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b9110af9
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DeepSpeech
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体验新版 GitCode,发现更多精彩内容 >>
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b9110af9
编写于
6月 11, 2021
作者:
H
Haoxin Ma
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
feat_dim, vocab_size
上级
3855522e
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
227 addition
and
139 deletion
+227
-139
deepspeech/exps/deepspeech2/model.py
deepspeech/exps/deepspeech2/model.py
+2
-2
deepspeech/frontend/featurizer/speech_featurizer.py
deepspeech/frontend/featurizer/speech_featurizer.py
+0
-43
deepspeech/frontend/utility.py
deepspeech/frontend/utility.py
+1
-1
deepspeech/io/collator.py
deepspeech/io/collator.py
+138
-28
deepspeech/io/dataset.py
deepspeech/io/dataset.py
+65
-63
examples/tiny/s0/conf/deepspeech2.yaml
examples/tiny/s0/conf/deepspeech2.yaml
+21
-2
未找到文件。
deepspeech/exps/deepspeech2/model.py
浏览文件 @
b9110af9
...
...
@@ -137,7 +137,7 @@ class DeepSpeech2Trainer(Trainer):
def
setup_dataloader
(
self
):
config
=
self
.
config
.
clone
()
config
.
defrost
()
config
.
data
.
keep_transcription_text
=
False
config
.
collator
.
keep_transcription_text
=
False
config
.
data
.
manifest
=
config
.
data
.
train_manifest
train_dataset
=
ManifestDataset
.
from_config
(
config
)
...
...
@@ -165,7 +165,7 @@ class DeepSpeech2Trainer(Trainer):
sortagrad
=
config
.
data
.
sortagrad
,
shuffle_method
=
config
.
data
.
shuffle_method
)
collate_fn
=
SpeechCollator
(
config
=
config
,
keep_transcription_text
=
False
)
collate_fn
=
SpeechCollator
.
from_config
(
config
)
self
.
train_loader
=
DataLoader
(
train_dataset
,
batch_sampler
=
batch_sampler
,
...
...
deepspeech/frontend/featurizer/speech_featurizer.py
浏览文件 @
b9110af9
...
...
@@ -104,50 +104,7 @@ class SpeechFeaturizer(object):
speech_segment
.
transcript
)
return
spec_feature
,
text_ids
@
property
def
vocab_size
(
self
):
"""Return the vocabulary size.
Returns:
int: Vocabulary size.
"""
return
self
.
_text_featurizer
.
vocab_size
@
property
def
vocab_list
(
self
):
"""Return the vocabulary in list.
Returns:
List[str]:
"""
return
self
.
_text_featurizer
.
vocab_list
@
property
def
vocab_dict
(
self
):
"""Return the vocabulary in dict.
Returns:
Dict[str, int]:
"""
return
self
.
_text_featurizer
.
vocab_dict
@
property
def
feature_size
(
self
):
"""Return the audio feature size.
Returns:
int: audio feature size.
"""
return
self
.
_audio_featurizer
.
feature_size
@
property
def
stride_ms
(
self
):
"""time length in `ms` unit per frame
Returns:
float: time(ms)/frame
"""
return
self
.
_audio_featurizer
.
stride_ms
@
property
def
text_feature
(
self
):
...
...
deepspeech/frontend/utility.py
浏览文件 @
b9110af9
...
...
@@ -82,7 +82,7 @@ def read_manifest(
]
if
all
(
conditions
):
manifest
.
append
(
json_data
)
return
manifest
return
manifest
,
json_data
[
"feat_shape"
][
-
1
]
def
rms_to_db
(
rms
:
float
):
...
...
deepspeech/io/collator.py
浏览文件 @
b9110af9
...
...
@@ -22,6 +22,8 @@ from deepspeech.frontend.normalizer import FeatureNormalizer
from
deepspeech.frontend.speech
import
SpeechSegment
import
io
import
time
from
yacs.config
import
CfgNode
from
typing
import
Optional
from
collections
import
namedtuple
...
...
@@ -33,51 +35,134 @@ logger = Log(__name__).getlog()
TarLocalData
=
namedtuple
(
'TarLocalData'
,
[
'tar2info'
,
'tar2object'
])
class
SpeechCollator
():
def
__init__
(
self
,
config
,
keep_transcription_text
=
True
):
"""
Padding audio features with zeros to make them have the same shape (or
a user-defined shape) within one bach.
@
classmethod
def
params
(
cls
,
config
:
Optional
[
CfgNode
]
=
None
)
->
CfgNode
:
default
=
CfgNode
(
dict
(
augmentation_config
=
""
,
random_seed
=
0
,
mean_std_filepath
=
""
,
unit_type
=
"char"
,
vocab_filepath
=
""
,
spm_model_prefix
=
""
,
specgram_type
=
'linear'
,
# 'linear', 'mfcc', 'fbank'
feat_dim
=
0
,
# 'mfcc', 'fbank'
delta_delta
=
False
,
# 'mfcc', 'fbank'
stride_ms
=
10.0
,
# ms
window_ms
=
20.0
,
# ms
n_fft
=
None
,
# fft points
max_freq
=
None
,
# None for samplerate/2
target_sample_rate
=
16000
,
# target sample rate
use_dB_normalization
=
True
,
target_dB
=-
20
,
dither
=
1.0
,
# feature dither
keep_transcription_text
=
True
))
if ``keep_transcription_text`` is False, text is token ids else is raw string.
if
config
is
not
None
:
config
.
merge_from_other_cfg
(
default
)
return
default
@
classmethod
def
from_config
(
cls
,
config
):
"""Build a SpeechCollator object from a config.
Args:
config (yacs.config.CfgNode): configs object.
Returns:
SpeechCollator: collator object.
"""
self
.
_keep_transcription_text
=
keep_transcription_text
assert
'augmentation_config'
in
config
.
collator
assert
'keep_transcription_text'
in
config
.
collator
assert
'mean_std_filepath'
in
config
.
collator
assert
'vocab_filepath'
in
config
.
data
assert
'specgram_type'
in
config
.
collator
assert
'n_fft'
in
config
.
collator
assert
config
.
collator
if
isinstance
(
config
.
data
.
augmentation_config
,
(
str
,
bytes
)):
if
config
.
data
.
augmentation_config
:
if
isinstance
(
config
.
collator
.
augmentation_config
,
(
str
,
bytes
)):
if
config
.
collator
.
augmentation_config
:
aug_file
=
io
.
open
(
config
.
data
.
augmentation_config
,
mode
=
'r'
,
encoding
=
'utf8'
)
config
.
collator
.
augmentation_config
,
mode
=
'r'
,
encoding
=
'utf8'
)
else
:
aug_file
=
io
.
StringIO
(
initial_value
=
'{}'
,
newline
=
''
)
else
:
aug_file
=
config
.
data
.
augmentation_config
aug_file
=
config
.
collator
.
augmentation_config
assert
isinstance
(
aug_file
,
io
.
StringIO
)
speech_collator
=
cls
(
aug_file
=
aug_file
,
random_seed
=
0
,
mean_std_filepath
=
config
.
collator
.
mean_std_filepath
,
unit_type
=
config
.
collator
.
unit_type
,
vocab_filepath
=
config
.
data
.
vocab_filepath
,
spm_model_prefix
=
config
.
collator
.
spm_model_prefix
,
specgram_type
=
config
.
collator
.
specgram_type
,
feat_dim
=
config
.
collator
.
feat_dim
,
delta_delta
=
config
.
collator
.
delta_delta
,
stride_ms
=
config
.
collator
.
stride_ms
,
window_ms
=
config
.
collator
.
window_ms
,
n_fft
=
config
.
collator
.
n_fft
,
max_freq
=
config
.
collator
.
max_freq
,
target_sample_rate
=
config
.
collator
.
target_sample_rate
,
use_dB_normalization
=
config
.
collator
.
use_dB_normalization
,
target_dB
=
config
.
collator
.
target_dB
,
dither
=
config
.
collator
.
dither
,
keep_transcription_text
=
config
.
collator
.
keep_transcription_text
)
return
speech_collator
def
__init__
(
self
,
aug_file
,
mean_std_filepath
,
vocab_filepath
,
spm_model_prefix
,
random_seed
=
0
,
unit_type
=
"char"
,
specgram_type
=
'linear'
,
# 'linear', 'mfcc', 'fbank'
feat_dim
=
0
,
# 'mfcc', 'fbank'
delta_delta
=
False
,
# 'mfcc', 'fbank'
stride_ms
=
10.0
,
# ms
window_ms
=
20.0
,
# ms
n_fft
=
None
,
# fft points
max_freq
=
None
,
# None for samplerate/2
target_sample_rate
=
16000
,
# target sample rate
use_dB_normalization
=
True
,
target_dB
=-
20
,
dither
=
1.0
,
keep_transcription_text
=
True
):
"""
Padding audio features with zeros to make them have the same shape (or
a user-defined shape) within one bach.
if ``keep_transcription_text`` is False, text is token ids else is raw string.
"""
self
.
_keep_transcription_text
=
keep_transcription_text
self
.
_local_data
=
TarLocalData
(
tar2info
=
{},
tar2object
=
{})
self
.
_augmentation_pipeline
=
AugmentationPipeline
(
augmentation_config
=
aug_file
.
read
(),
random_seed
=
config
.
data
.
random_seed
)
random_seed
=
random_seed
)
self
.
_normalizer
=
FeatureNormalizer
(
config
.
data
.
mean_std_filepath
)
if
config
.
data
.
mean_std_filepath
else
None
mean_std_filepath
)
if
mean_std_filepath
else
None
self
.
_stride_ms
=
config
.
data
.
stride_ms
self
.
_target_sample_rate
=
config
.
data
.
target_sample_rate
self
.
_stride_ms
=
stride_ms
self
.
_target_sample_rate
=
target_sample_rate
self
.
_speech_featurizer
=
SpeechFeaturizer
(
unit_type
=
config
.
data
.
unit_type
,
vocab_filepath
=
config
.
data
.
vocab_filepath
,
spm_model_prefix
=
config
.
data
.
spm_model_prefix
,
specgram_type
=
config
.
data
.
specgram_type
,
feat_dim
=
config
.
data
.
feat_dim
,
delta_delta
=
config
.
data
.
delta_delta
,
stride_ms
=
config
.
data
.
stride_ms
,
window_ms
=
config
.
data
.
window_ms
,
n_fft
=
config
.
data
.
n_fft
,
max_freq
=
config
.
data
.
max_freq
,
target_sample_rate
=
config
.
data
.
target_sample_rate
,
use_dB_normalization
=
config
.
data
.
use_dB_normalization
,
target_dB
=
config
.
data
.
target_dB
,
dither
=
config
.
data
.
dither
)
unit_type
=
unit_type
,
vocab_filepath
=
vocab_filepath
,
spm_model_prefix
=
spm_model_prefix
,
specgram_type
=
specgram_type
,
feat_dim
=
feat_dim
,
delta_delta
=
delta_delta
,
stride_ms
=
stride_ms
,
window_ms
=
window_ms
,
n_fft
=
n_fft
,
max_freq
=
max_freq
,
target_sample_rate
=
target_sample_rate
,
use_dB_normalization
=
use_dB_normalization
,
target_dB
=
target_dB
,
dither
=
dither
)
def
_parse_tar
(
self
,
file
):
"""Parse a tar file to get a tarfile object
...
...
@@ -196,3 +281,28 @@ class SpeechCollator():
texts
,
padding_value
=
IGNORE_ID
).
astype
(
np
.
int64
)
text_lens
=
np
.
array
(
text_lens
).
astype
(
np
.
int64
)
return
utts
,
padded_audios
,
audio_lens
,
padded_texts
,
text_lens
@
property
def
vocab_size
(
self
):
return
self
.
_speech_featurizer
.
vocab_size
@
property
def
vocab_list
(
self
):
return
self
.
_speech_featurizer
.
vocab_list
@
property
def
vocab_dict
(
self
):
return
self
.
_speech_featurizer
.
vocab_dict
@
property
def
text_feature
(
self
):
return
self
.
_text_featurizer
self
.
_speech_featurizer
.
text_feature
@
property
def
feature_size
(
self
):
return
self
.
_speech_featurizer
.
feature_size
@
property
def
stride_ms
(
self
):
return
self
.
_speech_featurizer
.
stride_ms
deepspeech/io/dataset.py
浏览文件 @
b9110af9
...
...
@@ -55,20 +55,6 @@ class ManifestDataset(Dataset):
min_output_len
=
0.0
,
max_output_input_ratio
=
float
(
'inf'
),
min_output_input_ratio
=
0.0
,
stride_ms
=
10.0
,
# ms
window_ms
=
20.0
,
# ms
n_fft
=
None
,
# fft points
max_freq
=
None
,
# None for samplerate/2
raw_wav
=
True
,
# use raw_wav or kaldi feature
specgram_type
=
'linear'
,
# 'linear', 'mfcc', 'fbank'
feat_dim
=
0
,
# 'mfcc', 'fbank'
delta_delta
=
False
,
# 'mfcc', 'fbank'
dither
=
1.0
,
# feature dither
target_sample_rate
=
16000
,
# target sample rate
use_dB_normalization
=
True
,
target_dB
=-
20
,
random_seed
=
0
,
keep_transcription_text
=
False
,
batch_size
=
32
,
# batch size
num_workers
=
0
,
# data loader workers
sortagrad
=
False
,
# sorted in first epoch when True
...
...
@@ -116,21 +102,19 @@ class ManifestDataset(Dataset):
min_output_len
=
config
.
data
.
min_output_len
,
max_output_input_ratio
=
config
.
data
.
max_output_input_ratio
,
min_output_input_ratio
=
config
.
data
.
min_output_input_ratio
,
stride_ms
=
config
.
data
.
stride_ms
,
window_ms
=
config
.
data
.
window_ms
,
n_fft
=
config
.
data
.
n_fft
,
max_freq
=
config
.
data
.
max_freq
,
target_sample_rate
=
config
.
data
.
target_sample_rate
,
specgram_type
=
config
.
data
.
specgram_type
,
feat_dim
=
config
.
data
.
feat_dim
,
delta_delta
=
config
.
data
.
delta_delta
,
dither
=
config
.
data
.
dither
,
use_dB_normalization
=
config
.
data
.
use_dB_normalization
,
target_dB
=
config
.
data
.
target_dB
,
random_seed
=
config
.
data
.
random_seed
,
keep_transcription_text
=
config
.
data
.
keep_transcription_text
)
)
return
dataset
def
_read_vocab
(
self
,
vocab_filepath
):
"""Load vocabulary from file."""
vocab_lines
=
[]
with
open
(
vocab_filepath
,
'r'
,
encoding
=
'utf-8'
)
as
file
:
vocab_lines
.
extend
(
file
.
readlines
())
vocab_list
=
[
line
[:
-
1
]
for
line
in
vocab_lines
]
return
vocab_list
def
__init__
(
self
,
manifest_path
,
unit_type
,
...
...
@@ -143,20 +127,7 @@ class ManifestDataset(Dataset):
max_output_len
=
float
(
'inf'
),
min_output_len
=
0.0
,
max_output_input_ratio
=
float
(
'inf'
),
min_output_input_ratio
=
0.0
,
stride_ms
=
10.0
,
window_ms
=
20.0
,
n_fft
=
None
,
max_freq
=
None
,
target_sample_rate
=
16000
,
specgram_type
=
'linear'
,
feat_dim
=
None
,
delta_delta
=
False
,
dither
=
1.0
,
use_dB_normalization
=
True
,
target_dB
=-
20
,
random_seed
=
0
,
keep_transcription_text
=
False
):
min_output_input_ratio
=
0.0
):
"""Manifest Dataset
Args:
...
...
@@ -186,30 +157,11 @@ class ManifestDataset(Dataset):
keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
"""
super
().
__init__
()
self
.
_stride_ms
=
stride_ms
self
.
_target_sample_rate
=
target_sample_rate
self
.
_speech_featurizer
=
SpeechFeaturizer
(
unit_type
=
unit_type
,
vocab_filepath
=
vocab_filepath
,
spm_model_prefix
=
spm_model_prefix
,
specgram_type
=
specgram_type
,
feat_dim
=
feat_dim
,
delta_delta
=
delta_delta
,
stride_ms
=
stride_ms
,
window_ms
=
window_ms
,
n_fft
=
n_fft
,
max_freq
=
max_freq
,
target_sample_rate
=
target_sample_rate
,
use_dB_normalization
=
use_dB_normalization
,
target_dB
=
target_dB
,
dither
=
dither
)
self
.
_rng
=
np
.
random
.
RandomState
(
random_seed
)
self
.
_keep_transcription_text
=
keep_transcription_text
# self._rng = np.random.RandomState(random_seed)
# read manifest
self
.
_manifest
=
read_manifest
(
self
.
_manifest
,
self
.
_feature_size
=
read_manifest
(
manifest_path
=
manifest_path
,
max_input_len
=
max_input_len
,
min_input_len
=
min_input_len
,
...
...
@@ -219,9 +171,59 @@ class ManifestDataset(Dataset):
min_output_input_ratio
=
min_output_input_ratio
)
self
.
_manifest
.
sort
(
key
=
lambda
x
:
x
[
"feat_shape"
][
0
])
self
.
_vocab_list
=
self
.
_read_vocab
(
vocab_filepath
)
@
property
def
manifest
(
self
):
return
self
.
_manifest
@
property
def
vocab_size
(
self
):
"""Return the vocabulary size.
Returns:
int: Vocabulary size.
"""
return
len
(
self
.
_vocab_list
)
@
property
def
vocab_list
(
self
):
"""Return the vocabulary in list.
Returns:
List[str]:
"""
return
self
.
_vocab_list
@
property
def
vocab_dict
(
self
):
"""Return the vocabulary in dict.
Returns:
Dict[str, int]:
"""
vocab_dict
=
dict
(
[(
token
,
idx
)
for
(
idx
,
token
)
in
enumerate
(
self
.
_vocab_list
)])
return
vocab_dict
@
property
def
feature_size
(
self
):
"""Return the audio feature size.
Returns:
int: audio feature size.
"""
return
self
.
_feature_size
@
property
def
stride_ms
(
self
):
"""time length in `ms` unit per frame
Returns:
float: time(ms)/frame
"""
return
self
.
_audio_featurizer
.
stride_ms
def
__len__
(
self
):
return
len
(
self
.
_manifest
)
...
...
examples/tiny/s0/conf/deepspeech2.yaml
浏览文件 @
b9110af9
...
...
@@ -4,9 +4,10 @@ data:
dev_manifest
:
data/manifest.tiny
test_manifest
:
data/manifest.tiny
mean_std_filepath
:
data/mean_std.json
unit_type
:
char
vocab_filepath
:
data/vocab.txt
augmentation_config
:
conf/augmentation.json
batch_size
:
2
batch_size
:
4
min_input_len
:
0.0
max_input_len
:
27.0
min_output_len
:
0.0
...
...
@@ -28,6 +29,24 @@ data:
sortagrad
:
True
shuffle_method
:
batch_shuffle
num_workers
:
0
collator
:
augmentation_config
:
conf/augmentation.json
random_seed
:
0
mean_std_filepath
:
data/mean_std.json
spm_model_prefix
:
specgram_type
:
linear
feat_dim
:
delta_delta
:
False
stride_ms
:
10.0
window_ms
:
20.0
n_fft
:
None
max_freq
:
None
target_sample_rate
:
16000
use_dB_normalization
:
True
target_dB
:
-20
dither
:
1.0
keep_transcription_text
:
True
model
:
num_conv_layers
:
2
...
...
@@ -37,7 +56,7 @@ model:
share_rnn_weights
:
True
training
:
n_epoch
:
10
n_epoch
:
21
lr
:
1e-5
lr_decay
:
1.0
weight_decay
:
1e-06
...
...
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