deepgeek/CosyVoice
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online feature

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This commit is contained in:
lyuxiang.lx
2026-01-28 15:19:07 +00:00
parent 1822c5c908
commit 66b80dbccb
22 changed files with 133 additions and 116 deletions
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50
tools/make_parquet_list.py
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@@ -29,31 +29,24 @@ def job(utt_list, parquet_file, utt2parquet_file, spk2parquet_file):
for utt in tqdm(utt_list):
data = open(utt2wav[utt], 'rb').read()
data_list.append(data)
wav_list = [utt2wav[utt] for utt in utt_list]
text_list = [utt2text[utt] for utt in utt_list]
spk_list = [utt2spk[utt] for utt in utt_list]
uttembedding_list = [utt2embedding[utt] for utt in utt_list]
spkembedding_list = [spk2embedding[utt2spk[utt]] for utt in utt_list]
speech_token_list = [utt2speech_token.get(utt, []) for utt in utt_list]
if args.dpo:
reject_speech_token_list = [utt2reject_speech_token[utt] for utt in utt_list]
if args.instruct:
instruct_list = [utt2instruct[utt] for utt in utt_list]
# 保存到parquet,utt2parquet_file,spk2parquet_file
df = pd.DataFrame()
df['utt'] = utt_list
df['wav'] = wav_list
df['audio_data'] = data_list
df['text'] = text_list
df['spk'] = spk_list
df['utt_embedding'] = uttembedding_list
df['spk_embedding'] = spkembedding_list
df['speech_token'] = speech_token_list
df['wav'] = [utt2wav[utt] for utt in utt_list]
df['text'] = [utt2text[utt] for utt in utt_list]
df['spk'] = [utt2spk[utt] for utt in utt_list]
if utt2embedding is not None:
df['utt_embedding'] = [utt2embedding[utt] for utt in utt_list]
if spk2embedding is not None:
df['spk_embedding'] = [spk2embedding[utt2spk[utt]] for utt in utt_list]
if utt2speech_token is not None:
df['speech_token'] = [utt2speech_token[utt] for utt in utt_list]
if utt2instruct is not None:
df['instruct'] = [utt2instruct[utt] for utt in utt_list]
if args.dpo:
df['reject_speech_token'] = reject_speech_token_list
if args.instruct:
df['instruct'] = instruct_list
df['reject_speech_token'] = [utt2reject_speech_token.get(utt, None) for utt in utt_list]
df.to_parquet(parquet_file)
with open(utt2parquet_file, 'w') as f:
json.dump({k: parquet_file for k in utt_list}, f, ensure_ascii=False, indent=2)
@@ -72,10 +65,6 @@ if __name__ == "__main__":
type=int,
default=1,
help='num processes for make parquets')
parser.add_argument('--instruct',
action='store_true',
default=False,
help='has instruct file or not')
parser.add_argument('--src_dir',
type=str)
parser.add_argument('--des_dir',
@@ -86,7 +75,7 @@ if __name__ == "__main__":
help='Use Direct Preference Optimization')
args = parser.parse_args()
utt2wav, utt2text, utt2spk, utt2instruct = {}, {}, {}, {}
utt2wav, utt2text, utt2spk = {}, {}, {}
with open('{}/wav.scp'.format(args.src_dir)) as f:
for l in f:
l = l.replace('\n', '').split()
@@ -99,16 +88,19 @@ if __name__ == "__main__":
for l in f:
l = l.replace('\n', '').split()
utt2spk[l[0]] = l[1]
if args.instruct is True:
if os.path.exists('{}/instruct'.format(args.src_dir)):
utt2instruct = {}
with open('{}/instruct'.format(args.src_dir)) as f:
for l in f:
l = l.replace('\n', '').split()
utt2instruct[l[0]] = ' '.join(l[1:])
utt2embedding = torch.load('{}/utt2embedding.pt'.format(args.src_dir))
spk2embedding = torch.load('{}/spk2embedding.pt'.format(args.src_dir))
utt2speech_token = torch.load('{}/utt2speech_token.pt'.format(args.src_dir))
else:
utt2instruct = None
utt2embedding = torch.load('{}/utt2embedding.pt'.format(args.src_dir)) if os.path.exists('{}/utt2embedding.pt'.format(args.src_dir)) else None
spk2embedding = torch.load('{}/spk2embedding.pt'.format(args.src_dir)) if os.path.exists('{}/spk2embedding.pt'.format(args.src_dir)) else None
utt2speech_token = torch.load('{}/utt2speech_token.pt'.format(args.src_dir)) if os.path.exists('{}/utt2speech_token.pt'.format(args.src_dir)) else None
if args.dpo:
utt2reject_speech_token = torch.load('{}_reject/utt2speech_token.pt'.format(args.src_dir))
utt2reject_speech_token = torch.load('{}_reject/utt2speech_token.pt'.format(args.src_dir)) if os.path.exists('{}_reject/utt2speech_token.pt'.format(args.src_dir)) else {}
utts = list(utt2wav.keys())
# Using process pool to speedup