deepgeek/CosyVoice
2
0
Fork 0
mirror of https://github.com/FunAudioLLM/CosyVoice.git synced 2026-02-05 18:09:24 +08:00
Code Issues Packages Projects Releases Wiki Activity

add prompt audio cache

Browse Source
This commit is contained in:
yuekaiz
2025-09-05 13:54:39 +08:00
parent 86e7c2d731
commit 6971536358
7 changed files with 112 additions and 53 deletions
Download Patch File Download Diff File Expand all files Collapse all files

70
runtime/triton_trtllm/model_repo/cosyvoice2/1/model.py
View File

@@ -43,6 +43,7 @@ import torchaudio
from matcha.utils.audio import mel_spectrogram
ORIGINAL_VOCAB_SIZE = 151663
torch.set_num_threads(1)
@@ -81,6 +82,12 @@ class TritonPythonModel:
self.flow_pre_lookahead_len = 3
self.token_hop_len = 15
spk_info_path = os.path.join(model_params["model_dir"], "spk2info.pt")
if not os.path.exists(spk_info_path):
raise ValueError(f"spk2info.pt not found in {model_params['model_dir']}")
spk_info = torch.load(spk_info_path, map_location="cpu", weights_only=False)
self.default_spk_info = spk_info["001"]
def forward_llm(self, input_ids):
"""
Prepares the response from the language model based on the provided
@@ -220,11 +227,11 @@ class TritonPythonModel:
def forward_token2wav(
self,
prompt_speech_tokens: torch.Tensor,
prompt_speech_feat: torch.Tensor,
prompt_spk_embedding: torch.Tensor,
target_speech_tokens: torch.Tensor,
request_id: str,
prompt_speech_tokens: torch.Tensor = None,
prompt_speech_feat: torch.Tensor = None,
prompt_spk_embedding: torch.Tensor = None,
token_offset: int = None,
finalize: bool = None) -> torch.Tensor:
"""Forward pass through the vocoder component.
@@ -238,12 +245,9 @@ class TritonPythonModel:
Returns:
Generated waveform tensor
"""
prompt_speech_tokens_tensor = pb_utils.Tensor.from_dlpack("prompt_speech_tokens", to_dlpack(prompt_speech_tokens))
prompt_speech_feat_tensor = pb_utils.Tensor.from_dlpack("prompt_speech_feat", to_dlpack(prompt_speech_feat))
prompt_spk_embedding_tensor = pb_utils.Tensor.from_dlpack("prompt_spk_embedding", to_dlpack(prompt_spk_embedding))
target_speech_tokens_tensor = pb_utils.Tensor.from_dlpack("target_speech_tokens", to_dlpack(target_speech_tokens))
inputs_tensor = [prompt_speech_tokens_tensor, prompt_speech_feat_tensor, prompt_spk_embedding_tensor, target_speech_tokens_tensor]
inputs_tensor = [target_speech_tokens_tensor]
if token_offset is not None:
assert finalize is not None
@@ -252,6 +256,13 @@ class TritonPythonModel:
inputs_tensor.append(token_offset_tensor)
inputs_tensor.append(finalize_tensor)
if prompt_spk_embedding is not None:
assert prompt_speech_feat is not None
prompt_speech_tokens_tensor = pb_utils.Tensor.from_dlpack("prompt_speech_tokens", to_dlpack(prompt_speech_tokens))
prompt_speech_feat_tensor = pb_utils.Tensor.from_dlpack("prompt_speech_feat", to_dlpack(prompt_speech_feat))
prompt_spk_embedding_tensor = pb_utils.Tensor.from_dlpack("prompt_spk_embedding", to_dlpack(prompt_spk_embedding))
inputs_tensor.extend([prompt_speech_tokens_tensor, prompt_speech_feat_tensor, prompt_spk_embedding_tensor])
# Create and execute inference request
inference_request = pb_utils.InferenceRequest(
model_name='token2wav',
@@ -318,25 +329,30 @@ class TritonPythonModel:
request_id = request.request_id()
# Extract input tensors
wav = pb_utils.get_input_tensor_by_name(request, "reference_wav")
wav_len = pb_utils.get_input_tensor_by_name(request, "reference_wav_len")
# Process reference audio through audio tokenizer
if wav is not None:
wav_len = pb_utils.get_input_tensor_by_name(request, "reference_wav_len")
prompt_speech_tokens = self.forward_audio_tokenizer(wav, wav_len)
prompt_speech_tokens = prompt_speech_tokens.unsqueeze(0)
prompt_speech_tokens = self.forward_audio_tokenizer(wav, wav_len)
prompt_speech_tokens = prompt_speech_tokens.unsqueeze(0)
wav_tensor = wav.as_numpy()
wav_tensor = torch.from_numpy(wav_tensor)[:, :wav_len.as_numpy()[0][0]]
prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=24000)(wav_tensor)
speech_feat = self._extract_speech_feat(prompt_speech_resample)
token_len = min(int(speech_feat.shape[1] / 2), prompt_speech_tokens.shape[-1])
prompt_speech_feat = speech_feat[:, :2 * token_len].contiguous().half()
prompt_speech_tokens = prompt_speech_tokens[:, :token_len].contiguous()
wav_tensor = wav.as_numpy()
wav_tensor = torch.from_numpy(wav_tensor)[:, :wav_len.as_numpy()[0][0]]
prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=24000)(wav_tensor)
speech_feat = self._extract_speech_feat(prompt_speech_resample)
token_len = min(int(speech_feat.shape[1] / 2), prompt_speech_tokens.shape[-1])
prompt_speech_feat = speech_feat[:, :2 * token_len].contiguous().half()
prompt_speech_tokens = prompt_speech_tokens[:, :token_len].contiguous()
flow_prompt_speech_token_len = prompt_speech_tokens.shape[-1]
reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy()
reference_text = reference_text[0][0].decode('utf-8')
reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy()
reference_text = reference_text[0][0].decode('utf-8')
prompt_spk_embedding = self.forward_speaker_embedding(wav_tensor)
else:
# using pre-cached reference text
reference_text = self.default_spk_info["prompt_text"]
prompt_speech_tokens = self.default_spk_info["speech_token"] + ORIGINAL_VOCAB_SIZE
prompt_speech_feat = None
prompt_spk_embedding = None
target_text = pb_utils.get_input_tensor_by_name(request, "target_text").as_numpy()
target_text = target_text[0][0].decode('utf-8')
@@ -350,7 +366,6 @@ class TritonPythonModel:
# Generate semantic tokens with LLM
generated_ids_iter = self.forward_llm(input_ids)
prompt_spk_embedding = self.forward_speaker_embedding(wav_tensor)
if self.decoupled:
response_sender = request.get_response_sender()
@@ -380,8 +395,9 @@ class TritonPythonModel:
this_tts_speech_token = torch.tensor(this_tts_speech_token).unsqueeze(dim=0).to(torch.int32).to(self.device)
sub_tts_speech = self.forward_token2wav(
prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding,
this_tts_speech_token, request_id, token_offset, False)
this_tts_speech_token, request_id, prompt_speech_tokens,
prompt_speech_feat, prompt_spk_embedding, token_offset, False
)
audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech))
inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor])
@@ -414,7 +430,7 @@ class TritonPythonModel:
time.sleep(0.02)
this_tts_speech_token = torch.tensor(semantic_token_ids_arr).unsqueeze(dim=0).to(torch.int32).to(self.device)
sub_tts_speech = self.forward_token2wav(prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding, this_tts_speech_token, request_id, token_offset, True)
sub_tts_speech = self.forward_token2wav(this_tts_speech_token, request_id, prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding, token_offset, True)
audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech))
inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor])
response_sender.send(inference_response)
@@ -428,7 +444,7 @@ class TritonPythonModel:
if generated_ids is None or len(generated_ids) == 0:
raise pb_utils.TritonModelException("Generated IDs is None or empty")
audio = self.forward_token2wav(prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding, generated_ids, request_id)
audio = self.forward_token2wav(generated_ids, request_id, prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding)
# Prepare response
audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio))

3
runtime/triton_trtllm/model_repo/cosyvoice2/config.pbtxt
View File

@@ -37,16 +37,19 @@ input [
name: "reference_wav"
data_type: TYPE_FP32
dims: [-1]
optional: true
},
{
name: "reference_wav_len"
data_type: TYPE_INT32
dims: [1]
optional: true
},
{
name: "reference_text"
data_type: TYPE_STRING
dims: [1]
optional: true
},
{
name: "target_text"