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fixed bug of compute_cmvn_stats.py

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This commit is contained in:
Jingyong Hou 2021-11-10 22:40:21 +08:00
parent 4db050eb67
commit 7df9ced666
8 changed files with 36 additions and 23 deletions
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9
examples/hi_xiaowen/s0/conf/ds_tcn.yaml
View File

@ -5,11 +5,12 @@ dataset_conf:
resample_conf:
resample_rate: 16000
speed_perturb: false
fbank_conf:
feature_extraction_conf:
feature_type: 'fbank'
num_mel_bins: 40
frame_shift: 10
frame_length: 25
dither: 0.1
dither: 1.0
spec_aug: true
spec_aug_conf:
num_t_mask: 1
@ -24,9 +25,9 @@ dataset_conf:
model:
hidden_dim: 64
subsampling:
preprocessing:
type: linear
body:
backbone:
type: tcn
ds: true
num_layers: 4

9
examples/hi_xiaowen/s0/conf/gru.yaml
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@ -5,11 +5,12 @@ dataset_conf:
resample_conf:
resample_rate: 16000
speed_perturb: false
fbank_conf:
feature_extraction_conf:
feature_type: 'fbank'
num_mel_bins: 40
frame_shift: 10
frame_length: 25
dither: 0.1
dither: 1.0
spec_aug: false
spec_aug_conf:
num_t_mask: 2
@ -24,9 +25,9 @@ dataset_conf:
model:
hidden_dim: 128
subsampling:
preprocessing:
type: linear
body:
backbone:
type: gru
num_layers: 2

5
examples/hi_xiaowen/s0/conf/mdtc.yaml
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@ -1,7 +1,4 @@
debug: false
input_dim: 80
output_dim: 2
dataset_conf:
filter_conf:
@ -38,6 +35,8 @@ dataset_conf:
batch_size: 100
model:
input_dim: 80
output_dim: 2
hidden_dim: 64
preprocessing:
type: none

9
examples/hi_xiaowen/s0/conf/tcn.yaml
View File

@ -5,11 +5,12 @@ dataset_conf:
resample_conf:
resample_rate: 16000
speed_perturb: false
fbank_conf:
feature_extraction_conf:
feature_type: 'fbank'
num_mel_bins: 40
frame_shift: 10
frame_length: 25
dither: 0.1
dither: 1.0
spec_aug: false
spec_aug_conf:
num_t_mask: 2
@ -24,9 +25,9 @@ dataset_conf:
model:
hidden_dim: 64
subsampling:
preprocessing:
type: linear
body:
backbone:
type: tcn
ds: false
num_layers: 4

4
examples/hi_xiaowen/s0/run.sh
View File

@ -5,8 +5,8 @@
export CUDA_VISIBLE_DEVICES="0"
stage=2
stop_stage=2
stage=0
stop_stage=4
num_keywords=2
config=conf/mdtc.yaml

2
kws/bin/train.py
View File

@ -135,7 +135,7 @@ def main():
num_workers=args.num_workers,
prefetch_factor=args.prefetch)
input_dim = configs['input_dim']
input_dim = configs['dataset_conf']['feature_extraction_conf']['num_mel_bins']
output_dim = args.num_keywords
# Write model_dir/config.yaml for inference and export

1
kws/dataset/processor.py
View File

@ -165,6 +165,7 @@ def compute_mfcc(
yield dict(key=sample['key'], label=sample['label'], feat=mat)
def compute_fbank(data,
feature_type='fbank',
num_mel_bins=23,
frame_length=25,
frame_shift=10,

20
tools/compute_cmvn_stats.py
View File

@ -18,9 +18,10 @@ torchaudio.set_audio_backend("sox_io")
class CollateFunc(object):
''' Collate function for AudioDataset
'''
def __init__(self, feat_dim, resample_rate):
def __init__(self, feat_dim, feat_type, resample_rate):
self.feat_dim = feat_dim
self.resample_rate = resample_rate
self.feat_type = feat_type
pass
def __call__(self, batch):
@ -50,12 +51,20 @@ class CollateFunc(object):
resample_rate = self.resample_rate
waveform = torchaudio.transforms.Resample(
orig_freq=sample_rate, new_freq=resample_rate)(waveform)
mat = kaldi.fbank(waveform,
if self.feat_type == 'fbank':
mat = kaldi.fbank(waveform,
num_mel_bins=self.feat_dim,
dither=0.0,
energy_floor=0.0,
sample_frequency=resample_rate)
elif self.feat_type == 'mfcc':
mat = kaldi.mfcc(waveform,
num_ceps=self.feat_dim,
num_mel_bins=self.feat_dim,
dither=0.0,
energy_floor=0.0,
sample_frequency=resample_rate,
)
mean_stat += torch.sum(mat, axis=0)
var_stat += torch.sum(torch.square(mat), axis=0)
number += mat.shape[0]
@ -95,13 +104,14 @@ if __name__ == '__main__':
with open(args.train_config, 'r') as fin:
configs = yaml.load(fin, Loader=yaml.FullLoader)
feat_dim = configs['input_dim']
feat_dim = configs['dataset_conf']['feature_extraction_conf']['num_mel_bins']
feat_type = configs['dataset_conf']['feature_extraction_conf']['feature_type']
resample_rate = 0
if 'resample_conf' in configs['dataset_conf']:
resample_rate = configs['dataset_conf']['resample_conf']['resample_rate']
print('using resample and new sample rate is {}'.format(resample_rate))
collate_func = CollateFunc(feat_dim, resample_rate)
collate_func = CollateFunc(feat_dim, feat_type, resample_rate)
dataset = AudioDataset(args.in_scp)
batch_size = 20
data_loader = DataLoader(dataset,