From 6f7e27eb7c2d0a7649ec8f14d167c8da8e29f906 Mon Sep 17 00:00:00 2001
From: jmwang66 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 16 五月 2023 15:07:20 +0800
Subject: [PATCH] Merge pull request #518 from alibaba-damo-academy/dev_wjm2
---
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 b91ac63..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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