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import os.path |
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from io import BytesIO |
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from pathlib import Path |
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import numpy as np |
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import onnxruntime as ort |
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import torch |
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from modules.hubert.cn_hubert import load_cn_model, get_cn_hubert_units |
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from modules.hubert.hubert_model import hubert_soft, get_units |
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from modules.hubert.hubert_onnx import get_onnx_units |
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from utils.hparams import hparams |
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class HubertEncoder: |
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def __init__(self, pt_path='checkpoints/hubert/hubert_soft.pt', hubert_mode='', onnx=False): |
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self.hubert_mode = hubert_mode |
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self.onnx = onnx |
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if 'use_cn_hubert' not in hparams.keys(): |
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hparams['use_cn_hubert'] = False |
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if hparams['use_cn_hubert'] or self.hubert_mode == 'cn_hubert': |
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pt_path = "checkpoints/cn_hubert/chinese-hubert-base-fairseq-ckpt.pt" |
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self.dev = torch.device("cuda") |
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self.hbt_model = load_cn_model(pt_path) |
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else: |
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if onnx: |
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self.hbt_model = ort.InferenceSession("onnx/hubert_soft.onnx", |
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providers=['CUDAExecutionProvider', 'CPUExecutionProvider', ]) |
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else: |
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pt_path = list(Path(pt_path).parent.rglob('*.pt'))[0] |
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if 'hubert_gpu' in hparams.keys(): |
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self.use_gpu = hparams['hubert_gpu'] |
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else: |
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self.use_gpu = True |
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self.dev = torch.device("cuda" if self.use_gpu and torch.cuda.is_available() else "cpu") |
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self.hbt_model = hubert_soft(str(pt_path)).to(self.dev) |
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print(f"| load 'model' from '{pt_path}'") |
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def encode(self, wav_path): |
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if isinstance(wav_path, BytesIO): |
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npy_path = "" |
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wav_path.seek(0) |
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else: |
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npy_path = Path(wav_path).with_suffix('.npy') |
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if os.path.exists(npy_path): |
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units = np.load(str(npy_path)) |
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elif self.onnx: |
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units = get_onnx_units(self.hbt_model, wav_path).squeeze(0) |
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elif hparams['use_cn_hubert'] or self.hubert_mode == 'cn_hubert': |
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units = get_cn_hubert_units(self.hbt_model, wav_path, self.dev).cpu().numpy()[0] |
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else: |
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units = get_units(self.hbt_model, wav_path, self.dev).cpu().numpy()[0] |
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return units |
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