mirror of
https://github.com/modelscope/FunASR
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511 lines
20 KiB
Python
511 lines
20 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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# Part of the implementation is borrowed from espnet/espnet.
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from typing import Tuple
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import numpy as np
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import torch
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import torch.nn as nn
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import torchaudio.compliance.kaldi as kaldi
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from torch.nn.utils.rnn import pad_sequence
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import funasr.frontends.eend_ola_feature as eend_ola_feature
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from funasr.register import tables
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def load_cmvn(cmvn_file):
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with open(cmvn_file, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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means_list = []
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vars_list = []
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for i in range(len(lines)):
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line_item = lines[i].split()
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if line_item[0] == '<AddShift>':
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line_item = lines[i + 1].split()
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if line_item[0] == '<LearnRateCoef>':
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add_shift_line = line_item[3:(len(line_item) - 1)]
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means_list = list(add_shift_line)
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continue
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elif line_item[0] == '<Rescale>':
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line_item = lines[i + 1].split()
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if line_item[0] == '<LearnRateCoef>':
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rescale_line = line_item[3:(len(line_item) - 1)]
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vars_list = list(rescale_line)
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continue
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means = np.array(means_list).astype(np.float32)
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vars = np.array(vars_list).astype(np.float32)
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cmvn = np.array([means, vars])
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cmvn = torch.as_tensor(cmvn, dtype=torch.float32)
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return cmvn
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def apply_cmvn(inputs, cmvn): # noqa
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"""
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Apply CMVN with mvn data
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"""
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device = inputs.device
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dtype = inputs.dtype
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frame, dim = inputs.shape
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means = cmvn[0:1, :dim]
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vars = cmvn[1:2, :dim]
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inputs += means.to(device)
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inputs *= vars.to(device)
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return inputs.type(torch.float32)
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def apply_lfr(inputs, lfr_m, lfr_n):
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LFR_inputs = []
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T = inputs.shape[0]
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T_lfr = int(np.ceil(T / lfr_n))
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left_padding = inputs[0].repeat((lfr_m - 1) // 2, 1)
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inputs = torch.vstack((left_padding, inputs))
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T = T + (lfr_m - 1) // 2
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for i in range(T_lfr):
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if lfr_m <= T - i * lfr_n:
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LFR_inputs.append((inputs[i * lfr_n:i * lfr_n + lfr_m]).view(1, -1))
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else: # process last LFR frame
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num_padding = lfr_m - (T - i * lfr_n)
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frame = (inputs[i * lfr_n:]).view(-1)
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for _ in range(num_padding):
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frame = torch.hstack((frame, inputs[-1]))
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LFR_inputs.append(frame)
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LFR_outputs = torch.vstack(LFR_inputs)
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return LFR_outputs.type(torch.float32)
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@tables.register("frontend_classes", "WavFrontend")
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class WavFrontend(nn.Module):
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"""Conventional frontend structure for ASR.
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"""
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def __init__(
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self,
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cmvn_file: str = None,
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fs: int = 16000,
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window: str = 'hamming',
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n_mels: int = 80,
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frame_length: int = 25,
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frame_shift: int = 10,
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filter_length_min: int = -1,
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filter_length_max: int = -1,
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lfr_m: int = 1,
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lfr_n: int = 1,
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dither: float = 1.0,
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snip_edges: bool = True,
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upsacle_samples: bool = True,
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**kwargs,
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):
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super().__init__()
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self.fs = fs
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self.window = window
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self.n_mels = n_mels
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self.frame_length = frame_length
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self.frame_shift = frame_shift
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self.filter_length_min = filter_length_min
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self.filter_length_max = filter_length_max
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self.lfr_m = lfr_m
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self.lfr_n = lfr_n
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self.cmvn_file = cmvn_file
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self.dither = dither
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self.snip_edges = snip_edges
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self.upsacle_samples = upsacle_samples
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self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file)
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def output_size(self) -> int:
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return self.n_mels * self.lfr_m
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def forward(
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self,
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input: torch.Tensor,
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input_lengths) -> Tuple[torch.Tensor, torch.Tensor]:
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batch_size = input.size(0)
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feats = []
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feats_lens = []
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for i in range(batch_size):
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waveform_length = input_lengths[i]
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waveform = input[i][:waveform_length]
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if self.upsacle_samples:
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waveform = waveform * (1 << 15)
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waveform = waveform.unsqueeze(0)
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mat = kaldi.fbank(waveform,
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num_mel_bins=self.n_mels,
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frame_length=self.frame_length,
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frame_shift=self.frame_shift,
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dither=self.dither,
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energy_floor=0.0,
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window_type=self.window,
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sample_frequency=self.fs,
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snip_edges=self.snip_edges)
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if self.lfr_m != 1 or self.lfr_n != 1:
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mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
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if self.cmvn is not None:
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mat = apply_cmvn(mat, self.cmvn)
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feat_length = mat.size(0)
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feats.append(mat)
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feats_lens.append(feat_length)
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feats_lens = torch.as_tensor(feats_lens)
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if batch_size == 1:
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feats_pad = feats[0][None, :, :]
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else:
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feats_pad = pad_sequence(feats,
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batch_first=True,
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padding_value=0.0)
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return feats_pad, feats_lens
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def forward_fbank(
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self,
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input: torch.Tensor,
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input_lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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batch_size = input.size(0)
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feats = []
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feats_lens = []
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for i in range(batch_size):
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waveform_length = input_lengths[i]
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waveform = input[i][:waveform_length]
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waveform = waveform * (1 << 15)
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waveform = waveform.unsqueeze(0)
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mat = kaldi.fbank(waveform,
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num_mel_bins=self.n_mels,
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frame_length=self.frame_length,
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frame_shift=self.frame_shift,
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dither=self.dither,
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energy_floor=0.0,
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window_type=self.window,
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sample_frequency=self.fs)
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feat_length = mat.size(0)
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feats.append(mat)
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feats_lens.append(feat_length)
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feats_lens = torch.as_tensor(feats_lens)
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feats_pad = pad_sequence(feats,
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batch_first=True,
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padding_value=0.0)
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return feats_pad, feats_lens
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def forward_lfr_cmvn(
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self,
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input: torch.Tensor,
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input_lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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batch_size = input.size(0)
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feats = []
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feats_lens = []
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for i in range(batch_size):
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mat = input[i, :input_lengths[i], :]
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if self.lfr_m != 1 or self.lfr_n != 1:
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mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
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if self.cmvn is not None:
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mat = apply_cmvn(mat, self.cmvn)
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feat_length = mat.size(0)
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feats.append(mat)
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feats_lens.append(feat_length)
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feats_lens = torch.as_tensor(feats_lens)
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feats_pad = pad_sequence(feats,
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batch_first=True,
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padding_value=0.0)
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return feats_pad, feats_lens
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@tables.register("frontend_classes", "WavFrontendOnline")
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class WavFrontendOnline(nn.Module):
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"""Conventional frontend structure for streaming ASR/VAD.
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"""
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def __init__(
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self,
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cmvn_file: str = None,
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fs: int = 16000,
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window: str = 'hamming',
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n_mels: int = 80,
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frame_length: int = 25,
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frame_shift: int = 10,
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filter_length_min: int = -1,
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filter_length_max: int = -1,
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lfr_m: int = 1,
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lfr_n: int = 1,
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dither: float = 1.0,
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snip_edges: bool = True,
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upsacle_samples: bool = True,
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**kwargs,
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):
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super().__init__()
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self.fs = fs
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self.window = window
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self.n_mels = n_mels
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self.frame_length = frame_length
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self.frame_shift = frame_shift
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self.frame_sample_length = int(self.frame_length * self.fs / 1000)
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self.frame_shift_sample_length = int(self.frame_shift * self.fs / 1000)
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self.filter_length_min = filter_length_min
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self.filter_length_max = filter_length_max
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self.lfr_m = lfr_m
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self.lfr_n = lfr_n
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self.cmvn_file = cmvn_file
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self.dither = dither
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self.snip_edges = snip_edges
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self.upsacle_samples = upsacle_samples
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self.waveforms = None
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self.reserve_waveforms = None
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self.fbanks = None
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self.fbanks_lens = None
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self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file)
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self.input_cache = None
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self.lfr_splice_cache = []
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def output_size(self) -> int:
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return self.n_mels * self.lfr_m
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@staticmethod
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def apply_cmvn(inputs: torch.Tensor, cmvn: torch.Tensor) -> torch.Tensor:
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"""
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Apply CMVN with mvn data
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"""
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device = inputs.device
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dtype = inputs.dtype
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frame, dim = inputs.shape
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means = np.tile(cmvn[0:1, :dim], (frame, 1))
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vars = np.tile(cmvn[1:2, :dim], (frame, 1))
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inputs += torch.from_numpy(means).type(dtype).to(device)
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inputs *= torch.from_numpy(vars).type(dtype).to(device)
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return inputs.type(torch.float32)
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@staticmethod
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# inputs tensor has catted the cache tensor
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# def apply_lfr(inputs: torch.Tensor, lfr_m: int, lfr_n: int, inputs_lfr_cache: torch.Tensor = None,
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# is_final: bool = False) -> Tuple[torch.Tensor, torch.Tensor, int]:
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def apply_lfr(inputs: torch.Tensor, lfr_m: int, lfr_n: int, is_final: bool = False) -> Tuple[
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torch.Tensor, torch.Tensor, int]:
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"""
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Apply lfr with data
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"""
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LFR_inputs = []
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# inputs = torch.vstack((inputs_lfr_cache, inputs))
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T = inputs.shape[0] # include the right context
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T_lfr = int(np.ceil((T - (lfr_m - 1) // 2) / lfr_n)) # minus the right context: (lfr_m - 1) // 2
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splice_idx = T_lfr
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for i in range(T_lfr):
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if lfr_m <= T - i * lfr_n:
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LFR_inputs.append((inputs[i * lfr_n:i * lfr_n + lfr_m]).view(1, -1))
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else: # process last LFR frame
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if is_final:
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num_padding = lfr_m - (T - i * lfr_n)
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frame = (inputs[i * lfr_n:]).view(-1)
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for _ in range(num_padding):
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frame = torch.hstack((frame, inputs[-1]))
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LFR_inputs.append(frame)
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else:
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# update splice_idx and break the circle
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splice_idx = i
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break
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splice_idx = min(T - 1, splice_idx * lfr_n)
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lfr_splice_cache = inputs[splice_idx:, :]
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LFR_outputs = torch.vstack(LFR_inputs)
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return LFR_outputs.type(torch.float32), lfr_splice_cache, splice_idx
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@staticmethod
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def compute_frame_num(sample_length: int, frame_sample_length: int, frame_shift_sample_length: int) -> int:
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frame_num = int((sample_length - frame_sample_length) / frame_shift_sample_length + 1)
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return frame_num if frame_num >= 1 and sample_length >= frame_sample_length else 0
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def forward_fbank(
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self,
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input: torch.Tensor,
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input_lengths: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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batch_size = input.size(0)
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if self.input_cache is None:
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self.input_cache = torch.empty(0)
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input = torch.cat((self.input_cache, input), dim=1)
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frame_num = self.compute_frame_num(input.shape[-1], self.frame_sample_length, self.frame_shift_sample_length)
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# update self.in_cache
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self.input_cache = input[:, -(input.shape[-1] - frame_num * self.frame_shift_sample_length):]
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waveforms = torch.empty(0)
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feats_pad = torch.empty(0)
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feats_lens = torch.empty(0)
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if frame_num:
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waveforms = []
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feats = []
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feats_lens = []
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for i in range(batch_size):
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waveform = input[i]
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# we need accurate wave samples that used for fbank extracting
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waveforms.append(
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waveform[:((frame_num - 1) * self.frame_shift_sample_length + self.frame_sample_length)])
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waveform = waveform * (1 << 15)
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waveform = waveform.unsqueeze(0)
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mat = kaldi.fbank(waveform,
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num_mel_bins=self.n_mels,
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frame_length=self.frame_length,
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frame_shift=self.frame_shift,
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dither=self.dither,
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energy_floor=0.0,
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window_type=self.window,
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sample_frequency=self.fs)
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feat_length = mat.size(0)
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feats.append(mat)
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feats_lens.append(feat_length)
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waveforms = torch.stack(waveforms)
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feats_lens = torch.as_tensor(feats_lens)
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feats_pad = pad_sequence(feats,
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batch_first=True,
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padding_value=0.0)
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self.fbanks = feats_pad
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import copy
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self.fbanks_lens = copy.deepcopy(feats_lens)
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return waveforms, feats_pad, feats_lens
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def get_fbank(self) -> Tuple[torch.Tensor, torch.Tensor]:
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return self.fbanks, self.fbanks_lens
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def forward_lfr_cmvn(
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self,
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input: torch.Tensor,
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input_lengths: torch.Tensor,
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is_final: bool = False
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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batch_size = input.size(0)
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feats = []
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feats_lens = []
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lfr_splice_frame_idxs = []
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for i in range(batch_size):
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mat = input[i, :input_lengths[i], :]
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if self.lfr_m != 1 or self.lfr_n != 1:
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# update self.lfr_splice_cache in self.apply_lfr
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# mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n, self.lfr_splice_cache[i],
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mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n,
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is_final)
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if self.cmvn_file is not None:
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mat = self.apply_cmvn(mat, self.cmvn)
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feat_length = mat.size(0)
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feats.append(mat)
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feats_lens.append(feat_length)
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lfr_splice_frame_idxs.append(lfr_splice_frame_idx)
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feats_lens = torch.as_tensor(feats_lens)
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feats_pad = pad_sequence(feats,
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batch_first=True,
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padding_value=0.0)
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lfr_splice_frame_idxs = torch.as_tensor(lfr_splice_frame_idxs)
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return feats_pad, feats_lens, lfr_splice_frame_idxs
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def forward(
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self, input: torch.Tensor, input_lengths: torch.Tensor, is_final: bool = False, reset: bool = False
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) -> Tuple[torch.Tensor, torch.Tensor]:
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if reset:
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self.cache_reset()
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batch_size = input.shape[0]
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assert batch_size == 1, 'we support to extract feature online only when the batch size is equal to 1 now'
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waveforms, feats, feats_lengths = self.forward_fbank(input, input_lengths) # input shape: B T D
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if feats.shape[0]:
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# if self.reserve_waveforms is None and self.lfr_m > 1:
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# self.reserve_waveforms = waveforms[:, :(self.lfr_m - 1) // 2 * self.frame_shift_sample_length]
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self.waveforms = waveforms if self.reserve_waveforms is None else torch.cat(
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(self.reserve_waveforms, waveforms), dim=1)
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if not self.lfr_splice_cache: # 初始化splice_cache
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for i in range(batch_size):
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self.lfr_splice_cache.append(feats[i][0, :].unsqueeze(dim=0).repeat((self.lfr_m - 1) // 2, 1))
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# need the number of the input frames + self.lfr_splice_cache[0].shape[0] is greater than self.lfr_m
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if feats_lengths[0] + self.lfr_splice_cache[0].shape[0] >= self.lfr_m:
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lfr_splice_cache_tensor = torch.stack(self.lfr_splice_cache) # B T D
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feats = torch.cat((lfr_splice_cache_tensor, feats), dim=1)
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feats_lengths += lfr_splice_cache_tensor[0].shape[0]
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frame_from_waveforms = int(
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(self.waveforms.shape[1] - self.frame_sample_length) / self.frame_shift_sample_length + 1)
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minus_frame = (self.lfr_m - 1) // 2 if self.reserve_waveforms is None else 0
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feats, feats_lengths, lfr_splice_frame_idxs = self.forward_lfr_cmvn(feats, feats_lengths, is_final)
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if self.lfr_m == 1:
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self.reserve_waveforms = None
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else:
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reserve_frame_idx = lfr_splice_frame_idxs[0] - minus_frame
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# print('reserve_frame_idx: ' + str(reserve_frame_idx))
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# print('frame_frame: ' + str(frame_from_waveforms))
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self.reserve_waveforms = self.waveforms[:, reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length]
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sample_length = (frame_from_waveforms - 1) * self.frame_shift_sample_length + self.frame_sample_length
|
|
self.waveforms = self.waveforms[:, :sample_length]
|
|
else:
|
|
# update self.reserve_waveforms and self.lfr_splice_cache
|
|
self.reserve_waveforms = self.waveforms[:,
|
|
:-(self.frame_sample_length - self.frame_shift_sample_length)]
|
|
for i in range(batch_size):
|
|
self.lfr_splice_cache[i] = torch.cat((self.lfr_splice_cache[i], feats[i]), dim=0)
|
|
return torch.empty(0), feats_lengths
|
|
else:
|
|
if is_final:
|
|
self.waveforms = waveforms if self.reserve_waveforms is None else self.reserve_waveforms
|
|
feats = torch.stack(self.lfr_splice_cache)
|
|
feats_lengths = torch.zeros(batch_size, dtype=torch.int) + feats.shape[1]
|
|
feats, feats_lengths, _ = self.forward_lfr_cmvn(feats, feats_lengths, is_final)
|
|
if is_final:
|
|
self.cache_reset()
|
|
return feats, feats_lengths
|
|
|
|
def get_waveforms(self):
|
|
return self.waveforms
|
|
|
|
def cache_reset(self):
|
|
self.reserve_waveforms = None
|
|
self.input_cache = None
|
|
self.lfr_splice_cache = []
|
|
|
|
|
|
class WavFrontendMel23(nn.Module):
|
|
"""Conventional frontend structure for ASR.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
fs: int = 16000,
|
|
frame_length: int = 25,
|
|
frame_shift: int = 10,
|
|
lfr_m: int = 1,
|
|
lfr_n: int = 1,
|
|
**kwargs,
|
|
):
|
|
super().__init__()
|
|
self.fs = fs
|
|
self.frame_length = frame_length
|
|
self.frame_shift = frame_shift
|
|
self.lfr_m = lfr_m
|
|
self.lfr_n = lfr_n
|
|
self.n_mels = 23
|
|
|
|
def output_size(self) -> int:
|
|
return self.n_mels * (2 * self.lfr_m + 1)
|
|
|
|
def forward(
|
|
self,
|
|
input: torch.Tensor,
|
|
input_lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
batch_size = input.size(0)
|
|
feats = []
|
|
feats_lens = []
|
|
for i in range(batch_size):
|
|
waveform_length = input_lengths[i]
|
|
waveform = input[i][:waveform_length]
|
|
waveform = waveform.numpy()
|
|
mat = eend_ola_feature.stft(waveform, self.frame_length, self.frame_shift)
|
|
mat = eend_ola_feature.transform(mat)
|
|
mat = eend_ola_feature.splice(mat, context_size=self.lfr_m)
|
|
mat = mat[::self.lfr_n]
|
|
mat = torch.from_numpy(mat)
|
|
feat_length = mat.size(0)
|
|
feats.append(mat)
|
|
feats_lens.append(feat_length)
|
|
|
|
feats_lens = torch.as_tensor(feats_lens)
|
|
feats_pad = pad_sequence(feats,
|
|
batch_first=True,
|
|
padding_value=0.0)
|
|
return feats_pad, feats_lens
|