deepgeek/CosyVoice
2
0
Fork 0
mirror of https://github.com/FunAudioLLM/CosyVoice.git synced 2026-02-04 17:39:25 +08:00
Code Issues Packages Projects Releases Wiki Activity

Enhance CosyVoice with CUDA stream management and estimator handling

Browse Source 
- Introduced a queue-based system for managing CUDA streams to improve inference performance.
- Updated inference methods to utilize CUDA streams for asynchronous processing.
- Added an EstimatorWrapper class to manage TensorRT estimators, allowing for efficient execution context handling.
- Modified model loading functions to support estimator count configuration.
- Improved logging and performance tracking during inference operations.
This commit is contained in:
禾息
2025-04-16 11:16:28 +08:00
parent 96950745a6
commit 62e04e8856
3 changed files with 207 additions and 113 deletions
Download Patch File Download Diff File Expand all files Collapse all files

43
cosyvoice/cli/model.py
View File

@@ -22,7 +22,7 @@ from contextlib import nullcontext
import uuid
from cosyvoice.utils.common import fade_in_out
from cosyvoice.utils.file_utils import convert_onnx_to_trt
from cosyvoice.flow.flow_matching import EstimatorWrapper
class CosyVoiceModel:
@@ -84,7 +84,7 @@ class CosyVoiceModel:
flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
self.flow.encoder = flow_encoder
def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, fp16):
def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, fp16, estimator_count=1):
assert torch.cuda.is_available(), 'tensorrt only supports gpu!'
if not os.path.exists(flow_decoder_estimator_model):
convert_onnx_to_trt(flow_decoder_estimator_model, flow_decoder_onnx_model, fp16)
@@ -96,7 +96,7 @@ class CosyVoiceModel:
self.flow.decoder.estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read())
if self.flow.decoder.estimator_engine is None:
raise ValueError('failed to load trt {}'.format(flow_decoder_estimator_model))
self.flow.decoder.estimator = self.flow.decoder.estimator_engine.create_execution_context()
self.flow.decoder.estimator = EstimatorWrapper(self.flow.decoder.estimator_engine, estimator_count=estimator_count)
def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
with self.llm_context:
@@ -122,13 +122,13 @@ class CosyVoiceModel:
def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
tts_mel, flow_cache = self.flow.inference(token=token.to(self.device),
token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
prompt_token=prompt_token.to(self.device),
prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
prompt_feat=prompt_feat.to(self.device),
prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
embedding=embedding.to(self.device),
flow_cache=self.flow_cache_dict[uuid])
token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
prompt_token=prompt_token.to(self.device),
prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
prompt_feat=prompt_feat.to(self.device),
prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
embedding=embedding.to(self.device),
flow_cache=self.flow_cache_dict[uuid])
self.flow_cache_dict[uuid] = flow_cache
# mel overlap fade in out
@@ -148,8 +148,8 @@ class CosyVoiceModel:
if self.hift_cache_dict[uuid] is not None:
tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
'source': tts_source[:, :, -self.source_cache_len:],
'speech': tts_speech[:, -self.source_cache_len:]}
'source': tts_source[:, :, -self.source_cache_len:],
'speech': tts_speech[:, -self.source_cache_len:]}
tts_speech = tts_speech[:, :-self.source_cache_len]
else:
if speed != 1.0:
@@ -319,14 +319,15 @@ class CosyVoice2Model(CosyVoiceModel):
self.flow.encoder = flow_encoder
def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, token_offset, finalize=False, speed=1.0):
tts_mel, _ = self.flow.inference(token=token.to(self.device),
token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
prompt_token=prompt_token.to(self.device),
prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
prompt_feat=prompt_feat.to(self.device),
prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
embedding=embedding.to(self.device),
finalize=finalize)
token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
prompt_token=prompt_token.to(self.device),
prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
prompt_feat=prompt_feat.to(self.device),
prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
embedding=embedding.to(self.device),
finalize=finalize)
tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:]
# append hift cache
if self.hift_cache_dict[uuid] is not None:
@@ -340,8 +341,8 @@ class CosyVoice2Model(CosyVoiceModel):
if self.hift_cache_dict[uuid] is not None:
tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
'source': tts_source[:, :, -self.source_cache_len:],
'speech': tts_speech[:, -self.source_cache_len:]}
'source': tts_source[:, :, -self.source_cache_len:],
'speech': tts_speech[:, -self.source_cache_len:]}
tts_speech = tts_speech[:, :-self.source_cache_len]
else:
if speed != 1.0: