From 33d3d2084403fd34b79c835d2f2fe04f6cd8f738 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 九月 2023 09:33:54 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/models/frontend/wav_frontend_kaldifeat.py | 119 ++---------------------------------------------------------
1 files changed, 4 insertions(+), 115 deletions(-)
diff --git a/funasr/models/frontend/wav_frontend_kaldifeat.py b/funasr/models/frontend/wav_frontend_kaldifeat.py
index 61cdd13..5372de3 100644
--- a/funasr/models/frontend/wav_frontend_kaldifeat.py
+++ b/funasr/models/frontend/wav_frontend_kaldifeat.py
@@ -1,15 +1,9 @@
# Copyright (c) Alibaba, Inc. and its affiliates.
# Part of the implementation is borrowed from espnet/espnet.
-from typing import Tuple
-
import numpy as np
import torch
-import torchaudio.compliance.kaldi as kaldi
-from funasr.models.frontend.abs_frontend import AbsFrontend
-from typeguard import check_argument_types
-from torch.nn.utils.rnn import pad_sequence
-import kaldifeat
+
def load_cmvn(cmvn_file):
with open(cmvn_file, 'r', encoding='utf-8') as f:
@@ -33,9 +27,9 @@
means = np.array(means_list).astype(np.float)
vars = np.array(vars_list).astype(np.float)
cmvn = np.array([means, vars])
- cmvn = torch.as_tensor(cmvn)
- return cmvn
-
+ cmvn = torch.as_tensor(cmvn)
+ return cmvn
+
def apply_cmvn(inputs, cmvn_file): # noqa
"""
@@ -73,108 +67,3 @@
LFR_inputs.append(frame)
LFR_outputs = torch.vstack(LFR_inputs)
return LFR_outputs.type(torch.float32)
-
-
-class WavFrontend_kaldifeat(AbsFrontend):
- """Conventional frontend structure for ASR.
- """
-
- def __init__(
- self,
- cmvn_file: str = None,
- fs: int = 16000,
- window: str = 'hamming',
- n_mels: int = 80,
- frame_length: int = 25,
- frame_shift: int = 10,
- lfr_m: int = 1,
- lfr_n: int = 1,
- dither: float = 1.0,
- snip_edges: bool = True,
- upsacle_samples: bool = True,
- device: str = 'cpu',
- **kwargs,
- ):
- super().__init__()
-
- opts = kaldifeat.FbankOptions()
- opts.device = device
- opts.frame_opts.samp_freq = fs
- opts.frame_opts.dither = dither
- opts.frame_opts.window_type = window
- opts.frame_opts.frame_shift_ms = float(frame_shift)
- opts.frame_opts.frame_length_ms = float(frame_length)
- opts.mel_opts.num_bins = n_mels
- opts.energy_floor = 0
- opts.frame_opts.snip_edges = snip_edges
- opts.mel_opts.debug_mel = False
- self.opts = opts
- self.fbank_fn = None
- self.fbank_beg_idx = 0
- self.reset_fbank_status()
-
- self.lfr_m = lfr_m
- self.lfr_n = lfr_n
- self.cmvn_file = cmvn_file
- self.upsacle_samples = upsacle_samples
-
- def output_size(self) -> int:
- return self.n_mels * self.lfr_m
-
- def forward_fbank(
- 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 * (1 << 15)
-
- self.fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
- frames = self.fbank_fn.num_frames_ready
- frames_cur = frames - self.fbank_beg_idx
- mat = torch.empty([frames_cur, self.opts.mel_opts.num_bins], dtype=torch.float32).to(
- device=self.opts.device)
- for i in range(self.fbank_beg_idx, frames):
- mat[i, :] = self.fbank_fn.get_frame(i)
- self.fbank_beg_idx += frames_cur
-
- 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
-
- def reset_fbank_status(self):
- self.fbank_fn = kaldifeat.OnlineFbank(self.opts)
- self.fbank_beg_idx = 0
-
- def forward_lfr_cmvn(
- 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):
- mat = input[i, :input_lengths[i], :]
- if self.lfr_m != 1 or self.lfr_n != 1:
- mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
- if self.cmvn_file is not None:
- mat = apply_cmvn(mat, self.cmvn_file)
- 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
--
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