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Merge pull request #3147 from tilmankamp/data_set_tool 

Resolves #3146 - Let build_sdb.py also output CSV files and rename it accordingly
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Tilman Kamp 2020-07-21 18:35:50 +02:00 committed by GitHub
parent ffcec7f9aa a982a61d83
commit b18a3a4ef5
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8 changed files with 198 additions and 98 deletions
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92
bin/build_sdb.py
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@ -1,92 +0,0 @@
#!/usr/bin/env python
"""
Tool for building Sample Databases (SDB files) from DeepSpeech CSV files and other SDB files
Use "python3 build_sdb.py -h" for help
"""
import argparse
import progressbar
from deepspeech_training.util.audio import (
AUDIO_TYPE_PCM,
AUDIO_TYPE_OPUS,
AUDIO_TYPE_WAV,
change_audio_types,
)
from deepspeech_training.util.downloader import SIMPLE_BAR
from deepspeech_training.util.sample_collections import (
DirectSDBWriter,
samples_from_sources,
)
from deepspeech_training.util.augmentations import (
parse_augmentations,
apply_sample_augmentations,
SampleAugmentation
)
AUDIO_TYPE_LOOKUP = {"wav": AUDIO_TYPE_WAV, "opus": AUDIO_TYPE_OPUS}
def build_sdb():
audio_type = AUDIO_TYPE_LOOKUP[CLI_ARGS.audio_type]
augmentations = parse_augmentations(CLI_ARGS.augment)
if any(not isinstance(a, SampleAugmentation) for a in augmentations):
print("Warning: Some of the augmentations cannot be applied by this command.")
with DirectSDBWriter(
CLI_ARGS.target, audio_type=audio_type, labeled=not CLI_ARGS.unlabeled
) as sdb_writer:
samples = samples_from_sources(CLI_ARGS.sources, labeled=not CLI_ARGS.unlabeled)
num_samples = len(samples)
if augmentations:
samples = apply_sample_augmentations(samples, audio_type=AUDIO_TYPE_PCM, augmentations=augmentations)
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for sample in bar(
change_audio_types(samples, audio_type=audio_type, bitrate=CLI_ARGS.bitrate, processes=CLI_ARGS.workers)
):
sdb_writer.add(sample)
def handle_args():
parser = argparse.ArgumentParser(
description="Tool for building Sample Databases (SDB files) "
"from DeepSpeech CSV files and other SDB files"
)
parser.add_argument(
"sources",
nargs="+",
help="Source CSV and/or SDB files - "
"Note: For getting a correctly ordered target SDB, source SDBs have to have their samples "
"already ordered from shortest to longest.",
)
parser.add_argument("target", help="SDB file to create")
parser.add_argument(
"--audio-type",
default="opus",
choices=AUDIO_TYPE_LOOKUP.keys(),
help="Audio representation inside target SDB",
)
parser.add_argument(
"--bitrate",
type=int,
help="Bitrate for lossy compressed SDB samples like in case of --audio-type opus",
)
parser.add_argument(
"--workers", type=int, default=None, help="Number of encoding SDB workers"
)
parser.add_argument(
"--unlabeled",
action="store_true",
help="If to build an SDB with unlabeled (audio only) samples - "
"typically used for building noise augmentation corpora",
)
parser.add_argument(
"--augment",
action='append',
help="Add an augmentation operation",
)
return parser.parse_args()
if __name__ == "__main__":
CLI_ARGS = handle_args()
build_sdb()

111
bin/data_set_tool.py Executable file
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@ -0,0 +1,111 @@
#!/usr/bin/env python
'''
Tool for building a combined SDB or CSV sample-set from other sets
Use 'python3 data_set_tool.py -h' for help
'''
import sys
import argparse
import progressbar
from pathlib import Path
from deepspeech_training.util.audio import (
AUDIO_TYPE_PCM,
AUDIO_TYPE_OPUS,
AUDIO_TYPE_WAV,
change_audio_types,
)
from deepspeech_training.util.downloader import SIMPLE_BAR
from deepspeech_training.util.sample_collections import (
CSVWriter,
DirectSDBWriter,
samples_from_sources,
)
from deepspeech_training.util.augmentations import (
parse_augmentations,
apply_sample_augmentations,
SampleAugmentation
)
AUDIO_TYPE_LOOKUP = {'wav': AUDIO_TYPE_WAV, 'opus': AUDIO_TYPE_OPUS}
def build_data_set():
audio_type = AUDIO_TYPE_LOOKUP[CLI_ARGS.audio_type]
augmentations = parse_augmentations(CLI_ARGS.augment)
if any(not isinstance(a, SampleAugmentation) for a in augmentations):
print('Warning: Some of the specified augmentations will not get applied, as this tool only supports '
'overlay, codec, reverb, resample and volume.')
extension = Path(CLI_ARGS.target).suffix.lower()
labeled = not CLI_ARGS.unlabeled
if extension == '.csv':
writer = CSVWriter(CLI_ARGS.target, absolute_paths=CLI_ARGS.absolute_paths, labeled=labeled)
elif extension == '.sdb':
writer = DirectSDBWriter(CLI_ARGS.target, audio_type=audio_type, labeled=labeled)
else:
print('Unknown extension of target file - has to be either .csv or .sdb')
sys.exit(1)
with writer:
samples = samples_from_sources(CLI_ARGS.sources, labeled=not CLI_ARGS.unlabeled)
num_samples = len(samples)
if augmentations:
samples = apply_sample_augmentations(samples, audio_type=AUDIO_TYPE_PCM, augmentations=augmentations)
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for sample in bar(change_audio_types(
samples,
audio_type=audio_type,
bitrate=CLI_ARGS.bitrate,
processes=CLI_ARGS.workers)):
writer.add(sample)
def handle_args():
parser = argparse.ArgumentParser(
description='Tool for building a combined SDB or CSV sample-set from other sets'
)
parser.add_argument(
'sources',
nargs='+',
help='Source CSV and/or SDB files - '
'Note: For getting a correctly ordered target set, source SDBs have to have their samples '
'already ordered from shortest to longest.',
)
parser.add_argument(
'target',
help='SDB or CSV file to create'
)
parser.add_argument(
'--audio-type',
default='opus',
choices=AUDIO_TYPE_LOOKUP.keys(),
help='Audio representation inside target SDB',
)
parser.add_argument(
'--bitrate',
type=int,
help='Bitrate for lossy compressed SDB samples like in case of --audio-type opus',
)
parser.add_argument(
'--workers', type=int, default=None, help='Number of encoding SDB workers'
)
parser.add_argument(
'--unlabeled',
action='store_true',
help='If to build an SDB with unlabeled (audio only) samples - '
'typically used for building noise augmentation corpora',
)
parser.add_argument(
'--absolute-paths',
action='store_true',
help='If to reference samples by their absolute paths when writing CSV files',
)
parser.add_argument(
'--augment',
action='append',
help='Add an augmentation operation',
)
return parser.parse_args()
if __name__ == '__main__':
CLI_ARGS = handle_args()
build_data_set()

2
bin/play.py
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@ -1,7 +1,7 @@
#!/usr/bin/env python
"""
Tool for playing (and augmenting) single samples or samples from Sample Databases (SDB files) and DeepSpeech CSV files
Use "python3 build_sdb.py -h" for help
Use "python3 play.py -h" for help
"""
import os

2
bin/run-tc-ldc93s1_checkpoint_sdb.sh
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@ -13,7 +13,7 @@ fi;
if [ ! -f "${ldc93s1_dir}/ldc93s1.sdb" ]; then
echo "Converting LDC93S1 example data, saving to ${ldc93s1_sdb}."
python -u bin/build_sdb.py ${ldc93s1_csv} ${ldc93s1_sdb}
python -u bin/data_set_tool.py ${ldc93s1_csv} ${ldc93s1_sdb}
fi;
# Force only one visible device because we have a single-sample dataset

2
bin/run-tc-ldc93s1_new_sdb.sh
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@ -16,7 +16,7 @@ fi;
if [ ! -f "${ldc93s1_dir}/ldc93s1.sdb" ]; then
echo "Converting LDC93S1 example data, saving to ${ldc93s1_sdb}."
python -u bin/build_sdb.py ${ldc93s1_csv} ${ldc93s1_sdb}
python -u bin/data_set_tool.py ${ldc93s1_csv} ${ldc93s1_sdb}
fi;
# Force only one visible device because we have a single-sample dataset

2
bin/run-tc-ldc93s1_new_sdb_csv.sh
View File

@ -16,7 +16,7 @@ fi;
if [ ! -f "${ldc93s1_dir}/ldc93s1.sdb" ]; then
echo "Converting LDC93S1 example data, saving to ${ldc93s1_sdb}."
python -u bin/build_sdb.py ${ldc93s1_csv} ${ldc93s1_sdb}
python -u bin/data_set_tool.py ${ldc93s1_csv} ${ldc93s1_sdb}
fi;
# Force only one visible device because we have a single-sample dataset

11
doc/TRAINING.rst
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@ -496,7 +496,7 @@ Example training with all augmentations:
[...]
The ``bin/play.py`` tool also supports ``--augment`` parameters (for sample domain augmentations) and can be used for experimenting with different configurations.
The ``bin/play.py`` and ``bin/data_set_tool.py`` tools also support ``--augment`` parameters (for sample domain augmentations) and can be used for experimenting with different configurations or creating augmented data sets.
Example of playing all samples with reverberation and maximized volume:
@ -510,3 +510,12 @@ Example simulation of the codec augmentation of a wav-file first at the beginnin
bin/play.py --augment codec[p=0.1,bitrate=48000:16000] --clock 0.0 test.wav
bin/play.py --augment codec[p=0.1,bitrate=48000:16000] --clock 1.0 test.wav
Example of creating a pre-augmented test set:
.. code-block:: bash
bin/data_set_tool.py \
--augment overlay[source=noise.sdb,layers=1,snr=20~10] \
--augment resample[rate=12000:8000~4000] \
test.sdb test-augmented.sdb

74
training/deepspeech_training/util/sample_collections.py
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@ -7,7 +7,15 @@ from pathlib import Path
from functools import partial
from .helpers import MEGABYTE, GIGABYTE, Interleaved
from .audio import Sample, DEFAULT_FORMAT, AUDIO_TYPE_OPUS, SERIALIZABLE_AUDIO_TYPES, get_audio_type_from_extension
from .audio import (
Sample,
DEFAULT_FORMAT,
AUDIO_TYPE_PCM,
AUDIO_TYPE_OPUS,
SERIALIZABLE_AUDIO_TYPES,
get_audio_type_from_extension,
write_wav
)
BIG_ENDIAN = 'big'
INT_SIZE = 4
@ -297,6 +305,70 @@ class SDB: # pylint: disable=too-many-instance-attributes
self.close()
class CSVWriter: # pylint: disable=too-many-instance-attributes
"""Sample collection writer for writing a CSV data-set and all its referenced WAV samples"""
def __init__(self,
csv_filename,
absolute_paths=False,
labeled=True):
"""
Parameters
----------
csv_filename : str
Path to the CSV file to write.
Will create a directory (CSV-filename without extension) next to it and fail if it already exists.
absolute_paths : bool
If paths in CSV file should be absolute instead of relative to the CSV file's parent directory.
labeled : bool or None
If True: Writes labeled samples (util.sample_collections.LabeledSample) only.
If False: Ignores transcripts (if available) and writes (unlabeled) util.audio.Sample instances.
"""
self.csv_filename = Path(csv_filename)
self.csv_base_dir = self.csv_filename.parent.resolve().absolute()
self.set_name = self.csv_filename.stem
self.csv_dir = self.csv_base_dir / self.set_name
if self.csv_dir.exists():
raise RuntimeError('"{}" already existing'.format(self.csv_dir))
os.mkdir(str(self.csv_dir))
self.absolute_paths = absolute_paths
fieldnames = ['wav_filename', 'wav_filesize']
self.labeled = labeled
if labeled:
fieldnames.append('transcript')
self.csv_file = open(csv_filename, 'w', encoding='utf-8', newline='')
self.csv_writer = csv.DictWriter(self.csv_file, fieldnames=fieldnames)
self.csv_writer.writeheader()
self.counter = 0
def __enter__(self):
return self
def add(self, sample):
sample_filename = self.csv_dir / 'sample{0:08d}.wav'.format(self.counter)
self.counter += 1
sample.change_audio_type(AUDIO_TYPE_PCM)
write_wav(str(sample_filename), sample.audio, audio_format=sample.audio_format)
sample.sample_id = str(sample_filename.relative_to(self.csv_base_dir))
row = {
'wav_filename': str(sample_filename.absolute()) if self.absolute_paths else sample.sample_id,
'wav_filesize': sample_filename.stat().st_size
}
if self.labeled:
row['transcript'] = sample.transcript
self.csv_writer.writerow(row)
return sample.sample_id
def close(self):
if self.csv_file:
self.csv_file.close()
def __len__(self):
return self.counter
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
class SampleList:
"""Sample collection base class with samples loaded from a list of in-memory paths."""
def __init__(self, samples, labeled=True):