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xuchen
Fairseq-S2T
Commits
13b6f080
Commit
13b6f080
authored
Apr 07, 2021
by
xuchen
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add the general scripts to process the st data
parent
0704bedb
隐藏空白字符变更
内嵌
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1 个修改的文件
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372 行增加
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-0
examples/speech_to_text/prep_st_data.py
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examples/speech_to_text/prep_st_data.py
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13b6f080
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import
argparse
import
logging
import
os
from
pathlib
import
Path
import
shutil
from
itertools
import
groupby
from
tempfile
import
NamedTemporaryFile
from
typing
import
Tuple
import
string
import
pickle
import
numpy
as
np
import
pandas
as
pd
import
torchaudio
from
examples.speech_to_text.data_utils
import
(
create_zip
,
extract_fbank_features
,
filter_manifest_df
,
gen_config_yaml
,
gen_vocab
,
get_zip_manifest
,
load_df_from_tsv
,
save_df_to_tsv
,
cal_gcmvn_stats
,
)
from
torch.utils.data
import
Dataset
from
tqdm
import
tqdm
log
=
logging
.
getLogger
(
__name__
)
MANIFEST_COLUMNS
=
[
"id"
,
"audio"
,
"n_frames"
,
"tgt_text"
,
"speaker"
]
class
ST_Dataset
(
Dataset
):
"""
Create a Dataset for MuST-C. Each item is a tuple of the form:
waveform, sample_rate, source utterance, target utterance, speaker_id,
utterance_id
"""
def
__init__
(
self
,
root
:
str
,
src_lang
,
tgt_lang
:
str
,
split
:
str
,
speed_perturb
:
bool
=
False
)
->
None
:
_root
=
Path
(
root
)
/
f
"{src_lang}-{tgt_lang}"
/
split
wav_root
,
txt_root
=
_root
/
"wav"
,
_root
/
"txt"
assert
_root
.
is_dir
()
and
wav_root
.
is_dir
()
and
txt_root
.
is_dir
(),
(
_root
,
wav_root
,
txt_root
)
# Load audio segments
try
:
import
yaml
except
ImportError
:
print
(
"Please install PyYAML to load the MuST-C YAML files"
)
with
open
(
txt_root
/
f
"{split}.yaml"
)
as
f
:
segments
=
yaml
.
load
(
f
,
Loader
=
yaml
.
BaseLoader
)
self
.
speed_perturb
=
[
0.9
,
1.0
,
1.1
]
if
speed_perturb
and
split
.
startswith
(
"train"
)
else
None
# Load source and target utterances
for
_lang
in
[
src_lang
,
tgt_lang
]:
with
open
(
txt_root
/
f
"{split}.{_lang}"
)
as
f
:
utterances
=
[
r
.
strip
()
for
r
in
f
]
assert
len
(
segments
)
==
len
(
utterances
)
for
i
,
u
in
enumerate
(
utterances
):
segments
[
i
][
_lang
]
=
u
# Gather info
self
.
data
=
[]
for
wav_filename
,
_seg_group
in
groupby
(
segments
,
lambda
x
:
x
[
"wav"
]):
wav_path
=
wav_root
/
wav_filename
try
:
sample_rate
=
torchaudio
.
info
(
wav_path
.
as_posix
())[
0
]
.
rate
except
TypeError
:
sample_rate
=
torchaudio
.
info
(
wav_path
.
as_posix
())
.
sample_rate
seg_group
=
sorted
(
_seg_group
,
key
=
lambda
x
:
x
[
"offset"
])
for
i
,
segment
in
enumerate
(
seg_group
):
offset
=
int
(
float
(
segment
[
"offset"
])
*
sample_rate
)
n_frames
=
int
(
float
(
segment
[
"duration"
])
*
sample_rate
)
_id
=
f
"{wav_path.stem}_{i}"
self
.
data
.
append
(
(
wav_path
.
as_posix
(),
offset
,
n_frames
,
sample_rate
,
segment
[
src_lang
],
segment
[
tgt_lang
],
segment
[
"speaker_id"
],
_id
,
)
)
def
__getitem__
(
self
,
n
:
int
):
wav_path
,
offset
,
n_frames
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
utt_id
=
self
.
data
[
n
]
items
=
[]
if
self
.
speed_perturb
is
None
:
waveform
,
_
=
torchaudio
.
load
(
wav_path
,
frame_offset
=
offset
,
num_frames
=
n_frames
)
items
.
append
([
waveform
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
utt_id
])
else
:
for
speed
in
self
.
speed_perturb
:
sp_utt_id
=
f
"sp{speed}_"
+
utt_id
if
speed
==
1.0
:
waveform
,
_
=
torchaudio
.
load
(
wav_path
,
frame_offset
=
offset
,
num_frames
=
n_frames
)
else
:
waveform
,
_
=
torchaudio
.
load
(
wav_path
,
frame_offset
=
offset
,
num_frames
=
n_frames
)
effects
=
[
[
"speed"
,
f
"{speed}"
],
[
"rate"
,
f
"{sr}"
]
]
waveform
,
_
=
torchaudio
.
sox_effects
.
apply_effects_tensor
(
waveform
,
sr
,
effects
)
items
.
append
([
waveform
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
sp_utt_id
])
return
items
def
get_fast
(
self
,
n
:
int
):
wav_path
,
offset
,
n_frames
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
utt_id
=
self
.
data
[
n
]
items
=
[]
if
self
.
speed_perturb
is
None
:
items
.
append
([
wav_path
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
utt_id
])
else
:
for
speed
in
self
.
speed_perturb
:
sp_utt_id
=
f
"sp{speed}_"
+
utt_id
items
.
append
([
wav_path
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
sp_utt_id
])
return
items
def
get_src_text
(
self
):
src_text
=
[]
for
item
in
self
.
data
:
src_text
.
append
(
item
[
4
])
return
src_text
def
get_tgt_text
(
self
):
tgt_text
=
[]
for
item
in
self
.
data
:
tgt_text
.
append
(
item
[
5
])
return
tgt_text
def
__len__
(
self
)
->
int
:
return
len
(
self
.
data
)
def
process
(
args
):
root
=
Path
(
args
.
data_root
)
.
absolute
()
splits
=
args
.
splits
.
split
(
","
)
src_lang
=
args
.
src_lang
tgt_lang
=
args
.
tgt_lang
cur_root
=
root
/
f
"{src_lang}-{tgt_lang}"
if
not
cur_root
.
is_dir
():
print
(
f
"{cur_root.as_posix()} does not exist. Skipped."
)
return
if
args
.
output_root
is
None
:
output_root
=
cur_root
else
:
output_root
=
Path
(
args
.
output_root
)
.
absolute
()
/
f
"{src_lang}-{tgt_lang}"
# Extract features
if
args
.
speed_perturb
:
feature_root
=
output_root
/
"fbank80_sp"
else
:
feature_root
=
output_root
/
"fbank80"
feature_root
.
mkdir
(
exist_ok
=
True
)
if
args
.
speed_perturb
:
zip_path
=
output_root
/
"fbank80_sp.zip"
else
:
zip_path
=
output_root
/
"fbank80.zip"
frame_path
=
output_root
/
"frame.pkl"
frame_dict
=
{}
index
=
0
gen_feature_flag
=
False
if
not
Path
.
exists
(
zip_path
):
gen_feature_flag
=
True
gen_frame_flag
=
False
if
not
Path
.
exists
(
frame_path
):
gen_frame_flag
=
True
if
args
.
overwrite
or
gen_feature_flag
or
gen_frame_flag
:
for
split
in
splits
:
print
(
f
"Fetching split {split}..."
)
dataset
=
ST_Dataset
(
root
.
as_posix
(),
src_lang
,
tgt_lang
,
split
,
args
.
speed_perturb
)
is_train_split
=
split
.
startswith
(
"train"
)
print
(
"Extracting log mel filter bank features..."
)
if
is_train_split
and
args
.
cmvn_type
==
"global"
:
print
(
"And estimating cepstral mean and variance stats..."
)
gcmvn_feature_list
=
[]
for
items
in
tqdm
(
dataset
):
for
item
in
items
:
index
+=
1
waveform
,
sr
,
_
,
_
,
_
,
utt_id
=
item
frame_dict
[
utt_id
]
=
waveform
.
size
(
1
)
if
gen_feature_flag
:
features_path
=
(
feature_root
/
f
"{utt_id}.npy"
)
.
as_posix
()
features
=
extract_fbank_features
(
waveform
,
sr
,
Path
(
features_path
))
if
split
==
'train'
and
args
.
cmvn_type
==
"global"
and
not
utt_id
.
startswith
(
"sp"
):
if
len
(
gcmvn_feature_list
)
<
args
.
gcmvn_max_num
:
gcmvn_feature_list
.
append
(
features
)
if
is_train_split
and
args
.
size
!=
-
1
and
index
>
args
.
size
:
break
if
is_train_split
and
args
.
cmvn_type
==
"global"
:
# Estimate and save cmv
stats
=
cal_gcmvn_stats
(
gcmvn_feature_list
)
with
open
(
output_root
/
"gcmvn.npz"
,
"wb"
)
as
f
:
np
.
savez
(
f
,
mean
=
stats
[
"mean"
],
std
=
stats
[
"std"
])
with
open
(
frame_path
,
"wb"
)
as
f
:
pickle
.
dump
(
frame_dict
,
f
)
# Pack features into ZIP
print
(
"ZIPing features..."
)
create_zip
(
feature_root
,
zip_path
)
gen_manifest_flag
=
False
for
split
in
splits
:
if
not
Path
.
exists
(
output_root
/
f
"{split}_{args.task}.tsv"
):
gen_manifest_flag
=
True
break
train_text
=
[]
if
args
.
overwrite
or
gen_manifest_flag
:
if
len
(
frame_dict
)
==
0
:
with
open
(
frame_path
,
"rb"
)
as
f
:
frame_dict
=
pickle
.
load
(
f
)
print
(
"Fetching ZIP manifest..."
)
zip_manifest
=
get_zip_manifest
(
zip_path
)
# Generate TSV manifest
print
(
"Generating manifest..."
)
for
split
in
splits
:
is_train_split
=
split
.
startswith
(
"train"
)
manifest
=
{
c
:
[]
for
c
in
MANIFEST_COLUMNS
}
if
args
.
task
==
"st"
and
args
.
add_src
:
manifest
[
"src_text"
]
=
[]
dataset
=
ST_Dataset
(
args
.
data_root
,
src_lang
,
tgt_lang
,
split
,
args
.
speed_perturb
)
for
idx
in
range
(
len
(
dataset
)):
items
=
dataset
.
get_fast
(
idx
)
for
item
in
items
:
_
,
sr
,
src_utt
,
tgt_utt
,
speaker_id
,
utt_id
=
item
manifest
[
"id"
]
.
append
(
utt_id
)
manifest
[
"audio"
]
.
append
(
zip_manifest
[
utt_id
])
duration_ms
=
int
(
frame_dict
[
utt_id
]
/
sr
*
1000
)
manifest
[
"n_frames"
]
.
append
(
int
(
1
+
(
duration_ms
-
25
)
/
10
))
if
args
.
lowercase_src
:
src_utt
=
src_utt
.
lower
()
if
args
.
rm_punc_src
:
for
w
in
string
.
punctuation
:
src_utt
=
src_utt
.
replace
(
w
,
""
)
manifest
[
"tgt_text"
]
.
append
(
src_utt
if
args
.
task
==
"asr"
else
tgt_utt
)
if
args
.
task
==
"st"
and
args
.
add_src
:
manifest
[
"src_text"
]
.
append
(
src_utt
)
manifest
[
"speaker"
]
.
append
(
speaker_id
)
if
is_train_split
and
args
.
size
!=
-
1
and
len
(
manifest
[
"id"
])
>
args
.
size
:
break
if
is_train_split
:
if
args
.
task
==
"st"
and
args
.
add_src
and
args
.
share
:
train_text
.
extend
(
manifest
[
"src_text"
])
train_text
.
extend
(
manifest
[
"tgt_text"
])
df
=
pd
.
DataFrame
.
from_dict
(
manifest
)
df
=
filter_manifest_df
(
df
,
is_train_split
=
is_train_split
)
save_df_to_tsv
(
df
,
output_root
/
f
"{split}_{args.task}.tsv"
)
# Generate vocab
v_size_str
=
""
if
args
.
vocab_type
==
"char"
else
str
(
args
.
vocab_size
)
spm_filename_prefix
=
f
"spm_{args.vocab_type}{v_size_str}_{args.task}"
if
args
.
task
==
"st"
and
args
.
add_src
:
if
args
.
share
:
spm_filename_prefix
=
f
"spm_{args.vocab_type}{v_size_str}_{args.task}_share"
asr_spm_filename
=
spm_filename_prefix
+
".model"
else
:
asr_spm_filename
=
args
.
asr_prefix
+
".model"
else
:
asr_spm_filename
=
None
if
len
(
train_text
)
==
0
:
print
(
"Loading the training text to build dictionary..."
)
for
split
in
splits
:
if
split
.
startswith
(
"train"
):
dataset
=
ST_Dataset
(
args
.
data_root
,
src_lang
,
tgt_lang
,
split
)
src_text
=
dataset
.
get_src_text
()
tgt_text
=
dataset
.
get_tgt_text
()
for
src_utt
,
tgt_utt
in
zip
(
src_text
,
tgt_text
):
if
args
.
task
==
"st"
and
args
.
add_src
and
args
.
share
:
if
args
.
lowercase_src
:
src_utt
=
src_utt
.
lower
()
if
args
.
rm_punc_src
:
src_utt
=
src_utt
.
translate
(
None
,
string
.
punctuation
)
train_text
.
append
(
src_utt
)
train_text
.
append
(
tgt_utt
)
with
NamedTemporaryFile
(
mode
=
"w"
)
as
f
:
for
t
in
train_text
:
f
.
write
(
t
+
"
\n
"
)
gen_vocab
(
Path
(
f
.
name
),
output_root
/
spm_filename_prefix
,
args
.
vocab_type
,
args
.
vocab_size
,
)
# Generate config YAML
yaml_filename
=
f
"config_{args.task}.yaml"
if
args
.
task
==
"st"
and
args
.
add_src
and
args
.
share
:
yaml_filename
=
f
"config_{args.task}_share.yaml"
gen_config_yaml
(
output_root
,
spm_filename_prefix
+
".model"
,
yaml_filename
=
yaml_filename
,
specaugment_policy
=
"lb"
,
cmvn_type
=
args
.
cmvn_type
,
gcmvn_path
=
(
output_root
/
"gcmvn.npz"
if
args
.
cmvn_type
==
"global"
else
None
),
asr_spm_filename
=
asr_spm_filename
,
share_src_and_tgt
=
True
if
args
.
task
==
"asr"
else
False
)
# Clean up
shutil
.
rmtree
(
feature_root
)
def
main
():
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"--data-root"
,
"-d"
,
required
=
True
,
type
=
str
)
parser
.
add_argument
(
"--output-root"
,
"-o"
,
default
=
None
,
type
=
str
)
parser
.
add_argument
(
"--vocab-type"
,
default
=
"unigram"
,
required
=
True
,
type
=
str
,
choices
=
[
"bpe"
,
"unigram"
,
"char"
],
),
parser
.
add_argument
(
"--vocab-size"
,
default
=
8000
,
type
=
int
)
parser
.
add_argument
(
"--task"
,
type
=
str
,
choices
=
[
"asr"
,
"st"
])
parser
.
add_argument
(
"--src-lang"
,
type
=
str
,
required
=
True
,
help
=
"source language"
)
parser
.
add_argument
(
"--tgt-lang"
,
type
=
str
,
required
=
True
,
help
=
"target language"
)
parser
.
add_argument
(
"--splits"
,
type
=
str
,
default
=
"train,dev,test"
,
help
=
"dataset splits"
)
parser
.
add_argument
(
"--size"
,
default
=-
1
,
type
=
int
)
parser
.
add_argument
(
"--speed-perturb"
,
action
=
"store_true"
,
default
=
False
,
help
=
"apply speed perturbation on wave file"
)
parser
.
add_argument
(
"--share"
,
action
=
"store_true"
,
help
=
"share the tokenizer and dictionary of the transcription and translation"
)
parser
.
add_argument
(
"--add-src"
,
action
=
"store_true"
,
help
=
"add the src text for st task"
)
parser
.
add_argument
(
"--asr-prefix"
,
type
=
str
,
help
=
"prefix of the asr dict"
)
parser
.
add_argument
(
"--lowercase-src"
,
action
=
"store_true"
,
help
=
"lowercase the source text"
)
parser
.
add_argument
(
"--rm-punc-src"
,
action
=
"store_true"
,
help
=
"remove the punctuation of the source text"
)
parser
.
add_argument
(
"--cmvn-type"
,
default
=
"utterance"
,
choices
=
[
"global"
,
"utterance"
],
help
=
"The type of cepstral mean and variance normalization"
)
parser
.
add_argument
(
"--overwrite"
,
action
=
"store_true"
,
help
=
"overwrite the existing files"
)
parser
.
add_argument
(
"--gcmvn-max-num"
,
default
=
150000
,
type
=
int
,
help
=
(
"Maximum number of sentences to use to estimate"
"global mean and variance"
))
args
=
parser
.
parse_args
()
process
(
args
)
if
__name__
==
"__main__"
:
main
()
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