deepgeek/CosyVoice
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add llm bistream

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lyuxiang.lx
2025-01-23 10:12:06 +08:00
parent 0b75c3a03f
commit 07e477519b
10 changed files with 163 additions and 39 deletions
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3
cosyvoice/cli/cosyvoice.py
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@@ -13,6 +13,7 @@
# limitations under the License.
import os
import time
from typing import Generator
from tqdm import tqdm
from hyperpyyaml import load_hyperpyyaml
from modelscope import snapshot_download
@@ -76,7 +77,7 @@ class CosyVoice:
def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend)
for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
if len(i) < 0.5 * len(prompt_text):
if (not isinstance(i, Generator)) and len(i) < 0.5 * len(prompt_text):
logging.warning('synthesis text {} too short than prompt text {}, this may lead to bad performance'.format(i, prompt_text))
model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k, self.sample_rate)
start_time = time.time()

24
cosyvoice/cli/frontend.py
View File

@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import Generator
import json
import onnxruntime
import torch
@@ -31,6 +32,7 @@ except ImportError:
from tn.chinese.normalizer import Normalizer as ZhNormalizer
from tn.english.normalizer import Normalizer as EnNormalizer
use_ttsfrd = False
from cosyvoice.utils.file_utils import logging
from cosyvoice.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph, is_only_punctuation
@@ -71,10 +73,21 @@ class CosyVoiceFrontEnd:
self.inflect_parser = inflect.engine()
def _extract_text_token(self, text):
text_token = self.tokenizer.encode(text, allowed_special=self.allowed_special)
text_token = torch.tensor([text_token], dtype=torch.int32).to(self.device)
text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.int32).to(self.device)
return text_token, text_token_len
if isinstance(text, Generator):
logging.info('get tts_text generator, will return _extract_text_token_generator!')
# NOTE add a dummy text_token_len for compatibility
return self._extract_text_token_generator(text), torch.tensor([0], dtype=torch.int32).to(self.device)
else:
text_token = self.tokenizer.encode(text, allowed_special=self.allowed_special)
text_token = torch.tensor([text_token], dtype=torch.int32).to(self.device)
text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.int32).to(self.device)
return text_token, text_token_len
def _extract_text_token_generator(self, text_generator):
for text in text_generator:
text_token, _ = self._extract_text_token(text)
for i in range(text_token.shape[1]):
yield text_token[:, i: i + 1]
def _extract_speech_token(self, speech):
assert speech.shape[1] / 16000 <= 30, 'do not support extract speech token for audio longer than 30s'
@@ -106,6 +119,9 @@ class CosyVoiceFrontEnd:
return speech_feat, speech_feat_len
def text_normalize(self, text, split=True, text_frontend=True):
if isinstance(text, Generator):
logging.info('get tts_text generator, will skip text_normalize!')
return [text]
if text_frontend is False:
return [text] if split is True else text
text = text.strip()

27
cosyvoice/cli/model.py
View File

@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Generator
import torch
import numpy as np
import threading
@@ -99,14 +100,24 @@ class CosyVoiceModel:
def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
with self.llm_context:
for i in self.llm.inference(text=text.to(self.device),
text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
prompt_text=prompt_text.to(self.device),
prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
prompt_speech_token=llm_prompt_speech_token.to(self.device),
prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
embedding=llm_embedding.to(self.device)):
self.tts_speech_token_dict[uuid].append(i)
if isinstance(text, Generator):
assert isinstance(self, CosyVoice2Model), 'streaming input text is only implemented for CosyVoice2!'
for i in self.llm.inference_bistream(text=text,
prompt_text=prompt_text.to(self.device),
prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
prompt_speech_token=llm_prompt_speech_token.to(self.device),
prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
embedding=llm_embedding.to(self.device)):
self.tts_speech_token_dict[uuid].append(i)
else:
for i in self.llm.inference(text=text.to(self.device),
text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
prompt_text=prompt_text.to(self.device),
prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
prompt_speech_token=llm_prompt_speech_token.to(self.device),
prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
embedding=llm_embedding.to(self.device)):
self.tts_speech_token_dict[uuid].append(i)
self.llm_end_dict[uuid] = True
def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):