From 3d9f094e9652d4b84894c6fd4eae39a4a753b0f0 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 16 五月 2023 23:48:00 +0800
Subject: [PATCH] train
---
funasr/models/frontend/wav_frontend.py | 35 ++++++++++++++---------------------
1 files changed, 14 insertions(+), 21 deletions(-)
diff --git a/funasr/models/frontend/wav_frontend.py b/funasr/models/frontend/wav_frontend.py
index ca22175..35fab57 100644
--- a/funasr/models/frontend/wav_frontend.py
+++ b/funasr/models/frontend/wav_frontend.py
@@ -11,8 +11,6 @@
import funasr.models.frontend.eend_ola_feature as eend_ola_feature
from funasr.models.frontend.abs_frontend import AbsFrontend
-from modelscope.utils.logger import get_logger
-logger = get_logger()
def load_cmvn(cmvn_file):
with open(cmvn_file, 'r', encoding='utf-8') as f:
@@ -36,11 +34,11 @@
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)
+ cmvn = torch.as_tensor(cmvn, dtype=torch.float32)
return cmvn
-def apply_cmvn(inputs, cmvn_file): # noqa
+def apply_cmvn(inputs, cmvn): # noqa
"""
Apply CMVN with mvn data
"""
@@ -49,11 +47,10 @@
dtype = inputs.dtype
frame, dim = inputs.shape
- cmvn = load_cmvn(cmvn_file)
- means = np.tile(cmvn[0:1, :dim], (frame, 1))
- vars = np.tile(cmvn[1:2, :dim], (frame, 1))
- inputs += torch.from_numpy(means).type(dtype).to(device)
- inputs *= torch.from_numpy(vars).type(dtype).to(device)
+ means = cmvn[0:1, :dim]
+ vars = cmvn[1:2, :dim]
+ inputs += means.to(device)
+ inputs *= vars.to(device)
return inputs.type(torch.float32)
@@ -113,6 +110,7 @@
self.dither = dither
self.snip_edges = snip_edges
self.upsacle_samples = upsacle_samples
+ self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file)
def output_size(self) -> int:
return self.n_mels * self.lfr_m
@@ -142,8 +140,8 @@
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)
+ if self.cmvn is not None:
+ mat = apply_cmvn(mat, self.cmvn)
feat_length = mat.size(0)
feats.append(mat)
feats_lens.append(feat_length)
@@ -196,8 +194,8 @@
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)
+ if self.cmvn is not None:
+ mat = apply_cmvn(mat, self.cmvn)
feat_length = mat.size(0)
feats.append(mat)
feats_lens.append(feat_length)
@@ -425,10 +423,8 @@
reserve_frame_idx = lfr_splice_frame_idxs[0] - minus_frame
# print('reserve_frame_idx: ' + str(reserve_frame_idx))
# print('frame_frame: ' + str(frame_from_waveforms))
- self.reserve_waveforms = self.waveforms[:,
- reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length]
- sample_length = (
- frame_from_waveforms - 1) * self.frame_shift_sample_length + self.frame_sample_length
+ self.reserve_waveforms = self.waveforms[:, reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length]
+ sample_length = (frame_from_waveforms - 1) * self.frame_shift_sample_length + self.frame_sample_length
self.waveforms = self.waveforms[:, :sample_length]
else:
# update self.reserve_waveforms and self.lfr_splice_cache
@@ -487,9 +483,6 @@
batch_size = input.size(0)
feats = []
feats_lens = []
- logger.info("batch_size: {}".format(batch_size))
- logger.info("input: {}".format(input))
- logger.info("input_lengths: {}".format(input_lengths))
for i in range(batch_size):
waveform_length = input_lengths[i]
waveform = input[i][:waveform_length]
@@ -507,4 +500,4 @@
feats_pad = pad_sequence(feats,
batch_first=True,
padding_value=0.0)
- return feats_pad, feats_lens
+ return feats_pad, feats_lens
\ No newline at end of file
--
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