import os.path from io import BytesIO from pathlib import Path import numpy as np import onnxruntime as ort import torch from modules.hubert.cn_hubert import load_cn_model, get_cn_hubert_units from modules.hubert.hubert_model import hubert_soft, get_units from modules.hubert.hubert_onnx import get_onnx_units from utils.hparams import hparams class HubertEncoder: def __init__(self, pt_path='checkpoints/hubert/hubert_soft.pt', hubert_mode='', onnx=False): self.hubert_mode = hubert_mode self.onnx = onnx if 'use_cn_hubert' not in hparams.keys(): hparams['use_cn_hubert'] = False if hparams['use_cn_hubert'] or self.hubert_mode == 'cn_hubert': pt_path = "checkpoints/cn_hubert/chinese-hubert-base-fairseq-ckpt.pt" self.dev = torch.device("cuda") self.hbt_model = load_cn_model(pt_path) else: if onnx: self.hbt_model = ort.InferenceSession("onnx/hubert_soft.onnx", providers=['CUDAExecutionProvider', 'CPUExecutionProvider', ]) else: pt_path = list(Path(pt_path).parent.rglob('*.pt'))[0] if 'hubert_gpu' in hparams.keys(): self.use_gpu = hparams['hubert_gpu'] else: self.use_gpu = True self.dev = torch.device("cuda" if self.use_gpu and torch.cuda.is_available() else "cpu") self.hbt_model = hubert_soft(str(pt_path)).to(self.dev) print(f"| load 'model' from '{pt_path}'") def encode(self, wav_path): if isinstance(wav_path, BytesIO): npy_path = "" wav_path.seek(0) else: npy_path = Path(wav_path).with_suffix('.npy') if os.path.exists(npy_path): units = np.load(str(npy_path)) elif self.onnx: units = get_onnx_units(self.hbt_model, wav_path).squeeze(0) elif hparams['use_cn_hubert'] or self.hubert_mode == 'cn_hubert': units = get_cn_hubert_units(self.hbt_model, wav_path, self.dev).cpu().numpy()[0] else: units = get_units(self.hbt_model, wav_path, self.dev).cpu().numpy()[0] return units # [T,256]