deepgeek/CosyVoice
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add stream code

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lyuxiang.lx
2024-07-23 00:02:30 +08:00
parent 2895d99b9a
commit a13411c561
4 changed files with 123 additions and 44 deletions
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cosyvoice/cli/model.py
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@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import numpy as np
class CosyVoiceModel:
@@ -23,6 +25,10 @@ class CosyVoiceModel:
self.llm = llm
self.flow = flow
self.hift = hift
self.stream_win_len = 60
self.stream_hop_len = 50
self.overlap = 4395 # 10 token equals 4395 sample point
self.window = np.hamming(2 * self.overlap)
def load(self, llm_model, flow_model, hift_model):
self.llm.load_state_dict(torch.load(llm_model, map_location=self.device))
@@ -36,25 +42,79 @@ class CosyVoiceModel:
prompt_text=torch.zeros(1, 0, dtype=torch.int32), prompt_text_len=torch.zeros(1, dtype=torch.int32),
llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), llm_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), flow_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
prompt_speech_feat=torch.zeros(1, 0, 80), prompt_speech_feat_len=torch.zeros(1, dtype=torch.int32)):
tts_speech_token = self.llm.inference(text=text.to(self.device),
text_len=text_len.to(self.device),
prompt_text=prompt_text.to(self.device),
prompt_text_len=prompt_text_len.to(self.device),
prompt_speech_token=llm_prompt_speech_token.to(self.device),
prompt_speech_token_len=llm_prompt_speech_token_len.to(self.device),
embedding=llm_embedding.to(self.device),
beam_size=1,
sampling=25,
max_token_text_ratio=30,
min_token_text_ratio=3)
tts_mel = self.flow.inference(token=tts_speech_token,
token_len=torch.tensor([tts_speech_token.size(1)], dtype=torch.int32).to(self.device),
prompt_token=flow_prompt_speech_token.to(self.device),
prompt_token_len=flow_prompt_speech_token_len.to(self.device),
prompt_feat=prompt_speech_feat.to(self.device),
prompt_feat_len=prompt_speech_feat_len.to(self.device),
embedding=flow_embedding.to(self.device))
tts_speech = self.hift.inference(mel=tts_mel).cpu()
torch.cuda.empty_cache()
return {'tts_speech': tts_speech}
prompt_speech_feat=torch.zeros(1, 0, 80), prompt_speech_feat_len=torch.zeros(1, dtype=torch.int32), stream=False):
if stream is True:
tts_speech_token, cache_speech = [], None
for i in self.llm.inference(text=text.to(self.device),
text_len=text_len.to(self.device),
prompt_text=prompt_text.to(self.device),
prompt_text_len=prompt_text_len.to(self.device),
prompt_speech_token=llm_prompt_speech_token.to(self.device),
prompt_speech_token_len=llm_prompt_speech_token_len.to(self.device),
embedding=llm_embedding.to(self.device),
beam_size=1,
sampling=25,
max_token_text_ratio=30,
min_token_text_ratio=3,
stream=stream):
tts_speech_token.append(i)
if len(tts_speech_token) == self.stream_win_len:
this_tts_speech_token = torch.concat(tts_speech_token, dim=1)
this_tts_mel = self.flow.inference(token=this_tts_speech_token,
token_len=torch.tensor([this_tts_speech_token.size(1)], dtype=torch.int32).to(self.device),
prompt_token=flow_prompt_speech_token.to(self.device),
prompt_token_len=flow_prompt_speech_token_len.to(self.device),
prompt_feat=prompt_speech_feat.to(self.device),
prompt_feat_len=prompt_speech_feat_len.to(self.device),
embedding=flow_embedding.to(self.device))
this_tts_speech = self.hift.inference(mel=this_tts_mel).cpu()
# fade in/out if necessary
if cache_speech is not None:
this_tts_speech[:, :self.overlap] = this_tts_speech[:, :self.overlap] * self.window[:self.overlap] + cache_speech * self.window[-self.overlap:]
yield {'tts_speech': this_tts_speech[:, :-self.overlap]}
cache_speech = this_tts_speech[:, -self.overlap:]
tts_speech_token = tts_speech_token[-(self.stream_win_len - self.stream_hop_len):]
# deal with remain tokens
if cache_speech is None or len(tts_speech_token) > self.stream_win_len - self.stream_hop_len:
this_tts_speech_token = torch.concat(tts_speech_token, dim=1)
this_tts_mel = self.flow.inference(token=this_tts_speech_token,
token_len=torch.tensor([this_tts_speech_token.size(1)], dtype=torch.int32).to(self.device),
prompt_token=flow_prompt_speech_token.to(self.device),
prompt_token_len=flow_prompt_speech_token_len.to(self.device),
prompt_feat=prompt_speech_feat.to(self.device),
prompt_feat_len=prompt_speech_feat_len.to(self.device),
embedding=flow_embedding.to(self.device))
this_tts_speech = self.hift.inference(mel=this_tts_mel).cpu()
if cache_speech is not None:
this_tts_speech[:, :self.overlap] = this_tts_speech[:, :self.overlap] * self.window[:self.overlap] + cache_speech * self.window[-self.overlap:]
yield {'tts_speech': this_tts_speech}
else:
assert len(tts_speech_token) == self.stream_win_len - self.stream_hop_len, 'tts_speech_token not equal to {}'.format(self.stream_win_len - self.stream_hop_len)
yield {'tts_speech': cache_speech}
else:
tts_speech_token = []
for i in self.llm.inference(text=text.to(self.device),
text_len=text_len.to(self.device),
prompt_text=prompt_text.to(self.device),
prompt_text_len=prompt_text_len.to(self.device),
prompt_speech_token=llm_prompt_speech_token.to(self.device),
prompt_speech_token_len=llm_prompt_speech_token_len.to(self.device),
embedding=llm_embedding.to(self.device),
beam_size=1,
sampling=25,
max_token_text_ratio=30,
min_token_text_ratio=3,
stream=stream):
tts_speech_token.append(i)
assert len(tts_speech_token) == 1, 'tts_speech_token len should be 1 when stream is {}'.format(stream)
tts_speech_token = torch.concat(tts_speech_token, dim=1)
tts_mel = self.flow.inference(token=tts_speech_token,
token_len=torch.tensor([tts_speech_token.size(1)], dtype=torch.int32).to(self.device),
prompt_token=flow_prompt_speech_token.to(self.device),
prompt_token_len=flow_prompt_speech_token_len.to(self.device),
prompt_feat=prompt_speech_feat.to(self.device),
prompt_feat_len=prompt_speech_feat_len.to(self.device),
embedding=flow_embedding.to(self.device))
tts_speech = self.hift.inference(mel=tts_mel).cpu()
torch.cuda.empty_cache()
yield {'tts_speech': tts_speech}