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