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 |  112 --------------------------------------------------------
 1 files changed, 0 insertions(+), 112 deletions(-)

diff --git a/funasr/models/frontend/wav_frontend_kaldifeat.py b/funasr/models/frontend/wav_frontend_kaldifeat.py
index 85adbb7..5372de3 100644
--- a/funasr/models/frontend/wav_frontend_kaldifeat.py
+++ b/funasr/models/frontend/wav_frontend_kaldifeat.py
@@ -1,17 +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:
@@ -75,107 +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
\ No newline at end of file

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