9c1009d16d
- vendor/CosyVoice: 패치된 CosyVoice 코드 + Matcha-TTS 벤더링 - cosyvoice_worker: 전용 venv에서 상주하는 합성 워커(FastAPI, 모델 1회 로드) - app/engines/cosyvoice_engine: HTTP 프록시 엔진(속도/피치) - Dockerfile: 전용 venv(torch cu128) + 모델 베이킹 + 빌드 워밍업 - entrypoint.sh: 워커(8001) + 메인앱(8788) 동시 기동
54 lines
2.8 KiB
Python
54 lines
2.8 KiB
Python
import onnxruntime
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import torch, random
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import os
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import torchaudio.compliance.kaldi as kaldi
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class SpeechTokenExtractor():
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def __init__(self, model_path):
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self.local_rank = int(os.environ.get("LOCAL_RANK", 0))
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option = onnxruntime.SessionOptions()
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option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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option.intra_op_num_threads = 1
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self.speech_tokenizer_session = onnxruntime.InferenceSession(model_path,
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sess_options=option,
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providers=[("CUDAExecutionProvider", {'device_id': self.local_rank})])
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def inference(self, feat, feat_lengths, device):
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speech_token = self.speech_tokenizer_session.run(None,
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{self.speech_tokenizer_session.get_inputs()[0].name:
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feat.transpose(1, 2).detach().cpu().numpy(),
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self.speech_tokenizer_session.get_inputs()[1].name:
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feat_lengths.detach().cpu().numpy()})[0]
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return torch.tensor(speech_token).to(torch.int32).to(device), (feat_lengths / 4).to(torch.int32).to(device)
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class EmbeddingExtractor():
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def __init__(self, model_path):
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option = onnxruntime.SessionOptions()
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option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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option.intra_op_num_threads = 1
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self.max_len = 10 * 16000
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self.campplus_session = onnxruntime.InferenceSession(model_path,
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sess_options=option,
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providers=["CPUExecutionProvider"])
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def inference(self, speech):
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if speech.shape[1] > self.max_len:
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start_index = random.randint(0, speech.shape[1] - self.max_len)
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speech = speech[:, start_index: start_index + self.max_len]
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feat = kaldi.fbank(speech,
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num_mel_bins=80,
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dither=0,
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sample_frequency=16000)
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feat = feat - feat.mean(dim=0, keepdim=True)
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embedding = self.campplus_session.run(None,
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{self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist()
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return torch.tensor(embedding).to(speech.device)
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# singleton mode, only initialized once
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onnx_path = os.environ.get('onnx_path')
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if onnx_path is not None:
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embedding_extractor, online_feature = EmbeddingExtractor(model_path=os.path.join(onnx_path, 'campplus.onnx')), True
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else:
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embedding_extractor, online_feature = None, False |