##########################
import shutil
from pathlib import Path
import torch
def compact_custom_model(xtts_checkpoint_copy, folder_path, overwrite_existing):
this_dir = Path(__file__).parent.resolve()
if not xtts_checkpoint_copy:
error_message = "No trained model was selected. Please click Refresh Dropdowns and try again."
print("[FINETUNE]", error_message)
return error_message
target_dir = this_dir / "models" / "xtts" / folder_path
if overwrite_existing == "Do not overwrite existing files" and target_dir.exists():
error_message = "The target folder already exists. Please change folder name or allow overwrites."
print("[FINETUNE]", error_message)
return error_message
xtts_checkpoint_copy = Path(xtts_checkpoint_copy)
try:
checkpoint = torch.load(xtts_checkpoint_copy, map_location=torch.device("cpu"))
except Exception as e:
print("[FINETUNE] Error loading checkpoint:", e)
raise
del checkpoint["optimizer"]
target_dir.mkdir(parents=True, exist_ok=True)
# Remove dvae-related keys from checkpoint
for key in list(checkpoint["model"].keys()):
if "dvae" in key:
del checkpoint["model"][key]
torch.save(checkpoint, target_dir / "model.pth")
def copy_files(src_folder, dest_folder, files):
for file_name in files:
src_path = src_folder / file_name
dest_path = dest_folder / file_name
if src_path.exists():
shutil.copy2(src_path, dest_path)
else:
print(f"[FINETUNE] Warning: {src_path} does not exist and will not be copied.")
# Copy first set of files
folder_path_new = xtts_checkpoint_copy.parent
files_to_copy = ["vocab.json", "config.json"]
copy_files(folder_path_new, target_dir, files_to_copy)
# Copy second set of files
chkptandnorm_path = this_dir / "finetune" / "tmp-trn" / "chkptandnorm"
files_to_copy2 = ["speakers_xtts.pth", "mel_stats.pth", "dvae.pth"]
copy_files(chkptandnorm_path, target_dir, files_to_copy2)
# Copy large wav files
source_wavs_dir = this_dir / "finetune" / "tmp-trn" / "wavs"
target_wavs_dir = target_dir / "wavs"
target_wavs_dir.mkdir(parents=True, exist_ok=True)
for file_path in source_wavs_dir.iterdir():
if file_path.is_file() and file_path.stat().st_size > 1000 * 1024: # File size greater than 1000 KB
shutil.copy2(file_path, target_wavs_dir / file_path.name)
print(f"[FINETUNE] Model & Suitable WAV samples copied to '/models/xtts/{folder_path}/'")
return f"Model & Suitable WAV samples copied to '/models/xtts/{folder_path}/'"
def delete_training_data():
# Define the folder to be deleted
folder_to_delete = Path(this_dir / "finetune" / "tmp-trn")
# Check if the folder exists before deleting
if folder_to_delete.exists():
# Iterate over all files and subdirectories
for item in folder_to_delete.iterdir():
# Exclude trainer_0_log.txt from deletion
if item.name != "trainer_0_log.txt":
try:
if item.is_file():
item.unlink()
elif item.is_dir():
shutil.rmtree(item)
except PermissionError:
print(f"[FINETUNE] PermissionError: Could not delete {item}. Skipping.")
print(f"[FINETUNE] Folder {folder_to_delete} contents (excluding trainer_0_log.txt) deleted successfully.")
return "Folder '/finetune/tmp-trn/' contents (excluding trainer_0_log.txt) deleted successfully."
else:
print(f"[FINETUNE] Folder {folder_to_delete} does not exist.")
return "Folder '/finetune/tmp-trn/' does not exist."
def clear_folder_contents(folder_path):
# Check if the folder exists before clearing its contents
if folder_path.exists() and folder_path.is_dir():
# List all files and subdirectories in the folder
for item in os.listdir(folder_path):
item_path = folder_path / item
if item_path.is_file():
# If it's a file, remove it
os.remove(item_path)
elif item_path.is_dir():
# If it's a subdirectory, remove it recursively
shutil.rmtree(item_path)
print(f"[FINETUNE] Contents of {folder_path} deleted successfully.")
return f"Contents of '{folder_path}' deleted successfully."
else:
print(f"[FINETUNE] Folder {folder_path} does not exist.")
return f"Folder '{folder_path}' does not exist."
def delete_voice_sample_contents():
# Define the folders to be cleared
voice_samples_folder = this_dir / "finetune" / "put-voice-samples-in-here"
gradio_temp_folder = this_dir / "finetune" / "gradio_temp"
# Clear the contents of the gradio_temp folder
gradio_temp_message = clear_folder_contents(gradio_temp_folder)
# Clear the contents of the voice samples folder
voice_samples_message = clear_folder_contents(voice_samples_folder)
return voice_samples_message
#######################
#### OTHER Generic ####
#######################
# define a logger to redirect
class Logger:
def __init__(self, filename="finetune.log"):
self.log_file = filename
self.terminal = sys.stdout
self.log = open(self.log_file, "w")
def write(self, message):
self.terminal.write(message)
self.log.write(message)
def flush(self):
self.terminal.flush()
self.log.flush()
def isatty(self):
return False
# redirect stdout and stderr to a file
sys.stdout = Logger()
sys.stderr = sys.stdout
# logging.basicConfig(stream=sys.stdout, level=logging.INFO)
import logging
logging.basicConfig(
level=logging.INFO,
format="[FINETUNE] %(message)s",
handlers=[
logging.StreamHandler(sys.stdout)
]
)
def read_logs():
sys.stdout.flush()
with open(sys.stdout.log_file, "r") as f:
return f.read()
def cleanup_before_exit(signum, frame):
print("[FINETUNE] Received interrupt signal. Cleaning up and exiting...")
# Perform cleanup operations here if necessary
sys.exit(0)
def create_refresh_button(refresh_components, refresh_methods, elem_class, interactive=True):
def refresh():
updates = {}
for component, method in zip(refresh_components, refresh_methods):
args = method() if callable(method) else method
if args and 'choices' in args:
# Select the most recent file (last in the sorted list)
args['value'] = args['choices'][-1] if args['choices'] else ""
for k, v in args.items():
setattr(component, k, v)
updates[component] = gr.update(**(args or {}))
return updates
refresh_button = gr.Button("Refresh Dropdowns", elem_classes=elem_class, interactive=interactive)
refresh_button.click(
fn=refresh,
inputs=[],
outputs=refresh_components
)
return refresh_button
pfc_markdown = f"""
### 🚀 Pre-flight Checklist for Fine-tuning
◽ Ensure each criterion is marked with a green check mark ✅ and a Pass status. Finetuning will fail otherwise.
◽ The help tabs along the top will assist in resolving issues and you can also find additional help guides on the AllTalk [GitHub repository](https://github.com/erew123/alltalk_tts#-finetuning-a-model).
◽ For an overview of fine-tuning procedures, please refer to the "General Finetuning info" tab or visit the AllTalk [GitHub repository](https://github.com/erew123/alltalk_tts#-finetuning-a-model).
"""
custom_css = """
body {
font-size: 16px; /* Adjust the base font size as needed */
}
h1, h2, h3, h4, h5, h6 {
font-size: 1.25em; /* Adjust heading sizes relative to the base size */
}
p {
font-size: 1.1em; /* Paragraph font size, relative to the base size */
margin-bottom: 10px; /* Adjust paragraph spacing */
}
.gradio_container {
zoom: 1.1; /* Adjust the zoom to scale the entire container */
}
"""
if __name__ == "__main__":
# Register the signal handler
signal.signal(signal.SIGINT, cleanup_before_exit)
################
#### GRADIO ####
################
parser = argparse.ArgumentParser(
description="""XTTS fine-tuning demo\n\n"""
"""
Example runs:
python3 TTS/demos/xtts_ft_demo/xtts_demo.py --port
""",
formatter_class=argparse.RawTextHelpFormatter,
)
parser.add_argument(
"--num_epochs",
type=int,
help="Number of epochs to train. Default: 10",
default=10,
)
parser.add_argument(
"--batch_size",
type=int,
help="Batch size. Default: 4",
default=4,
)
parser.add_argument(
"--grad_acumm",
type=int,
help="Grad accumulation steps. Default: 1",
default=1,
)
parser.add_argument(
"--max_audio_length",
type=int,
help="Max permitted audio size in seconds. Default: 11",
default=11,
)
args = parser.parse_args()
#####################
#### GRADIO INFO ####
#####################
with gr.Blocks(theme=gr.themes.Base(), css=custom_css) as demo:
with gr.Row():
gr.Markdown("## XTTS Models Finetuning")
gr.Markdown("")
gr.Markdown("")
dark_mode_btn = gr.Button("Light/Dark Mode", variant="primary", size="sm")
dark_mode_btn.click(None, None, None,
js="""() => {
if (document.querySelectorAll('.dark').length) {
document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
// localStorage.setItem('darkMode', 'disabled');
} else {
document.querySelector('body').classList.add('dark');
// localStorage.setItem('darkMode', 'enabled');
}
}""", show_api=False)
with gr.Tab("🚀 PFC"):
with gr.Tab("🚀 Pre-Flight Checklist"):
gr.Markdown(
f"""
{pfc_markdown}
{disk_space_results}
{system_ram_results}
{cuda_results}
{pytorch_results}
{base_model_results}
{tts_version_status}
"""
)
with gr.Tab("🟩 Disks Help"):
gr.Markdown(
f"""
{disk_space_results}
◽ During actual training (Step 2) Finetuning will require approximately 18GB's of free disk space while performing training and will fail or perform badly if there is any less disk space. The majority of this disk space is used temporarily and will be cleared when you reach Step 4 and move & compact the model then delete the training data.
◽ Because lots of data is being copied around, mechanical hard disks will be slow.
"""
)
with gr.Tab("🟪 RAM & VRAM Help"):
gr.Markdown(
f"""
{system_ram_results}
◽ During actual training (Step 2) Finetuning will use around **14GB's** of VRAM. If your GPU doesnt have 14GB's of VRAM:
- **Windows** systems will attempt to extend VRAM into your System RAM, and so should work.
- **Linux** systems can only use the available VRAM, so may fail on 12GB VRAM or smaller GPU's.
◽ For **Windows** users with 12GB or less, if you also have very low or slow System RAM you can expect bad performance or the training to fail.
- 12GB cards may need 2GB System RAM.
- 8GB cards may need 6GB System RAM.
- 6GB cards may need 8GB System RAM.
◽ Its hard to estimate what performance impact this could will have, due to different memory speeds, PCI speeds, GPU speeds etc.
◽ If you have a low VRAM scenario and also are attempting to run on a mechanical hard drive, Finetuning could take ??? amount of time.
◽ On Windows machines, please ensure you have **not** disabled System Memory Fallback for Stable Diffusion link here
"""
)
with gr.Tab("🟨 CUDA & Cublas Help"):
gr.Markdown(
f"""
{cuda_results}
◽ It DOESNT matter what version of CUDA you have installed within Python either, CUDA 11.8, CUDA 12.1 etc. The NVIDIA CUDA Development Toolkit is a completly different and seperate thing from Python/PyTorch.
◽ Finetuning simply wants to access a tool within the CUDA Development Toolkit 11.8 called Cublas64_11.
◽ If you dont have the toolkit installed, the idea is just to install the smallest bit possible and this will not affect or impact other things on your system.
◽ You will need to download the Nvidia Cuda Toolkit 11.8 network install from link here
◽ 1) Run the installer and select Custom Advanced Uncheck everything at the top then expand CUDA, Development > Compiler > and select nvcc then expand Libraries and select CUBLAS.
◽ 2) Back at the top of CUDA, expand Runtime > Libraries and select CUBLAS. Click Next, accept the default path (taking a note of its location) and let the install run.
◽ 3) You should be able to drop to your terminal or command prompt and type nvcc --version and have it report Cuda compilation tools, release 11.8. If it does you are good to go. If it doesn't > Step 4.
◽ 4) Linux users, you can temporarily add these paths on your current terminal window with (you may need to confirm these are correct for your flavour of Linux):
export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} (Add it to your ~/.bashrc if you want this to be permanent)
export LD_LIBRARY_PATH=/usr/local/cuda-11.8/bin
Windows users need the add the following to the PATH environment variable. Start menu and search for "Environment Variables" or "Edit the system environment variables.".
Find and select the "Path" variable, then click on the "Edit...". Click on the "New" button and add:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin.
◽ 5) Once you have these set correctly, you should be able to open a new command prompt/terminal and nvcc --version at the command prompt/terminal, resulting in Cuda compilation tools, release 11.8.
◽ 6) If the nvcc command doesn't work OR it reports a version different from 11.8, finetuning wont work, so you will to double check your environment variables and get them working correctly.
"""
)
with gr.Tab("🟦 Python & PyTorch Help"):
gr.Markdown(
f"""
{pytorch_results}
◽ On the PyTorch version the:
- first few digits are the version of PyTorch e.g. 2.1.0 is PyTorch 2.1.0
- last few digits refer to the CUDA version e.g. cu118 is Cuda 11.8. cu121 is Cuda 12.1.
◽ Ensure you have started your Python envuronment before running finetuning otherwise you will have failures on the above checks.
◽ If PyTorch does not show a CUDA version, then PyTorch will need reinstalling with CUDA. I would suggest running pip cache purge before installing PyTorch again.
◽ It DOESNT matter what version of PyTorch and CUDA you have installed within Python, CUDA 11.8, CUDA 12.1 etc. The NVIDIA CUDA Development Toolkit is a completly different and seperate thing.
◽ Finetuning simply wants to access a tool within the CUDA Development Toolkit called Cublas64_11.
◽ If you dont have the toolkit installed, the idea is just to install the smallest bit possible and this will not affect or impact other things on your system.
"""
)
with gr.Tab("⬛ XTTS Base Model Help"):
gr.Markdown(
f"""
{base_model_results}
◽ If your basemodel is not being detected, please ensure that finetune.py is being run from the AllTalk main folder.
◽ Ensure you have started AllTalk normally at least once. You can start it again and it will download any missing files.
◽ Check that there is an XTTS model within the models folder e.g. /models/xttsv2_2.0.2/
◽ The files required are "model.pth", "vocab.json", "config.json", "dvae.pth", "mel_stats.pth", "speakers_xtts.pth".
"""
)
with gr.Tab("🟥 TTS Version Help"):
gr.Markdown(
f"""
{tts_version_status}
◽ If your TTS version is showing as the incorrect version, please reinstall the Finetuning requirements at the command prompt/terminal.
◽ pip install -r requirements_finetune.txt
"""
)
with gr.Tab("ℹ️ General Finetuning info"):
gr.Markdown(
f"""
### 🟥 Important Note
◽ finetune.py needs to be run from the /alltalk_tts/ folder. Don't move the location of this script.
### 🟦 What you need to run finetuning
◽ An Nvidia GPU.
◽ If you have multiple Nvidia GPU's in your system, please see the Github Help section [Multiple GPU's](https://github.com/erew123/alltalk_tts#performance-and-compatibility-issues).
◽ Some decent quality audio, multiple files if you like. Minimum of 2 minutes and Ive tested up to 20 minutes of audio.
◽ There is no major need to chop down your audio files into small slices as Step 1 will do that for you automatically and prepare the training set. But this can be helpful in cases where:
- Whisper doesnt correctly detect and split down audio files.
- You do not get a lot of "Speaker Reference Audio" files at the end of training.
◽ This process will need access to all your GPU and VRAM, so close any other software that's using your GPU currently.
### 🟨 What do I do from here?
◽ Proceed through Step 1, 2, 3 and onto "What to do next".
### 🟩 Additional Information
◽ Guidance is provided on each step of the process however, if you are after more detailed information please visit:
◽ [AllTalk Github Finetuning](https://github.com/erew123/alltalk_tts#-finetuning-a-model)
◽ [Coqui XTTS Documentation](https://docs.coqui.ai/en/latest/index.html)
"""
)
#######################
#### GRADIO STEP 1 ####
#######################
with gr.Tab("📁 Step 1 - Generating the dataset"):
with gr.Tab("Generate Dataset"):
out_path = gr.Textbox(
label="Output path (where data and checkpoints will be saved):",
value=out_path,
visible=False,
)
# Define directories
this_dir = Path(__file__).parent.resolve()
voice_samples_dir = this_dir / "finetune" / "put-voice-samples-in-here"
training_data_dir = this_dir / "finetune" / "tmp-trn" / "wavs"
metadata_files = [
this_dir / "finetune" / "tmp-trn" / "metadata_eval.csv",
this_dir / "finetune" / "tmp-trn" / "metadata_train.csv",
this_dir / "finetune" / "tmp-trn" / "lang.txt"
]
# Ensure directories exist
voice_samples_dir.mkdir(parents=True, exist_ok=True)
training_data_dir.mkdir(parents=True, exist_ok=True)
def upload_audio(files):
for file in files:
shutil.copy(file.name, voice_samples_dir)
return f"Uploaded {len(files)} files to {voice_samples_dir}"
def delete_existing_audio():
for file in voice_samples_dir.iterdir():
if file.is_file():
file.unlink()
return f"Deleted all files in {voice_samples_dir}"
def delete_existing_training_data():
if training_data_dir.exists():
for file in training_data_dir.iterdir():
if file.is_file():
file.unlink()
for metadata_file in metadata_files:
if metadata_file.exists():
metadata_file.unlink()
return f"Deleted all files in {training_data_dir} and related metadata files"
with gr.Row():
with gr.Column(scale=1):
audio_files_upload = gr.Files(label="Upload Audio Files")
with gr.Column(scale=3):
with gr.Row():
with gr.Column(scale=1):
audio_upload_button = gr.Button("Upload Audio")
delete_audio_button = gr.Button("Delete Existing Audio")
delete_dataset_button = gr.Button("Delete Existing Training Dataset")
with gr.Column(scale=2):
gr.Markdown("""
You can manually copy your audio files to `/finetune/put-voice-samples-in-here/` or use the upload to the left and click "Upload Audio". Once you have uploaded files, you can start creating your dataset.
- If you wish to delete previously uploaded audio files then use 'Delete Existing Audio'.
- If you wish to delete previously generated training datasets, please use 'Delete Existing Training Dataset'.
- If you wish to re-use your previously created training data, just click 'Create Dataset'.
""")
with gr.Row():
output_text = gr.Textbox(label="Audio File Management Result", interactive=False)
# Define actions for buttons
audio_upload_button.click(upload_audio, inputs=audio_files_upload, outputs=output_text)
delete_audio_button.click(delete_existing_audio, outputs=output_text)
delete_dataset_button.click(delete_existing_training_data, outputs=output_text)
with gr.Row():
whisper_model = gr.Dropdown(
label="Whisper Model",
value="large-v2",
choices=[
"large-v3",
"large-v2",
"large",
"medium",
"small"
],
scale=1,
)
lang = gr.Dropdown(
label="Dataset Language",
value="en",
choices=[
"en",
"es",
"fr",
"de",
"it",
"pt",
"pl",
"tr",
"ru",
"nl",
"cs",
"ar",
"zh",
"hu",
"ko",
"ja"
],
scale=1,
)
max_sample_length = gr.Dropdown(
label="Max Audio Length Created by Whisper (seconds)",
value="30",
choices=["10", "15", "20", "25", "30", "35", "40", "45", "50", "55", "60", "65", "70", "75", "80", "85", "90"],
scale=2
)
eval_split_number = gr.Number(
label="Evaluation data Split (the % to use for Evaluation data)",
value=15, # Default value
minimum=5, # Minimum value
maximum=95, # Maximum value
step=1, # Increment step
scale=2,
)
speaker_name_input = gr.Textbox(
label="The name of the speaker/person you are training",
value="personsname",
visible=True,
scale=2,
)
progress_data = gr.Label(
label="Progress:"
)
logs = gr.Textbox(
label="Logs:",
interactive=False,
lines=10,
)
demo.load(read_logs, None, logs, every=1)
prompt_compute_btn = gr.Button(value="Step 1 - Create dataset")
def preprocess_dataset(language, whisper_model, out_path, max_sample_length, 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, max_sample_length=max_sample_length, eval_split_number=eval_split_number, speaker_name_input=speaker_name_input, gradio_progress=progress)
except:
traceback.print_exc()
error = traceback.format_exc()
return f"The data processing was interrupted due an error !! Please check the console to verify the full error message! \n Error summary: {error}", "", ""
clear_gpu_cache()
# if audio total len is less than 2 minutes raise an error
if audio_total_size < 120:
message = "The total duration of the audio file or files you provided, was less than 2 minutes in length. Please add more audio samples."
print("[FINETUNE] ",message)
return message, "", ""
print("[FINETUNE] Dataset Generated. Move to Step 2")
return "Dataset Generated. Move to Step 2", train_meta, eval_meta
with gr.Tab("Generate Dataset Instructions"):
gr.Markdown(
f"""
### 📁 Generating the dataset
### 🟥 Important Note - Windows - "UserWarning: huggingface_hub cache-system uses symlinks."
◽ 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.
◽ 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)
### 🟥 Important Note - Language support.
◽ 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.
◽ 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.
### 🟦 What you need to do
◽ 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)
◽ Place your audio files in {str(audio_folder)}
◽ Your audio samples can be in the format mp3, wav, or flac.
◽ You will need a minimum of 2 minutes of audio in either one or multiple audio files. 5 to 10 minutes of audio would probably be better, allowing for more varied sample data to be generated.
◽ Very small sample files cause errors, so I would recommend that the samples are at least 30 seconds and longer.
◽ FYI Anecdotal evidence suggests that the Whisper 2 model may yield superior results in audio splitting and dataset creation.
### 🟨 What this step is doing
◽ With step one, we are going to be stripping your audio file(s) into smaller files, using Whisper to find spoken words/sentences, compile that into excel sheets of training data, ready for Step 2.
◽ 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).
◽ 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".
◽ If you are using Audio that has multiple speakers/people speaking, you will HAVE to edit the generated CSV files and remove the **OTHER** person out of the training list.
◽ 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.
◽ /alltalk_tts/finetune/tmp-trn/lang.txt Contains a two digit langauage code e.g. `en`, `fr` etc.
◽ /alltalk_tts/finetune/tmp-trn/metadata_train.csv Contains the list of wavs to text that will be used. This is what the model is trained with.
◽ /alltalk_tts/finetune/tmp-trn/metadata_eval.csv Lists the evaluation wav to text, used to generate TTS while training and evaluate the quality of generated TTS to the original sample.
◽ /alltalk_tts/finetune/tmp-trn/wavs/ These are the wavs split out from your original training samples and match the contents of the CSV files.
◽ Whilst you can choose multiple Whisper models, its best only to use the 1 model as each one is about 3GB in size and will download to your local huggingface cache on first-time use.
◽ If and when you have completed training, should you wish to delete this 3GB Whisper model from your system, you are welcome to do so.
### 🟩 How long will this take?
◽ First time, it needs to download the Whisper model which is 3GB. After that a few minutes on an average 3-4 year old system.
"""
)
#######################
#### GRADIO STEP 2 ####
#######################
with gr.Tab("💻 Step 2 - Training"):
with gr.Tab("Training the model"):
with gr.Row():
model_to_train_choices = list(available_models.keys())
model_to_train = gr.Dropdown(
choices=model_to_train_choices,
label="Select the Model to train",
value=model_to_train_choices[0] if model_to_train_choices else None
)
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,
)
progress_train = gr.Label(
label="Progress:"
)
logs_tts_train = gr.Textbox(
label="Logs:",
interactive=False,
lines=10,
)
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, model_to_train, num_epochs, batch_size, grad_acumm, output_path, max_audio_length, progress=gr.Progress()):
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
progress(0, "Initializing training...")
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, model_to_train, output_path=str(output_path), max_audio_length=max_audio_length, progress=gr.Progress())
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("Training the model Instructions"):
gr.Markdown(
f"""
### 💻 Training
### 🟥 Important Note - Language support.
◽ 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.
◽ Ignore 'fatal: not a git repository (or any of the parent directories): .git' as this is a legacy training script issue.
### 🟦 What you need to do
◽ The Train CSV and Eval CSV should already be populated. If not, just go back to Step 1 and click "Create Dataset" again.
◽ The default settings below are the suggested settings for most purposes, however you may choose to alter them depending on your specific use case.
◽ There are no absolute correct settings for training. It will vary based on:
- What you are training (A human voice, A cartoon voice, A new language entirely etc)
- How much audio you have (you may want less or more eval or epochs)
◽ 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.
### 🟨 What this step is doing
◽ 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.
### 🟩 How long will this take?
◽ 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.
◽ Look at your command prompt/terminal window if you want to see what it is doing.
"""
)
with gr.Tab("Info - Grad Accumulation"):
gr.Markdown(
f"""
### 🟩 Grad Accumulation
◽ 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:
◽ Instead of updating the model's parameters after every batch, gradient accumulation enables you to process multiple batches and accumulate their gradients.
◽ 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.
◽ 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.
◽ 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.
◽ However, it's important to note that increasing the gradient accumulation steps does have an impact on the training process:
◽ 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.
◽ 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.
◽ The training progress will be slower in terms of the number of optimization steps per epoch, as the model updates occur less frequently.
◽ 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.
◽ 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"""
### 🟩 Batch Size
◽ Batch size refers to the number of training examples processed in a single iteration (forward and backward pass) before updating the model's parameters.
◽ A larger batch size allows for more efficient computation and utilization of hardware resources, especially when using GPUs.
◽ However, increasing the batch size also increases the memory consumption during training, as more examples need to be stored in memory.
◽ If the batch size is too large, it can lead to out-of-memory (OOM) errors, especially when training on GPUs with limited memory.
◽ On the other hand, using a smaller batch size can result in slower training and may require more iterations to converge.
◽ The optimal batch size depends on various factors, such as the model architecture, available hardware resources, and the specific problem being solved.
◽ It's common to experiment with different batch sizes to find a balance between training speed and memory efficiency.
◽ If you encounter OOM errors, try reducing the batch size until the training can proceed without memory issues.
◽ 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"""
### 🟩 Learning Rate
◽ 1e-6: Very small learning rate, slow but stable learning.
◽ 5e-6: Small learning rate, slow but stable learning.
◽ 1e-5: Moderate learning rate, balanced between stability and convergence speed.
◽ 5e-5: Higher learning rate, faster convergence but potential instability.
◽ 1e-4: High learning rate, faster convergence but increased risk of instability.
◽ 5e-4: Very high learning rate, fast convergence but higher risk of instability.
◽ 1e-3: Extremely high learning rate, very fast convergence but high risk of instability.
The optimal learning rate depends on the model architecture, dataset, and other hyperparameters.
"""
)
with gr.Tab("Info - Epochs"):
gr.Markdown(
f"""
### 🟩 Epochs
◽ An epoch represents a single pass through the entire training dataset during the training process.
◽ In each epoch, the model sees and learns from all the training examples once.
◽ The number of epochs determines how many times the model will iterate over the complete training dataset.
◽ Increasing the number of epochs allows the model to learn more from the data and potentially improve its performance.
◽ 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.
◽ 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.
◽ It's common to use techniques like early stopping or validation monitoring to determine the appropriate number of epochs.
◽ 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.
◽ 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.
◽ 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"""
### 🟩 Max Permitted Audio Size (in seconds)
◽ 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.
◽ It is specified in seconds and represents the longest audio clip that the model can process.
◽ Limiting the audio size helps to control the memory usage and computational requirements of the model.
◽ 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.
◽ Audio files longer than the max permitted size will typically be truncated or split into smaller segments before being fed into the model.
◽ The choice of max permitted audio size depends on the specific requirements of your application and the available hardware resources.
◽ Increasing the max permitted audio size allows the model to handle longer audio clips but also increases the memory consumption and computational burden.
◽ If you encounter memory issues or slow processing times, you may need to reduce the max permitted audio size to find a suitable balance.
◽ 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.
"""
)
#######################
#### GRADIO STEP 3 ####
#######################
with gr.Tab("✅ Step 3 - Testing"):
with gr.Tab("Testing"):
with gr.Row():
with gr.Column() as col1:
xtts_checkpoint = gr.Dropdown(
[str(file) for file in xtts_checkpoint_files],
label="XTTS checkpoint path (best_model.pth):",
value="",
allow_custom_value=True,
)
xtts_config = gr.Dropdown(
[str(file) for file in xtts_config_files],
label="XTTS config path (config.json):",
value="",
allow_custom_value=True,
)
xtts_vocab = gr.Dropdown(
[str(file) for file in xtts_vocab_files],
label="XTTS vocab path (vocab.json):",
value="",
allow_custom_value=True,
)
progress_load = gr.Label(
label="Progress:"
)
load_btn = gr.Button(value="Step 3 - Load Fine-tuned XTTS model")
with gr.Column() as col2:
with gr.Row():
# Gather the voice files
available_speaker_audios = get_available_voices()
# Create Dropdown for speaker reference audio
speaker_reference_audio = gr.Dropdown(
available_speaker_audios,
label="Speaker reference audio:",
value="", # Set the default value if needed
allow_custom_value=True, # Allow custom values
scale=1,
)
with gr.Row():
tts_language = gr.Dropdown(
label="Language",
value="en",
choices=[
"en",
"es",
"fr",
"de",
"it",
"pt",
"pl",
"tr",
"ru",
"nl",
"cs",
"ar",
"zh",
"hu",
"ko",
"ja",
]
)
# Create refresh button
refresh_button = create_refresh_button(
[xtts_checkpoint, xtts_config, xtts_vocab, speaker_reference_audio],
[
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": ""},
],
elem_class="refresh-button-class"
)
tts_text = gr.Textbox(
label="Input Text:",
value="I've just fine tuned a text to speech language model and this is how it sounds. If it doesn't sound right, I will try a different Speaker Reference Audio file.",
lines=5,
)
tts_btn = gr.Button(value="Step 4 - Inference (Generate TTS)")
with gr.Row():
progress_gen = gr.Label(
label="Progress:"
)
with gr.Row():
tts_output_audio = gr.Audio(label="TTS Generated Speech.")
reference_audio = gr.Audio(label="Speaker Reference Audio Sample.")
with gr.Tab("Testing Instructions"):
gr.Markdown(
f"""
### ✅ Testing
### 🟥 Important Note
◽ If you dont believe the dropdown lists are populated correctly, you can use the "Refresh Dropdowns" button to search the finetuning folder path and update the lists. You can also manually edit the path to the listed files.
◽ You can use any config.json or vocab.json, as long as they match the model version you trained on. Only the XTTS checkpoint matters.
◽ Upon successful processing of your speech data, expect to find multiple speaker reference files if your input consisted of over 3 minutes of speech.
◽ Audio clips shorter than 7 seconds are automatically excluded from the reference list, as they generally do not provide sufficient length for effective TTS generation.
◽ Should you find a lower number of reference files than anticipated, it may be due to the initial segmentation performed by Whisper (Step 1), which can occasionally result in clips that are too brief for our criteria or indeed too long. In such cases, consider manually segmenting your audio to give Whisper a better chance at generating training data and repeating the process.
### 🟦 What you need to do
◽ **Click** the **Refresh Dropdowns** button to correctly populate the **Speaker Reference Audio** with all the available WAV samples.
◽ The model is now trained and you are at the testing stage. Hopefully all the dropdowns should be pre-populated now.
◽ You need to Load Fine-tuned XTTS model and then select your Speaker Reference Audio. You can choose various Speaker Reference Audios to see which works best.
◽ All the Speaker Reference Audios in the dropdown are ones that are 8 seconds long or more. You can use one of these later for your voice sample in All Talk, so remember the one you like.
◽ Some Speaker Reference Audios are a bit long and may cause issues generating the TTS, so try different ones.
◽ Once you are happy, move to the next tab for instruction on cleaning out the training data and how to copy your newly trained model over your current model.
### 🟨 What this step is doing
◽ Its loading the finetuned model into memory, loading a voice sample and generating TTS, so that you can test out how well fine tuning worked.
"""
)
with gr.Tab("🔜 What to do next"):
gr.Markdown(
f"""
### 🔜 What to do next
### 🟦 What you need to do
You have a few options below:
◽ **Compact & move model:** This will compress the raw finetuned model and move it, along with any large enough wav files created to the folder name of your choice.
◽ **Delete Generated Training data:** This will delete the finetuned model and any other training data generated during this process. You will more than likely want to do this after you have Compacted & moved the model.
◽ **Delete original voice samples:** This will delete the voice samples you placed in put-voice-samples-in-here (if you no longer have a use for them).
◽ **Note:** Your 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.
### 🟨 Clearing up disk space
◽ If you are not going to train anything again, you can delete the whisper model from inside of your huggingface cache (3GB approx)
◽ **Linux:** ~/.cache/huggingface/hub/(folder-here)
◽ **Windows:** C:\users(your-username)\.cache\huggingface\hub\(folder-here).
"""
)
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=2,
)
# 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():
overwrite_existing = gr.Dropdown(value="Do not overwrite existing files", choices=["Overwrite existing files", "Do not overwrite existing files"], label="File Overwrite Options",)
folder_path = gr.Textbox(label="Enter a new folder name (will be sub the models folder)", lines=1, value="mycustomfolder")
compact_custom_btn = gr.Button(value="Compact and move model to a folder name of your choosing")
with gr.Row():
gr.Textbox(value="This will DELETE your training data and the raw finetuned model from /finetune/tmp-trn", scale=2, show_label=False, interactive=False)
delete_training_btn = gr.Button(value="Delete generated training data")
with gr.Row():
gr.Textbox(value="This will DELETE your original voice samples from /finetune/put-voice-samples-in-here/.", scale=2, show_label=False, interactive=False)
delete_voicesamples_btn = gr.Button(value="Delete original voice samples")
prompt_compute_btn.click(
fn=preprocess_dataset,
inputs=[
lang,
whisper_model,
out_path,
max_sample_length,
eval_split_number,
speaker_name_input,
],
outputs=[
progress_data,
train_csv,
eval_csv,
],
)
train_btn.click(
fn=train_model,
inputs=[
lang,
train_csv,
eval_csv,
learning_rates,
model_to_train,
num_epochs,
batch_size,
grad_acumm,
out_path,
max_audio_length,
],
outputs=[progress_train, xtts_config, xtts_vocab, xtts_checkpoint, speaker_reference_audio],
)
load_btn.click(
fn=load_model,
inputs=[
xtts_checkpoint,
xtts_config,
xtts_vocab
],
outputs=[progress_load],
)
tts_btn.click(
fn=run_tts,
inputs=[
tts_language,
tts_text,
speaker_reference_audio,
],
outputs=[progress_gen, tts_output_audio, reference_audio],
)
compact_custom_btn.click(
fn=compact_custom_model,
inputs=[xtts_checkpoint_copy, folder_path, overwrite_existing],
outputs=[final_progress_data],
)
delete_training_btn.click(
fn=delete_training_data,
outputs=[final_progress_data],
)
delete_voicesamples_btn.click(
fn=delete_voice_sample_contents,
outputs=[final_progress_data],
)
model_to_train.change(basemodel_or_finetunedmodel_choice, model_to_train, None)
demo.queue().launch(
show_api=False,
inbrowser=True,
share=False,
debug=False,
server_port=7052,
server_name="127.0.0.1",
)