From 3f7f737587ac0f233c3f60e4f3caf25256bcc1e5 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 22 二月 2023 20:11:31 +0800
Subject: [PATCH] fbank online
---
funasr/models/frontend/wav_frontend_kaldifeat.py | 208 ++++++++++++++++++++++++++--------------------------
1 files changed, 104 insertions(+), 104 deletions(-)
diff --git a/funasr/models/frontend/wav_frontend_kaldifeat.py b/funasr/models/frontend/wav_frontend_kaldifeat.py
index 61cdd13..b91ac63 100644
--- a/funasr/models/frontend/wav_frontend_kaldifeat.py
+++ b/funasr/models/frontend/wav_frontend_kaldifeat.py
@@ -9,7 +9,7 @@
from funasr.models.frontend.abs_frontend import AbsFrontend
from typeguard import check_argument_types
from torch.nn.utils.rnn import pad_sequence
-import kaldifeat
+# import kaldifeat
def load_cmvn(cmvn_file):
with open(cmvn_file, 'r', encoding='utf-8') as f:
@@ -75,106 +75,106 @@
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
+# 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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