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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51
cosyvoice/cli/cosyvoice.py
View File

@@ -12,11 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
import time
from hyperpyyaml import load_hyperpyyaml
from modelscope import snapshot_download
from cosyvoice.cli.frontend import CosyVoiceFrontEnd
from cosyvoice.cli.model import CosyVoiceModel
from cosyvoice.utils.file_utils import logging
class CosyVoice:
@@ -44,40 +45,48 @@ class CosyVoice:
spks = list(self.frontend.spk2info.keys())
return spks
def inference_sft(self, tts_text, spk_id):
tts_speeches = []
def inference_sft(self, tts_text, spk_id, stream=False):
start_time = time.time()
for i in self.frontend.text_normalize(tts_text, split=True):
model_input = self.frontend.frontend_sft(i, spk_id)
model_output = self.model.inference(**model_input)
tts_speeches.append(model_output['tts_speech'])
return {'tts_speech': torch.concat(tts_speeches, dim=1)}
for model_output in self.model.inference(**model_input, stream=stream):
speech_len = model_output['tts_speech'].shape[1] / 22050
logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
yield model_output
start_time = time.time()
def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k):
def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=False):
start_time = time.time()
prompt_text = self.frontend.text_normalize(prompt_text, split=False)
tts_speeches = []
for i in self.frontend.text_normalize(tts_text, split=True):
model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k)
model_output = self.model.inference(**model_input)
tts_speeches.append(model_output['tts_speech'])
return {'tts_speech': torch.concat(tts_speeches, dim=1)}
for model_output in self.model.inference(**model_input, stream=stream):
speech_len = model_output['tts_speech'].shape[1] / 22050
logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
yield model_output
start_time = time.time()
def inference_cross_lingual(self, tts_text, prompt_speech_16k):
def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False):
if self.frontend.instruct is True:
raise ValueError('{} do not support cross_lingual inference'.format(self.model_dir))
tts_speeches = []
start_time = time.time()
for i in self.frontend.text_normalize(tts_text, split=True):
model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k)
model_output = self.model.inference(**model_input)
tts_speeches.append(model_output['tts_speech'])
return {'tts_speech': torch.concat(tts_speeches, dim=1)}
for model_output in self.model.inference(**model_input, stream=stream):
speech_len = model_output['tts_speech'].shape[1] / 22050
logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
yield model_output
start_time = time.time()
def inference_instruct(self, tts_text, spk_id, instruct_text):
def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False):
if self.frontend.instruct is False:
raise ValueError('{} do not support instruct inference'.format(self.model_dir))
start_time = time.time()
instruct_text = self.frontend.text_normalize(instruct_text, split=False)
tts_speeches = []
for i in self.frontend.text_normalize(tts_text, split=True):
model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
model_output = self.model.inference(**model_input)
tts_speeches.append(model_output['tts_speech'])
return {'tts_speech': torch.concat(tts_speeches, dim=1)}
for model_output in self.model.inference(**model_input, stream=stream):
speech_len = model_output['tts_speech'].shape[1] / 22050
logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
yield model_output
start_time = time.time()

104
cosyvoice/cli/model.py
View File

@@ -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}

8
cosyvoice/llm/llm.py
View File

@@ -158,6 +158,7 @@ class TransformerLM(torch.nn.Module):
sampling: int = 25,
max_token_text_ratio: float = 20,
min_token_text_ratio: float = 2,
stream: bool = False,
) -> torch.Tensor:
device = text.device
text = torch.concat([prompt_text, text], dim=1)
@@ -199,8 +200,13 @@ class TransformerLM(torch.nn.Module):
top_ids = self.sampling_ids(logp.squeeze(dim=0), sampling, beam_size, ignore_eos=True if i < min_len else False).item()
if top_ids == self.speech_token_size:
break
# in stream mode, yield token one by one
if stream is True:
yield torch.tensor([[top_ids]], dtype=torch.int64, device=device)
out_tokens.append(top_ids)
offset += lm_input.size(1)
lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)
return torch.tensor([out_tokens], dtype=torch.int64, device=device)
# in non-stream mode, yield all token
if stream is False:
yield torch.tensor([out_tokens], dtype=torch.int64, device=device)

4
cosyvoice/utils/file_utils.py
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@@ -15,6 +15,10 @@
import json
import torchaudio
import logging
logging.getLogger('matplotlib').setLevel(logging.WARNING)
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s')
def read_lists(list_file):