mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
54 lines
2.8 KiB
Python
54 lines
2.8 KiB
Python
import onnxruntime
|
|
import torch, random
|
|
import os
|
|
import torchaudio.compliance.kaldi as kaldi
|
|
|
|
|
|
class SpeechTokenExtractor():
|
|
def __init__(self, model_path):
|
|
self.local_rank = int(os.environ.get("LOCAL_RANK", 0))
|
|
option = onnxruntime.SessionOptions()
|
|
option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
|
|
option.intra_op_num_threads = 1
|
|
self.speech_tokenizer_session = onnxruntime.InferenceSession(model_path,
|
|
sess_options=option,
|
|
providers=[("CUDAExecutionProvider", {'device_id': self.local_rank})])
|
|
|
|
def inference(self, feat, feat_lengths, device):
|
|
speech_token = self.speech_tokenizer_session.run(None,
|
|
{self.speech_tokenizer_session.get_inputs()[0].name:
|
|
feat.transpose(1, 2).detach().cpu().numpy(),
|
|
self.speech_tokenizer_session.get_inputs()[1].name:
|
|
feat_lengths.detach().cpu().numpy()})[0]
|
|
return torch.tensor(speech_token).to(torch.int32).to(device), (feat_lengths / 4).to(torch.int32).to(device)
|
|
|
|
|
|
class EmbeddingExtractor():
|
|
def __init__(self, model_path):
|
|
option = onnxruntime.SessionOptions()
|
|
option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
|
|
option.intra_op_num_threads = 1
|
|
self.max_len = 10 * 16000
|
|
self.campplus_session = onnxruntime.InferenceSession(model_path,
|
|
sess_options=option,
|
|
providers=["CPUExecutionProvider"])
|
|
|
|
def inference(self, speech):
|
|
if speech.shape[1] > self.max_len:
|
|
start_index = random.randint(0, speech.shape[1] - self.max_len)
|
|
speech = speech[:, start_index: start_index + self.max_len]
|
|
feat = kaldi.fbank(speech,
|
|
num_mel_bins=80,
|
|
dither=0,
|
|
sample_frequency=16000)
|
|
feat = feat - feat.mean(dim=0, keepdim=True)
|
|
embedding = self.campplus_session.run(None,
|
|
{self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist()
|
|
return torch.tensor(embedding).to(speech.device)
|
|
|
|
# singleton mode, only initialized once
|
|
onnx_path = os.environ.get('onnx_path')
|
|
if onnx_path is not None:
|
|
embedding_extractor, online_feature = EmbeddingExtractor(model_path=os.path.join(onnx_path, 'campplus.onnx')), True
|
|
else:
|
|
embedding_extractor, online_feature = None, False |