File size: 3,230 Bytes
cb35c36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | 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() |