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# Add this to your processor.py file or create a new file
import logging
import json
import torch
import glob
import os
from pathlib import Path
def individual_file_opener(data, mode='train', tts_data={}):
"""Load data from individual files instead of parquet
Args:
data: Iterable[{src}] where src is either:
- Path to index JSON file
- Directory path containing wav files
mode: 'train' or 'test'
tts_data: Dict for TTS mode
Yields:
Dict with all required fields for training
"""
for sample in data:
src = sample['src']
# Check if src is a JSON index file or a directory
if src.endswith('.json'):
# Load from index file
with open(src, 'r') as f:
index_data = json.load(f)
file_list = index_data.get('data', [])
else:
# Find all wav files in directory
wav_files = glob.glob(os.path.join(src, '*/*/*wav'))
file_list = []
for wav_path in wav_files:
# Check if all required files exist
txt_path = wav_path.replace('.wav', '.normalized.txt')
embedding_path = wav_path.replace('.wav', '_embedding.pt')
token_path = wav_path.replace('.wav', '_tokens.pt')
if not all(os.path.exists(p) for p in [txt_path, embedding_path, token_path]):
logging.warning(f'Missing files for {wav_path}, skipping')
continue
# Extract metadata
utt = os.path.basename(wav_path).replace('.wav', '')
spk = utt.split('_')[0]
file_list.append({
'utt': utt,
'spk': spk,
'wav': wav_path,
'text_path': txt_path,
'embedding_path': embedding_path,
'token_path': token_path,
'spk_embedding_path': os.path.join(os.path.dirname(src), f"spk_embeddings/{spk}_embedding.pt")
})
# Process each file
for file_info in file_list:
try:
# Read audio data
with open(file_info['wav'], 'rb') as f:
audio_data = f.read()
# Read text
with open(file_info['text_path'], 'r') as f:
text = ''.join(l.strip() for l in f.readlines())
# Load embeddings
utt_embedding = torch.load(file_info['embedding_path']).tolist()
speech_token = torch.load(file_info['token_path'])
# Load speaker embedding
if os.path.exists(file_info['spk_embedding_path']):
spk_embedding = torch.load(file_info['spk_embedding_path']).tolist()
else:
logging.warning(f"Speaker embedding not found: {file_info['spk_embedding_path']}")
spk_embedding = utt_embedding # Fallback to utterance embedding
# Build sample dict
sample_dict = {
'utt': file_info['utt'],
'spk': file_info['spk'],
'audio_data': audio_data,
'text': text,
'text_token': [], # Will be filled by tokenize processor
'utt_embedding': utt_embedding,
'spk_embedding': spk_embedding,
'speech_token': speech_token,
'wav': file_info['wav'], # Keep original path for reference
}
# Merge with original sample data
sample_dict.update(sample)
if mode == 'train':
yield sample_dict
else:
# For TTS mode
if file_info['utt'] in tts_data:
for index, tts_text in enumerate(tts_data[file_info['utt']]):
yield {**sample_dict, 'tts_index': index, 'tts_text': tts_text}
else:
yield sample_dict
except Exception as ex:
logging.warning(f'Failed to process {file_info["wav"]}: {ex}') |