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import os
import glob
import torch
import torchaudio
from tqdm import tqdm
from src.chatterbox_.tts import ChatterboxTTS, punc_norm
from src.chatterbox_.models.s3tokenizer import S3_SR
from src.utils import setup_logger
logger = setup_logger(__name__)
def preprocess_dataset_file_based(config, tts_engine: ChatterboxTTS):
"""
Reads .wav and .txt file pairs in a folder, processes them, and saves them as .pt.
Structure:
ID.wav (Audio)
ID.txt (Text)
"""
os.makedirs(config.preprocessed_dir, exist_ok=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tts_engine.ve.to(device)
tts_engine.s3gen.to(device)
tts_engine.ve.eval()
tts_engine.s3gen.eval()
search_path = os.path.join(config.wav_dir, "*.wav")
wav_files = glob.glob(search_path)
if len(wav_files) == 0:
logger.error(f"ERROR: No .wav files found in folder '{config.wav_dir}'!")
return
logger.info(f"Processing dataset... Found audio file: {len(wav_files)}")
success_count = 0
for wav_path in tqdm(wav_files, desc="Preprocessing"):
try:
filename = os.path.basename(wav_path)
file_id = os.path.splitext(filename)[0]
txt_path = os.path.join(config.wav_dir, f"{file_id}.txt")
if not os.path.exists(txt_path):
logger.warning(f"Text file not found, skipping: {file_id}")
continue
with open(txt_path, "r", encoding="utf-8") as f:
raw_text = f.read().strip()
if not raw_text:
continue
wav, sr = torchaudio.load(wav_path)
if wav.shape[0] > 1:
wav = wav.mean(dim=0, keepdim=True)
if sr != S3_SR:
resampler = torchaudio.transforms.Resample(sr, S3_SR)
wav = resampler(wav)
wav = wav.to(device)
with torch.no_grad():
wav_np = wav.cpu().squeeze().numpy()
spk_emb_np = tts_engine.ve.embeds_from_wavs([wav_np], sample_rate=S3_SR)
speaker_emb = torch.from_numpy(spk_emb_np[0]).cpu()
s_tokens, _ = tts_engine.s3gen.tokenizer(wav.unsqueeze(0))
speech_tokens = s_tokens.squeeze().cpu()
prompt_samples = int(config.prompt_duration * S3_SR)
if wav.shape[1] < prompt_samples:
prompt_wav = torch.nn.functional.pad(wav, (0, prompt_samples - wav.shape[1]))
else:
prompt_wav = wav[:, :prompt_samples]
p_tokens, _ = tts_engine.s3gen.tokenizer(prompt_wav.unsqueeze(0))
prompt_tokens = p_tokens.squeeze().cpu()
clean_text = punc_norm(raw_text)
# Tokenizer
if config.is_turbo:
token_output = tts_engine.tokenizer(clean_text, return_tensors="pt")
text_tokens = token_output.input_ids[0]
else:
text_tokens = tts_engine.tokenizer.text_to_tokens(clean_text).squeeze(0).cpu()
# --- 5. SAVING ---
# We keep the file name: ID.pt
save_path = os.path.join(config.preprocessed_dir, f"{file_id}.pt")
torch.save({
"speech_tokens": speech_tokens,
"speaker_emb": speaker_emb,
"prompt_tokens": prompt_tokens,
"text_tokens": text_tokens,
}, save_path)
success_count += 1
except Exception as e:
logger.error(f"Error ({filename}): {e}")
continue
logger.info(f"Preprocessing completed! Success: {success_count}/{len(wav_files)}")
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