import argparse import os import sys 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 pandas import glob import json from pathlib import Path from tqdm import tqdm from faster_whisper import WhisperModel from TTS.tts.layers.xtts.tokenizer import multilingual_cleaners # 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' # Define the path to the modeldownload config file file modeldownload_config_file_path = this_dir / "modeldownload.json" # Check if the JSON file exists if modeldownload_config_file_path.exists(): with open(modeldownload_config_file_path, "r") as config_file: settings = json.load(config_file) # Extract settings from the loaded JSON base_path = Path(settings.get("base_path", "")) model_path = Path(settings.get("model_path", "")) base_model_path = Path(settings.get("model_path", "")) files_to_download = settings.get("files_to_download", {}) else: # Default settings if the JSON file doesn't exist or is empty print("[FINETUNE] \033[91mWarning\033[0m modeldownload.json is missing. Please run this script in the /alltalk_tts/ folder") sys.exit(1) ################################################## #### Check to see if a finetuned model exists #### ################################################## # Set the path to the directory trained_model_directory = this_dir / "models" / "trainedmodel" # Check if the directory "trainedmodel" exists finetuned_model = trained_model_directory.exists() # If the directory exists, check for the existence of the required files # If true, this will add a extra option in the Gradio interface for loading Xttsv2 FT if finetuned_model: required_files = ["model.pth", "config.json", "vocab.json", "mel_stats.pth", "dvae.pth"] finetuned_model = all((trained_model_directory / file).exists() for file in required_files) basemodel_or_finetunedmodel = True ##################### #### 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, gradio_progress=progress): audio_files = [os.path.join(audio_folder, file) for file in os.listdir(audio_folder) if file.endswith(('.mp3', '.flac', '.wav'))] buffer=0.2 eval_percentage=0.15 speaker_name="coqui" audio_total_size = 0 os.makedirs(out_path, exist_ok=True) temp_folder = os.path.join(out_path, "temp") # Update with your folder name os.makedirs(temp_folder, exist_ok=True) print("[FINETUNE] \033[94mPart of AllTalk\033[0m https://github.com/erew123/alltalk_tts/") print("[FINETUNE] \033[94mCoqui Public Model License\033[0m") print("[FINETUNE] \033[94mhttps://coqui.ai/cpml.txt\033[0m") print("[FINETUNE] \033[94mStarting Step 1\033[0m - Preparing Audio/Generating the dataset") # Write the target language to lang.txt in the output directory lang_file_path = os.path.join(out_path, "lang.txt") # Check if lang.txt already exists and contains a different language 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: # Only update lang.txt if target language is different from the current 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") # Loading Whisper device = "cuda" if torch.cuda.is_available() else "cpu" print("[FINETUNE] Loading Whisper Model:", whisper_model) asr_model = WhisperModel(whisper_model, device=device, compute_type="float16") 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'] = pandas.read_csv(train_metadata_path, sep="|") print("[FINETUNE] Existing training metadata found and loaded.") if os.path.exists(eval_metadata_path): existing_metadata['eval'] = pandas.read_csv(eval_metadata_path, sep="|") print("[FINETUNE] Existing evaluation metadata found and loaded.") for idx, audio_path in tqdm(enumerate(audio_files)): if isinstance(audio_path, str): audio_file_name_without_ext, _ = os.path.splitext(os.path.basename(audio_path)) # If it's a string, it's already the path to the file audio_path_name = audio_path elif hasattr(audio_path, 'read'): # If it has a 'read' attribute, treat it as a file-like object # and use a temporary file to save its content audio_file_name_without_ext, _ = os.path.splitext(os.path.basename(audio_path.name)) audio_path_name = create_temporary_file(temp_folder) with open(audio_path, 'rb') as original_file: file_content = original_file.read() with open(audio_path_name, 'wb') as temp_file: temp_file.write(file_content) # Create a temporary file path within the new folder temp_audio_path = create_temporary_file(temp_folder) try: if isinstance(audio_path, str): audio_path_name = audio_path elif hasattr(audio_path, 'name'): audio_path_name = audio_path.name else: raise ValueError(f"Unsupported audio_path type: {type(audio_path)}") except Exception as e: print("[FINETUNE] Error reading original file: {e}") # Handle the error or raise it if needed print("[FINETUNE] Current working file:", audio_path_name) try: # Copy the audio content time.sleep(0.5) # Introduce a small delay shutil.copy2(audio_path_name, temp_audio_path) except Exception as e: print("[FINETUNE] Error copying file: {e}") # Handle the error or raise it if needed # Load the temporary audio file 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}_" # Check both training and evaluation metadata for an entry that starts with the file name. 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 break # If we found that we've already processed this file before, continue to the next iteration. if skip_processing: continue wav, sr = torchaudio.load(audio_path) # stereo to mono if needed 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 # added all segments words in a unique list words_list = [] for _, segment in enumerate(segments): words = list(segment.words) words_list.extend(words) # process each word for word_idx, word in enumerate(words_list): if first_word: sentence_start = word.start # If it is the first sentence, add buffer or get the beginning of the file if word_idx == 0: sentence_start = max(sentence_start - buffer, 0) # Add buffer to the sentence start else: # get the previous sentence end previous_word_end = words_list[word_idx - 1].end # add buffer or get the silence middle between the previous sentence and the current one sentence_start = max(sentence_start - buffer, (previous_word_end + sentence_start) / 2) sentence = word.word first_word = False else: sentence += word.word if word.word[-1] in ["!", ".", "?"]: sentence = sentence[1:] # Expand number and abbreviations plus normalization sentence = multilingual_cleaners(sentence, target_language) audio_file_name, _ = os.path.splitext(os.path.basename(audio_path)) audio_file = f"wavs/{audio_file_name}_{str(i).zfill(8)}.wav" # Check for the next word's existence if word_idx + 1 < len(words_list): next_word_start = words_list[word_idx + 1].start else: # If don't have more words it means that it is the last sentence then use the audio len as next word start next_word_start = (wav.shape[0] - 1) / sr # Average the current word end and next word start word_end = min((word.end + next_word_start) / 2, word.end + buffer) absolute_path = os.path.join(out_path, audio_file) 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 short, ignore it (i.e., < 0.33 seconds) if audio.size(-1) >= sr / 3: torchaudio.save( absolute_path, audio, sr ) else: continue metadata["audio_file"].append(audio_file) metadata["text"].append(sentence) metadata["speaker_name"].append(speaker_name) os.remove(temp_audio_path) 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'] audio_total_size = 121 else: existing_train_df = pandas.DataFrame(columns=["audio_file", "text", "speaker_name"]) existing_eval_df = pandas.DataFrame(columns=["audio_file", "text", "speaker_name"]) new_data_df = pandas.DataFrame(metadata) combined_train_df = pandas.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) # deallocate VRAM and RAM del asr_model, final_eval_set, final_training_set, new_data_df, existing_metadata gc.collect() existing_train_df = None existing_eval_df = None print("[FINETUNE] Train CSV:", train_metadata_path) print("[FINETUNE] Eval CSV:", eval_metadata_path) print("[FINETUNE] Audio Total:", audio_total_size) 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, output_path, max_audio_length=255995): # Logging parameters RUN_NAME = "XTTS_FT" PROJECT_NAME = "XTTS_trainer" DASHBOARD_LOGGER = "tensorboard" LOGGER_URI = None # 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") # 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] if basemodel_or_finetunedmodel: # BASE XTTS model checkpoints for fine-tuning. print("[FINETUNE] Starting finetuning on Base Model") TOKENIZER_FILE = str(this_dir / base_path / model_path / "vocab.json") XTTS_CHECKPOINT = str(this_dir / base_path / model_path / "model.pth") XTTS_CONFIG_FILE = str(this_dir / base_path / model_path / "config.json") DVAE_CHECKPOINT = str(this_dir / base_path / model_path / "dvae.pth") MEL_NORM_FILE = str(this_dir / base_path / model_path / "mel_stats.pth") else: # FINETUNED XTTS model checkpoints for fine-tuning. print("[FINETUNE] Starting finetuning on Existing Finetuned Model") TOKENIZER_FILE = str(this_dir / base_path / "trainedmodel" / "vocab.json") XTTS_CHECKPOINT = str(this_dir / base_path / "trainedmodel" / "model.pth") XTTS_CONFIG_FILE = str(this_dir / base_path / "trainedmodel" / "config.json") DVAE_CHECKPOINT = str(this_dir / base_path / "trainedmodel" / "dvae.pth") MEL_NORM_FILE = str(this_dir / base_path / "trainedmodel" / "mel_stats.pth") # 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) # 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=8, 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=5e-06, # 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=[], ) # 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 return XTTS_CONFIG_FILE, XTTS_CHECKPOINT, TOKENIZER_FILE, trainer_out_path, speaker_ref ########################## #### STEP 3 AND OTHER #### ########################## def clear_gpu_cache(): # clear the GPU cache if torch.cuda.is_available(): torch.cuda.empty_cache() XTTS_MODEL = None def load_model(xtts_checkpoint, xtts_config, xtts_vocab): global XTTS_MODEL clear_gpu_cache() if not xtts_checkpoint or not xtts_config or not xtts_vocab: return "You need to run the previous steps or manually set the `XTTS checkpoint path`, `XTTS config path`, and `XTTS vocab path` fields !!" 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) 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_models(directory, extension): """Find files with a specific extension in the given directory.""" return [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)) # Your main directory main_directory = Path(this_dir) / "finetune" / "tmp-trn" # XTTS checkpoint files (*.pth) xtts_checkpoint_files = find_models(main_directory, "pth") # 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 #### ########################## def find_latest_best_model(folder_path): search_path = folder_path / "XTTS_FT-*" / "best_model.pth" files = glob.glob(str(search_path), recursive=True) latest_file = max(files, key=os.path.getctime, default=None) return latest_file def compact_model(): this_dir = Path(__file__).parent.resolve() best_model_path_str = find_latest_best_model(this_dir / "finetune" / "tmp-trn" / "training") # Check if the best model file exists 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) # Attempt to load the model try: checkpoint = torch.load(best_model_path, map_location=torch.device("cpu")) 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) # 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"] 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/trainedmodel/'") return "Model copied to '/models/trainedmodel/'" def compact_lastfinetuned_model(): this_dir = Path(__file__).parent.resolve() best_model_path_str = find_latest_best_model(this_dir / "finetune" / "tmp-trn" / "training") # Check if the best model file exists 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) # Attempt to load the model try: checkpoint = torch.load(best_model_path, map_location=torch.device("cpu")) 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" / "lastfinetuned" # Create the target directory if it doesn't exist target_dir.mkdir(parents=True, exist_ok=True) # 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"] 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/lastfinetuned/'") return "Model copied to '/models/lastfinetuned/'" def compact_legacy_model(): this_dir = Path(__file__).parent.resolve() best_model_path_str = os.path.join(this_dir, "finetune", "best_model.pth") # 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/" # Convert model_path_str to Path best_model_path = Path(best_model_path_str) # Attempt to load the model try: checkpoint = torch.load(best_model_path, map_location=torch.device("cpu")) 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 / "finetune" # Create the target directory if it doesn't exist target_dir.mkdir(parents=True, exist_ok=True) # 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/'" 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 delete_voice_sample_contents(): # Define the folder to be cleared folder_to_clear = Path(this_dir / "finetune" / "put-voice-samples-in-here") # Check if the folder exists before clearing its contents if folder_to_clear.exists() and folder_to_clear.is_dir(): # List all files and subdirectories in the folder for item in os.listdir(folder_to_clear): item_path = folder_to_clear / 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_to_clear} deleted successfully.") return f"Contents of 'put-voice-samples-in-here deleted' successfully." else: print(f"[FINETUNE] Folder {folder_to_clear} does not exist.") return f"Folder 'put-voice-samples-in-here' does not exist." ####################### #### 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_component, refresh_method, refreshed_args, elem_class, interactive=True): """ Copied from https://github.com/AUTOMATIC1111/stable-diffusion-webui """ def refresh(): refresh_method() args = refreshed_args() if callable(refreshed_args) else refreshed_args for k, v in args.items(): setattr(refresh_component, k, v) return gr.update(**(args or {})) refresh_button = gr.Button(refresh_symbol, elem_classes=elem_class, interactive=interactive, scale=0) refresh_button.click( fn=refresh, inputs=[], outputs=[refresh_component] ) return refresh_button 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=theme) as demo: with gr.Tab("Information/Guide"): gr.Markdown( f""" ## Finetuning Information
### 🟥 Important Note ####      - finetune.py needs to be run from the /alltalk_tts/ folder. Don't move the location of this script. ####      - Have you run AllTalk at least once? It needs to have downloaded+updated the voice model, before we can finetune it. ### 🟦 What you need to run finetuning ####      - An Nvidia GPU. Tested on Windows with extended shared VRAM and training used about 16GB's total (which worked on a 12GB card). ####      - If you have multiple Nvidia GPU's in your system, please see this [important note](https://github.com/erew123/alltalk_tts#-i-have-multiple-gpus-and-i-have-problems-running-finetuning). ####      - I have not been able to test this on a GPU with less than 12GB of VRAM, so cannot say if it will work or how that would affect performance. ####      - Version 11.8 of Nvidia cuBLAS and cuDNN (guide below). Only 11.8 of cuBLAS and cuDNN work for this process currently. ####      - Minimum 18GB free disk space (most of it is used temporarily). ####      - 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. Ive been testing with 5 minute long clips. ####      - This process will need access to all your GPU and VRAM, so close any other software that's using your GPU currently. ### 🟨 Setting up cuBLAS and cuDNN Version 11.8 ####      - If you have the Nvidia CUDA Toolkit Version 11.8 installed and can type nvcc --version at the command prompt/terminal and it reports Cuda compilation tools, release 11.8 you should be good to go. ####      - 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 [here](https://developer.nvidia.com/cuda-11-8-0-download-archive) ####          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. """ ) ####################### #### GRADIO STEP 1 #### ####################### with gr.Tab("Step 1 - Preparing Audio/Generating the dataset"): gr.Markdown( f""" ## STEP 1 - Preparing Audio/Generating the dataset
### 🟦 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. Very small sample files cause errors, so I would suggest 30 seconds and longer samples. ####      - When you have completed Steps 1, 2, and 3, you are welcome to delete your samples from "put-voice-samples-in-here".
### 🟨 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 finetuning the model on Step 2. ####      - Whilst you can choose multiple Whisper models, its best only to use the 1 model as each one is about 3GB in size and will download to your local huggingface cache on first-time use. If and when you have completed training, you wish to delete this 3GB model from your system, you are welcome to do so. ### 🟩 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. """ ) out_path = gr.Textbox( label="Output path (where data and checkpoints will be saved):", value=out_path, visible=False, ) with gr.Row(): whisper_model = gr.Dropdown( label="Whisper Model", value="large-v3", choices=[ "large-v3", "large-v2", "large", "medium", "small" ], ) 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" ], ) progress_data = gr.Label( label="Progress:" ) logs = gr.Textbox( label="Logs:", interactive=False, ) 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, progress=gr.Progress(track_tqdm=True)): clear_gpu_cache() test_for_audio_files = [file for file in os.listdir(audio_folder) if any(file.lower().endswith(ext) for ext in ['.wav', '.mp3', '.flac'])] if not test_for_audio_files: return "I cannot find any mp3, wav or flac files in the folder called 'put-voice-samples-in-here'", "", "" else: try: train_meta, eval_meta, audio_total_size = format_audio_list(target_language=language, whisper_model=whisper_model ,out_path=out_path, gradio_progress=progress) 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 ####################### #### GRADIO STEP 2 #### ####################### with gr.Tab("Step 2 - Fine-tuning the XTTS Encoder"): gr.Markdown( f""" ## STEP 2 - Fine-tuning the XTTS Encoder
### 🟦 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 the button again. ####      - If for any reason it crashed, close the app and re-start the finetuning and kick it off again, clicking through setp 1 (it doesnt need to regenerate the data) ####      - The settings below are factory and should be ok.
### 🟨 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. ### 🟩 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.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" ) gr.Markdown( f""" ##### If you have an existing finetuned model in /models/trainedmodel/ you will have an option to select Existing finetuned model, 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, 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 os.system(f"cp {config_path} {exp_path}") os.system(f"cp {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 #### ####################### with gr.Tab("Step 3 - Inference/Testing"): gr.Markdown( f""" ## STEP 3 - Inference/Testing
### 🟥 Important Note ####      - This step will error if you are using TTS version 0.22.0. Please re-run pip install -r requirements_finetune.txt if it errors loading the model. ####      - If you dont see multiple speaker reference files and you used more than 3 minutes of speech, try refreshing the page as it may not have loaded them correctly. ### 🟦 What you need to do ####      - 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.Row(): with gr.Column() as col1: xtts_checkpoint = gr.Dropdown( [str(file) for file in xtts_checkpoint_files], label="XTTS checkpoint path:", value="", allow_custom_value=True, ) xtts_config = gr.Dropdown( [str(file) for file in xtts_config_files], label="XTTS config path:", value="", allow_custom_value=True, ) xtts_vocab = gr.Dropdown( [str(file) for file in xtts_vocab_files], label="XTTS vocab path:", 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, ) # Create refresh button refresh_button = create_refresh_button( speaker_reference_audio, lambda: None, # Specify your refresh logic if needed lambda: {"choices": get_available_voices(), "value": ""}, # Update choices and value "refresh-button", ) 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", ] ) 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=6, ) tts_btn = gr.Button(value="Step 4 - Inference (Generate TTS)") with gr.Column() as col3: progress_gen = gr.Label( label="Progress:" ) tts_output_audio = gr.Audio(label="TTS Generated Audio.") reference_audio = gr.Audio(label="Speaker Reference Audio Used.") with gr.Tab("What to do next"): 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 one of two locations (please read text next to the button).
                 You can use the wav files in that location as sample voices with your model. Models moved to /models/trainedmodel/ will be loadable within Text-gen-webui on the XTTSv2 FT loader. ####          🔹 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). ####          🔹 Compress a legacy finetuned model. For any models that were generated pre the current version of finetuning, you can still compact them down. ####      - Instructions on using the finetuned model in Text-generation-webui and also further tuning this model you just created are on OPTION A - Compact and move model to '/trainedmodel/'. ### 🟨 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). ####      - You can also uninstall the Nvidia CUDA 11.8 Toolkit if you wish and remove your environment variable entries. """ ) final_progress_data = gr.Label( label="Progress:" ) with gr.Row(): compact_btn = gr.Button(value="OPTION A - Compact and move model to '/trainedmodel/'") gr.Markdown( f""" ##### This will compact the model and move it, along with the reference wav's to /models/trainedmodel/ and will OVERWRITE any model in that location. This model will become available to load in Text-gen-webui with the XTTSv2 FT loader. It will also be detected by Finetune as a model that can be further trained (Reload finetune.py). """ ) with gr.Row(): compact_lastfinetuned_btn = gr.Button(value="OPTION B - Compact and move model to '/lastfinetuned/'") gr.Markdown( f""" ##### This will compact the model and move it, along with the reference wav's to /models/lastfinetuned/ and will OVERWRITE any model in that location. This will just leave you a finetuned model that you can do with as you please. It will NOT be available in Text-gen-webui unless you copy it over one of the other model loader locations. """ ) with gr.Row(): delete_training_btn = gr.Button(value="Delete generated training data") gr.Markdown( f""" ##### This will DELETE your training data and the raw finetuned model from /finetune/tmp-trn/. """ ) with gr.Row(): delete_voicesamples_btn = gr.Button(value="Delete original voice samples") gr.Markdown( f""" ##### This will DELETE your original voice samples from /finetune/put-voice-samples-in-here/. """ ) with gr.Row(): compact_legacy_btn = gr.Button(value="Compact a legacy finetuned model") gr.Markdown( f""" ##### This will compact your old finetuned models from 5GB to 1.9GB. Place your 5GB base_model.pth file in /finetune/ (rename your file to base_model.pth if necessary). This will generate you a model.pth file. """ ) prompt_compute_btn.click( fn=preprocess_dataset, inputs=[ lang, whisper_model, out_path, ], outputs=[ progress_data, train_csv, eval_csv, ], ) train_btn.click( fn=train_model, inputs=[ lang, train_csv, eval_csv, 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_btn.click( fn=compact_model, outputs=[final_progress_data], ) compact_lastfinetuned_btn.click( fn=compact_lastfinetuned_model, 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], ) compact_legacy_btn.click( fn=compact_legacy_model, outputs=[final_progress_data], ) model_to_train.change(basemodel_or_finetunedmodel_choice, model_to_train, None) demo.queue().launch( show_api=False, inbrowser=True, share=False, debug=False, server_port=7052, server_name="127.0.0.1", )