deepgeek/CosyVoice
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update dpo

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lyuxiang.lx
2025-06-13 16:14:05 +08:00
parent cc234bd322
commit 63856565f3
23 changed files with 345 additions and 2024 deletions
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39
cosyvoice/dataset/processor.py
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@@ -43,8 +43,6 @@ def parquet_opener(data, mode='train', tts_data={}):
for df in pq.ParquetFile(url).iter_batches(batch_size=64):
df = df.to_pandas()
for i in range(len(df)):
if mode == 'inference' and df.loc[i, 'utt'] not in tts_data:
continue
sample.update(dict(df.loc[i]))
if mode == 'train':
# NOTE do not return sample directly, must initialize a new dict
@@ -100,6 +98,8 @@ def filter(data,
continue
if len(sample['speech_token']) == 0:
continue
if 'reject_speech_token' in sample and len(sample['reject_speech_token']) == 0:
continue
if num_frames != 0:
if len(sample['text_token']) / num_frames < min_output_input_ratio:
continue
@@ -242,8 +242,6 @@ def tokenize(data, get_tokenizer, allowed_special, mode='train'):
for sample in data:
assert 'text' in sample
sample['text_token'] = tokenizer.encode(sample['text'], allowed_special=allowed_special)
if mode == 'inference':
sample['tts_text_token'] = tokenizer.encode(sample['tts_text'], allowed_special=allowed_special)
yield sample
@@ -351,18 +349,15 @@ def dynamic_batch(data, max_frames_in_batch=12000, mode='train'):
def batch(data, batch_type='static', batch_size=16, max_frames_in_batch=12000, mode='train'):
""" Wrapper for static/dynamic batch
"""
if mode == 'inference':
return static_batch(data, 1)
if batch_type == 'static':
return static_batch(data, batch_size)
elif batch_type == 'dynamic':
return dynamic_batch(data, max_frames_in_batch)
else:
if batch_type == 'static':
return static_batch(data, batch_size)
elif batch_type == 'dynamic':
return dynamic_batch(data, max_frames_in_batch)
else:
logging.fatal('Unsupported batch type {}'.format(batch_type))
logging.fatal('Unsupported batch type {}'.format(batch_type))
def padding(data, use_spk_embedding, mode='train', gan=False):
def padding(data, use_spk_embedding, mode='train', gan=False, dpo=False):
""" Padding the data into training data
Args:
@@ -424,16 +419,14 @@ def padding(data, use_spk_embedding, mode='train', gan=False):
# only gan train needs speech, delete it to save memory
del batch["speech"]
del batch["speech_len"]
if mode == 'inference':
tts_text = [sample[i]['tts_text'] for i in order]
tts_index = [sample[i]['tts_index'] for i in order]
tts_text_token = [torch.tensor(sample[i]['tts_text_token']) for i in order]
tts_text_token_len = torch.tensor([i.size(0) for i in tts_text_token], dtype=torch.int32)
tts_text_token = pad_sequence(tts_text_token, batch_first=True, padding_value=-1)
batch.update({'tts_text': tts_text,
'tts_index': tts_index,
'tts_text_token': tts_text_token,
'tts_text_token_len': tts_text_token_len})
if dpo is True:
reject_speech_token = [torch.tensor(sample[i]['reject_speech_token']) for i in order]
reject_speech_token_len = torch.tensor([i.size(0) for i in reject_speech_token], dtype=torch.int32)
reject_speech_token = pad_sequence(reject_speech_token,
batch_first=True,
padding_value=0)
batch['reject_speech_token'] = reject_speech_token
batch['reject_speech_token_len'] = reject_speech_token_len
if use_spk_embedding is True:
batch["embedding"] = batch["spk_embedding"]
else: