103 lines
3.0 KiB
Python
103 lines
3.0 KiB
Python
import argparse
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import torch
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from pathlib import Path
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import yaml
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from .frontend import DefaultFrontend
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from .utterance_mvn import UtteranceMVN
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from .encoder.conformer_encoder import ConformerEncoder
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_model = None # type: PPGModel
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_device = None
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class PPGModel(torch.nn.Module):
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def __init__(
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self,
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frontend,
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normalizer,
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encoder,
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):
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super().__init__()
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self.frontend = frontend
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self.normalize = normalizer
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self.encoder = encoder
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def forward(self, speech, speech_lengths):
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"""
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Args:
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speech (tensor): (B, L)
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speech_lengths (tensor): (B, )
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Returns:
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bottle_neck_feats (tensor): (B, L//hop_size, 144)
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"""
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feats, feats_lengths = self._extract_feats(speech, speech_lengths)
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feats, feats_lengths = self.normalize(feats, feats_lengths)
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encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
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return encoder_out
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def _extract_feats(
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self, speech: torch.Tensor, speech_lengths: torch.Tensor
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):
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assert speech_lengths.dim() == 1, speech_lengths.shape
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# for data-parallel
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speech = speech[:, : speech_lengths.max()]
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if self.frontend is not None:
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# Frontend
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# e.g. STFT and Feature extract
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# data_loader may send time-domain signal in this case
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# speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim)
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feats, feats_lengths = self.frontend(speech, speech_lengths)
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else:
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# No frontend and no feature extract
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feats, feats_lengths = speech, speech_lengths
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return feats, feats_lengths
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def extract_from_wav(self, src_wav):
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src_wav_tensor = torch.from_numpy(src_wav).unsqueeze(0).float().to(_device)
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src_wav_lengths = torch.LongTensor([len(src_wav)]).to(_device)
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return self(src_wav_tensor, src_wav_lengths)
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def build_model(args):
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normalizer = UtteranceMVN(**args.normalize_conf)
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frontend = DefaultFrontend(**args.frontend_conf)
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encoder = ConformerEncoder(input_size=80, **args.encoder_conf)
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model = PPGModel(frontend, normalizer, encoder)
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return model
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def load_model(model_file, device=None):
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global _model, _device
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if device is None:
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_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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else:
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_device = device
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# search a config file
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model_config_fpaths = list(model_file.parent.rglob("*.yaml"))
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config_file = model_config_fpaths[0]
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with config_file.open("r", encoding="utf-8") as f:
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args = yaml.safe_load(f)
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args = argparse.Namespace(**args)
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model = build_model(args)
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model_state_dict = model.state_dict()
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ckpt_state_dict = torch.load(model_file, map_location=_device)
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ckpt_state_dict = {k:v for k,v in ckpt_state_dict.items() if 'encoder' in k}
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model_state_dict.update(ckpt_state_dict)
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model.load_state_dict(model_state_dict)
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_model = model.eval().to(_device)
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return _model
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