From 8762d9973585fdceaaa886516a06e0ada303d3b5 Mon Sep 17 00:00:00 2001
From: speech_asr <wangjiaming.wjm@alibaba-inc.com>
Date: 星期一, 13 三月 2023 15:30:17 +0800
Subject: [PATCH] update ola
---
funasr/models/frontend/wav_frontend.py | 111 ++++++++++++++++++++++++++++++++++++++++++++-----------
1 files changed, 88 insertions(+), 23 deletions(-)
diff --git a/funasr/models/frontend/wav_frontend.py b/funasr/models/frontend/wav_frontend.py
index ed8cb36..4e52b90 100644
--- a/funasr/models/frontend/wav_frontend.py
+++ b/funasr/models/frontend/wav_frontend.py
@@ -1,14 +1,14 @@
# Copyright (c) Alibaba, Inc. and its affiliates.
# Part of the implementation is borrowed from espnet/espnet.
-from typing import Tuple
-
+import funasr.models.frontend.eend_ola_feature
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
+from typeguard import check_argument_types
+from typing import Tuple
def load_cmvn(cmvn_file):
@@ -33,9 +33,9 @@
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
"""
@@ -78,21 +78,22 @@
class WavFrontend(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,
- filter_length_min: int = -1,
- filter_length_max: int = -1,
- lfr_m: int = 1,
- lfr_n: int = 1,
- dither: float = 1.0,
- snip_edges: bool = True,
- upsacle_samples: bool = True,
+ self,
+ cmvn_file: str = None,
+ fs: int = 16000,
+ window: str = 'hamming',
+ n_mels: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ filter_length_min: int = -1,
+ filter_length_max: int = -1,
+ lfr_m: int = 1,
+ lfr_n: int = 1,
+ dither: float = 1.0,
+ snip_edges: bool = True,
+ upsacle_samples: bool = True,
):
assert check_argument_types()
super().__init__()
@@ -135,11 +136,11 @@
window_type=self.window,
sample_frequency=self.fs,
snip_edges=self.snip_edges)
-
+
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)
+ mat = apply_cmvn(mat, self.cmvn_file)
feat_length = mat.size(0)
feats.append(mat)
feats_lens.append(feat_length)
@@ -171,7 +172,6 @@
window_type=self.window,
sample_frequency=self.fs)
-
feat_length = mat.size(0)
feats.append(mat)
feats_lens.append(feat_length)
@@ -204,3 +204,68 @@
batch_first=True,
padding_value=0.0)
return feats_pad, feats_lens
+
+
+class WavFrontendMel23(AbsFrontend):
+ """Conventional frontend structure for ASR.
+ """
+
+ def __init__(
+ self,
+ fs: int = 16000,
+ window: str = 'hamming',
+ n_mels: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ filter_length_min: int = -1,
+ filter_length_max: int = -1,
+ lfr_m: int = 1,
+ lfr_n: int = 1,
+ dither: float = 1.0,
+ snip_edges: bool = True,
+ upsacle_samples: bool = True,
+ ):
+ assert check_argument_types()
+ super().__init__()
+ self.fs = fs
+ self.window = window
+ self.n_mels = n_mels
+ self.frame_length = frame_length
+ self.frame_shift = frame_shift
+ self.filter_length_min = filter_length_min
+ self.filter_length_max = filter_length_max
+ self.lfr_m = lfr_m
+ self.lfr_n = lfr_n
+ self.cmvn_file = cmvn_file
+ self.dither = dither
+ self.snip_edges = snip_edges
+ self.upsacle_samples = upsacle_samples
+
+ def output_size(self) -> int:
+ return self.n_mels * self.lfr_m
+
+ 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.unsqueeze(0).numpy()
+ mat = eend_ola_feature.stft(waveform, self.frame_length, self.frame_shift)
+ mat = eend_ola_feature.transform(mat)
+ mat = mat.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
--
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