| import os |
| import torch |
| import torchaudio |
| from tqdm import tqdm |
| from chatterbox.tts_turbo import ChatterboxTurboTTS |
|
|
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" |
| BASE_DIR = "/home/cloud/StyleTTS2-fine-tuning" |
| OUTPUT_DIR = os.path.join(BASE_DIR, "Data") |
|
|
| REFERENCE_AUDIO_PATH = os.path.join(OUTPUT_DIR, "reference_wavs/british_accent_audio.wav") |
| INPUT_TEXT_FILE = os.path.join(OUTPUT_DIR, "source_text_final.txt") |
| TRAIN_LIST_PATH = os.path.join(OUTPUT_DIR, "train_list_new.txt") |
| WAVS_DIR = os.path.join(OUTPUT_DIR, "wavs") |
| TARGET_SAMPLE_RATE = 24000 |
|
|
| os.makedirs(WAVS_DIR, exist_ok=True) |
|
|
| model = ChatterboxTurboTTS.from_pretrained(device=DEVICE) |
|
|
| def get_sentences(text_path): |
| if not os.path.exists(text_path): |
| return [] |
| |
| with open(text_path, 'r', encoding='utf-8') as f: |
| lines = f.readlines() |
| |
| valid_sentences = [] |
| for line in lines: |
| cleaned = line.strip() |
| if cleaned: |
| valid_sentences.append(cleaned) |
| |
| return valid_sentences |
|
|
| def get_completed_indices(): |
| if not os.path.exists(TRAIN_LIST_PATH): |
| return set() |
| |
| completed = set() |
| with open(TRAIN_LIST_PATH, "r", encoding="utf-8") as f: |
| for line in f: |
| parts = line.strip().split("|") |
| if parts and len(parts) >= 1: |
| filename = parts[0] |
| try: |
| number_part = filename.split("_")[1].split(".")[0] |
| completed.add(int(number_part)) |
| except: |
| continue |
| return completed |
|
|
| def generate_dataset(): |
| sentences = get_sentences(INPUT_TEXT_FILE) |
| completed_indices = get_completed_indices() |
| |
| print(f"Total sentences: {len(sentences)}") |
| print(f"Already done: {len(completed_indices)}") |
| |
| resampler = None |
| if model.sr != TARGET_SAMPLE_RATE: |
| resampler = torchaudio.transforms.Resample(orig_freq=model.sr, new_freq=TARGET_SAMPLE_RATE).to(DEVICE) |
|
|
| for i, sentence in enumerate(tqdm(sentences)): |
| if (i + 1) in completed_indices: |
| continue |
|
|
| filename = f"file_{i+1:04d}.wav" |
| filepath = os.path.join(WAVS_DIR, filename) |
| print(f"Generating {filename}...") |
| try: |
| with torch.inference_mode(): |
| wav_tensor = model.generate( |
| sentence, |
| audio_prompt_path=REFERENCE_AUDIO_PATH |
| ) |
|
|
| if wav_tensor.dim() == 1: |
| wav_tensor = wav_tensor.unsqueeze(0) |
|
|
| if wav_tensor.shape[0] > 1: |
| wav_tensor = wav_tensor.mean(dim=0, keepdim=True) |
|
|
| wav_tensor = wav_tensor.cpu() |
|
|
| if resampler: |
| wav_tensor = resampler(wav_tensor) |
|
|
| torchaudio.save(filepath, wav_tensor, TARGET_SAMPLE_RATE) |
|
|
| with open(TRAIN_LIST_PATH, "a", encoding="utf-8") as f: |
| f.write(f"{filename}|{sentence}|0\n") |
|
|
| del wav_tensor |
| torch.cuda.empty_cache() |
|
|
| if (i + 1) % 50 == 0: |
| torch.cuda.synchronize() |
|
|
| except Exception as e: |
| print(f"Error at sample {i+1}: {e}") |
| continue |
|
|
|
|
| if __name__ == "__main__": |
| generate_dataset() |
