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

28
runtime/triton_trtllm/model_repo/token2wav/1/model.py
View File

@@ -187,6 +187,12 @@ class TritonPythonModel:
model_dir, load_jit=False, load_trt=True, fp16=True, device=self.device
)
spk_info_path = os.path.join(model_dir, "spk2info.pt")
if not os.path.exists(spk_info_path):
raise ValueError(f"spk2info.pt not found in {model_dir}")
spk_info = torch.load(spk_info_path, map_location="cpu", weights_only=False)
self.default_spk_info = spk_info["001"]
logger.info("Token2Wav initialized successfully")
def execute(self, requests):
@@ -202,17 +208,23 @@ class TritonPythonModel:
# Process each request in batch
for request in requests:
target_speech_tokens_tensor = pb_utils.get_input_tensor_by_name(request, "target_speech_tokens").as_numpy()
prompt_speech_tokens_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_speech_tokens").as_numpy()
prompt_speech_feat_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_speech_feat").as_numpy()
prompt_spk_embedding_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_spk_embedding").as_numpy()
target_speech_tokens = torch.from_numpy(target_speech_tokens_tensor).to(self.device)
prompt_speech_tokens = torch.from_numpy(prompt_speech_tokens_tensor).to(self.device)
prompt_speech_feat = torch.from_numpy(prompt_speech_feat_tensor).to(self.device)
prompt_spk_embedding = torch.from_numpy(prompt_spk_embedding_tensor).to(self.device)
prompt_speech_tokens_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_speech_tokens")
if prompt_speech_tokens_tensor is not None:
prompt_speech_tokens_tensor = prompt_speech_tokens_tensor.as_numpy()
prompt_speech_feat_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_speech_feat").as_numpy()
prompt_spk_embedding_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_spk_embedding").as_numpy()
prompt_speech_tokens = torch.from_numpy(prompt_speech_tokens_tensor).to(self.device)
prompt_speech_feat = torch.from_numpy(prompt_speech_feat_tensor).to(self.device)
prompt_spk_embedding = torch.from_numpy(prompt_spk_embedding_tensor).to(self.device)
prompt_speech_tokens = prompt_speech_tokens - ORIGINAL_VOCAB_SIZE
else:
prompt_speech_tokens = self.default_spk_info["speech_token"].to(self.device)
prompt_speech_feat = self.default_spk_info["speech_feat"].to(torch.float16).to(self.device)
prompt_spk_embedding = self.default_spk_info["embedding"].to(torch.float16).to(self.device)
# shift the speech tokens according to the original vocab size
prompt_speech_tokens = prompt_speech_tokens - ORIGINAL_VOCAB_SIZE
target_speech_tokens = target_speech_tokens - ORIGINAL_VOCAB_SIZE
# We set token_offset as an optional input to support streaming/offline tts. It has to be None when offline tts.