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This commit is contained in:
Reuben Morais 2021-05-18 13:45:52 +02:00
parent 14aee5d35b
commit 43a6c3e62a
140 changed files with 4008 additions and 2214 deletions
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4
.pre-commit-config.yaml
View File

@ -22,7 +22,3 @@ repos:
- id: isort
name: isort (pyi)
types: [pyi]
- repo: https://github.com/pycqa/pylint
rev: v2.8.2
hooks:
- id: pylint

1
README.rst
View File

@ -67,4 +67,3 @@ Links & Resources
- `see the latest release on GitHub <https://github.com/coqui-ai/STT/releases/latest>`_
* - 🤝 **Contribution Guidelines**
- `CONTRIBUTING.rst <CONTRIBUTING.rst>`_

30
bin/compare_samples.py
View File

@ -2,10 +2,10 @@
"""
Tool for comparing two wav samples
"""
import sys
import argparse
import numpy as np
import sys
import numpy as np
from coqui_stt_training.util.audio import AUDIO_TYPE_NP, mean_dbfs
from coqui_stt_training.util.sample_collections import load_sample
@ -19,9 +19,17 @@ def compare_samples():
sample1 = load_sample(CLI_ARGS.sample1).unpack()
sample2 = load_sample(CLI_ARGS.sample2).unpack()
if sample1.audio_format != sample2.audio_format:
fail('Samples differ on: audio-format ({} and {})'.format(sample1.audio_format, sample2.audio_format))
fail(
"Samples differ on: audio-format ({} and {})".format(
sample1.audio_format, sample2.audio_format
)
)
if abs(sample1.duration - sample2.duration) > 0.001:
fail('Samples differ on: duration ({} and {})'.format(sample1.duration, sample2.duration))
fail(
"Samples differ on: duration ({} and {})".format(
sample1.duration, sample2.duration
)
)
sample1.change_audio_type(AUDIO_TYPE_NP)
sample2.change_audio_type(AUDIO_TYPE_NP)
samples = [sample1, sample2]
@ -30,8 +38,10 @@ def compare_samples():
samples[largest].audio = samples[largest].audio[: len(samples[smallest].audio)]
audio_diff = samples[largest].audio - samples[smallest].audio
diff_dbfs = mean_dbfs(audio_diff)
differ_msg = 'Samples differ on: sample data ({:0.2f} dB difference) '.format(diff_dbfs)
equal_msg = 'Samples are considered equal ({:0.2f} dB difference)'.format(diff_dbfs)
differ_msg = "Samples differ on: sample data ({:0.2f} dB difference) ".format(
diff_dbfs
)
equal_msg = "Samples are considered equal ({:0.2f} dB difference)".format(diff_dbfs)
if CLI_ARGS.if_differ:
if diff_dbfs <= CLI_ARGS.threshold:
fail(equal_msg)
@ -50,8 +60,12 @@ def handle_args():
)
parser.add_argument("sample1", help="Filename of sample 1 to compare")
parser.add_argument("sample2", help="Filename of sample 2 to compare")
parser.add_argument("--threshold", type=float, default=-60.0,
help="dB of sample deltas above which they are considered different")
parser.add_argument(
"--threshold",
type=float,
default=-60.0,
help="dB of sample deltas above which they are considered different",
)
parser.add_argument(
"--if-differ",
action="store_true",

124
bin/data_set_tool.py
View File

@ -1,19 +1,24 @@
#!/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
import sys
from pathlib import Path
import progressbar
from coqui_stt_training.util.audio import (
AUDIO_TYPE_PCM,
AUDIO_TYPE_OPUS,
AUDIO_TYPE_PCM,
AUDIO_TYPE_WAV,
change_audio_types,
)
from coqui_stt_training.util.augmentations import (
SampleAugmentation,
apply_sample_augmentations,
parse_augmentations,
)
from coqui_stt_training.util.downloader import SIMPLE_BAR
from coqui_stt_training.util.sample_collections import (
CSVWriter,
@ -21,101 +26,110 @@ from coqui_stt_training.util.sample_collections import (
TarWriter,
samples_from_sources,
)
from coqui_stt_training.util.augmentations import (
parse_augmentations,
apply_sample_augmentations,
SampleAugmentation
)
AUDIO_TYPE_LOOKUP = {'wav': AUDIO_TYPE_WAV, 'opus': AUDIO_TYPE_OPUS}
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.')
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)
elif extension == '.tar':
writer = TarWriter(CLI_ARGS.target, labeled=labeled, gz=False, include=CLI_ARGS.include)
elif extension == '.tgz' or CLI_ARGS.target.lower().endswith('.tar.gz'):
writer = TarWriter(CLI_ARGS.target, labeled=labeled, gz=True, include=CLI_ARGS.include)
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
)
elif extension == ".tar":
writer = TarWriter(
CLI_ARGS.target, labeled=labeled, gz=False, include=CLI_ARGS.include
)
elif extension == ".tgz" or CLI_ARGS.target.lower().endswith(".tar.gz"):
writer = TarWriter(
CLI_ARGS.target, labeled=labeled, gz=True, include=CLI_ARGS.include
)
else:
print('Unknown extension of target file - has to be either .csv, .sdb, .tar, .tar.gz or .tgz')
print(
"Unknown extension of target file - has to be either .csv, .sdb, .tar, .tar.gz or .tgz"
)
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)
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(
for sample in bar(
change_audio_types(
samples,
audio_type=audio_type,
bitrate=CLI_ARGS.bitrate,
processes=CLI_ARGS.workers)):
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'
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.',
"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, CSV or TAR(.gz) file to create")
parser.add_argument(
'target',
help='SDB, CSV or TAR(.gz) file to create'
)
parser.add_argument(
'--audio-type',
default='opus',
"--audio-type",
default="opus",
choices=AUDIO_TYPE_LOOKUP.keys(),
help='Audio representation inside target SDB',
help="Audio representation inside target SDB",
)
parser.add_argument(
'--bitrate',
"--bitrate",
type=int,
help='Bitrate for lossy compressed SDB samples like in case of --audio-type opus',
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'
"--workers", type=int, default=None, help="Number of encoding SDB workers"
)
parser.add_argument(
'--unlabeled',
action='store_true',
help='If to build an data-set with unlabeled (audio only) samples - '
'typically used for building noise augmentation corpora',
"--unlabeled",
action="store_true",
help="If to build an data-set 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',
"--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',
"--augment",
action="append",
help="Add an augmentation operation",
)
parser.add_argument(
'--include',
action='append',
help='Adds a file to the root directory of .tar(.gz) targets',
"--include",
action="append",
help="Adds a file to the root directory of .tar(.gz) targets",
)
return parser.parse_args()
if __name__ == '__main__':
if __name__ == "__main__":
CLI_ARGS = handle_args()
build_data_set()

3
bin/graphdef_binary_to_text.py
View File

@ -3,9 +3,10 @@
import sys
import tensorflow.compat.v1 as tfv1
from google.protobuf import text_format
import tensorflow.compat.v1 as tfv1
def main():
# Load and export as string

1
bin/import_aidatatang.py
View File

@ -4,7 +4,6 @@ import os
import tarfile
import pandas
from coqui_stt_training.util.importers import get_importers_parser
COLUMN_NAMES = ["wav_filename", "wav_filesize", "transcript"]

1
bin/import_aishell.py
View File

@ -4,7 +4,6 @@ import os
import tarfile
import pandas
from coqui_stt_training.util.importers import get_importers_parser
COLUMNNAMES = ["wav_filename", "wav_filesize", "transcript"]

417
bin/import_ccpmf.py
View File

@ -5,21 +5,21 @@ Ministère de l'Économie, des Finances et de la Relance
"""
import csv
import sys
import decimal
import hashlib
import math
import os
import progressbar
import re
import subprocess
import sys
import unicodedata
import xml.etree.ElementTree as ET
import zipfile
from glob import glob
from multiprocessing import Pool
import hashlib
import decimal
import math
import unicodedata
import re
import progressbar
import sox
import xml.etree.ElementTree as ET
try:
from num2words import num2words
@ -27,19 +27,19 @@ except ImportError as ex:
print("pip install num2words")
sys.exit(1)
import requests
import json
import requests
from coqui_stt_ctcdecoder import Alphabet
from coqui_stt_training.util.downloader import SIMPLE_BAR, maybe_download
from coqui_stt_training.util.helpers import secs_to_hours
from coqui_stt_training.util.importers import (
get_counter,
get_importers_parser,
get_imported_samples,
get_importers_parser,
get_validate_label,
print_import_report,
)
from coqui_stt_ctcdecoder import Alphabet
FIELDNAMES = ["wav_filename", "wav_filesize", "transcript"]
SAMPLE_RATE = 16000
@ -50,58 +50,187 @@ MIN_SECS = 0.85
DATASET_RELEASE_CSV = "https://data.economie.gouv.fr/explore/dataset/transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020/download/?format=csv&timezone=Europe/Berlin&lang=fr&use_labels_for_header=true&csv_separator=%3B"
DATASET_RELEASE_SHA = [
("863d39a06a388c6491c6ff2f6450b151f38f1b57", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.001"),
("2f3a0305aa04c61220bb00b5a4e553e45dbf12e1", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.002"),
("5e55e9f1f844097349188ac875947e5a3d7fe9f1", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.003"),
("8bf54842cf07948ca5915e27a8bd5fa5139c06ae", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.004"),
("c8963504aadc015ac48f9af80058a0bb3440b94f", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.005"),
("d95e225e908621d83ce4e9795fd108d9d310e244", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.006"),
("de6ed9c2b0ee80ca879aae8ba7923cc93217d811", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.007"),
("234283c47dacfcd4450d836c52c25f3e807fc5f2", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.008"),
("4e6b67a688639bb72f8cd81782eaba604a8d32a6", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.009"),
("4165a51389777c8af8e6253d87bdacb877e8b3b0", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.010"),
("34322e7009780d97ef5bd02bf2f2c7a31f00baff", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.011"),
("48c5be3b2ca9d6108d525da6a03e91d93a95dbac", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.012"),
("87573172f506a189c2ebc633856fe11a2e9cd213", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.013"),
("6ab2c9e508e9278d5129f023e018725c4a7c69e8", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.014"),
("4f84df831ef46dce5d3ab3e21817687a2d8c12d0", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.015"),
("e69bfb079885c299cb81080ef88b1b8b57158aa6", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.016"),
("5f764ba788ee273981cf211b242c29b49ca22c5e", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.017"),
("b6aa81a959525363223494830c1e7307d4c4bae6", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.018"),
("91ddcf43c7bf113a6f2528b857c7ec22a50a148a", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.019"),
("fa1b29273dd77b9a7494983a2f9ae52654b931d7", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.020"),
("1113aef4f5e2be2f7fbf2d54b6c710c1c0e7135f", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.021"),
("ce6420d5d0b6b5135ba559f83e1a82d4d615c470", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.022"),
("d0976ed292ac24fcf1590d1ea195077c74b05471", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.023"),
("ec746cd6af066f62d9bf8d3b2f89174783ff4e3c", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.024"),
("570d9e1e84178e32fd867171d4b3aaecda1fd4fb", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.025"),
("c29ccc7467a75b2cae3d7f2e9fbbb2ab276cb8ac", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.026"),
("08406a51146d88e208704ce058c060a1e44efa50", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.027"),
("199aedad733a78ea1e7d47def9c71c6fd5795e02", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.028"),
("db856a068f92fb4f01f410bba42c7271de0f231a", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.029"),
("e3c0135f16c6c9d25a09dcb4f99a685438a84740", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.030"),
("e51b8bb9c0ae4339f98b4f21e6d29b825109f0ac", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.031"),
("be5e80cbc49b59b31ae33c30576ef0e1a162d84e", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.032"),
("501df58e3ff55fcfd75b93dab57566dc536948b8", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.033"),
("1a114875811a8cdcb8d85a9f6dbee78be3e05131", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.034"),
("465d824e7ee46448369182c0c28646d155a2249b", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.035"),
("37f341b1b266d143eb73138c31cfff3201b9d619", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.036"),
("9e7d8255987a8a77a90e0d4b55c8fd38b9fb5694", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.037"),
("54886755630cb080a53098cb1b6c951c6714a143", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.038"),
("4b7cbb0154697be795034f7a49712e882a97197a", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.039"),
("c8e1e565a0e7a1f6ff1dbfcefe677aa74a41d2f2", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.040"),
(
"863d39a06a388c6491c6ff2f6450b151f38f1b57",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.001",
),
(
"2f3a0305aa04c61220bb00b5a4e553e45dbf12e1",
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),
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),
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),
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),
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"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.011",
),
(
"48c5be3b2ca9d6108d525da6a03e91d93a95dbac",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.012",
),
(
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),
(
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),
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),
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),
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"ce6420d5d0b6b5135ba559f83e1a82d4d615c470",
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),
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"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.023",
),
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"ec746cd6af066f62d9bf8d3b2f89174783ff4e3c",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.024",
),
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"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.025",
),
(
"c29ccc7467a75b2cae3d7f2e9fbbb2ab276cb8ac",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.026",
),
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),
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),
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"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.029",
),
(
"e3c0135f16c6c9d25a09dcb4f99a685438a84740",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.030",
),
(
"e51b8bb9c0ae4339f98b4f21e6d29b825109f0ac",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.031",
),
(
"be5e80cbc49b59b31ae33c30576ef0e1a162d84e",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.032",
),
(
"501df58e3ff55fcfd75b93dab57566dc536948b8",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.033",
),
(
"1a114875811a8cdcb8d85a9f6dbee78be3e05131",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.034",
),
(
"465d824e7ee46448369182c0c28646d155a2249b",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.035",
),
(
"37f341b1b266d143eb73138c31cfff3201b9d619",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.036",
),
(
"9e7d8255987a8a77a90e0d4b55c8fd38b9fb5694",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.037",
),
(
"54886755630cb080a53098cb1b6c951c6714a143",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.038",
),
(
"4b7cbb0154697be795034f7a49712e882a97197a",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.039",
),
(
"c8e1e565a0e7a1f6ff1dbfcefe677aa74a41d2f2",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.040",
),
]
def _download_and_preprocess_data(csv_url, target_dir):
dataset_sources = os.path.join(target_dir, "transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020", "data.txt")
dataset_sources = os.path.join(
target_dir, "transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020", "data.txt"
)
if os.path.exists(dataset_sources):
return dataset_sources
# Making path absolute
target_dir = os.path.abspath(target_dir)
csv_ref = requests.get(csv_url).text.split('\r\n')[1:-1]
csv_ref = requests.get(csv_url).text.split("\r\n")[1:-1]
for part in csv_ref:
part_filename = requests.head(part).headers.get("Content-Disposition").split(" ")[1].split("=")[1].replace('"', "")
part_filename = (
requests.head(part)
.headers.get("Content-Disposition")
.split(" ")[1]
.split("=")[1]
.replace('"', "")
)
if not os.path.exists(os.path.join(target_dir, part_filename)):
part_path = maybe_download(part_filename, target_dir, part)
@ -126,10 +255,18 @@ def _download_and_preprocess_data(csv_url, target_dir):
assert csum == sha1
# Conditionally extract data
_maybe_extract(target_dir, "transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip", "transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020.zip")
_maybe_extract(
target_dir,
"transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020",
"transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip",
"transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020.zip",
)
# Produce source text for extraction / conversion
return _maybe_create_sources(os.path.join(target_dir, "transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020"))
return _maybe_create_sources(
os.path.join(target_dir, "transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020")
)
def _maybe_extract(target_dir, extracted_data, archive, final):
# If target_dir/extracted_data does not exist, extract archive in target_dir
@ -147,7 +284,10 @@ def _maybe_extract(target_dir, extracted_data, archive, final):
subprocess.check_call(cmdline, shell=True, cwd=target_dir)
assert os.path.exists(archive_path)
print('No directory "%s" - extracting archive %s ...' % (extracted_path, archive_path))
print(
'No directory "%s" - extracting archive %s ...'
% (extracted_path, archive_path)
)
with zipfile.ZipFile(archive_path) as zip_f:
zip_f.extractall(extracted_path)
@ -156,6 +296,7 @@ def _maybe_extract(target_dir, extracted_data, archive, final):
else:
print('Found directory "%s" - not extracting it from archive.' % extracted_path)
def _maybe_create_sources(dir):
dataset_sources = os.path.join(dir, "data.txt")
MP3 = glob(os.path.join(dir, "**", "*.mp3"))
@ -168,8 +309,8 @@ def _maybe_create_sources(dir):
for f_xml in XML:
b_mp3 = os.path.splitext(os.path.basename(f_mp3))[0]
b_xml = os.path.splitext(os.path.basename(f_xml))[0]
a_mp3 = b_mp3.split('_')
a_xml = b_xml.split('_')
a_mp3 = b_mp3.split("_")
a_xml = b_xml.split("_")
score = 0
date_mp3 = a_mp3[0]
date_xml = a_xml[0]
@ -178,7 +319,7 @@ def _maybe_create_sources(dir):
continue
for i in range(min(len(a_mp3), len(a_xml))):
if (a_mp3[i] == a_xml[i]):
if a_mp3[i] == a_xml[i]:
score += 1
if score >= 1:
@ -208,13 +349,14 @@ def _maybe_create_sources(dir):
if os.path.getsize(mp3) > 0 and os.path.getsize(xml) > 0:
mp3 = os.path.relpath(mp3, dir)
xml = os.path.relpath(xml, dir)
ds.write('{},{},{:0.2e}\n'.format(xml, mp3, 2.5e-4))
ds.write("{},{},{:0.2e}\n".format(xml, mp3, 2.5e-4))
else:
print("Empty file {} or {}".format(mp3, xml), file=sys.stderr)
print("Missing XML pairs:", MP3, file=sys.stderr)
return dataset_sources
def maybe_normalize_for_digits(label):
# first, try to identify numbers like "50 000", "260 000"
if " " in label:
@ -238,22 +380,36 @@ def maybe_normalize_for_digits(label):
maybe_minutes = maybe_date_or_time[0][1]
maybe_seconds = maybe_date_or_time[0][2]
if len(maybe_seconds) > 0:
label = label.replace("{}:{}:{}".format(maybe_hours, maybe_minutes, maybe_seconds), "{} heures {} minutes et {} secondes".format(maybe_hours, maybe_minutes, maybe_seconds))
label = label.replace(
"{}:{}:{}".format(
maybe_hours, maybe_minutes, maybe_seconds
),
"{} heures {} minutes et {} secondes".format(
maybe_hours, maybe_minutes, maybe_seconds
),
)
else:
label = label.replace("{}:{}".format(maybe_hours, maybe_minutes), "{} heures et {} minutes".format(maybe_hours, maybe_minutes))
label = label.replace(
"{}:{}".format(maybe_hours, maybe_minutes),
"{} heures et {} minutes".format(
maybe_hours, maybe_minutes
),
)
new_label = []
# pylint: disable=too-many-nested-blocks
for s in label.split(" "):
if any(i.isdigit() for i in s):
s = s.replace(",", ".") # num2words requires "." for floats
s = s.replace("\"", "") # clean some data, num2words would choke on 1959"
s = s.replace('"', "") # clean some data, num2words would choke on 1959"
last_c = s[-1]
if not last_c.isdigit(): # num2words will choke on "0.6.", "24 ?"
s = s[:-1]
if any(i.isalpha() for i in s): # So we have any(isdigit()) **and** any(sialpha), like "3D"
if any(
i.isalpha() for i in s
): # So we have any(isdigit()) **and** any(sialpha), like "3D"
ns = []
for c in s:
nc = c
@ -274,22 +430,36 @@ def maybe_normalize_for_digits(label):
new_label.append(s)
return " ".join(new_label)
def maybe_normalize_for_specials_chars(label):
label = label.replace("%", "pourcents")
label = label.replace("/", ", ") # clean intervals like 2019/2022 to "2019 2022"
label = label.replace("-", ", ") # clean intervals like 70-80 to "70 80"
label = label.replace("+", " plus ") # clean + and make it speakable
label = label.replace("€", " euros ") # clean euro symbol and make it speakable
label = label.replace("., ", ", ") # clean some strange "4.0., " (20181017_Innovation.xml)
label = label.replace("°", " degré ") # clean some strange "°5" (20181210_EtatsGeneraux-1000_fre_750_und.xml)
label = label.replace(
"., ", ", "
) # clean some strange "4.0., " (20181017_Innovation.xml)
label = label.replace(
"°", " degré "
) # clean some strange "°5" (20181210_EtatsGeneraux-1000_fre_750_und.xml)
label = label.replace("...", ".") # remove ellipsis
label = label.replace("..", ".") # remove broken ellipsis
label = label.replace("m²", "mètre-carrés") # 20150616_Defi_Climat_3_wmv_0_fre_minefi.xml
label = label.replace("[end]", "") # broken tag in 20150123_Entretiens_Tresor_PGM_wmv_0_fre_minefi.xml
label = label.replace(u'\xB8c', " ç") # strange cedilla in 20150417_Printemps_Economie_2_wmv_0_fre_minefi.xml
label = label.replace("C0²", "CO 2") # 20121016_Syteme_sante_copie_wmv_0_fre_minefi.xml
label = label.replace(
"m²", "mètre-carrés"
) # 20150616_Defi_Climat_3_wmv_0_fre_minefi.xml
label = label.replace(
"[end]", ""
) # broken tag in 20150123_Entretiens_Tresor_PGM_wmv_0_fre_minefi.xml
label = label.replace(
u"\xB8c", " ç"
) # strange cedilla in 20150417_Printemps_Economie_2_wmv_0_fre_minefi.xml
label = label.replace(
"C0²", "CO 2"
) # 20121016_Syteme_sante_copie_wmv_0_fre_minefi.xml
return label
def maybe_normalize_for_anglicisms(label):
label = label.replace("B2B", "B to B")
label = label.replace("B2C", "B to C")
@ -297,12 +467,14 @@ def maybe_normalize_for_anglicisms(label):
label = label.replace("@", "at ")
return label
def maybe_normalize(label):
label = maybe_normalize_for_specials_chars(label)
label = maybe_normalize_for_anglicisms(label)
label = maybe_normalize_for_digits(label)
return label
def one_sample(sample):
file_size = -1
frames = 0
@ -316,14 +488,33 @@ def one_sample(sample):
label = label_filter_fun(sample[5])
sample_id = sample[6]
_wav_filename = os.path.basename(audio_source.replace(".wav", "_{:06}.wav".format(sample_id)))
_wav_filename = os.path.basename(
audio_source.replace(".wav", "_{:06}.wav".format(sample_id))
)
wav_fullname = os.path.join(target_dir, dataset_basename, _wav_filename)
if not os.path.exists(wav_fullname):
subprocess.check_output(["ffmpeg", "-i", audio_source, "-ss", str(start_time), "-t", str(duration), "-c", "copy", wav_fullname], stdin=subprocess.DEVNULL, stderr=subprocess.STDOUT)
subprocess.check_output(
[
"ffmpeg",
"-i",
audio_source,
"-ss",
str(start_time),
"-t",
str(duration),
"-c",
"copy",
wav_fullname,
],
stdin=subprocess.DEVNULL,
stderr=subprocess.STDOUT,
)
file_size = os.path.getsize(wav_fullname)
frames = int(subprocess.check_output(["soxi", "-s", wav_fullname], stderr=subprocess.STDOUT))
frames = int(
subprocess.check_output(["soxi", "-s", wav_fullname], stderr=subprocess.STDOUT)
)
_counter = get_counter()
_rows = []
@ -352,13 +543,16 @@ def one_sample(sample):
return (_counter, _rows)
def _maybe_import_data(xml_file, audio_source, target_dir, rel_tol=1e-1):
dataset_basename = os.path.splitext(os.path.split(xml_file)[1])[0]
wav_root = os.path.join(target_dir, dataset_basename)
if not os.path.exists(wav_root):
os.makedirs(wav_root)
source_frames = int(subprocess.check_output(["soxi", "-s", audio_source], stderr=subprocess.STDOUT))
source_frames = int(
subprocess.check_output(["soxi", "-s", audio_source], stderr=subprocess.STDOUT)
)
print("Source audio length: %s" % secs_to_hours(source_frames / SAMPLE_RATE))
# Get audiofile path and transcript for each sentence in tsv
@ -384,16 +578,28 @@ def _maybe_import_data(xml_file, audio_source, target_dir, rel_tol=1e-1):
# rel_tol value is made from trial/error to try and compromise between:
# - cutting enough to skip missing words
# - not too short, not too long sentences
is_close = math.isclose(cur_time, this_time + this_duration, rel_tol=rel_tol)
is_short = ((this_duration + cur_duration + delta) < MAX_SECS)
is_close = math.isclose(
cur_time, this_time + this_duration, rel_tol=rel_tol
)
is_short = (this_duration + cur_duration + delta) < MAX_SECS
# when the previous element is close enough **and** this does not
# go over MAX_SECS, we append content
if (is_close and is_short):
if is_close and is_short:
this_duration += cur_duration + delta
this_text += cur_text
else:
samples.append((audio_source, target_dir, dataset_basename, this_time, this_duration, this_text, seq_id))
samples.append(
(
audio_source,
target_dir,
dataset_basename,
this_time,
this_duration,
this_text,
seq_id,
)
)
this_time = cur_time
this_duration = cur_duration
@ -425,21 +631,27 @@ def _maybe_import_data(xml_file, audio_source, target_dir, rel_tol=1e-1):
assert len(_rows) == imported_samples
print_import_report(_counter, SAMPLE_RATE, MAX_SECS)
print("Import efficiency: %.1f%%" % ((_counter["total_time"] / source_frames)*100))
print(
"Import efficiency: %.1f%%" % ((_counter["total_time"] / source_frames) * 100)
)
print("")
return _counter, _rows
def _maybe_convert_wav(mp3_filename, _wav_filename):
if not os.path.exists(_wav_filename):
print("Converting {} to WAV file: {}".format(mp3_filename, _wav_filename))
transformer = sox.Transformer()
transformer.convert(samplerate=SAMPLE_RATE, n_channels=CHANNELS, bitdepth=BIT_DEPTH)
transformer.convert(
samplerate=SAMPLE_RATE, n_channels=CHANNELS, bitdepth=BIT_DEPTH
)
try:
transformer.build(mp3_filename, _wav_filename)
except sox.core.SoxError:
pass
def write_general_csv(target_dir, _rows, _counter):
target_csv_template = os.path.join(target_dir, "ccpmf_{}.csv")
with open(target_csv_template.format("train"), "w") as train_csv_file: # 80%
@ -461,7 +673,13 @@ def write_general_csv(target_dir, _rows, _counter):
writer = dev_writer
else:
writer = train_writer
writer.writerow({"wav_filename": item[0], "wav_filesize": item[1], "transcript": item[2]})
writer.writerow(
{
"wav_filename": item[0],
"wav_filesize": item[1],
"transcript": item[2],
}
)
print("")
print("~~~~ FINAL STATISTICS ~~~~")
@ -469,11 +687,21 @@ def write_general_csv(target_dir, _rows, _counter):
print("~~~~ (FINAL STATISTICS) ~~~~")
print("")
if __name__ == "__main__":
PARSER = get_importers_parser(description="Import XML from Conference Centre for Economics, France")
PARSER = get_importers_parser(
description="Import XML from Conference Centre for Economics, France"
)
PARSER.add_argument("target_dir", help="Destination directory")
PARSER.add_argument("--filter_alphabet", help="Exclude samples with characters not in provided alphabet")
PARSER.add_argument("--normalize", action="store_true", help="Converts diacritic characters to their base ones")
PARSER.add_argument(
"--filter_alphabet",
help="Exclude samples with characters not in provided alphabet",
)
PARSER.add_argument(
"--normalize",
action="store_true",
help="Converts diacritic characters to their base ones",
)
PARAMS = PARSER.parse_args()
validate_label = get_validate_label(PARAMS)
@ -481,9 +709,11 @@ if __name__ == "__main__":
def label_filter_fun(label):
if PARAMS.normalize:
label = unicodedata.normalize("NFKD", label.strip()) \
.encode("ascii", "ignore") \
label = (
unicodedata.normalize("NFKD", label.strip())
.encode("ascii", "ignore")
.decode("ascii", "ignore")
)
label = maybe_normalize(label)
label = validate_label(label)
if ALPHABET and label:
@ -493,7 +723,9 @@ if __name__ == "__main__":
label = None
return label
dataset_sources = _download_and_preprocess_data(csv_url=DATASET_RELEASE_CSV, target_dir=PARAMS.target_dir)
dataset_sources = _download_and_preprocess_data(
csv_url=DATASET_RELEASE_CSV, target_dir=PARAMS.target_dir
)
sources_root_dir = os.path.dirname(dataset_sources)
all_counter = get_counter()
all_rows = []
@ -504,9 +736,14 @@ if __name__ == "__main__":
this_mp3 = os.path.join(sources_root_dir, d[1])
this_rel = float(d[2])
wav_filename = os.path.join(sources_root_dir, os.path.splitext(os.path.basename(this_mp3))[0] + ".wav")
wav_filename = os.path.join(
sources_root_dir,
os.path.splitext(os.path.basename(this_mp3))[0] + ".wav",
)
_maybe_convert_wav(this_mp3, wav_filename)
counter, rows = _maybe_import_data(this_xml, wav_filename, sources_root_dir, this_rel)
counter, rows = _maybe_import_data(
this_xml, wav_filename, sources_root_dir, this_rel
)
all_counter += counter
all_rows += rows

3
bin/import_cv.py
View File

@ -1,15 +1,14 @@
#!/usr/bin/env python
import csv
import os
import sys
import subprocess
import sys
import tarfile
from glob import glob
from multiprocessing import Pool
import progressbar
import sox
from coqui_stt_training.util.downloader import SIMPLE_BAR, maybe_download
from coqui_stt_training.util.importers import (
get_counter,

49
bin/import_cv2.py
View File

@ -14,7 +14,7 @@ from multiprocessing import Pool
import progressbar
import sox
from coqui_stt_ctcdecoder import Alphabet
from coqui_stt_training.util.downloader import SIMPLE_BAR
from coqui_stt_training.util.importers import (
get_counter,
@ -23,7 +23,6 @@ from coqui_stt_training.util.importers import (
get_validate_label,
print_import_report,
)
from coqui_stt_ctcdecoder import Alphabet
FIELDNAMES = ["wav_filename", "wav_filesize", "transcript"]
SAMPLE_RATE = 16000
@ -41,7 +40,11 @@ class LabelFilter:
def filter(self, label):
if self.normalize:
label = unicodedata.normalize("NFKD", label.strip()).encode("ascii", "ignore").decode("ascii", "ignore")
label = (
unicodedata.normalize("NFKD", label.strip())
.encode("ascii", "ignore")
.decode("ascii", "ignore")
)
label = self.validate_fun(label)
if self.alphabet and label and not self.alphabet.CanEncode(label):
label = None
@ -97,7 +100,15 @@ def one_sample(sample):
return (counter, rows)
def _maybe_convert_set(dataset, tsv_dir, audio_dir, filter_obj, space_after_every_character=None, rows=None, exclude=None):
def _maybe_convert_set(
dataset,
tsv_dir,
audio_dir,
filter_obj,
space_after_every_character=None,
rows=None,
exclude=None,
):
exclude_transcripts = set()
exclude_speakers = set()
if exclude is not None:
@ -116,7 +127,13 @@ def _maybe_convert_set(dataset, tsv_dir, audio_dir, filter_obj, space_after_ever
with open(input_tsv, encoding="utf-8") as input_tsv_file:
reader = csv.DictReader(input_tsv_file, delimiter="\t")
for row in reader:
samples.append((os.path.join(audio_dir, row["path"]), row["sentence"], row["client_id"]))
samples.append(
(
os.path.join(audio_dir, row["path"]),
row["sentence"],
row["client_id"],
)
)
counter = get_counter()
num_samples = len(samples)
@ -124,7 +141,9 @@ def _maybe_convert_set(dataset, tsv_dir, audio_dir, filter_obj, space_after_ever
print("Importing mp3 files...")
pool = Pool(initializer=init_worker, initargs=(PARAMS,))
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
for i, processed in enumerate(
pool.imap_unordered(one_sample, samples), start=1
):
counter += processed[0]
rows += processed[1]
bar.update(i)
@ -169,12 +188,20 @@ def _maybe_convert_set(dataset, tsv_dir, audio_dir, filter_obj, space_after_ever
def _preprocess_data(tsv_dir, audio_dir, space_after_every_character=False):
exclude = []
for dataset in ["test", "dev", "train", "validated", "other"]:
set_samples = _maybe_convert_set(dataset, tsv_dir, audio_dir, space_after_every_character)
set_samples = _maybe_convert_set(
dataset, tsv_dir, audio_dir, space_after_every_character
)
if dataset in ["test", "dev"]:
exclude += set_samples
if dataset == "validated":
_maybe_convert_set("train-all", tsv_dir, audio_dir, space_after_every_character,
rows=set_samples, exclude=exclude)
_maybe_convert_set(
"train-all",
tsv_dir,
audio_dir,
space_after_every_character,
rows=set_samples,
exclude=exclude,
)
def _maybe_convert_wav(mp3_filename, wav_filename):
@ -212,7 +239,9 @@ def parse_args():
def main():
audio_dir = PARAMS.audio_dir if PARAMS.audio_dir else os.path.join(PARAMS.tsv_dir, "clips")
audio_dir = (
PARAMS.audio_dir if PARAMS.audio_dir else os.path.join(PARAMS.tsv_dir, "clips")
)
_preprocess_data(PARAMS.tsv_dir, audio_dir, PARAMS.space_after_every_character)

4
bin/import_fisher.py
View File

@ -10,7 +10,6 @@ import unicodedata
import librosa
import pandas
import soundfile # <= Has an external dependency on libsndfile
from coqui_stt_training.util.importers import validate_label_eng as validate_label
# Prerequisite: Having the sph2pipe tool in your PATH:
@ -261,8 +260,7 @@ def _split_sets(filelist):
def get_sample_size(population_size):
"""calculates the sample size for a 99% confidence and 1% margin of error
"""
"""calculates the sample size for a 99% confidence and 1% margin of error"""
margin_of_error = 0.01
fraction_picking = 0.50
z_score = 2.58 # Corresponds to confidence level 99%

1
bin/import_freestmandarin.py
View File

@ -5,7 +5,6 @@ import tarfile
import numpy as np
import pandas
from coqui_stt_training.util.importers import get_importers_parser
COLUMN_NAMES = ["wav_filename", "wav_filesize", "transcript"]

3
bin/import_gram_vaani.py
View File

@ -9,10 +9,9 @@ import urllib
from pathlib import Path
import pandas as pd
from sox import Transformer
import swifter
from coqui_stt_training.util.importers import get_importers_parser, get_validate_label
from sox import Transformer
__version__ = "0.1.0"
_logger = logging.getLogger(__name__)

1
bin/import_ldc93s1.py
View File

@ -3,7 +3,6 @@ import os
import sys
import pandas
from coqui_stt_training.util.downloader import maybe_download

6
bin/import_librivox.py
View File

@ -9,10 +9,10 @@ import unicodedata
import pandas
import progressbar
from sox import Transformer
from tensorflow.python.platform import gfile
from coqui_stt_training.util.downloader import maybe_download
from sox import Transformer
from tensorflow.python.platform import gfile
SAMPLE_RATE = 16000

19
bin/import_lingua_libre.py
View File

@ -11,7 +11,7 @@ from multiprocessing import Pool
import progressbar
import sox
from coqui_stt_ctcdecoder import Alphabet
from coqui_stt_training.util.downloader import SIMPLE_BAR, maybe_download
from coqui_stt_training.util.importers import (
get_counter,
@ -20,7 +20,6 @@ from coqui_stt_training.util.importers import (
get_validate_label,
print_import_report,
)
from coqui_stt_ctcdecoder import Alphabet
FIELDNAMES = ["wav_filename", "wav_filesize", "transcript"]
SAMPLE_RATE = 16000
@ -137,9 +136,15 @@ def _maybe_convert_sets(target_dir, extracted_data):
pool.close()
pool.join()
with open(target_csv_template.format("train"), "w", encoding="utf-8", newline="") as train_csv_file: # 80%
with open(target_csv_template.format("dev"), "w", encoding="utf-8", newline="") as dev_csv_file: # 10%
with open(target_csv_template.format("test"), "w", encoding="utf-8", newline="") as test_csv_file: # 10%
with open(
target_csv_template.format("train"), "w", encoding="utf-8", newline=""
) as train_csv_file: # 80%
with open(
target_csv_template.format("dev"), "w", encoding="utf-8", newline=""
) as dev_csv_file: # 10%
with open(
target_csv_template.format("test"), "w", encoding="utf-8", newline=""
) as test_csv_file: # 10%
train_writer = csv.DictWriter(train_csv_file, fieldnames=FIELDNAMES)
train_writer.writeheader()
dev_writer = csv.DictWriter(dev_csv_file, fieldnames=FIELDNAMES)
@ -179,7 +184,9 @@ def _maybe_convert_sets(target_dir, extracted_data):
def _maybe_convert_wav(ogg_filename, wav_filename):
if not os.path.exists(wav_filename):
transformer = sox.Transformer()
transformer.convert(samplerate=SAMPLE_RATE, n_channels=N_CHANNELS, bitdepth=BITDEPTH)
transformer.convert(
samplerate=SAMPLE_RATE, n_channels=N_CHANNELS, bitdepth=BITDEPTH
)
try:
transformer.build(ogg_filename, wav_filename)
except sox.core.SoxError as ex:

30
bin/import_m-ailabs.py
View File

@ -9,7 +9,7 @@ from glob import glob
from multiprocessing import Pool
import progressbar
from coqui_stt_ctcdecoder import Alphabet
from coqui_stt_training.util.downloader import SIMPLE_BAR, maybe_download
from coqui_stt_training.util.importers import (
get_counter,
@ -18,7 +18,6 @@ from coqui_stt_training.util.importers import (
get_validate_label,
print_import_report,
)
from coqui_stt_ctcdecoder import Alphabet
FIELDNAMES = ["wav_filename", "wav_filesize", "transcript"]
SAMPLE_RATE = 16000
@ -60,9 +59,20 @@ def one_sample(sample):
file_size = -1
frames = 0
if os.path.exists(wav_filename):
tmp_filename = os.path.splitext(wav_filename)[0]+'.tmp.wav'
tmp_filename = os.path.splitext(wav_filename)[0] + ".tmp.wav"
subprocess.check_call(
['sox', wav_filename, '-r', str(SAMPLE_RATE), '-c', '1', '-b', '16', tmp_filename], stderr=subprocess.STDOUT
[
"sox",
wav_filename,
"-r",
str(SAMPLE_RATE),
"-c",
"1",
"-b",
"16",
tmp_filename,
],
stderr=subprocess.STDOUT,
)
os.rename(tmp_filename, wav_filename)
file_size = os.path.getsize(wav_filename)
@ -138,9 +148,15 @@ def _maybe_convert_sets(target_dir, extracted_data):
pool.close()
pool.join()
with open(target_csv_template.format("train"), "w", encoding="utf-8", newline="") as train_csv_file: # 80%
with open(target_csv_template.format("dev"), "w", encoding="utf-8", newline="") as dev_csv_file: # 10%
with open(target_csv_template.format("test"), "w", encoding="utf-8", newline="") as test_csv_file: # 10%
with open(
target_csv_template.format("train"), "w", encoding="utf-8", newline=""
) as train_csv_file: # 80%
with open(
target_csv_template.format("dev"), "w", encoding="utf-8", newline=""
) as dev_csv_file: # 10%
with open(
target_csv_template.format("test"), "w", encoding="utf-8", newline=""
) as test_csv_file: # 10%
train_writer = csv.DictWriter(train_csv_file, fieldnames=FIELDNAMES)
train_writer.writeheader()
dev_writer = csv.DictWriter(dev_csv_file, fieldnames=FIELDNAMES)

1
bin/import_magicdata.py
View File

@ -5,7 +5,6 @@ import tarfile
import wave
import pandas
from coqui_stt_training.util.importers import get_importers_parser
COLUMN_NAMES = ["wav_filename", "wav_filesize", "transcript"]

3
bin/import_mls_english.py
View File

@ -2,10 +2,9 @@
import argparse
import ctypes
import os
from pathlib import Path
import pandas
from pathlib import Path
from tqdm import tqdm

1
bin/import_primewords.py
View File

@ -6,7 +6,6 @@ import tarfile
import numpy as np
import pandas
from coqui_stt_training.util.importers import get_importers_parser
COLUMN_NAMES = ["wav_filename", "wav_filesize", "transcript"]

15
bin/import_slr57.py
View File

@ -8,7 +8,7 @@ from glob import glob
from multiprocessing import Pool
import progressbar
from coqui_stt_ctcdecoder import Alphabet
from coqui_stt_training.util.downloader import SIMPLE_BAR, maybe_download
from coqui_stt_training.util.importers import (
get_counter,
@ -17,7 +17,6 @@ from coqui_stt_training.util.importers import (
get_validate_label,
print_import_report,
)
from coqui_stt_ctcdecoder import Alphabet
FIELDNAMES = ["wav_filename", "wav_filesize", "transcript"]
SAMPLE_RATE = 16000
@ -157,9 +156,15 @@ def _maybe_convert_sets(target_dir, extracted_data):
pool.close()
pool.join()
with open(target_csv_template.format("train"), "w", encoding="utf-8", newline="") as train_csv_file: # 80%
with open(target_csv_template.format("dev"), "w", encoding="utf-8", newline="") as dev_csv_file: # 10%
with open(target_csv_template.format("test"), "w", encoding="utf-8", newline="") as test_csv_file: # 10%
with open(
target_csv_template.format("train"), "w", encoding="utf-8", newline=""
) as train_csv_file: # 80%
with open(
target_csv_template.format("dev"), "w", encoding="utf-8", newline=""
) as dev_csv_file: # 10%
with open(
target_csv_template.format("test"), "w", encoding="utf-8", newline=""
) as test_csv_file: # 10%
train_writer = csv.DictWriter(train_csv_file, fieldnames=FIELDNAMES)
train_writer.writeheader()
dev_writer = csv.DictWriter(dev_csv_file, fieldnames=FIELDNAMES)

4
bin/import_swb.py
View File

@ -16,7 +16,6 @@ import librosa
import pandas
import requests
import soundfile # <= Has an external dependency on libsndfile
from coqui_stt_training.util.importers import validate_label_eng as validate_label
# ARCHIVE_NAME refers to ISIP alignments from 01/29/03
@ -315,8 +314,7 @@ def _split_sets(filelist):
def get_sample_size(population_size):
"""calculates the sample size for a 99% confidence and 1% margin of error
"""
"""calculates the sample size for a 99% confidence and 1% margin of error"""
margin_of_error = 0.01
fraction_picking = 0.50
z_score = 2.58 # Corresponds to confidence level 99%

16
bin/import_swc.py
View File

@ -21,10 +21,9 @@ from multiprocessing.pool import ThreadPool
import progressbar
import sox
from coqui_stt_ctcdecoder import Alphabet
from coqui_stt_training.util.downloader import SIMPLE_BAR, maybe_download
from coqui_stt_training.util.importers import validate_label_eng as validate_label
from coqui_stt_ctcdecoder import Alphabet
SWC_URL = "https://www2.informatik.uni-hamburg.de/nats/pub/SWC/SWC_{language}.tar"
SWC_ARCHIVE = "SWC_{language}.tar"
@ -173,7 +172,6 @@ def in_alphabet(alphabet, c):
return alphabet.CanEncode(c) if alphabet else True
ALPHABETS = {}
@ -202,8 +200,16 @@ def label_filter(label, language):
dont_normalize = DONT_NORMALIZE[language] if language in DONT_NORMALIZE else ""
alphabet = get_alphabet(language)
for c in label:
if CLI_ARGS.normalize and c not in dont_normalize and not in_alphabet(alphabet, c):
c = unicodedata.normalize("NFKD", c).encode("ascii", "ignore").decode("ascii", "ignore")
if (
CLI_ARGS.normalize
and c not in dont_normalize
and not in_alphabet(alphabet, c)
):
c = (
unicodedata.normalize("NFKD", c)
.encode("ascii", "ignore")
.decode("ascii", "ignore")
)
for sc in c:
if not in_alphabet(alphabet, sc):
return None, "illegal character"

6
bin/import_ted.py
View File

@ -7,11 +7,11 @@ from glob import glob
from os import makedirs, path, remove, rmdir
import pandas
from sox import Transformer
from tensorflow.python.platform import gfile
from coqui_stt_training.util.downloader import maybe_download
from coqui_stt_training.util.stm import parse_stm_file
from sox import Transformer
from tensorflow.python.platform import gfile
def _download_and_preprocess_data(data_dir):

13
bin/import_ts.py
View File

@ -8,7 +8,6 @@ from multiprocessing import Pool
import progressbar
import sox
import unidecode
from coqui_stt_training.util.downloader import SIMPLE_BAR, maybe_download
from coqui_stt_training.util.importers import (
@ -132,9 +131,15 @@ def _maybe_convert_sets(target_dir, extracted_data, english_compatible=False):
pool.close()
pool.join()
with open(target_csv_template.format("train"), "w", encoding="utf-8", newline="") as train_csv_file: # 80%
with open(target_csv_template.format("dev"), "w", encoding="utf-8", newline="") as dev_csv_file: # 10%
with open(target_csv_template.format("test"), "w", encoding="utf-8", newline="") as test_csv_file: # 10%
with open(
target_csv_template.format("train"), "w", encoding="utf-8", newline=""
) as train_csv_file: # 80%
with open(
target_csv_template.format("dev"), "w", encoding="utf-8", newline=""
) as dev_csv_file: # 10%
with open(
target_csv_template.format("test"), "w", encoding="utf-8", newline=""
) as test_csv_file: # 10%
train_writer = csv.DictWriter(train_csv_file, fieldnames=FIELDNAMES)
train_writer.writeheader()
dev_writer = csv.DictWriter(dev_csv_file, fieldnames=FIELDNAMES)

11
bin/import_tuda.py
View File

@ -13,10 +13,9 @@ import xml.etree.ElementTree as ET
from collections import Counter
import progressbar
from coqui_stt_ctcdecoder import Alphabet
from coqui_stt_training.util.downloader import SIMPLE_BAR, maybe_download
from coqui_stt_training.util.importers import validate_label_eng as validate_label
from coqui_stt_ctcdecoder import Alphabet
TUDA_VERSION = "v2"
TUDA_PACKAGE = "german-speechdata-package-{}".format(TUDA_VERSION)
@ -55,7 +54,11 @@ def check_and_prepare_sentence(sentence):
chars = []
for c in sentence:
if CLI_ARGS.normalize and c not in "äöüß" and not in_alphabet(c):
c = unicodedata.normalize("NFKD", c).encode("ascii", "ignore").decode("ascii", "ignore")
c = (
unicodedata.normalize("NFKD", c)
.encode("ascii", "ignore")
.decode("ascii", "ignore")
)
for sc in c:
if not in_alphabet(c):
return None
@ -118,7 +121,7 @@ def write_csvs(extracted):
sentence = list(meta.iter("cleaned_sentence"))[0].text
sentence = check_and_prepare_sentence(sentence)
if sentence is None:
reasons['alphabet filter'] += 1
reasons["alphabet filter"] += 1
continue
for wav_name in wav_names:
sample_counter += 1

1
bin/import_vctk.py
View File

@ -10,7 +10,6 @@ from zipfile import ZipFile
import librosa
import progressbar
from coqui_stt_training.util.downloader import SIMPLE_BAR, maybe_download
from coqui_stt_training.util.importers import (
get_counter,

3
bin/import_voxforge.py
View File

@ -13,9 +13,10 @@ from os import makedirs, path
import pandas
from bs4 import BeautifulSoup
from tensorflow.python.platform import gfile
from coqui_stt_training.util.downloader import maybe_download
from tensorflow.python.platform import gfile
"""The number of jobs to run in parallel"""
NUM_PARALLEL = 8

46
bin/play.py
View File

@ -4,14 +4,26 @@ Tool for playing (and augmenting) single samples or samples from Sample Database
Use "python3 play.py -h" for help
"""
import os
import sys
import random
import argparse
import os
import random
import sys
from coqui_stt_training.util.audio import get_loadable_audio_type_from_extension, AUDIO_TYPE_PCM, AUDIO_TYPE_WAV
from coqui_stt_training.util.sample_collections import SampleList, LabeledSample, samples_from_source
from coqui_stt_training.util.augmentations import parse_augmentations, apply_sample_augmentations, SampleAugmentation
from coqui_stt_training.util.audio import (
AUDIO_TYPE_PCM,
AUDIO_TYPE_WAV,
get_loadable_audio_type_from_extension,
)
from coqui_stt_training.util.augmentations import (
SampleAugmentation,
apply_sample_augmentations,
parse_augmentations,
)
from coqui_stt_training.util.sample_collections import (
LabeledSample,
SampleList,
samples_from_source,
)
def get_samples_in_play_order():
@ -43,11 +55,13 @@ def play_collection():
if any(not isinstance(a, SampleAugmentation) for a in augmentations):
print("Warning: Some of the augmentations cannot be simulated by this command.")
samples = get_samples_in_play_order()
samples = apply_sample_augmentations(samples,
samples = apply_sample_augmentations(
samples,
audio_type=AUDIO_TYPE_PCM,
augmentations=augmentations,
process_ahead=0,
clock=CLI_ARGS.clock)
clock=CLI_ARGS.clock,
)
for sample in samples:
if not CLI_ARGS.quiet:
print('Sample "{}"'.format(sample.sample_id), file=sys.stderr)
@ -57,10 +71,12 @@ def play_collection():
sample.change_audio_type(AUDIO_TYPE_WAV)
sys.stdout.buffer.write(sample.audio.getvalue())
return
wave_obj = simpleaudio.WaveObject(sample.audio,
wave_obj = simpleaudio.WaveObject(
sample.audio,
sample.audio_format.channels,
sample.audio_format.width,
sample.audio_format.rate)
sample.audio_format.rate,
)
play_obj = wave_obj.play()
play_obj.wait_done()
@ -70,7 +86,9 @@ def handle_args():
description="Tool for playing (and augmenting) single samples or samples from Sample Databases (SDB files) "
"and Coqui STT CSV files"
)
parser.add_argument("source", help="Sample DB, CSV or WAV file to play samples from")
parser.add_argument(
"source", help="Sample DB, CSV or WAV file to play samples from"
)
parser.add_argument(
"--start",
type=int,
@ -90,7 +108,7 @@ def handle_args():
)
parser.add_argument(
"--augment",
action='append',
action="append",
help="Add an augmentation operation",
)
parser.add_argument(
@ -120,7 +138,9 @@ if __name__ == "__main__":
try:
import simpleaudio
except ModuleNotFoundError:
print('Unless using the --pipe flag, play.py requires Python package "simpleaudio" for playing samples')
print(
'Unless using the --pipe flag, play.py requires Python package "simpleaudio" for playing samples'
)
sys.exit(1)
try:
play_collection()

1
data/README.rst
View File

@ -8,4 +8,3 @@ This directory contains language-specific data files. Most importantly, you will
2. A script used to generate a binary n-gram language model: ``data/lm/generate_lm.py``.
For more information on how to build these resources from scratch, see the ``External scorer scripts`` section on `stt.readthedocs.io <https://stt.readthedocs.io/>`_.

100
doc/conf.py
View File

@ -22,21 +22,27 @@
import os
import sys
sys.path.insert(0, os.path.abspath('../'))
sys.path.insert(0, os.path.abspath("../"))
autodoc_mock_imports = ['stt']
autodoc_mock_imports = ["stt"]
# This is in fact only relevant on ReadTheDocs, but we want to run the same way
# on our CI as in RTD to avoid regressions on RTD that we would not catch on CI
import subprocess
parent = subprocess.check_output("cd ../ && pwd", shell=True).decode().strip()
os.environ["PATH"] = os.path.join(parent, 'node_modules', '.bin') + ':' + os.environ["PATH"]
subprocess.check_call('cd ../ && npm install typedoc@0.17.4 typescript@3.8.3 @types/node@13.9.x', shell=True)
subprocess.check_call('env', shell=True)
subprocess.check_call('which typedoc', shell=True)
subprocess.check_call('cd ../ && doxygen doc/doxygen-c.conf', shell=True)
subprocess.check_call('cd ../ && doxygen doc/doxygen-java.conf', shell=True)
subprocess.check_call('cd ../ && doxygen doc/doxygen-dotnet.conf', shell=True)
os.environ["PATH"] = (
os.path.join(parent, "node_modules", ".bin") + ":" + os.environ["PATH"]
)
subprocess.check_call(
"cd ../ && npm install typedoc@0.17.4 typescript@3.8.3 @types/node@13.9.x",
shell=True,
)
subprocess.check_call("env", shell=True)
subprocess.check_call("which typedoc", shell=True)
subprocess.check_call("cd ../ && doxygen doc/doxygen-c.conf", shell=True)
subprocess.check_call("cd ../ && doxygen doc/doxygen-java.conf", shell=True)
subprocess.check_call("cd ../ && doxygen doc/doxygen-dotnet.conf", shell=True)
# -- General configuration ------------------------------------------------
@ -44,11 +50,11 @@ import semver
# -- Project information -----------------------------------------------------
project = u'Coqui STT'
copyright = '2021 Coqui GmbH, 2020 DeepSpeech authors, 2019-2020 Mozilla Corporation'
author = 'Coqui GmbH'
project = u"Coqui STT"
copyright = "2021 Coqui GmbH, 2020 DeepSpeech authors, 2019-2020 Mozilla Corporation"
author = "Coqui GmbH"
with open('../VERSION', 'r') as ver:
with open("../VERSION", "r") as ver:
v = ver.read().strip()
vv = semver.parse(v)
@ -56,7 +62,7 @@ vv = semver.parse(v)
# |version| and |release|, also used in various other places throughout the
# built documents.
# The short X.Y version
version = '{}.{}'.format(vv['major'], vv['minor'])
version = "{}.{}".format(vv["major"], vv["minor"])
# The full version, including alpha/beta/rc tags
release = v
@ -68,15 +74,15 @@ release = v
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.extlinks',
'sphinx.ext.intersphinx',
'sphinx.ext.mathjax',
'sphinx.ext.viewcode',
'sphinx_js',
'sphinx_csharp',
'breathe',
'recommonmark',
"sphinx.ext.autodoc",
"sphinx.ext.extlinks",
"sphinx.ext.intersphinx",
"sphinx.ext.mathjax",
"sphinx.ext.viewcode",
"sphinx_js",
"sphinx_csharp",
"breathe",
"recommonmark",
]
@ -91,16 +97,16 @@ js_language = "typescript"
jsdoc_config_path = "../native_client/javascript/tsconfig.json"
# Add any paths that contain templates here, relative to this directory.
templates_path = ['.templates']
templates_path = [".templates"]
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
source_suffix = ".rst"
# The main toctree document.
master_doc = 'index'
master_doc = "index"
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
@ -112,10 +118,10 @@ language = None
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = ['.build', 'Thumbs.db', '.DS_Store', 'node_modules', 'examples']
exclude_patterns = [".build", "Thumbs.db", ".DS_Store", "node_modules", "examples"]
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
pygments_style = "sphinx"
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False
@ -128,18 +134,18 @@ add_module_names = False
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'furo'
html_theme = "furo"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['.static']
html_static_path = [".static"]
# -- Options for HTMLHelp output ------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = 'STTdoc'
htmlhelp_basename = "STTdoc"
# -- Options for LaTeX output ---------------------------------------------
@ -148,15 +154,12 @@ latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',
# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
@ -166,8 +169,7 @@ latex_elements = {
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'STT.tex', u'Coqui STT Documentation',
u'Coqui GmbH', 'manual'),
(master_doc, "STT.tex", u"Coqui STT Documentation", u"Coqui GmbH", "manual"),
]
@ -175,10 +177,7 @@ latex_documents = [
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'stt', u'Coqui STT Documentation',
[author], 1)
]
man_pages = [(master_doc, "stt", u"Coqui STT Documentation", [author], 1)]
# -- Options for Texinfo output -------------------------------------------
@ -187,16 +186,21 @@ man_pages = [
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'STT', u'Coqui STT Documentation',
author, 'STT', 'One line description of project.',
'Miscellaneous'),
(
master_doc,
"STT",
u"Coqui STT Documentation",
author,
"STT",
"One line description of project.",
"Miscellaneous",
),
]
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {'https://docs.python.org/': None}
intersphinx_mapping = {"https://docs.python.org/": None}
extlinks = {'github': ('https://github.com/coqui-ai/STT/blob/v{}/%s'.format(release),
'%s')}
extlinks = {
"github": ("https://github.com/coqui-ai/STT/blob/v{}/%s".format(release), "%s")
}

4
evaluate.py
View File

@ -2,11 +2,11 @@
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
if __name__ == '__main__':
if __name__ == "__main__":
try:
from coqui_stt_training import evaluate as ds_evaluate
except ImportError:
print('Training package is not installed. See training documentation.')
print("Training package is not installed. See training documentation.")
raise
ds_evaluate.run_script()

120
evaluate_tflite.py
View File

@ -2,22 +2,22 @@
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import absl.app
import argparse
import numpy as np
import wave
import csv
import os
import sys
import wave
from functools import partial
from multiprocessing import JoinableQueue, Manager, Process, cpu_count
from stt import Model
import absl.app
import numpy as np
from coqui_stt_training.util.evaluate_tools import calculate_and_print_report
from coqui_stt_training.util.flags import create_flags
from functools import partial
from multiprocessing import JoinableQueue, Process, cpu_count, Manager
from six.moves import zip, range
from six.moves import range, zip
from stt import Model
r'''
r"""
This module should be self-contained:
- build libstt.so with TFLite:
- bazel build [...] --define=runtime=tflite [...] //native_client:libstt.so
@ -27,10 +27,11 @@ This module should be self-contained:
- pip install -r requirements_eval_tflite.txt
Then run with a TFLite model, a scorer and a CSV test file
'''
"""
def tflite_worker(model, scorer, queue_in, queue_out, gpu_mask):
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_mask)
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_mask)
ds = Model(model)
ds.enableExternalScorer(scorer)
@ -38,20 +39,27 @@ def tflite_worker(model, scorer, queue_in, queue_out, gpu_mask):
try:
msg = queue_in.get()
filename = msg['filename']
fin = wave.open(filename, 'rb')
filename = msg["filename"]
fin = wave.open(filename, "rb")
audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
fin.close()
decoded = ds.stt(audio)
queue_out.put({'wav': filename, 'prediction': decoded, 'ground_truth': msg['transcript']})
queue_out.put(
{
"wav": filename,
"prediction": decoded,
"ground_truth": msg["transcript"],
}
)
except FileNotFoundError as ex:
print('FileNotFoundError: ', ex)
print("FileNotFoundError: ", ex)
print(queue_out.qsize(), end='\r') # Update the current progress
print(queue_out.qsize(), end="\r") # Update the current progress
queue_in.task_done()
def main(args, _):
manager = Manager()
work_todo = JoinableQueue() # this is where we are going to store input data
@ -59,7 +67,12 @@ def main(args, _):
processes = []
for i in range(args.proc):
worker_process = Process(target=tflite_worker, args=(args.model, args.scorer, work_todo, work_done, i), daemon=True, name='tflite_process_{}'.format(i))
worker_process = Process(
target=tflite_worker,
args=(args.model, args.scorer, work_todo, work_done, i),
daemon=True,
name="tflite_process_{}".format(i),
)
worker_process.start() # Launch reader() as a separate python process
processes.append(worker_process)
@ -71,56 +84,75 @@ def main(args, _):
losses = []
wav_filenames = []
with open(args.csv, 'r') as csvfile:
with open(args.csv, "r") as csvfile:
csvreader = csv.DictReader(csvfile)
count = 0
for row in csvreader:
count += 1
# Relative paths are relative to the folder the CSV file is in
if not os.path.isabs(row['wav_filename']):
row['wav_filename'] = os.path.join(os.path.dirname(args.csv), row['wav_filename'])
work_todo.put({'filename': row['wav_filename'], 'transcript': row['transcript']})
wav_filenames.extend(row['wav_filename'])
if not os.path.isabs(row["wav_filename"]):
row["wav_filename"] = os.path.join(
os.path.dirname(args.csv), row["wav_filename"]
)
work_todo.put(
{"filename": row["wav_filename"], "transcript": row["transcript"]}
)
wav_filenames.extend(row["wav_filename"])
print('Totally %d wav entries found in csv\n' % count)
print("Totally %d wav entries found in csv\n" % count)
work_todo.join()
print('\nTotally %d wav file transcripted' % work_done.qsize())
print("\nTotally %d wav file transcripted" % work_done.qsize())
while not work_done.empty():
msg = work_done.get()
losses.append(0.0)
ground_truths.append(msg['ground_truth'])
predictions.append(msg['prediction'])
wavlist.append(msg['wav'])
ground_truths.append(msg["ground_truth"])
predictions.append(msg["prediction"])
wavlist.append(msg["wav"])
# Print test summary
_ = calculate_and_print_report(wav_filenames, ground_truths, predictions, losses, args.csv)
_ = calculate_and_print_report(
wav_filenames, ground_truths, predictions, losses, args.csv
)
if args.dump:
with open(args.dump + '.txt', 'w') as ftxt, open(args.dump + '.out', 'w') as fout:
with open(args.dump + ".txt", "w") as ftxt, open(
args.dump + ".out", "w"
) as fout:
for wav, txt, out in zip(wavlist, ground_truths, predictions):
ftxt.write('%s %s\n' % (wav, txt))
fout.write('%s %s\n' % (wav, out))
print('Reference texts dumped to %s.txt' % args.dump)
print('Transcription dumped to %s.out' % args.dump)
ftxt.write("%s %s\n" % (wav, txt))
fout.write("%s %s\n" % (wav, out))
print("Reference texts dumped to %s.txt" % args.dump)
print("Transcription dumped to %s.out" % args.dump)
def parse_args():
parser = argparse.ArgumentParser(description='Computing TFLite accuracy')
parser.add_argument('--model', required=True,
help='Path to the model (protocol buffer binary file)')
parser.add_argument('--scorer', required=True,
help='Path to the external scorer file')
parser.add_argument('--csv', required=True,
help='Path to the CSV source file')
parser.add_argument('--proc', required=False, default=cpu_count(), type=int,
help='Number of processes to spawn, defaulting to number of CPUs')
parser.add_argument('--dump', required=False,
help='Path to dump the results as text file, with one line for each wav: "wav transcription".')
parser = argparse.ArgumentParser(description="Computing TFLite accuracy")
parser.add_argument(
"--model", required=True, help="Path to the model (protocol buffer binary file)"
)
parser.add_argument(
"--scorer", required=True, help="Path to the external scorer file"
)
parser.add_argument("--csv", required=True, help="Path to the CSV source file")
parser.add_argument(
"--proc",
required=False,
default=cpu_count(),
type=int,
help="Number of processes to spawn, defaulting to number of CPUs",
)
parser.add_argument(
"--dump",
required=False,
help='Path to dump the results as text file, with one line for each wav: "wav transcription".',
)
args, unknown = parser.parse_known_args()
# Reconstruct argv for absl.flags
sys.argv = [sys.argv[0]] + unknown
return args
if __name__ == '__main__':
if __name__ == "__main__":
create_flags()
absl.app.run(partial(main, parse_args()))

47
lm_optimizer.py
View File

@ -2,35 +2,39 @@
# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function
import sys
import absl.app
import optuna
import sys
import tensorflow.compat.v1 as tfv1
from coqui_stt_ctcdecoder import Scorer
from coqui_stt_training.evaluate import evaluate
from coqui_stt_training.train import create_model
from coqui_stt_training.util.config import Config, initialize_globals
from coqui_stt_training.util.flags import create_flags, FLAGS
from coqui_stt_training.util.logging import log_error
from coqui_stt_training.util.evaluate_tools import wer_cer_batch
from coqui_stt_ctcdecoder import Scorer
from coqui_stt_training.util.flags import FLAGS, create_flags
from coqui_stt_training.util.logging import log_error
import tensorflow.compat.v1 as tfv1
def character_based():
is_character_based = False
if FLAGS.scorer_path:
scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.scorer_path, Config.alphabet)
scorer = Scorer(
FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.scorer_path, Config.alphabet
)
is_character_based = scorer.is_utf8_mode()
return is_character_based
def objective(trial):
FLAGS.lm_alpha = trial.suggest_uniform('lm_alpha', 0, FLAGS.lm_alpha_max)
FLAGS.lm_beta = trial.suggest_uniform('lm_beta', 0, FLAGS.lm_beta_max)
is_character_based = trial.study.user_attrs['is_character_based']
def objective(trial):
FLAGS.lm_alpha = trial.suggest_uniform("lm_alpha", 0, FLAGS.lm_alpha_max)
FLAGS.lm_beta = trial.suggest_uniform("lm_beta", 0, FLAGS.lm_beta_max)
is_character_based = trial.study.user_attrs["is_character_based"]
samples = []
for step, test_file in enumerate(FLAGS.test_files.split(',')):
for step, test_file in enumerate(FLAGS.test_files.split(",")):
tfv1.reset_default_graph()
current_samples = evaluate([test_file], create_model)
@ -47,12 +51,15 @@ def objective(trial):
wer, cer = wer_cer_batch(samples)
return cer if is_character_based else wer
def main(_):
initialize_globals()
if not FLAGS.test_files:
log_error('You need to specify what files to use for evaluation via '
'the --test_files flag.')
log_error(
"You need to specify what files to use for evaluation via "
"the --test_files flag."
)
sys.exit(1)
is_character_based = character_based()
@ -60,11 +67,15 @@ def main(_):
study = optuna.create_study()
study.set_user_attr("is_character_based", is_character_based)
study.optimize(objective, n_jobs=1, n_trials=FLAGS.n_trials)
print('Best params: lm_alpha={} and lm_beta={} with WER={}'.format(study.best_params['lm_alpha'],
study.best_params['lm_beta'],
study.best_value))
print(
"Best params: lm_alpha={} and lm_beta={} with WER={}".format(
study.best_params["lm_alpha"],
study.best_params["lm_beta"],
study.best_value,
)
)
if __name__ == '__main__':
if __name__ == "__main__":
create_flags()
absl.app.run(main)

1
native_client/ctcdecode/LICENSE.parlance
View File

@ -20,4 +20,3 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

133
native_client/ctcdecode/__init__.py
View File

@ -4,14 +4,15 @@ from . import swigwrapper # pylint: disable=import-self
# This module is built with SWIG_PYTHON_STRICT_BYTE_CHAR so we must handle
# string encoding explicitly, here and throughout this file.
__version__ = swigwrapper.__version__.decode('utf-8')
__version__ = swigwrapper.__version__.decode("utf-8")
# Hack: import error codes by matching on their names, as SWIG unfortunately
# does not support binding enums to Python in a scoped manner yet.
for symbol in dir(swigwrapper):
if symbol.startswith('STT_ERR_'):
if symbol.startswith("STT_ERR_"):
globals()[symbol] = getattr(swigwrapper, symbol)
class Scorer(swigwrapper.Scorer):
"""Wrapper for Scorer.
@ -23,130 +24,140 @@ class Scorer(swigwrapper.Scorer):
:alphabet: Alphabet
:type scorer_path: basestring
"""
def __init__(self, alpha=None, beta=None, scorer_path=None, alphabet=None):
super(Scorer, self).__init__()
# Allow bare initialization
if alphabet:
assert alpha is not None, 'alpha parameter is required'
assert beta is not None, 'beta parameter is required'
assert scorer_path, 'scorer_path parameter is required'
assert alpha is not None, "alpha parameter is required"
assert beta is not None, "beta parameter is required"
assert scorer_path, "scorer_path parameter is required"
err = self.init(scorer_path.encode('utf-8'), alphabet)
err = self.init(scorer_path.encode("utf-8"), alphabet)
if err != 0:
raise ValueError('Scorer initialization failed with error code 0x{:X}'.format(err))
raise ValueError(
"Scorer initialization failed with error code 0x{:X}".format(err)
)
self.reset_params(alpha, beta)
class Alphabet(swigwrapper.Alphabet):
"""Convenience wrapper for Alphabet which calls init in the constructor"""
def __init__(self, config_path):
super(Alphabet, self).__init__()
err = self.init(config_path.encode('utf-8'))
err = self.init(config_path.encode("utf-8"))
if err != 0:
raise ValueError('Alphabet initialization failed with error code 0x{:X}'.format(err))
raise ValueError(
"Alphabet initialization failed with error code 0x{:X}".format(err)
)
def CanEncodeSingle(self, input):
'''
"""
Returns true if the single character/output class has a corresponding label
in the alphabet.
'''
return super(Alphabet, self).CanEncodeSingle(input.encode('utf-8'))
"""
return super(Alphabet, self).CanEncodeSingle(input.encode("utf-8"))
def CanEncode(self, input):
'''
"""
Returns true if the entire string can be encoded into labels in this
alphabet.
'''
return super(Alphabet, self).CanEncode(input.encode('utf-8'))
"""
return super(Alphabet, self).CanEncode(input.encode("utf-8"))
def EncodeSingle(self, input):
'''
"""
Encode a single character/output class into a label. Character must be in
the alphabet, this method will assert that. Use `CanEncodeSingle` to test.
'''
return super(Alphabet, self).EncodeSingle(input.encode('utf-8'))
"""
return super(Alphabet, self).EncodeSingle(input.encode("utf-8"))
def Encode(self, input):
'''
"""
Encode a sequence of character/output classes into a sequence of labels.
Characters are assumed to always take a single Unicode codepoint.
Characters must be in the alphabet, this method will assert that. Use
`CanEncode` and `CanEncodeSingle` to test.
'''
"""
# Convert SWIG's UnsignedIntVec to a Python list
res = super(Alphabet, self).Encode(input.encode('utf-8'))
res = super(Alphabet, self).Encode(input.encode("utf-8"))
return [el for el in res]
def DecodeSingle(self, input):
res = super(Alphabet, self).DecodeSingle(input)
return res.decode('utf-8')
return res.decode("utf-8")
def Decode(self, input):
'''Decode a sequence of labels into a string.'''
"""Decode a sequence of labels into a string."""
res = super(Alphabet, self).Decode(input)
return res.decode('utf-8')
return res.decode("utf-8")
class UTF8Alphabet(swigwrapper.UTF8Alphabet):
"""Convenience wrapper for Alphabet which calls init in the constructor"""
def __init__(self):
super(UTF8Alphabet, self).__init__()
err = self.init(b'')
err = self.init(b"")
if err != 0:
raise ValueError('UTF8Alphabet initialization failed with error code 0x{:X}'.format(err))
raise ValueError(
"UTF8Alphabet initialization failed with error code 0x{:X}".format(err)
)
def CanEncodeSingle(self, input):
'''
"""
Returns true if the single character/output class has a corresponding label
in the alphabet.
'''
return super(UTF8Alphabet, self).CanEncodeSingle(input.encode('utf-8'))
"""
return super(UTF8Alphabet, self).CanEncodeSingle(input.encode("utf-8"))
def CanEncode(self, input):
'''
"""
Returns true if the entire string can be encoded into labels in this
alphabet.
'''
return super(UTF8Alphabet, self).CanEncode(input.encode('utf-8'))
"""
return super(UTF8Alphabet, self).CanEncode(input.encode("utf-8"))
def EncodeSingle(self, input):
'''
"""
Encode a single character/output class into a label. Character must be in
the alphabet, this method will assert that. Use `CanEncodeSingle` to test.
'''
return super(UTF8Alphabet, self).EncodeSingle(input.encode('utf-8'))
"""
return super(UTF8Alphabet, self).EncodeSingle(input.encode("utf-8"))
def Encode(self, input):
'''
"""
Encode a sequence of character/output classes into a sequence of labels.
Characters are assumed to always take a single Unicode codepoint.
Characters must be in the alphabet, this method will assert that. Use
`CanEncode` and `CanEncodeSingle` to test.
'''
"""
# Convert SWIG's UnsignedIntVec to a Python list
res = super(UTF8Alphabet, self).Encode(input.encode('utf-8'))
res = super(UTF8Alphabet, self).Encode(input.encode("utf-8"))
return [el for el in res]
def DecodeSingle(self, input):
res = super(UTF8Alphabet, self).DecodeSingle(input)
return res.decode('utf-8')
return res.decode("utf-8")
def Decode(self, input):
'''Decode a sequence of labels into a string.'''
"""Decode a sequence of labels into a string."""
res = super(UTF8Alphabet, self).Decode(input)
return res.decode('utf-8')
return res.decode("utf-8")
def ctc_beam_search_decoder(probs_seq,
def ctc_beam_search_decoder(
probs_seq,
alphabet,
beam_size,
cutoff_prob=1.0,
cutoff_top_n=40,
scorer=None,
hot_words=dict(),
num_results=1):
num_results=1,
):
"""Wrapper for the CTC Beam Search Decoder.
:param probs_seq: 2-D list of probability distributions over each time
@ -175,13 +186,23 @@ def ctc_beam_search_decoder(probs_seq,
:rtype: list
"""
beam_results = swigwrapper.ctc_beam_search_decoder(
probs_seq, alphabet, beam_size, cutoff_prob, cutoff_top_n,
scorer, hot_words, num_results)
beam_results = [(res.confidence, alphabet.Decode(res.tokens)) for res in beam_results]
probs_seq,
alphabet,
beam_size,
cutoff_prob,
cutoff_top_n,
scorer,
hot_words,
num_results,
)
beam_results = [
(res.confidence, alphabet.Decode(res.tokens)) for res in beam_results
]
return beam_results
def ctc_beam_search_decoder_batch(probs_seq,
def ctc_beam_search_decoder_batch(
probs_seq,
seq_lengths,
alphabet,
beam_size,
@ -190,7 +211,8 @@ def ctc_beam_search_decoder_batch(probs_seq,
cutoff_top_n=40,
scorer=None,
hot_words=dict(),
num_results=1):
num_results=1,
):
"""Wrapper for the batched CTC beam search decoder.
:param probs_seq: 3-D list with each element as an instance of 2-D list
@ -222,7 +244,18 @@ def ctc_beam_search_decoder_batch(probs_seq,
results, in descending order of the confidence.
:rtype: list
"""
batch_beam_results = swigwrapper.ctc_beam_search_decoder_batch(probs_seq, seq_lengths, alphabet, beam_size, num_processes, cutoff_prob, cutoff_top_n, scorer, hot_words, num_results)
batch_beam_results = swigwrapper.ctc_beam_search_decoder_batch(
probs_seq,
seq_lengths,
alphabet,
beam_size,
num_processes,
cutoff_prob,
cutoff_top_n,
scorer,
hot_words,
num_results,
)
batch_beam_results = [
[(res.confidence, alphabet.Decode(res.tokens)) for res in beam_results]
for beam_results in batch_beam_results

127
native_client/ctcdecode/build_archive.py
View File

@ -6,84 +6,95 @@ import os
import shlex
import subprocess
import sys
from multiprocessing.dummy import Pool
if sys.platform.startswith('win'):
ARGS = ['/nologo', '/D KENLM_MAX_ORDER=6', '/EHsc', '/source-charset:utf-8']
OPT_ARGS = ['/O2', '/MT', '/D NDEBUG']
DBG_ARGS = ['/Od', '/MTd', '/Zi', '/U NDEBUG', '/D DEBUG']
OPENFST_DIR = 'third_party/openfst-1.6.9-win'
if sys.platform.startswith("win"):
ARGS = ["/nologo", "/D KENLM_MAX_ORDER=6", "/EHsc", "/source-charset:utf-8"]
OPT_ARGS = ["/O2", "/MT", "/D NDEBUG"]
DBG_ARGS = ["/Od", "/MTd", "/Zi", "/U NDEBUG", "/D DEBUG"]
OPENFST_DIR = "third_party/openfst-1.6.9-win"
else:
ARGS = ['-fPIC', '-DKENLM_MAX_ORDER=6', '-std=c++11', '-Wno-unused-local-typedefs', '-Wno-sign-compare']
OPT_ARGS = ['-O3', '-DNDEBUG']
DBG_ARGS = ['-O0', '-g', '-UNDEBUG', '-DDEBUG']
OPENFST_DIR = 'third_party/openfst-1.6.7'
ARGS = [
"-fPIC",
"-DKENLM_MAX_ORDER=6",
"-std=c++11",
"-Wno-unused-local-typedefs",
"-Wno-sign-compare",
]
OPT_ARGS = ["-O3", "-DNDEBUG"]
DBG_ARGS = ["-O0", "-g", "-UNDEBUG", "-DDEBUG"]
OPENFST_DIR = "third_party/openfst-1.6.7"
INCLUDES = [
'..',
'../kenlm',
OPENFST_DIR + '/src/include',
'third_party/ThreadPool',
'third_party/object_pool'
"..",
"../kenlm",
OPENFST_DIR + "/src/include",
"third_party/ThreadPool",
"third_party/object_pool",
]
KENLM_FILES = (glob.glob('../kenlm/util/*.cc')
+ glob.glob('../kenlm/lm/*.cc')
+ glob.glob('../kenlm/util/double-conversion/*.cc'))
KENLM_FILES = (
glob.glob("../kenlm/util/*.cc")
+ glob.glob("../kenlm/lm/*.cc")
+ glob.glob("../kenlm/util/double-conversion/*.cc")
)
KENLM_FILES += glob.glob(OPENFST_DIR + '/src/lib/*.cc')
KENLM_FILES += glob.glob(OPENFST_DIR + "/src/lib/*.cc")
KENLM_FILES = [
fn for fn in KENLM_FILES
if not (fn.endswith('main.cc') or fn.endswith('test.cc') or fn.endswith(
'unittest.cc'))
fn
for fn in KENLM_FILES
if not (
fn.endswith("main.cc") or fn.endswith("test.cc") or fn.endswith("unittest.cc")
)
]
CTC_DECODER_FILES = [
'ctc_beam_search_decoder.cpp',
'scorer.cpp',
'path_trie.cpp',
'decoder_utils.cpp',
'workspace_status.cc',
'../alphabet.cc',
"ctc_beam_search_decoder.cpp",
"scorer.cpp",
"path_trie.cpp",
"decoder_utils.cpp",
"workspace_status.cc",
"../alphabet.cc",
]
def build_archive(srcs=[], out_name='', build_dir='temp_build/temp_build', debug=False, num_parallel=1):
compiler = os.environ.get('CXX', 'g++')
if sys.platform.startswith('win'):
def build_archive(
srcs=[], out_name="", build_dir="temp_build/temp_build", debug=False, num_parallel=1
):
compiler = os.environ.get("CXX", "g++")
if sys.platform.startswith("win"):
compiler = '"{}"'.format(compiler)
ar = os.environ.get('AR', 'ar')
libexe = os.environ.get('LIBEXE', 'lib.exe')
libtool = os.environ.get('LIBTOOL', 'libtool')
cflags = os.environ.get('CFLAGS', '') + os.environ.get('CXXFLAGS', '')
ar = os.environ.get("AR", "ar")
libexe = os.environ.get("LIBEXE", "lib.exe")
libtool = os.environ.get("LIBTOOL", "libtool")
cflags = os.environ.get("CFLAGS", "") + os.environ.get("CXXFLAGS", "")
args = ARGS + (DBG_ARGS if debug else OPT_ARGS)
for file in srcs:
outfile = os.path.join(build_dir, os.path.splitext(file)[0] + '.o')
outfile = os.path.join(build_dir, os.path.splitext(file)[0] + ".o")
outdir = os.path.dirname(outfile)
if not os.path.exists(outdir):
print('mkdir', outdir)
print("mkdir", outdir)
os.makedirs(outdir)
def build_one(file):
outfile = os.path.join(build_dir, os.path.splitext(file)[0] + '.o')
outfile = os.path.join(build_dir, os.path.splitext(file)[0] + ".o")
if os.path.exists(outfile):
return
if sys.platform.startswith('win'):
file = '"{}"'.format(file.replace('\\', '/'))
output = '/Fo"{}"'.format(outfile.replace('\\', '/'))
if sys.platform.startswith("win"):
file = '"{}"'.format(file.replace("\\", "/"))
output = '/Fo"{}"'.format(outfile.replace("\\", "/"))
else:
output = '-o ' + outfile
output = "-o " + outfile
cmd = '{cc} -c {cflags} {args} {includes} {infile} {output}'.format(
cmd = "{cc} -c {cflags} {args} {includes} {infile} {output}".format(
cc=compiler,
cflags=cflags,
args=' '.join(args),
includes=' '.join('-I' + i for i in INCLUDES),
args=" ".join(args),
includes=" ".join("-I" + i for i in INCLUDES),
infile=file,
output=output,
)
@ -94,30 +105,28 @@ def build_archive(srcs=[], out_name='', build_dir='temp_build/temp_build', debug
pool = Pool(num_parallel)
obj_files = list(pool.imap_unordered(build_one, srcs))
if sys.platform.startswith('darwin'):
cmd = '{libtool} -static -o {outfile} {infiles}'.format(
if sys.platform.startswith("darwin"):
cmd = "{libtool} -static -o {outfile} {infiles}".format(
libtool=libtool,
outfile=out_name,
infiles=' '.join(obj_files),
infiles=" ".join(obj_files),
)
print(cmd)
subprocess.check_call(shlex.split(cmd))
elif sys.platform.startswith('win'):
elif sys.platform.startswith("win"):
cmd = '"{libexe}" /OUT:"{outfile}" {infiles} /MACHINE:X64 /NOLOGO'.format(
libexe=libexe,
outfile=out_name,
infiles=' '.join(obj_files))
cmd = cmd.replace('\\', '/')
libexe=libexe, outfile=out_name, infiles=" ".join(obj_files)
)
cmd = cmd.replace("\\", "/")
print(cmd)
subprocess.check_call(shlex.split(cmd))
else:
cmd = '{ar} rcs {outfile} {infiles}'.format(
ar=ar,
outfile=out_name,
infiles=' '.join(obj_files)
cmd = "{ar} rcs {outfile} {infiles}".format(
ar=ar, outfile=out_name, infiles=" ".join(obj_files)
)
print(cmd)
subprocess.check_call(shlex.split(cmd))
if __name__ == '__main__':
if __name__ == "__main__":
build_common()

1
native_client/ctcdecode/setup.cfg
View File

@ -13,4 +13,3 @@ bdist-dir=temp_build/temp_build
[install_lib]
build-dir=temp_build/temp_build

72
native_client/ctcdecode/setup.py
View File

@ -1,95 +1,105 @@
#!/usr/bin/env python
from __future__ import absolute_import, division, print_function
from distutils.command.build import build
from setuptools import setup, Extension, distutils
import argparse
import multiprocessing.pool
import os
import platform
import sys
from distutils.command.build import build
from build_archive import *
from setuptools import Extension, distutils, setup
try:
import numpy
try:
numpy_include = numpy.get_include()
except AttributeError:
numpy_include = numpy.get_numpy_include()
except ImportError:
numpy_include = ''
assert 'NUMPY_INCLUDE' in os.environ
numpy_include = ""
assert "NUMPY_INCLUDE" in os.environ
numpy_include = os.getenv('NUMPY_INCLUDE', numpy_include)
numpy_min_ver = os.getenv('NUMPY_DEP_VERSION', '')
numpy_include = os.getenv("NUMPY_INCLUDE", numpy_include)
numpy_min_ver = os.getenv("NUMPY_DEP_VERSION", "")
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--num_processes",
default=1,
type=int,
help="Number of cpu processes to build package. (default: %(default)d)")
help="Number of cpu processes to build package. (default: %(default)d)",
)
known_args, unknown_args = parser.parse_known_args()
debug = '--debug' in unknown_args
debug = "--debug" in unknown_args
# reconstruct sys.argv to pass to setup below
sys.argv = [sys.argv[0]] + unknown_args
def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
def maybe_rebuild(srcs, out_name, build_dir):
if not os.path.exists(out_name):
if not os.path.exists(build_dir):
os.makedirs(build_dir)
build_archive(srcs=srcs,
build_archive(
srcs=srcs,
out_name=out_name,
build_dir=build_dir,
num_parallel=known_args.num_processes,
debug=debug)
debug=debug,
)
project_version = read('../../training/coqui_stt_training/VERSION').strip()
build_dir = 'temp_build/temp_build'
project_version = read("../../training/coqui_stt_training/VERSION").strip()
if sys.platform.startswith('win'):
archive_ext = 'lib'
build_dir = "temp_build/temp_build"
if sys.platform.startswith("win"):
archive_ext = "lib"
else:
archive_ext = 'a'
archive_ext = "a"
third_party_build = 'third_party.{}'.format(archive_ext)
ctc_decoder_build = 'first_party.{}'.format(archive_ext)
third_party_build = "third_party.{}".format(archive_ext)
ctc_decoder_build = "first_party.{}".format(archive_ext)
maybe_rebuild(KENLM_FILES, third_party_build, build_dir)
maybe_rebuild(CTC_DECODER_FILES, ctc_decoder_build, build_dir)
decoder_module = Extension(
name='coqui_stt_ctcdecoder._swigwrapper',
sources=['swigwrapper.i'],
swig_opts=['-c++', '-extranative'],
language='c++',
name="coqui_stt_ctcdecoder._swigwrapper",
sources=["swigwrapper.i"],
swig_opts=["-c++", "-extranative"],
language="c++",
include_dirs=INCLUDES + [numpy_include],
extra_compile_args=ARGS + (DBG_ARGS if debug else OPT_ARGS),
extra_link_args=[ctc_decoder_build, third_party_build],
)
class BuildExtFirst(build):
sub_commands = [('build_ext', build.has_ext_modules),
('build_py', build.has_pure_modules),
('build_clib', build.has_c_libraries),
('build_scripts', build.has_scripts)]
sub_commands = [
("build_ext", build.has_ext_modules),
("build_py", build.has_pure_modules),
("build_clib", build.has_c_libraries),
("build_scripts", build.has_scripts),
]
setup(
name='coqui_stt_ctcdecoder',
name="coqui_stt_ctcdecoder",
version=project_version,
description="""DS CTC decoder""",
cmdclass = {'build': BuildExtFirst},
cmdclass={"build": BuildExtFirst},
ext_modules=[decoder_module],
package_dir = {'coqui_stt_ctcdecoder': '.'},
py_modules=['coqui_stt_ctcdecoder', 'coqui_stt_ctcdecoder.swigwrapper'],
install_requires = ['numpy%s' % numpy_min_ver],
package_dir={"coqui_stt_ctcdecoder": "."},
py_modules=["coqui_stt_ctcdecoder", "coqui_stt_ctcdecoder.swigwrapper"],
install_requires=["numpy%s" % numpy_min_ver],
)

2
native_client/java/gradle.properties
View File

@ -11,5 +11,3 @@ org.gradle.jvmargs=-Xmx1536m
# This option should only be used with decoupled projects. More details, visit
# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
# org.gradle.parallel=true

1
native_client/java/libstt/src/main/java/ai/coqui/libstt_doc/STT_Error_Codes.java
View File

@ -70,4 +70,3 @@ public enum STT_Error_Codes {
private static int next = 0;
}
}

30
native_client/javascript/binding.gyp
View File

@ -3,27 +3,25 @@
{
"target_name": "stt",
"sources": ["stt_wrap.cxx"],
"libraries": [
"$(LIBS)"
],
"include_dirs": [
"../"
],
"libraries": ["$(LIBS)"],
"include_dirs": ["../"],
"conditions": [
[ "OS=='mac'", {
[
"OS=='mac'",
{
"xcode_settings": {
"OTHER_CXXFLAGS": [
"-stdlib=libc++",
"-mmacosx-version-min=10.10"
"-mmacosx-version-min=10.10",
],
"OTHER_LDFLAGS": [
"-stdlib=libc++",
"-mmacosx-version-min=10.10"
]
}
"-mmacosx-version-min=10.10",
],
}
},
]
]
],
},
{
"target_name": "action_after_build",
@ -32,15 +30,15 @@
"copies": [
{
"files": ["<(PRODUCT_DIR)/<(module_name).node"],
"destination": "<(module_path)"
}
]
"destination": "<(module_path)",
}
],
},
],
"variables": {
"build_v8_with_gn": 0,
"v8_enable_pointer_compression": 0,
"v8_enable_31bit_smis_on_64bit_arch": 0,
"enable_lto": 1
"enable_lto": 1,
},
}

73
native_client/python/__init__.py
View File

@ -5,23 +5,24 @@ import platform
# pylint: disable=invalid-name
if platform.system().lower() == "windows":
dslib_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'lib')
dslib_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "lib")
# On Windows, we can't rely on RPATH being set to $ORIGIN/lib/ or on
# @loader_path/lib
if hasattr(os, 'add_dll_directory'):
if hasattr(os, "add_dll_directory"):
# Starting with Python 3.8 this properly handles the problem
os.add_dll_directory(dslib_path)
else:
# Before Pythin 3.8 we need to change the PATH to include the proper
# directory for the dynamic linker
os.environ['PATH'] = dslib_path + ';' + os.environ['PATH']
os.environ["PATH"] = dslib_path + ";" + os.environ["PATH"]
import stt
# rename for backwards compatibility
from stt.impl import Version as version
class Model(object):
"""
Class holding a Coqui STT model
@ -29,13 +30,18 @@ class Model(object):
:param aModelPath: Path to model file to load
:type aModelPath: str
"""
def __init__(self, model_path):
# make sure the attribute is there if CreateModel fails
self._impl = None
status, impl = stt.impl.CreateModel(model_path)
if status != 0:
raise RuntimeError("CreateModel failed with '{}' (0x{:X})".format(stt.impl.ErrorCodeToErrorMessage(status),status))
raise RuntimeError(
"CreateModel failed with '{}' (0x{:X})".format(
stt.impl.ErrorCodeToErrorMessage(status), status
)
)
self._impl = impl
def __del__(self):
@ -85,7 +91,11 @@ class Model(object):
"""
status = stt.impl.EnableExternalScorer(self._impl, scorer_path)
if status != 0:
raise RuntimeError("EnableExternalScorer failed with '{}' (0x{:X})".format(stt.impl.ErrorCodeToErrorMessage(status),status))
raise RuntimeError(
"EnableExternalScorer failed with '{}' (0x{:X})".format(
stt.impl.ErrorCodeToErrorMessage(status), status
)
)
def disableExternalScorer(self):
"""
@ -111,7 +121,11 @@ class Model(object):
"""
status = stt.impl.AddHotWord(self._impl, word, boost)
if status != 0:
raise RuntimeError("AddHotWord failed with '{}' (0x{:X})".format(stt.impl.ErrorCodeToErrorMessage(status),status))
raise RuntimeError(
"AddHotWord failed with '{}' (0x{:X})".format(
stt.impl.ErrorCodeToErrorMessage(status), status
)
)
def eraseHotWord(self, word):
"""
@ -124,7 +138,11 @@ class Model(object):
"""
status = stt.impl.EraseHotWord(self._impl, word)
if status != 0:
raise RuntimeError("EraseHotWord failed with '{}' (0x{:X})".format(stt.impl.ErrorCodeToErrorMessage(status),status))
raise RuntimeError(
"EraseHotWord failed with '{}' (0x{:X})".format(
stt.impl.ErrorCodeToErrorMessage(status), status
)
)
def clearHotWords(self):
"""
@ -134,7 +152,11 @@ class Model(object):
"""
status = stt.impl.ClearHotWords(self._impl)
if status != 0:
raise RuntimeError("ClearHotWords failed with '{}' (0x{:X})".format(stt.impl.ErrorCodeToErrorMessage(status),status))
raise RuntimeError(
"ClearHotWords failed with '{}' (0x{:X})".format(
stt.impl.ErrorCodeToErrorMessage(status), status
)
)
def setScorerAlphaBeta(self, alpha, beta):
"""
@ -190,7 +212,11 @@ class Model(object):
"""
status, ctx = stt.impl.CreateStream(self._impl)
if status != 0:
raise RuntimeError("CreateStream failed with '{}' (0x{:X})".format(stt.impl.ErrorCodeToErrorMessage(status),status))
raise RuntimeError(
"CreateStream failed with '{}' (0x{:X})".format(
stt.impl.ErrorCodeToErrorMessage(status), status
)
)
return Stream(ctx)
@ -199,6 +225,7 @@ class Stream(object):
Class wrapping a stt stream. The constructor cannot be called directly.
Use :func:`Model.createStream()`
"""
def __init__(self, native_stream):
self._impl = native_stream
@ -216,7 +243,9 @@ class Stream(object):
:throws: RuntimeError if the stream object is not valid
"""
if not self._impl:
raise RuntimeError("Stream object is not valid. Trying to feed an already finished stream?")
raise RuntimeError(
"Stream object is not valid. Trying to feed an already finished stream?"
)
stt.impl.FeedAudioContent(self._impl, audio_buffer)
def intermediateDecode(self):
@ -229,7 +258,9 @@ class Stream(object):
:throws: RuntimeError if the stream object is not valid
"""
if not self._impl:
raise RuntimeError("Stream object is not valid. Trying to decode an already finished stream?")
raise RuntimeError(
"Stream object is not valid. Trying to decode an already finished stream?"
)
return stt.impl.IntermediateDecode(self._impl)
def intermediateDecodeWithMetadata(self, num_results=1):
@ -245,7 +276,9 @@ class Stream(object):
:throws: RuntimeError if the stream object is not valid
"""
if not self._impl:
raise RuntimeError("Stream object is not valid. Trying to decode an already finished stream?")
raise RuntimeError(
"Stream object is not valid. Trying to decode an already finished stream?"
)
return stt.impl.IntermediateDecodeWithMetadata(self._impl, num_results)
def finishStream(self):
@ -260,7 +293,9 @@ class Stream(object):
:throws: RuntimeError if the stream object is not valid
"""
if not self._impl:
raise RuntimeError("Stream object is not valid. Trying to finish an already finished stream?")
raise RuntimeError(
"Stream object is not valid. Trying to finish an already finished stream?"
)
result = stt.impl.FinishStream(self._impl)
self._impl = None
return result
@ -281,7 +316,9 @@ class Stream(object):
:throws: RuntimeError if the stream object is not valid
"""
if not self._impl:
raise RuntimeError("Stream object is not valid. Trying to finish an already finished stream?")
raise RuntimeError(
"Stream object is not valid. Trying to finish an already finished stream?"
)
result = stt.impl.FinishStreamWithMetadata(self._impl, num_results)
self._impl = None
return result
@ -294,7 +331,9 @@ class Stream(object):
:throws: RuntimeError if the stream object is not valid
"""
if not self._impl:
raise RuntimeError("Stream object is not valid. Trying to free an already finished stream?")
raise RuntimeError(
"Stream object is not valid. Trying to free an already finished stream?"
)
stt.impl.FreeStream(self._impl)
self._impl = None
@ -311,13 +350,11 @@ class TokenMetadata(object):
The text for this token
"""
def timestep(self):
"""
Position of the token in units of 20ms
"""
def start_time(self):
"""
Position of the token in seconds
@ -328,6 +365,7 @@ class CandidateTranscript(object):
"""
Stores the entire CTC output as an array of character metadata objects
"""
def tokens(self):
"""
List of tokens
@ -336,7 +374,6 @@ class CandidateTranscript(object):
:type: list
"""
def confidence(self):
"""
Approximated confidence value for this transcription. This is roughly the

136
native_client/python/client.py
View File

@ -3,16 +3,16 @@
from __future__ import absolute_import, division, print_function
import argparse
import numpy as np
import json
import shlex
import subprocess
import sys
import wave
import json
from stt import Model, version
from timeit import default_timer as timer
import numpy as np
from stt import Model, version
try:
from shhlex import quote
except ImportError:
@ -20,19 +20,26 @@ except ImportError:
def convert_samplerate(audio_path, desired_sample_rate):
sox_cmd = 'sox {} --type raw --bits 16 --channels 1 --rate {} --encoding signed-integer --endian little --compression 0.0 --no-dither - '.format(quote(audio_path), desired_sample_rate)
sox_cmd = "sox {} --type raw --bits 16 --channels 1 --rate {} --encoding signed-integer --endian little --compression 0.0 --no-dither - ".format(
quote(audio_path), desired_sample_rate
)
try:
output = subprocess.check_output(shlex.split(sox_cmd), stderr=subprocess.PIPE)
except subprocess.CalledProcessError as e:
raise RuntimeError('SoX returned non-zero status: {}'.format(e.stderr))
raise RuntimeError("SoX returned non-zero status: {}".format(e.stderr))
except OSError as e:
raise OSError(e.errno, 'SoX not found, use {}hz files or install it: {}'.format(desired_sample_rate, e.strerror))
raise OSError(
e.errno,
"SoX not found, use {}hz files or install it: {}".format(
desired_sample_rate, e.strerror
),
)
return desired_sample_rate, np.frombuffer(output, np.int16)
def metadata_to_string(metadata):
return ''.join(token.text for token in metadata.tokens)
return "".join(token.text for token in metadata.tokens)
def words_from_candidate_transcript(metadata):
@ -70,56 +77,78 @@ def words_from_candidate_transcript(metadata):
def metadata_json_output(metadata):
json_result = dict()
json_result["transcripts"] = [{
json_result["transcripts"] = [
{
"confidence": transcript.confidence,
"words": words_from_candidate_transcript(transcript),
} for transcript in metadata.transcripts]
}
for transcript in metadata.transcripts
]
return json.dumps(json_result, indent=2)
class VersionAction(argparse.Action):
def __init__(self, *args, **kwargs):
super(VersionAction, self).__init__(nargs=0, *args, **kwargs)
def __call__(self, *args, **kwargs):
print('Coqui STT ', version())
print("Coqui STT ", version())
exit(0)
def main():
parser = argparse.ArgumentParser(description='Running Coqui STT inference.')
parser.add_argument('--model', required=True,
help='Path to the model (protocol buffer binary file)')
parser.add_argument('--scorer', required=False,
help='Path to the external scorer file')
parser.add_argument('--audio', required=True,
help='Path to the audio file to run (WAV format)')
parser.add_argument('--beam_width', type=int,
help='Beam width for the CTC decoder')
parser.add_argument('--lm_alpha', type=float,
help='Language model weight (lm_alpha). If not specified, use default from the scorer package.')
parser.add_argument('--lm_beta', type=float,
help='Word insertion bonus (lm_beta). If not specified, use default from the scorer package.')
parser.add_argument('--version', action=VersionAction,
help='Print version and exits')
parser.add_argument('--extended', required=False, action='store_true',
help='Output string from extended metadata')
parser.add_argument('--json', required=False, action='store_true',
help='Output json from metadata with timestamp of each word')
parser.add_argument('--candidate_transcripts', type=int, default=3,
help='Number of candidate transcripts to include in JSON output')
parser.add_argument('--hot_words', type=str,
help='Hot-words and their boosts.')
parser = argparse.ArgumentParser(description="Running Coqui STT inference.")
parser.add_argument(
"--model", required=True, help="Path to the model (protocol buffer binary file)"
)
parser.add_argument(
"--scorer", required=False, help="Path to the external scorer file"
)
parser.add_argument(
"--audio", required=True, help="Path to the audio file to run (WAV format)"
)
parser.add_argument("--beam_width", type=int, help="Beam width for the CTC decoder")
parser.add_argument(
"--lm_alpha",
type=float,
help="Language model weight (lm_alpha). If not specified, use default from the scorer package.",
)
parser.add_argument(
"--lm_beta",
type=float,
help="Word insertion bonus (lm_beta). If not specified, use default from the scorer package.",
)
parser.add_argument(
"--version", action=VersionAction, help="Print version and exits"
)
parser.add_argument(
"--extended",
required=False,
action="store_true",
help="Output string from extended metadata",
)
parser.add_argument(
"--json",
required=False,
action="store_true",
help="Output json from metadata with timestamp of each word",
)
parser.add_argument(
"--candidate_transcripts",
type=int,
default=3,
help="Number of candidate transcripts to include in JSON output",
)
parser.add_argument("--hot_words", type=str, help="Hot-words and their boosts.")
args = parser.parse_args()
print('Loading model from file {}'.format(args.model), file=sys.stderr)
print("Loading model from file {}".format(args.model), file=sys.stderr)
model_load_start = timer()
# sphinx-doc: python_ref_model_start
ds = Model(args.model)
# sphinx-doc: python_ref_model_stop
model_load_end = timer() - model_load_start
print('Loaded model in {:.3}s.'.format(model_load_end), file=sys.stderr)
print("Loaded model in {:.3}s.".format(model_load_end), file=sys.stderr)
if args.beam_width:
ds.setBeamWidth(args.beam_width)
@ -127,25 +156,30 @@ def main():
desired_sample_rate = ds.sampleRate()
if args.scorer:
print('Loading scorer from files {}'.format(args.scorer), file=sys.stderr)
print("Loading scorer from files {}".format(args.scorer), file=sys.stderr)
scorer_load_start = timer()
ds.enableExternalScorer(args.scorer)
scorer_load_end = timer() - scorer_load_start
print('Loaded scorer in {:.3}s.'.format(scorer_load_end), file=sys.stderr)
print("Loaded scorer in {:.3}s.".format(scorer_load_end), file=sys.stderr)
if args.lm_alpha and args.lm_beta:
ds.setScorerAlphaBeta(args.lm_alpha, args.lm_beta)
if args.hot_words:
print('Adding hot-words', file=sys.stderr)
for word_boost in args.hot_words.split(','):
word,boost = word_boost.split(':')
print("Adding hot-words", file=sys.stderr)
for word_boost in args.hot_words.split(","):
word, boost = word_boost.split(":")
ds.addHotWord(word, float(boost))
fin = wave.open(args.audio, 'rb')
fin = wave.open(args.audio, "rb")
fs_orig = fin.getframerate()
if fs_orig != desired_sample_rate:
print('Warning: original sample rate ({}) is different than {}hz. Resampling might produce erratic speech recognition.'.format(fs_orig, desired_sample_rate), file=sys.stderr)
print(
"Warning: original sample rate ({}) is different than {}hz. Resampling might produce erratic speech recognition.".format(
fs_orig, desired_sample_rate
),
file=sys.stderr,
)
fs_new, audio = convert_samplerate(args.audio, desired_sample_rate)
else:
audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
@ -153,18 +187,24 @@ def main():
audio_length = fin.getnframes() * (1 / fs_orig)
fin.close()
print('Running inference.', file=sys.stderr)
print("Running inference.", file=sys.stderr)
inference_start = timer()
# sphinx-doc: python_ref_inference_start
if args.extended:
print(metadata_to_string(ds.sttWithMetadata(audio, 1).transcripts[0]))
elif args.json:
print(metadata_json_output(ds.sttWithMetadata(audio, args.candidate_transcripts)))
print(
metadata_json_output(ds.sttWithMetadata(audio, args.candidate_transcripts))
)
else:
print(ds.stt(audio))
# sphinx-doc: python_ref_inference_stop
inference_end = timer() - inference_start
print('Inference took %0.3fs for %0.3fs audio file.' % (inference_end, audio_length), file=sys.stderr)
print(
"Inference took %0.3fs for %0.3fs audio file." % (inference_end, audio_length),
file=sys.stderr,
)
if __name__ == '__main__':
if __name__ == "__main__":
main()

139
native_client/python/setup.py
View File

@ -1,107 +1,122 @@
#! /usr/bin/env python
from setuptools import setup, Extension
from distutils.command.build import build
import os
import subprocess
import sys
from distutils.command.build import build
from setuptools import Extension, setup
def main():
try:
import numpy
try:
numpy_include = numpy.get_include()
except AttributeError:
numpy_include = numpy.get_numpy_include()
except ImportError:
numpy_include = ''
assert 'NUMPY_INCLUDE' in os.environ
numpy_include = ""
assert "NUMPY_INCLUDE" in os.environ
def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
numpy_include = os.getenv('NUMPY_INCLUDE', numpy_include)
numpy_min_ver = os.getenv('NUMPY_DEP_VERSION', '')
numpy_include = os.getenv("NUMPY_INCLUDE", numpy_include)
numpy_min_ver = os.getenv("NUMPY_DEP_VERSION", "")
project_name = 'STT'
if '--project_name' in sys.argv:
project_name_idx = sys.argv.index('--project_name')
project_name = "STT"
if "--project_name" in sys.argv:
project_name_idx = sys.argv.index("--project_name")
project_name = sys.argv[project_name_idx + 1]
sys.argv.remove('--project_name')
sys.argv.remove("--project_name")
sys.argv.pop(project_name_idx)
with open('../../training/coqui_stt_training/VERSION', 'r') as ver:
with open("../../training/coqui_stt_training/VERSION", "r") as ver:
project_version = ver.read().strip()
class BuildExtFirst(build):
sub_commands = [('build_ext', build.has_ext_modules),
('build_py', build.has_pure_modules),
('build_clib', build.has_c_libraries),
('build_scripts', build.has_scripts)]
sub_commands = [
("build_ext", build.has_ext_modules),
("build_py", build.has_pure_modules),
("build_clib", build.has_c_libraries),
("build_scripts", build.has_scripts),
]
# Properly pass arguments for linking, setuptools will perform some checks
def lib_dirs_split(a):
if os.name == 'posix':
return a.split('-L')[1:]
if os.name == "posix":
return a.split("-L")[1:]
if os.name == 'nt':
if os.name == "nt":
return []
raise AssertionError('os.name == java not expected')
raise AssertionError("os.name == java not expected")
def libs_split(a):
if os.name == 'posix':
return a.split('-l')[1:]
if os.name == "posix":
return a.split("-l")[1:]
if os.name == 'nt':
return a.split('.lib')[0:1]
if os.name == "nt":
return a.split(".lib")[0:1]
raise AssertionError('os.name == java not expected')
raise AssertionError("os.name == java not expected")
ds_ext = Extension(name='stt._impl',
sources=['impl.i'],
include_dirs=[numpy_include, '../'],
library_dirs=list(map(lambda x: x.strip(), lib_dirs_split(os.getenv('MODEL_LDFLAGS', '')))),
libraries=list(map(lambda x: x.strip(), libs_split(os.getenv('MODEL_LIBS', '')))),
swig_opts=['-c++', '-keyword'])
ds_ext = Extension(
name="stt._impl",
sources=["impl.i"],
include_dirs=[numpy_include, "../"],
library_dirs=list(
map(lambda x: x.strip(), lib_dirs_split(os.getenv("MODEL_LDFLAGS", "")))
),
libraries=list(
map(lambda x: x.strip(), libs_split(os.getenv("MODEL_LIBS", "")))
),
swig_opts=["-c++", "-keyword"],
)
setup(name=project_name,
description='A library for doing speech recognition using a Coqui STT model',
long_description=read('README.rst'),
long_description_content_type='text/x-rst; charset=UTF-8',
author='Coqui GmbH',
setup(
name=project_name,
description="A library for doing speech recognition using a Coqui STT model",
long_description=read("README.rst"),
long_description_content_type="text/x-rst; charset=UTF-8",
author="Coqui GmbH",
version=project_version,
package_dir={'stt': '.'},
cmdclass={'build': BuildExtFirst},
license='MPL-2.0',
url='https://github.com/coqui-ai/STT',
package_dir={"stt": "."},
cmdclass={"build": BuildExtFirst},
license="MPL-2.0",
url="https://github.com/coqui-ai/STT",
project_urls={
'Documentation': 'https://stt.readthedocs.io',
'Tracker': 'https://github.com/coqui-ai/STT/issues',
'Repository': 'https://github.com/coqui-ai/STT/tree/v{}'.format(project_version),
'Discussions': 'https://github.com/coqui-ai/STT/discussions',
"Documentation": "https://stt.readthedocs.io",
"Tracker": "https://github.com/coqui-ai/STT/issues",
"Repository": "https://github.com/coqui-ai/STT/tree/v{}".format(
project_version
),
"Discussions": "https://github.com/coqui-ai/STT/discussions",
},
ext_modules=[ds_ext],
py_modules=['stt', 'stt.client', 'stt.impl'],
entry_points={'console_scripts':['stt=stt.client:main']},
install_requires=['numpy%s' % numpy_min_ver],
py_modules=["stt", "stt.client", "stt.impl"],
entry_points={"console_scripts": ["stt=stt.client:main"]},
install_requires=["numpy%s" % numpy_min_ver],
include_package_data=True,
classifiers=[
'Development Status :: 3 - Alpha',
'Environment :: Console',
'Intended Audience :: Developers',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Topic :: Multimedia :: Sound/Audio :: Speech',
'Topic :: Scientific/Engineering :: Human Machine Interfaces',
'Topic :: Scientific/Engineering',
'Topic :: Utilities',
])
"Development Status :: 3 - Alpha",
"Environment :: Console",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Topic :: Multimedia :: Sound/Audio :: Speech",
"Topic :: Scientific/Engineering :: Human Machine Interfaces",
"Topic :: Scientific/Engineering",
"Topic :: Utilities",
],
)
if __name__ == '__main__':
if __name__ == "__main__":
main()

2
native_client/swift/stt_ios/stt_ios.h
View File

@ -9,5 +9,3 @@
#import <Foundation/Foundation.h>
// In this header, you should import all the public headers of your framework using statements like #import <stt_ios/PublicHeader.h>

1
native_client/swift/stt_ios_test/SceneDelegate.swift
View File

@ -62,4 +62,3 @@ class SceneDelegate: UIResponder, UIWindowSceneDelegate {
}

31
native_client/test/concurrent_streams.py
View File

@ -3,22 +3,26 @@
from __future__ import absolute_import, division, print_function
import argparse
import numpy as np
import wave
import numpy as np
from stt import Model
def main():
parser = argparse.ArgumentParser(description='Running STT inference.')
parser.add_argument('--model', required=True,
help='Path to the model (protocol buffer binary file)')
parser.add_argument('--scorer', nargs='?',
help='Path to the external scorer file')
parser.add_argument('--audio1', required=True,
help='First audio file to use in interleaved streams')
parser.add_argument('--audio2', required=True,
help='Second audio file to use in interleaved streams')
parser = argparse.ArgumentParser(description="Running STT inference.")
parser.add_argument(
"--model", required=True, help="Path to the model (protocol buffer binary file)"
)
parser.add_argument("--scorer", nargs="?", help="Path to the external scorer file")
parser.add_argument(
"--audio1", required=True, help="First audio file to use in interleaved streams"
)
parser.add_argument(
"--audio2",
required=True,
help="Second audio file to use in interleaved streams",
)
args = parser.parse_args()
ds = Model(args.model)
@ -26,12 +30,12 @@ def main():
if args.scorer:
ds.enableExternalScorer(args.scorer)
fin = wave.open(args.audio1, 'rb')
fin = wave.open(args.audio1, "rb")
fs1 = fin.getframerate()
audio1 = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
fin.close()
fin = wave.open(args.audio2, 'rb')
fin = wave.open(args.audio2, "rb")
fs2 = fin.getframerate()
audio2 = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
fin.close()
@ -49,5 +53,6 @@ def main():
print(stream1.finishStream())
print(stream2.finishStream())
if __name__ == '__main__':
if __name__ == "__main__":
main()

81
setup.py
View File

@ -8,77 +8,74 @@ from setuptools import find_packages, setup
def main():
version_file = Path(__file__).parent / 'VERSION'
version_file = Path(__file__).parent / "VERSION"
with open(str(version_file)) as fin:
version = fin.read().strip()
install_requires_base = [
'absl-py',
'attrdict',
'bs4',
'numpy',
'optuna',
'opuslib == 2.0.0',
'pandas',
'progressbar2',
'pyogg >= 0.6.14a1',
'pyxdg',
'resampy >= 0.2.2',
'requests',
'semver',
'six',
'sox',
'soundfile',
"absl-py",
"attrdict",
"bs4",
"numpy",
"optuna",
"opuslib == 2.0.0",
"pandas",
"progressbar2",
"pyogg >= 0.6.14a1",
"pyxdg",
"resampy >= 0.2.2",
"requests",
"semver",
"six",
"sox",
"soundfile",
]
decoder_pypi_dep = [
'coqui_stt_ctcdecoder == {}'.format(version)
]
decoder_pypi_dep = ["coqui_stt_ctcdecoder == {}".format(version)]
tensorflow_pypi_dep = [
'tensorflow == 1.15.4'
]
tensorflow_pypi_dep = ["tensorflow == 1.15.4"]
if os.environ.get('DS_NODECODER', ''):
if os.environ.get("DS_NODECODER", ""):
install_requires = install_requires_base
else:
install_requires = install_requires_base + decoder_pypi_dep
if os.environ.get('DS_NOTENSORFLOW', ''):
if os.environ.get("DS_NOTENSORFLOW", ""):
install_requires = install_requires
else:
install_requires = install_requires + tensorflow_pypi_dep
setup(
name='coqui_stt_training',
name="coqui_stt_training",
version=version,
description='Training code for Coqui STT',
url='https://github.com/coqui-ai/STT',
author='Coqui STT authors',
license='MPL-2.0',
description="Training code for Coqui STT",
url="https://github.com/coqui-ai/STT",
author="Coqui STT authors",
license="MPL-2.0",
# Classifiers help users find your project by categorizing it.
#
# For a list of valid classifiers, see https://pypi.org/classifiers/
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'Topic :: Multimedia :: Sound/Audio :: Speech',
'License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)',
'Programming Language :: Python :: 3',
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Topic :: Multimedia :: Sound/Audio :: Speech",
"License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)",
"Programming Language :: Python :: 3",
],
package_dir={'': 'training'},
packages=find_packages(where='training'),
python_requires='>=3.5, <4',
package_dir={"": "training"},
packages=find_packages(where="training"),
python_requires=">=3.5, <4",
install_requires=install_requires,
# If there are data files included in your packages that need to be
# installed, specify them here.
package_data={
'coqui_stt_training': [
'VERSION',
'GRAPH_VERSION',
"coqui_stt_training": [
"VERSION",
"GRAPH_VERSION",
],
},
)
if __name__ == '__main__':
if __name__ == "__main__":
main()

59
stats.py
View File

@ -1,11 +1,11 @@
#!/usr/bin/env python3
import argparse
import functools
import pandas
from coqui_stt_training.util.helpers import secs_to_hours
from pathlib import Path
import pandas
from coqui_stt_training.util.helpers import secs_to_hours
def read_csvs(csv_files):
# Relative paths are relative to CSV location
@ -17,32 +17,59 @@ def read_csvs(csv_files):
sets = []
for csv in csv_files:
file = pandas.read_csv(csv, encoding='utf-8', na_filter=False)
file['wav_filename'] = file['wav_filename'].apply(functools.partial(absolutify, csv))
file = pandas.read_csv(csv, encoding="utf-8", na_filter=False)
file["wav_filename"] = file["wav_filename"].apply(
functools.partial(absolutify, csv)
)
sets.append(file)
# Concat all sets, drop any extra columns, re-index the final result as 0..N
return pandas.concat(sets, join='inner', ignore_index=True)
return pandas.concat(sets, join="inner", ignore_index=True)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-csv", "--csv-files", help="Str. Filenames as a comma separated list", required=True)
parser.add_argument("--sample-rate", type=int, default=16000, required=False, help="Audio sample rate")
parser.add_argument("--channels", type=int, default=1, required=False, help="Audio channels")
parser.add_argument("--bits-per-sample", type=int, default=16, required=False, help="Audio bits per sample")
parser.add_argument(
"-csv",
"--csv-files",
help="Str. Filenames as a comma separated list",
required=True,
)
parser.add_argument(
"--sample-rate",
type=int,
default=16000,
required=False,
help="Audio sample rate",
)
parser.add_argument(
"--channels", type=int, default=1, required=False, help="Audio channels"
)
parser.add_argument(
"--bits-per-sample",
type=int,
default=16,
required=False,
help="Audio bits per sample",
)
args = parser.parse_args()
in_files = [Path(i).absolute() for i in args.csv_files.split(",")]
csv_dataframe = read_csvs(in_files)
total_bytes = csv_dataframe['wav_filesize'].sum()
total_bytes = csv_dataframe["wav_filesize"].sum()
total_files = len(csv_dataframe)
total_seconds = ((csv_dataframe['wav_filesize'] - 44) / args.sample_rate / args.channels / (args.bits_per_sample // 8)).sum()
total_seconds = (
(csv_dataframe["wav_filesize"] - 44)
/ args.sample_rate
/ args.channels
/ (args.bits_per_sample // 8)
).sum()
print('Total bytes:', total_bytes)
print('Total files:', total_files)
print('Total time:', secs_to_hours(total_seconds))
print("Total bytes:", total_bytes)
print("Total files:", total_files)
print("Total time:", secs_to_hours(total_seconds))
if __name__ == '__main__':
if __name__ == "__main__":
main()

1
tests/test_data/alphabet_windows.txt
View File

@ -1,4 +1,3 @@
a
b
c

35
tests/test_importers.py
View File

@ -1,42 +1,49 @@
import unittest
from argparse import Namespace
from coqui_stt_training.util.importers import validate_label_eng, get_validate_label
from pathlib import Path
from coqui_stt_training.util.importers import get_validate_label, validate_label_eng
def from_here(path):
here = Path(__file__)
return here.parent / path
class TestValidateLabelEng(unittest.TestCase):
def test_numbers(self):
label = validate_label_eng("this is a 1 2 3 test")
self.assertEqual(label, None)
class TestGetValidateLabel(unittest.TestCase):
class TestGetValidateLabel(unittest.TestCase):
def test_no_validate_label_locale(self):
f = get_validate_label(Namespace())
self.assertEqual(f('toto'), 'toto')
self.assertEqual(f('toto1234'), None)
self.assertEqual(f('toto1234[{[{[]'), None)
self.assertEqual(f("toto"), "toto")
self.assertEqual(f("toto1234"), None)
self.assertEqual(f("toto1234[{[{[]"), None)
def test_validate_label_locale_default(self):
f = get_validate_label(Namespace(validate_label_locale=None))
self.assertEqual(f('toto'), 'toto')
self.assertEqual(f('toto1234'), None)
self.assertEqual(f('toto1234[{[{[]'), None)
self.assertEqual(f("toto"), "toto")
self.assertEqual(f("toto1234"), None)
self.assertEqual(f("toto1234[{[{[]"), None)
def test_get_validate_label_missing(self):
args = Namespace(validate_label_locale=from_here('test_data/validate_locale_ger.py'))
args = Namespace(
validate_label_locale=from_here("test_data/validate_locale_ger.py")
)
f = get_validate_label(args)
self.assertEqual(f, None)
def test_get_validate_label(self):
args = Namespace(validate_label_locale=from_here('test_data/validate_locale_fra.py'))
args = Namespace(
validate_label_locale=from_here("test_data/validate_locale_fra.py")
)
f = get_validate_label(args)
l = f('toto')
self.assertEqual(l, 'toto')
l = f("toto")
self.assertEqual(l, "toto")
if __name__ == '__main__':
if __name__ == "__main__":
unittest.main()

17
tests/test_text.py
View File

@ -1,13 +1,13 @@
import unittest
import os
import unittest
from coqui_stt_ctcdecoder import Alphabet
class TestAlphabetParsing(unittest.TestCase):
class TestAlphabetParsing(unittest.TestCase):
def _ending_tester(self, file, expected):
alphabet = Alphabet(os.path.join(os.path.dirname(__file__), 'test_data', file))
label = ''
alphabet = Alphabet(os.path.join(os.path.dirname(__file__), "test_data", file))
label = ""
label_id = -1
for expected_label, expected_label_id in expected:
try:
@ -22,13 +22,14 @@ class TestAlphabetParsing(unittest.TestCase):
self.assertEqual(label, expected_label)
def test_macos_ending(self):
self._ending_tester('alphabet_macos.txt', [('a', 0), ('b', 1), ('c', 2)])
self._ending_tester("alphabet_macos.txt", [("a", 0), ("b", 1), ("c", 2)])
def test_unix_ending(self):
self._ending_tester('alphabet_unix.txt', [('a', 0), ('b', 1), ('c', 2)])
self._ending_tester("alphabet_unix.txt", [("a", 0), ("b", 1), ("c", 2)])
def test_windows_ending(self):
self._ending_tester('alphabet_windows.txt', [('a', 0), ('b', 1), ('c', 2)])
self._ending_tester("alphabet_windows.txt", [("a", 0), ("b", 1), ("c", 2)])
if __name__ == '__main__':
if __name__ == "__main__":
unittest.main()

74
tests/test_value_range.py
View File

@ -1,27 +1,32 @@
import unittest
import numpy as np
from coqui_stt_training.util.helpers import (
ValueRange,
get_value_range,
pick_value_from_range,
tf_pick_value_from_range,
)
import tensorflow as tf
from coqui_stt_training.util.helpers import ValueRange, get_value_range, pick_value_from_range, tf_pick_value_from_range
class TestValueRange(unittest.TestCase):
def _ending_tester(self, value, value_type, expected):
result = get_value_range(value, value_type)
self.assertEqual(result, expected)
def test_int_str_scalar(self):
self._ending_tester('1', int, ValueRange(1, 1, 0))
self._ending_tester("1", int, ValueRange(1, 1, 0))
def test_int_str_scalar_radius(self):
self._ending_tester('1~3', int, ValueRange(1, 1, 3))
self._ending_tester("1~3", int, ValueRange(1, 1, 3))
def test_int_str_range(self):
self._ending_tester('1:2', int, ValueRange(1, 2, 0))
self._ending_tester("1:2", int, ValueRange(1, 2, 0))
def test_int_str_range_radius(self):
self._ending_tester('1:2~3', int, ValueRange(1, 2, 3))
self._ending_tester("1:2~3", int, ValueRange(1, 2, 3))
def test_int_scalar(self):
self._ending_tester(1, int, ValueRange(1, 1, 0))
@ -33,16 +38,16 @@ class TestValueRange(unittest.TestCase):
self._ending_tester((1, 2, 3), int, ValueRange(1, 2, 3))
def test_float_str_scalar(self):
self._ending_tester('1.0', float, ValueRange(1.0, 1.0, 0.0))
self._ending_tester("1.0", float, ValueRange(1.0, 1.0, 0.0))
def test_float_str_scalar_radius(self):
self._ending_tester('1.0~3.0', float, ValueRange(1.0, 1.0, 3.0))
self._ending_tester("1.0~3.0", float, ValueRange(1.0, 1.0, 3.0))
def test_float_str_range(self):
self._ending_tester('1.0:2.0', float, ValueRange(1.0, 2.0, 0.0))
self._ending_tester("1.0:2.0", float, ValueRange(1.0, 2.0, 0.0))
def test_float_str_range_radius(self):
self._ending_tester('1.0:2.0~3.0', float, ValueRange(1.0, 2.0, 3.0))
self._ending_tester("1.0:2.0~3.0", float, ValueRange(1.0, 2.0, 3.0))
def test_float_scalar(self):
self._ending_tester(1.0, float, ValueRange(1.0, 1.0, 0.0))
@ -61,7 +66,7 @@ class TestPickValueFromFixedRange(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(TestPickValueFromFixedRange, self).__init__(*args, **kwargs)
self.session = tf.Session()
self.clock_ph = tf.placeholder(dtype=tf.float64, name='clock')
self.clock_ph = tf.placeholder(dtype=tf.float64, name="clock")
def _ending_tester(self, value_range, clock, expected):
with tf.Session() as session:
@ -71,7 +76,10 @@ class TestPickValueFromFixedRange(unittest.TestCase):
return session.run(tf_pick, feed_dict={self.clock_ph: c})
is_int = isinstance(value_range.start, int)
for pick, int_type, float_type in [(pick_value_from_range, int, float), (run_pick, np.int32, np.float32)]:
for pick, int_type, float_type in [
(pick_value_from_range, int, float),
(run_pick, np.int32, np.float32),
]:
result = pick(value_range, clock)
self.assertEqual(result, expected)
self.assertTrue(isinstance(result, int_type if is_int else float_type))
@ -99,9 +107,11 @@ class TestPickValueFromRandomizedRange(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(TestPickValueFromRandomizedRange, self).__init__(*args, **kwargs)
self.session = tf.Session()
self.clock_ph = tf.placeholder(dtype=tf.float64, name='clock')
self.clock_ph = tf.placeholder(dtype=tf.float64, name="clock")
def _ending_tester(self, value_range, clock_min, clock_max, expected_min, expected_max):
def _ending_tester(
self, value_range, clock_min, clock_max, expected_min, expected_max
):
with self.session as session:
tf_pick = tf_pick_value_from_range(value_range, clock=self.clock_ph)
@ -109,12 +119,26 @@ class TestPickValueFromRandomizedRange(unittest.TestCase):
return session.run(tf_pick, feed_dict={self.clock_ph: c})
is_int = isinstance(value_range.start, int)
clock_range = np.arange(clock_min, clock_max, (clock_max - clock_min) / 100.0)
for pick, int_type, float_type in [(pick_value_from_range, int, float), (run_pick, np.int32, np.float32)]:
clock_range = np.arange(
clock_min, clock_max, (clock_max - clock_min) / 100.0
)
for pick, int_type, float_type in [
(pick_value_from_range, int, float),
(run_pick, np.int32, np.float32),
]:
results = [pick(value_range, c) for c in clock_range]
self.assertGreater(len(set(results)), 80)
self.assertTrue(all(map(lambda x: expected_min <= x <= expected_max, results)))
self.assertTrue(all(map(lambda x: isinstance(x, int_type if is_int else float_type), results)))
self.assertTrue(
all(map(lambda x: expected_min <= x <= expected_max, results))
)
self.assertTrue(
all(
map(
lambda x: isinstance(x, int_type if is_int else float_type),
results,
)
)
)
def test_int_0(self):
self._ending_tester(ValueRange(10000, 30000, 10000), 0.0, 0.1, 0, 22000)
@ -126,14 +150,20 @@ class TestPickValueFromRandomizedRange(unittest.TestCase):
self._ending_tester(ValueRange(10000, 30000, 10000), 0.8, 1.0, 16000, 40000)
def test_float_0(self):
self._ending_tester(ValueRange(10000.0, 30000.0, 10000.0), 0.0, 0.1, 0.0, 22000.0)
self._ending_tester(
ValueRange(10000.0, 30000.0, 10000.0), 0.0, 0.1, 0.0, 22000.0
)
def test_float_half(self):
self._ending_tester(ValueRange(10000.0, 30000.0, 10000.0), 0.4, 0.6, 8000.0, 32000.0)
self._ending_tester(
ValueRange(10000.0, 30000.0, 10000.0), 0.4, 0.6, 8000.0, 32000.0
)
def test_float_1(self):
self._ending_tester(ValueRange(10000.0, 30000.0, 10000.0), 0.8, 1.0, 16000.0, 40000.0)
self._ending_tester(
ValueRange(10000.0, 30000.0, 10000.0), 0.8, 1.0, 16000.0, 40000.0
)
if __name__ == '__main__':
if __name__ == "__main__":
unittest.main()

4
train.py
View File

@ -2,11 +2,11 @@
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
if __name__ == '__main__':
if __name__ == "__main__":
try:
from coqui_stt_training import train as ds_train
except ImportError:
print('Training package is not installed. See training documentation.')
print("Training package is not installed. See training documentation.")
raise
ds_train.run_script()

107
training/coqui_stt_training/evaluate.py
View File

@ -4,33 +4,36 @@ from __future__ import absolute_import, division, print_function
import json
import sys
from multiprocessing import cpu_count
import absl.app
import progressbar
from coqui_stt_ctcdecoder import Scorer, ctc_beam_search_decoder_batch
from six.moves import zip
import tensorflow as tf
import tensorflow.compat.v1 as tfv1
from coqui_stt_ctcdecoder import ctc_beam_search_decoder_batch, Scorer
from six.moves import zip
from .util.augmentations import NormalizeSampleRate
from .util.config import Config, initialize_globals
from .util.checkpoints import load_graph_for_evaluation
from .util.config import Config, initialize_globals
from .util.evaluate_tools import calculate_and_print_report, save_samples_json
from .util.feeding import create_dataset
from .util.flags import create_flags, FLAGS
from .util.flags import FLAGS, create_flags
from .util.helpers import check_ctcdecoder_version
from .util.logging import create_progressbar, log_error, log_progress
check_ctcdecoder_version()
def sparse_tensor_value_to_texts(value, alphabet):
r"""
Given a :class:`tf.SparseTensor` ``value``, return an array of Python strings
representing its values, converting tokens to strings using ``alphabet``.
"""
return sparse_tuple_to_texts((value.indices, value.values, value.dense_shape), alphabet)
return sparse_tuple_to_texts(
(value.indices, value.values, value.dense_shape), alphabet
)
def sparse_tuple_to_texts(sp_tuple, alphabet):
@ -45,36 +48,42 @@ def sparse_tuple_to_texts(sp_tuple, alphabet):
def evaluate(test_csvs, create_model):
if FLAGS.scorer_path:
scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta,
FLAGS.scorer_path, Config.alphabet)
scorer = Scorer(
FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.scorer_path, Config.alphabet
)
else:
scorer = None
test_sets = [create_dataset([csv],
test_sets = [
create_dataset(
[csv],
batch_size=FLAGS.test_batch_size,
train_phase=False,
augmentations=[NormalizeSampleRate(FLAGS.audio_sample_rate)],
reverse=FLAGS.reverse_test,
limit=FLAGS.limit_test) for csv in test_csvs]
iterator = tfv1.data.Iterator.from_structure(tfv1.data.get_output_types(test_sets[0]),
limit=FLAGS.limit_test,
)
for csv in test_csvs
]
iterator = tfv1.data.Iterator.from_structure(
tfv1.data.get_output_types(test_sets[0]),
tfv1.data.get_output_shapes(test_sets[0]),
output_classes=tfv1.data.get_output_classes(test_sets[0]))
output_classes=tfv1.data.get_output_classes(test_sets[0]),
)
test_init_ops = [iterator.make_initializer(test_set) for test_set in test_sets]
batch_wav_filename, (batch_x, batch_x_len), batch_y = iterator.get_next()
# One rate per layer
no_dropout = [None] * 6
logits, _ = create_model(batch_x=batch_x,
seq_length=batch_x_len,
dropout=no_dropout)
logits, _ = create_model(
batch_x=batch_x, seq_length=batch_x_len, dropout=no_dropout
)
# Transpose to batch major and apply softmax for decoder
transposed = tf.nn.softmax(tf.transpose(a=logits, perm=[1, 0, 2]))
loss = tfv1.nn.ctc_loss(labels=batch_y,
inputs=logits,
sequence_length=batch_x_len)
loss = tfv1.nn.ctc_loss(labels=batch_y, inputs=logits, sequence_length=batch_x_len)
tfv1.train.get_or_create_global_step()
@ -93,9 +102,11 @@ def evaluate(test_csvs, create_model):
predictions = []
ground_truths = []
bar = create_progressbar(prefix='Test epoch | ',
widgets=['Steps: ', progressbar.Counter(), ' | ', progressbar.Timer()]).start()
log_progress('Test epoch...')
bar = create_progressbar(
prefix="Test epoch | ",
widgets=["Steps: ", progressbar.Counter(), " | ", progressbar.Timer()],
).start()
log_progress("Test epoch...")
step_count = 0
@ -105,17 +116,35 @@ def evaluate(test_csvs, create_model):
# First pass, compute losses and transposed logits for decoding
while True:
try:
batch_wav_filenames, batch_logits, batch_loss, batch_lengths, batch_transcripts = \
session.run([batch_wav_filename, transposed, loss, batch_x_len, batch_y])
(
batch_wav_filenames,
batch_logits,
batch_loss,
batch_lengths,
batch_transcripts,
) = session.run(
[batch_wav_filename, transposed, loss, batch_x_len, batch_y]
)
except tf.errors.OutOfRangeError:
break
decoded = ctc_beam_search_decoder_batch(batch_logits, batch_lengths, Config.alphabet, FLAGS.beam_width,
num_processes=num_processes, scorer=scorer,
cutoff_prob=FLAGS.cutoff_prob, cutoff_top_n=FLAGS.cutoff_top_n)
decoded = ctc_beam_search_decoder_batch(
batch_logits,
batch_lengths,
Config.alphabet,
FLAGS.beam_width,
num_processes=num_processes,
scorer=scorer,
cutoff_prob=FLAGS.cutoff_prob,
cutoff_top_n=FLAGS.cutoff_top_n,
)
predictions.extend(d[0][1] for d in decoded)
ground_truths.extend(sparse_tensor_value_to_texts(batch_transcripts, Config.alphabet))
wav_filenames.extend(wav_filename.decode('UTF-8') for wav_filename in batch_wav_filenames)
ground_truths.extend(
sparse_tensor_value_to_texts(batch_transcripts, Config.alphabet)
)
wav_filenames.extend(
wav_filename.decode("UTF-8") for wav_filename in batch_wav_filenames
)
losses.extend(batch_loss)
step_count += 1
@ -124,12 +153,14 @@ def evaluate(test_csvs, create_model):
bar.finish()
# Print test summary
test_samples = calculate_and_print_report(wav_filenames, ground_truths, predictions, losses, dataset)
test_samples = calculate_and_print_report(
wav_filenames, ground_truths, predictions, losses, dataset
)
return test_samples
samples = []
for csv, init_op in zip(test_csvs, test_init_ops):
print('Testing model on {}'.format(csv))
print("Testing model on {}".format(csv))
samples.extend(run_test(init_op, dataset=csv))
return samples
@ -138,12 +169,17 @@ def main(_):
initialize_globals()
if not FLAGS.test_files:
log_error('You need to specify what files to use for evaluation via '
'the --test_files flag.')
log_error(
"You need to specify what files to use for evaluation via "
"the --test_files flag."
)
sys.exit(1)
from .train import create_model # pylint: disable=cyclic-import,import-outside-toplevel
samples = evaluate(FLAGS.test_files.split(','), create_model)
from .train import ( # pylint: disable=cyclic-import,import-outside-toplevel
create_model,
)
samples = evaluate(FLAGS.test_files.split(","), create_model)
if FLAGS.test_output_file:
save_samples_json(samples, FLAGS.test_output_file)
@ -153,5 +189,6 @@ def run_script():
create_flags()
absl.app.run(main)
if __name__ == '__main__':
if __name__ == "__main__":
run_script()

816
training/coqui_stt_training/train.py
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241
training/coqui_stt_training/util/audio.py
View File

@ -2,28 +2,29 @@ import collections
import ctypes
import io
import math
import numpy as np
import os
import pyogg
import tempfile
import wave
from collections import namedtuple
import numpy as np
import pyogg
from .helpers import LimitingPool
from collections import namedtuple
from .io import open_remote, remove_remote, copy_remote, is_remote_path
from .io import copy_remote, is_remote_path, open_remote, remove_remote
AudioFormat = namedtuple('AudioFormat', 'rate channels width')
AudioFormat = namedtuple("AudioFormat", "rate channels width")
DEFAULT_RATE = 16000
DEFAULT_CHANNELS = 1
DEFAULT_WIDTH = 2
DEFAULT_FORMAT = AudioFormat(DEFAULT_RATE, DEFAULT_CHANNELS, DEFAULT_WIDTH)
AUDIO_TYPE_NP = 'application/vnd.mozilla.np'
AUDIO_TYPE_PCM = 'application/vnd.mozilla.pcm'
AUDIO_TYPE_WAV = 'audio/wav'
AUDIO_TYPE_OPUS = 'application/vnd.mozilla.opus'
AUDIO_TYPE_OGG_OPUS = 'application/vnd.deepspeech.ogg_opus'
AUDIO_TYPE_NP = "application/vnd.mozilla.np"
AUDIO_TYPE_PCM = "application/vnd.mozilla.pcm"
AUDIO_TYPE_WAV = "audio/wav"
AUDIO_TYPE_OPUS = "application/vnd.mozilla.opus"
AUDIO_TYPE_OGG_OPUS = "application/vnd.deepspeech.ogg_opus"
SERIALIZABLE_AUDIO_TYPES = [AUDIO_TYPE_WAV, AUDIO_TYPE_OPUS, AUDIO_TYPE_OGG_OPUS]
@ -49,6 +50,7 @@ class Sample:
duration : float
Audio duration of the sample in seconds
"""
def __init__(self, audio_type, raw_data, audio_format=None, sample_id=None):
"""
Parameters
@ -74,20 +76,26 @@ class Sample:
self.audio_format = audio_format
self.sample_id = sample_id
if audio_type in SERIALIZABLE_AUDIO_TYPES:
self.audio = raw_data if isinstance(raw_data, io.BytesIO) else io.BytesIO(raw_data)
self.audio = (
raw_data if isinstance(raw_data, io.BytesIO) else io.BytesIO(raw_data)
)
self.duration = read_duration(audio_type, self.audio)
if not self.audio_format:
self.audio_format = read_format(audio_type, self.audio)
else:
self.audio = raw_data
if self.audio_format is None:
raise ValueError('For audio type "{}" parameter "audio_format" is mandatory'.format(self.audio_type))
raise ValueError(
'For audio type "{}" parameter "audio_format" is mandatory'.format(
self.audio_type
)
)
if audio_type == AUDIO_TYPE_PCM:
self.duration = get_pcm_duration(len(self.audio), self.audio_format)
elif audio_type == AUDIO_TYPE_NP:
self.duration = get_np_duration(len(self.audio), self.audio_format)
else:
raise ValueError('Unsupported audio type: {}'.format(self.audio_type))
raise ValueError("Unsupported audio type: {}".format(self.audio_type))
def change_audio_type(self, new_audio_type, bitrate=None):
"""
@ -102,7 +110,10 @@ class Sample:
"""
if self.audio_type == new_audio_type:
return
if new_audio_type == AUDIO_TYPE_PCM and self.audio_type in SERIALIZABLE_AUDIO_TYPES:
if (
new_audio_type == AUDIO_TYPE_PCM
and self.audio_type in SERIALIZABLE_AUDIO_TYPES
):
self.audio_format, audio = read_audio(self.audio_type, self.audio)
self.audio.close()
self.audio = audio
@ -114,18 +125,27 @@ class Sample:
elif new_audio_type in SERIALIZABLE_AUDIO_TYPES:
self.change_audio_type(AUDIO_TYPE_PCM)
audio_bytes = io.BytesIO()
write_audio(new_audio_type, audio_bytes, self.audio, audio_format=self.audio_format, bitrate=bitrate)
write_audio(
new_audio_type,
audio_bytes,
self.audio,
audio_format=self.audio_format,
bitrate=bitrate,
)
audio_bytes.seek(0)
self.audio = audio_bytes
else:
raise RuntimeError('Changing audio representation type from "{}" to "{}" not supported'
.format(self.audio_type, new_audio_type))
raise RuntimeError(
'Changing audio representation type from "{}" to "{}" not supported'.format(
self.audio_type, new_audio_type
)
)
self.audio_type = new_audio_type
def _unpack_and_change_audio_type(sample_and_audio_type):
packed_sample, audio_type, bitrate = sample_and_audio_type
if hasattr(packed_sample, 'unpack'):
if hasattr(packed_sample, "unpack"):
sample = packed_sample.unpack()
else:
sample = packed_sample
@ -133,20 +153,31 @@ def _unpack_and_change_audio_type(sample_and_audio_type):
return sample
def change_audio_types(packed_samples, audio_type=AUDIO_TYPE_PCM, bitrate=None, processes=None, process_ahead=None):
def change_audio_types(
packed_samples,
audio_type=AUDIO_TYPE_PCM,
bitrate=None,
processes=None,
process_ahead=None,
):
with LimitingPool(processes=processes, process_ahead=process_ahead) as pool:
yield from pool.imap(_unpack_and_change_audio_type, map(lambda s: (s, audio_type, bitrate), packed_samples))
yield from pool.imap(
_unpack_and_change_audio_type,
map(lambda s: (s, audio_type, bitrate), packed_samples),
)
def get_loadable_audio_type_from_extension(ext):
return {
'.wav': AUDIO_TYPE_WAV,
'.opus': AUDIO_TYPE_OGG_OPUS,
".wav": AUDIO_TYPE_WAV,
".opus": AUDIO_TYPE_OGG_OPUS,
}.get(ext, None)
def read_audio_format_from_wav_file(wav_file):
return AudioFormat(wav_file.getframerate(), wav_file.getnchannels(), wav_file.getsampwidth())
return AudioFormat(
wav_file.getframerate(), wav_file.getnchannels(), wav_file.getsampwidth()
)
def get_num_samples(pcm_buffer_size, audio_format=DEFAULT_FORMAT):
@ -163,13 +194,18 @@ def get_np_duration(np_len, audio_format=DEFAULT_FORMAT):
return np_len / audio_format.rate
def convert_audio(src_audio_path, dst_audio_path, file_type=None, audio_format=DEFAULT_FORMAT):
def convert_audio(
src_audio_path, dst_audio_path, file_type=None, audio_format=DEFAULT_FORMAT
):
import sox
transformer = sox.Transformer()
transformer.set_output_format(file_type=file_type,
transformer.set_output_format(
file_type=file_type,
rate=audio_format.rate,
channels=audio_format.channels,
bits=audio_format.width * 8)
bits=audio_format.width * 8,
)
transformer.build(src_audio_path, dst_audio_path)
@ -178,6 +214,7 @@ class AudioFile:
Audio data file wrapper that ensures that the file is loaded with the correct sample rate, channels,
and width, and converts the file on the fly otherwise.
"""
def __init__(self, audio_path, as_path=False, audio_format=DEFAULT_FORMAT):
self.audio_path = audio_path
self.audio_format = audio_format
@ -188,8 +225,8 @@ class AudioFile:
self.tmp_src_file_path = None
def __enter__(self):
if self.audio_path.endswith('.wav'):
self.open_file = open_remote(self.audio_path, 'rb')
if self.audio_path.endswith(".wav"):
self.open_file = open_remote(self.audio_path, "rb")
self.open_wav = wave.open(self.open_file)
if read_audio_format_from_wav_file(self.open_wav) == self.audio_format:
if self.as_path:
@ -202,15 +239,20 @@ class AudioFile:
# If the format isn't right, copy the file to local tmp dir and do the conversion on disk
if is_remote_path(self.audio_path):
_, self.tmp_src_file_path = tempfile.mkstemp(suffix='.wav')
_, self.tmp_src_file_path = tempfile.mkstemp(suffix=".wav")
copy_remote(self.audio_path, self.tmp_src_file_path, True)
self.audio_path = self.tmp_src_file_path
_, self.tmp_file_path = tempfile.mkstemp(suffix='.wav')
convert_audio(self.audio_path, self.tmp_file_path, file_type='wav', audio_format=self.audio_format)
_, self.tmp_file_path = tempfile.mkstemp(suffix=".wav")
convert_audio(
self.audio_path,
self.tmp_file_path,
file_type="wav",
audio_format=self.audio_format,
)
if self.as_path:
return self.tmp_file_path
self.open_wav = wave.open(self.tmp_file_path, 'rb')
self.open_wav = wave.open(self.tmp_file_path, "rb")
return self.open_wav
def __exit__(self, *args):
@ -230,33 +272,49 @@ def read_frames(wav_file, frame_duration_ms=30, yield_remainder=False):
while True:
try:
data = wav_file.readframes(frame_size)
if not yield_remainder and get_pcm_duration(len(data), audio_format) * 1000 < frame_duration_ms:
if (
not yield_remainder
and get_pcm_duration(len(data), audio_format) * 1000 < frame_duration_ms
):
break
yield data
except EOFError:
break
def read_frames_from_file(audio_path, audio_format=DEFAULT_FORMAT, frame_duration_ms=30, yield_remainder=False):
def read_frames_from_file(
audio_path, audio_format=DEFAULT_FORMAT, frame_duration_ms=30, yield_remainder=False
):
with AudioFile(audio_path, audio_format=audio_format) as wav_file:
for frame in read_frames(wav_file, frame_duration_ms=frame_duration_ms, yield_remainder=yield_remainder):
for frame in read_frames(
wav_file,
frame_duration_ms=frame_duration_ms,
yield_remainder=yield_remainder,
):
yield frame
def vad_split(audio_frames,
def vad_split(
audio_frames,
audio_format=DEFAULT_FORMAT,
num_padding_frames=10,
threshold=0.5,
aggressiveness=3):
aggressiveness=3,
):
from webrtcvad import Vad # pylint: disable=import-outside-toplevel
if audio_format.channels != 1:
raise ValueError('VAD-splitting requires mono samples')
raise ValueError("VAD-splitting requires mono samples")
if audio_format.width != 2:
raise ValueError('VAD-splitting requires 16 bit samples')
raise ValueError("VAD-splitting requires 16 bit samples")
if audio_format.rate not in [8000, 16000, 32000, 48000]:
raise ValueError('VAD-splitting only supported for sample rates 8000, 16000, 32000, or 48000')
raise ValueError(
"VAD-splitting only supported for sample rates 8000, 16000, 32000, or 48000"
)
if aggressiveness not in [0, 1, 2, 3]:
raise ValueError('VAD-splitting aggressiveness mode has to be one of 0, 1, 2, or 3')
raise ValueError(
"VAD-splitting aggressiveness mode has to be one of 0, 1, 2, or 3"
)
ring_buffer = collections.deque(maxlen=num_padding_frames)
triggered = False
vad = Vad(int(aggressiveness))
@ -266,7 +324,9 @@ def vad_split(audio_frames,
for frame_index, frame in enumerate(audio_frames):
frame_duration_ms = get_pcm_duration(len(frame), audio_format) * 1000
if int(frame_duration_ms) not in [10, 20, 30]:
raise ValueError('VAD-splitting only supported for frame durations 10, 20, or 30 ms')
raise ValueError(
"VAD-splitting only supported for frame durations 10, 20, or 30 ms"
)
is_speech = vad.is_speech(frame, audio_format.rate)
if not triggered:
ring_buffer.append((frame, is_speech))
@ -282,23 +342,23 @@ def vad_split(audio_frames,
num_unvoiced = len([f for f, speech in ring_buffer if not speech])
if num_unvoiced > threshold * ring_buffer.maxlen:
triggered = False
yield b''.join(voiced_frames), \
frame_duration_ms * max(0, frame_index - len(voiced_frames)), \
frame_duration_ms * frame_index
yield b"".join(voiced_frames), frame_duration_ms * max(
0, frame_index - len(voiced_frames)
), frame_duration_ms * frame_index
ring_buffer.clear()
voiced_frames = []
if len(voiced_frames) > 0:
yield b''.join(voiced_frames), \
frame_duration_ms * (frame_index - len(voiced_frames)), \
frame_duration_ms * (frame_index + 1)
yield b"".join(voiced_frames), frame_duration_ms * (
frame_index - len(voiced_frames)
), frame_duration_ms * (frame_index + 1)
def pack_number(n, num_bytes):
return n.to_bytes(num_bytes, 'big', signed=False)
return n.to_bytes(num_bytes, "big", signed=False)
def unpack_number(data):
return int.from_bytes(data, 'big', signed=False)
return int.from_bytes(data, "big", signed=False)
def get_opus_frame_size(rate):
@ -308,7 +368,8 @@ def get_opus_frame_size(rate):
def write_opus(opus_file, audio_data, audio_format=DEFAULT_FORMAT, bitrate=None):
frame_size = get_opus_frame_size(audio_format.rate)
import opuslib # pylint: disable=import-outside-toplevel
encoder = opuslib.Encoder(audio_format.rate, audio_format.channels, 'audio')
encoder = opuslib.Encoder(audio_format.rate, audio_format.channels, "audio")
if bitrate is not None:
encoder.bitrate = bitrate
chunk_size = frame_size * audio_format.channels * audio_format.width
@ -320,7 +381,7 @@ def write_opus(opus_file, audio_data, audio_format=DEFAULT_FORMAT, bitrate=None)
chunk = audio_data[i : i + chunk_size]
# Preventing non-deterministic encoding results from uninitialized remainder of the encoder buffer
if len(chunk) < chunk_size:
chunk = chunk + b'\0' * (chunk_size - len(chunk))
chunk = chunk + b"\0" * (chunk_size - len(chunk))
encoded = encoder.encode(chunk, frame_size)
opus_file.write(pack_number(len(encoded), OPUS_CHUNK_LEN_SIZE))
opus_file.write(encoded)
@ -339,6 +400,7 @@ def read_opus(opus_file):
pcm_buffer_size, audio_format = read_opus_header(opus_file)
frame_size = get_opus_frame_size(audio_format.rate)
import opuslib # pylint: disable=import-outside-toplevel
decoder = opuslib.Decoder(audio_format.rate, audio_format.channels)
audio_data = bytearray()
while len(audio_data) < pcm_buffer_size:
@ -357,13 +419,12 @@ def read_ogg_opus(ogg_file):
opusfile = pyogg.opus.op_open_memory(
ubyte_array.from_buffer(ogg_file_buffer),
len(ogg_file_buffer),
ctypes.pointer(error)
ctypes.pointer(error),
)
if error.value != 0:
raise ValueError(
("Ogg/Opus buffer could not be read."
"Error code: {}").format(error.value)
("Ogg/Opus buffer could not be read." "Error code: {}").format(error.value)
)
channel_count = pyogg.opus.op_channel_count(opusfile, -1)
@ -380,10 +441,7 @@ def read_ogg_opus(ogg_file):
# seems we can only do pointer arithmetic on void
# pointers. See
# https://mattgwwalker.wordpress.com/2020/05/30/pointer-manipulation-in-python/
buf_ptr = ctypes.cast(
ctypes.pointer(buf),
ctypes.c_void_p
)
buf_ptr = ctypes.cast(ctypes.pointer(buf), ctypes.c_void_p)
assert buf_ptr.value is not None # for mypy
buf_ptr_zero = buf_ptr.value
@ -396,37 +454,23 @@ def read_ogg_opus(ogg_file):
while True:
# Calculate remaining buffer size
remaining_buffer = (
len(buf) # int
- (buf_ptr.value - buf_ptr_zero) // bytes_per_sample
len(buf) - (buf_ptr.value - buf_ptr_zero) // bytes_per_sample # int
)
# Convert buffer pointer to the desired type
ptr = ctypes.cast(
buf_ptr,
ctypes.POINTER(pyogg.opus.opus_int16)
)
ptr = ctypes.cast(buf_ptr, ctypes.POINTER(pyogg.opus.opus_int16))
# Read the next section of PCM
ns = pyogg.opus.op_read(
opusfile,
ptr,
remaining_buffer,
pyogg.ogg.c_int_p()
)
ns = pyogg.opus.op_read(opusfile, ptr, remaining_buffer, pyogg.ogg.c_int_p())
# Check for errors
if ns < 0:
raise ValueError(
"Error while reading OggOpus buffer. "+
"Error code: {}".format(ns)
"Error while reading OggOpus buffer. " + "Error code: {}".format(ns)
)
# Increment the pointer
buf_ptr.value += (
ns
* bytes_per_sample
* channel_count
)
buf_ptr.value += ns * bytes_per_sample * channel_count
assert buf_ptr.value is not None # for mypy
samples += ns
@ -448,7 +492,7 @@ def read_ogg_opus(ogg_file):
def write_wav(wav_file, pcm_data, audio_format=DEFAULT_FORMAT):
# wav_file is already a file-pointer here
with wave.open(wav_file, 'wb') as wav_file_writer:
with wave.open(wav_file, "wb") as wav_file_writer:
wav_file_writer.setframerate(audio_format.rate)
wav_file_writer.setnchannels(audio_format.channels)
wav_file_writer.setsampwidth(audio_format.width)
@ -457,7 +501,7 @@ def write_wav(wav_file, pcm_data, audio_format=DEFAULT_FORMAT):
def read_wav(wav_file):
wav_file.seek(0)
with wave.open(wav_file, 'rb') as wav_file_reader:
with wave.open(wav_file, "rb") as wav_file_reader:
audio_format = read_audio_format_from_wav_file(wav_file_reader)
pcm_data = wav_file_reader.readframes(wav_file_reader.getnframes())
return audio_format, pcm_data
@ -470,20 +514,24 @@ def read_audio(audio_type, audio_file):
return read_opus(audio_file)
if audio_type == AUDIO_TYPE_OGG_OPUS:
return read_ogg_opus(audio_file)
raise ValueError('Unsupported audio type: {}'.format(audio_type))
raise ValueError("Unsupported audio type: {}".format(audio_type))
def write_audio(audio_type, audio_file, pcm_data, audio_format=DEFAULT_FORMAT, bitrate=None):
def write_audio(
audio_type, audio_file, pcm_data, audio_format=DEFAULT_FORMAT, bitrate=None
):
if audio_type == AUDIO_TYPE_WAV:
return write_wav(audio_file, pcm_data, audio_format=audio_format)
if audio_type == AUDIO_TYPE_OPUS:
return write_opus(audio_file, pcm_data, audio_format=audio_format, bitrate=bitrate)
raise ValueError('Unsupported audio type: {}'.format(audio_type))
return write_opus(
audio_file, pcm_data, audio_format=audio_format, bitrate=bitrate
)
raise ValueError("Unsupported audio type: {}".format(audio_type))
def read_wav_duration(wav_file):
wav_file.seek(0)
with wave.open(wav_file, 'rb') as wav_file_reader:
with wave.open(wav_file, "rb") as wav_file_reader:
return wav_file_reader.getnframes() / wav_file_reader.getframerate()
@ -499,13 +547,12 @@ def read_ogg_opus_duration(ogg_file):
opusfile = pyogg.opus.op_open_memory(
ubyte_array.from_buffer(ogg_file_buffer),
len(ogg_file_buffer),
ctypes.pointer(error)
ctypes.pointer(error),
)
if error.value != 0:
raise ValueError(
("Ogg/Opus buffer could not be read."
"Error code: {}").format(error.value)
("Ogg/Opus buffer could not be read." "Error code: {}").format(error.value)
)
pcm_buffer_size = pyogg.opus.op_pcm_total(opusfile, -1)
@ -524,12 +571,12 @@ def read_duration(audio_type, audio_file):
return read_opus_duration(audio_file)
if audio_type == AUDIO_TYPE_OGG_OPUS:
return read_ogg_opus_duration(audio_file)
raise ValueError('Unsupported audio type: {}'.format(audio_type))
raise ValueError("Unsupported audio type: {}".format(audio_type))
def read_wav_format(wav_file):
wav_file.seek(0)
with wave.open(wav_file, 'rb') as wav_file_reader:
with wave.open(wav_file, "rb") as wav_file_reader:
return read_audio_format_from_wav_file(wav_file_reader)
@ -545,13 +592,12 @@ def read_ogg_opus_format(ogg_file):
opusfile = pyogg.opus.op_open_memory(
ubyte_array.from_buffer(ogg_file_buffer),
len(ogg_file_buffer),
ctypes.pointer(error)
ctypes.pointer(error),
)
if error.value != 0:
raise ValueError(
("Ogg/Opus buffer could not be read."
"Error code: {}").format(error.value)
("Ogg/Opus buffer could not be read." "Error code: {}").format(error.value)
)
channel_count = pyogg.opus.op_channel_count(opusfile, -1)
@ -569,12 +615,12 @@ def read_format(audio_type, audio_file):
return read_opus_format(audio_file)
if audio_type == AUDIO_TYPE_OGG_OPUS:
return read_ogg_opus_format(audio_file)
raise ValueError('Unsupported audio type: {}'.format(audio_type))
raise ValueError("Unsupported audio type: {}".format(audio_type))
def get_dtype(audio_format):
if audio_format.width not in [1, 2, 4]:
raise ValueError('Unsupported sample width: {}'.format(audio_format.width))
raise ValueError("Unsupported sample width: {}".format(audio_format.width))
return [None, np.int8, np.int16, None, np.int32][audio_format.width]
@ -624,4 +670,7 @@ def gain_db_to_ratio(gain_db):
def normalize_audio(sample_data, dbfs=3.0103):
return np.maximum(np.minimum(sample_data * gain_db_to_ratio(dbfs - max_dbfs(sample_data)), 1.0), -1.0)
return np.maximum(
np.minimum(sample_data * gain_db_to_ratio(dbfs - max_dbfs(sample_data)), 1.0),
-1.0,
)

338
training/coqui_stt_training/util/augmentations.py
View File

@ -1,18 +1,33 @@
import os
import re
import math
import os
import random
import resampy
import numpy as np
import re
from multiprocessing import Process, Queue
from multiprocessing import Queue, Process
from .audio import gain_db_to_ratio, max_dbfs, normalize_audio, AUDIO_TYPE_NP, AUDIO_TYPE_PCM, AUDIO_TYPE_OPUS
from .helpers import LimitingPool, int_range, float_range, pick_value_from_range, tf_pick_value_from_range, MEGABYTE
from .sample_collections import samples_from_source, unpack_maybe
import numpy as np
import resampy
from .audio import (
AUDIO_TYPE_NP,
AUDIO_TYPE_OPUS,
AUDIO_TYPE_PCM,
gain_db_to_ratio,
max_dbfs,
normalize_audio,
)
from .helpers import (
MEGABYTE,
LimitingPool,
float_range,
int_range,
pick_value_from_range,
tf_pick_value_from_range,
)
from .logging import log_info
from .sample_collections import samples_from_source, unpack_maybe
BUFFER_SIZE = 1 * MEGABYTE
SPEC_PARSER = re.compile(r'^(?P<cls>[a-z_]+)(\[(?P<params>.*)\])?$')
SPEC_PARSER = re.compile(r"^(?P<cls>[a-z_]+)(\[(?P<params>.*)\])?$")
class Augmentation:
@ -32,10 +47,10 @@ class SampleAugmentation(Augmentation):
class GraphAugmentation(Augmentation):
def __init__(self, p=1.0, domain='spectrogram'):
def __init__(self, p=1.0, domain="spectrogram"):
super(GraphAugmentation, self).__init__(p)
if domain not in ['signal', 'spectrogram', 'features']:
raise ValueError('Unsupported augmentation domain: {}'.format(domain))
if domain not in ["signal", "spectrogram", "features"]:
raise ValueError("Unsupported augmentation domain: {}".format(domain))
self.domain = domain
def apply(self, tensor, transcript=None, clock=0.0):
@ -43,19 +58,31 @@ class GraphAugmentation(Augmentation):
def apply_with_probability(self, tensor, transcript=None, clock=0.0):
import tensorflow as tf # pylint: disable=import-outside-toplevel
rv = tf.random.stateless_uniform([], seed=(clock * tf.int32.min, clock * tf.int32.max))
return tf.cond(tf.less(rv, self.probability),
rv = tf.random.stateless_uniform(
[], seed=(clock * tf.int32.min, clock * tf.int32.max)
)
return tf.cond(
tf.less(rv, self.probability),
lambda: self.apply(tensor, transcript=transcript, clock=clock),
lambda: tensor)
lambda: tensor,
)
def maybe_apply(self, domain, tensor, transcript=None, clock=0.0):
if domain == self.domain:
return self.apply_with_probability(tensor, transcript=transcript, clock=clock)
return self.apply_with_probability(
tensor, transcript=transcript, clock=clock
)
return tensor
def units_per_ms(self):
from .flags import FLAGS # pylint: disable=import-outside-toplevel
return FLAGS.audio_sample_rate / 1000.0 if self.domain == 'signal' else 1.0 / FLAGS.feature_win_step
return (
FLAGS.audio_sample_rate / 1000.0
if self.domain == "signal"
else 1.0 / FLAGS.feature_win_step
)
def parse_augmentation(augmentation_spec):
@ -73,24 +100,34 @@ def parse_augmentation(augmentation_spec):
"""
match = SPEC_PARSER.match(augmentation_spec)
if not match:
raise ValueError('Augmentation specification has wrong format')
cls_name = ''.join(map(lambda p: p[0].upper() + p[1:], match.group('cls').split('_')))
raise ValueError("Augmentation specification has wrong format")
cls_name = "".join(
map(lambda p: p[0].upper() + p[1:], match.group("cls").split("_"))
)
augmentation_cls = globals()[cls_name] if cls_name in globals() else None
if augmentation_cls is None or not issubclass(augmentation_cls, Augmentation) or augmentation_cls == Augmentation:
raise ValueError('Unknown augmentation: {}'.format(cls_name))
parameters = match.group('params')
parameters = [] if parameters is None else parameters.split(',')
if (
augmentation_cls is None
or not issubclass(augmentation_cls, Augmentation)
or augmentation_cls == Augmentation
):
raise ValueError("Unknown augmentation: {}".format(cls_name))
parameters = match.group("params")
parameters = [] if parameters is None else parameters.split(",")
args = []
kwargs = {}
for parameter in parameters:
pair = tuple(list(map(str.strip, (parameter.split('=')))))
pair = tuple(list(map(str.strip, (parameter.split("=")))))
if len(pair) == 1:
args.append(pair)
elif len(pair) == 2:
kwargs[pair[0]] = pair[1]
else:
raise ValueError('Unable to parse augmentation value assignment')
log_info('Processed augmentation type: [{}] with parameter settings: {}'.format(augmentation_cls.__name__, kwargs))
raise ValueError("Unable to parse augmentation value assignment")
log_info(
"Processed augmentation type: [{}] with parameter settings: {}".format(
augmentation_cls.__name__, kwargs
)
)
return augmentation_cls(*args, **kwargs)
@ -110,7 +147,9 @@ def parse_augmentations(augmentation_specs):
return list(map(parse_augmentation, augmentation_specs or []))
def apply_graph_augmentations(domain, tensor, augmentations, transcript=None, clock=0.0):
def apply_graph_augmentations(
domain, tensor, augmentations, transcript=None, clock=0.0
):
"""
Augments training sample tensor of a certain domain with matching augmentations of passed list.
@ -134,7 +173,9 @@ def apply_graph_augmentations(domain, tensor, augmentations, transcript=None, cl
if augmentations:
for augmentation in augmentations:
if isinstance(augmentation, GraphAugmentation):
tensor = augmentation.maybe_apply(domain, tensor, transcript=transcript, clock=clock)
tensor = augmentation.maybe_apply(
domain, tensor, transcript=transcript, clock=clock
)
return tensor
@ -168,13 +209,15 @@ def _augment_sample(timed_sample, context=None):
return sample
def apply_sample_augmentations(samples,
def apply_sample_augmentations(
samples,
augmentations,
audio_type=AUDIO_TYPE_NP,
buffering=BUFFER_SIZE,
process_ahead=None,
clock=0.0,
final_clock=None):
final_clock=None,
):
"""
Prepares samples for being used during training.
This includes parallel and buffered application of augmentations and a conversion to a specified audio-type.
@ -201,20 +244,27 @@ def apply_sample_augmentations(samples,
-------
iterable of util.sample_collections.LabeledSample or util.audio.Sample
"""
def timed_samples():
if final_clock is None:
for sample in samples:
yield sample, clock
else:
for sample_index, sample in enumerate(samples):
sample_clock = clock + (final_clock - clock) * (sample_index / len(samples))
sample_clock = clock + (final_clock - clock) * (
sample_index / len(samples)
)
yield sample, sample_clock
assert 0.0 <= clock <= 1.0
if final_clock is not None:
assert 0.0 <= final_clock <= 1.0
assert clock <= final_clock
augmentations = [aug for aug in augmentations if isinstance(aug, SampleAugmentation)] if augmentations else []
augmentations = (
[aug for aug in augmentations if isinstance(aug, SampleAugmentation)]
if augmentations
else []
)
try:
for augmentation in augmentations:
augmentation.start(buffering=buffering)
@ -223,9 +273,11 @@ def apply_sample_augmentations(samples,
for timed_sample in timed_samples():
yield _load_and_augment_sample(timed_sample, context=context)
else:
with LimitingPool(process_ahead=process_ahead,
with LimitingPool(
process_ahead=process_ahead,
initializer=_init_augmentation_worker,
initargs=(context,)) as pool:
initargs=(context,),
) as pool:
yield from pool.imap(_load_and_augment_sample, timed_samples())
finally:
for augmentation in augmentations:
@ -247,6 +299,7 @@ def _enqueue_overlay_samples(sample_source, queue, buffering=BUFFER_SIZE):
class Overlay(SampleAugmentation):
"""See "Overlay augmentation" in training documentation"""
def __init__(self, source, p=1.0, snr=3.0, layers=1):
super(Overlay, self).__init__(p)
self.source = source
@ -257,10 +310,14 @@ class Overlay(SampleAugmentation):
self.enqueue_process = None
def start(self, buffering=BUFFER_SIZE):
self.queue = Queue(max(1, math.floor(self.probability * self.layers[1] * os.cpu_count())))
self.enqueue_process = Process(target=_enqueue_overlay_samples,
self.queue = Queue(
max(1, math.floor(self.probability * self.layers[1] * os.cpu_count()))
)
self.enqueue_process = Process(
target=_enqueue_overlay_samples,
args=(self.source, self.queue),
kwargs={'buffering': buffering})
kwargs={"buffering": buffering},
)
self.enqueue_process.start()
def apply(self, sample, clock=0.0):
@ -280,11 +337,15 @@ class Overlay(SampleAugmentation):
n_required = len(audio) - overlay_offset
n_current = len(self.current_sample)
if n_required >= n_current: # take it completely
overlay_data[overlay_offset:overlay_offset + n_current] += self.current_sample
overlay_data[
overlay_offset : overlay_offset + n_current
] += self.current_sample
overlay_offset += n_current
self.current_sample = None
else: # take required slice from head and keep tail for next layer or sample
overlay_data[overlay_offset:overlay_offset + n_required] += self.current_sample[0:n_required]
overlay_data[
overlay_offset : overlay_offset + n_required
] += self.current_sample[0:n_required]
overlay_offset += n_required
self.current_sample = self.current_sample[n_required:]
snr_db = pick_value_from_range(self.snr, clock=clock)
@ -303,18 +364,24 @@ class Overlay(SampleAugmentation):
class Codec(SampleAugmentation):
"""See "Codec augmentation" in training documentation"""
def __init__(self, p=1.0, bitrate=3200):
super(Codec, self).__init__(p)
self.bitrate = int_range(bitrate)
def apply(self, sample, clock=0.0):
bitrate = pick_value_from_range(self.bitrate, clock=clock)
sample.change_audio_type(new_audio_type=AUDIO_TYPE_PCM) # decoding to ensure it has to get encoded again
sample.change_audio_type(new_audio_type=AUDIO_TYPE_OPUS, bitrate=bitrate) # will get decoded again downstream
sample.change_audio_type(
new_audio_type=AUDIO_TYPE_PCM
) # decoding to ensure it has to get encoded again
sample.change_audio_type(
new_audio_type=AUDIO_TYPE_OPUS, bitrate=bitrate
) # will get decoded again downstream
class Reverb(SampleAugmentation):
"""See "Reverb augmentation" in training documentation"""
def __init__(self, p=1.0, delay=20.0, decay=10.0):
super(Reverb, self).__init__(p)
self.delay = float_range(delay)
@ -331,8 +398,12 @@ class Reverb(SampleAugmentation):
primes = [17, 19, 23, 29, 31]
for delay_prime in primes: # primes to minimize comb filter interference
layer = np.copy(audio)
n_delay = math.floor(delay * (delay_prime / primes[0]) * sample.audio_format.rate / 1000.0)
n_delay = max(16, n_delay) # 16 samples minimum to avoid performance trap and risk of division by zero
n_delay = math.floor(
delay * (delay_prime / primes[0]) * sample.audio_format.rate / 1000.0
)
n_delay = max(
16, n_delay
) # 16 samples minimum to avoid performance trap and risk of division by zero
for w_index in range(0, math.floor(len(audio) / n_delay)):
w1 = w_index * n_delay
w2 = (w_index + 1) * n_delay
@ -345,6 +416,7 @@ class Reverb(SampleAugmentation):
class Resample(SampleAugmentation):
"""See "Resample augmentation" in training documentation"""
def __init__(self, p=1.0, rate=8000):
super(Resample, self).__init__(p)
self.rate = int_range(rate)
@ -353,8 +425,12 @@ class Resample(SampleAugmentation):
sample.change_audio_type(new_audio_type=AUDIO_TYPE_NP)
rate = pick_value_from_range(self.rate, clock=clock)
orig_len = len(sample.audio)
resampled = resampy.resample(sample.audio, sample.audio_format.rate, rate, axis=0, filter='kaiser_fast')
sample.audio = resampy.resample(resampled, rate, sample.audio_format.rate, axis=0, filter='kaiser_fast')[:orig_len]
resampled = resampy.resample(
sample.audio, sample.audio_format.rate, rate, axis=0, filter="kaiser_fast"
)
sample.audio = resampy.resample(
resampled, rate, sample.audio_format.rate, axis=0, filter="kaiser_fast"
)[:orig_len]
class NormalizeSampleRate(SampleAugmentation):
@ -367,12 +443,19 @@ class NormalizeSampleRate(SampleAugmentation):
return
sample.change_audio_type(new_audio_type=AUDIO_TYPE_NP)
sample.audio = resampy.resample(sample.audio, sample.audio_format.rate, self.rate, axis=0, filter='kaiser_fast')
sample.audio = resampy.resample(
sample.audio,
sample.audio_format.rate,
self.rate,
axis=0,
filter="kaiser_fast",
)
sample.audio_format = sample.audio_format._replace(rate=self.rate)
class Volume(SampleAugmentation):
"""See "Volume augmentation" in training documentation"""
def __init__(self, p=1.0, dbfs=3.0103):
super(Volume, self).__init__(p)
self.target_dbfs = float_range(dbfs)
@ -385,55 +468,76 @@ class Volume(SampleAugmentation):
class Pitch(GraphAugmentation):
"""See "Pitch augmentation" in training documentation"""
def __init__(self, p=1.0, pitch=(1.075, 1.075, 0.125)):
super(Pitch, self).__init__(p, domain='spectrogram')
super(Pitch, self).__init__(p, domain="spectrogram")
self.pitch = float_range(pitch)
def apply(self, tensor, transcript=None, clock=0.0):
import tensorflow as tf # pylint: disable=import-outside-toplevel
original_shape = tf.shape(tensor)
pitch = tf_pick_value_from_range(self.pitch, clock=clock)
new_freq_size = tf.cast(tf.cast(original_shape[2], tf.float32) * pitch, tf.int32)
spectrogram_aug = tf.image.resize_bilinear(tf.expand_dims(tensor, -1), [original_shape[1], new_freq_size])
spectrogram_aug = tf.image.crop_to_bounding_box(spectrogram_aug,
new_freq_size = tf.cast(
tf.cast(original_shape[2], tf.float32) * pitch, tf.int32
)
spectrogram_aug = tf.image.resize_bilinear(
tf.expand_dims(tensor, -1), [original_shape[1], new_freq_size]
)
spectrogram_aug = tf.image.crop_to_bounding_box(
spectrogram_aug,
offset_height=0,
offset_width=0,
target_height=original_shape[1],
target_width=tf.math.minimum(original_shape[2], new_freq_size))
spectrogram_aug = tf.cond(pitch < 1,
lambda: tf.image.pad_to_bounding_box(spectrogram_aug,
target_width=tf.math.minimum(original_shape[2], new_freq_size),
)
spectrogram_aug = tf.cond(
pitch < 1,
lambda: tf.image.pad_to_bounding_box(
spectrogram_aug,
offset_height=0,
offset_width=0,
target_height=tf.shape(spectrogram_aug)[1],
target_width=original_shape[2]),
lambda: spectrogram_aug)
target_width=original_shape[2],
),
lambda: spectrogram_aug,
)
return spectrogram_aug[:, :, :, 0]
class Tempo(GraphAugmentation):
"""See "Tempo augmentation" in training documentation"""
def __init__(self, p=1.0, factor=1.1, max_time=-1):
super(Tempo, self).__init__(p, domain='spectrogram')
super(Tempo, self).__init__(p, domain="spectrogram")
self.factor = float_range(factor)
self.max_time = float(max_time)
def apply(self, tensor, transcript=None, clock=0.0):
import tensorflow as tf # pylint: disable=import-outside-toplevel
factor = tf_pick_value_from_range(self.factor, clock=clock)
original_shape = tf.shape(tensor)
new_time_size = tf.cast(tf.cast(original_shape[1], tf.float32) / factor, tf.int32)
new_time_size = tf.cast(
tf.cast(original_shape[1], tf.float32) / factor, tf.int32
)
if transcript is not None:
new_time_size = tf.math.maximum(new_time_size, tf.shape(transcript)[1])
if self.max_time > 0:
new_time_size = tf.math.minimum(new_time_size, tf.cast(self.max_time * self.units_per_ms(), tf.int32))
spectrogram_aug = tf.image.resize_bilinear(tf.expand_dims(tensor, -1), [new_time_size, original_shape[2]])
new_time_size = tf.math.minimum(
new_time_size, tf.cast(self.max_time * self.units_per_ms(), tf.int32)
)
spectrogram_aug = tf.image.resize_bilinear(
tf.expand_dims(tensor, -1), [new_time_size, original_shape[2]]
)
return spectrogram_aug[:, :, :, 0]
class Warp(GraphAugmentation):
"""See "Warp augmentation" in training documentation"""
def __init__(self, p=1.0, nt=1, nf=1, wt=0.1, wf=0.0):
super(Warp, self).__init__(p, domain='spectrogram')
super(Warp, self).__init__(p, domain="spectrogram")
self.num_t = int_range(nt)
self.num_f = int_range(nf)
self.warp_t = float_range(wt)
@ -441,6 +545,7 @@ class Warp(GraphAugmentation):
def apply(self, tensor, transcript=None, clock=0.0):
import tensorflow as tf # pylint: disable=import-outside-toplevel
original_shape = tf.shape(tensor)
size_t, size_f = original_shape[1], original_shape[2]
seed = (clock * tf.int32.min, clock * tf.int32.max)
@ -449,25 +554,43 @@ class Warp(GraphAugmentation):
def get_flows(n, size, warp):
warp = tf_pick_value_from_range(warp, clock=clock)
warp = warp * tf.cast(size, dtype=tf.float32) / tf.cast(2 * (n + 1), dtype=tf.float32)
f = tf.random.stateless_normal([num_t, num_f], seed, mean=0.0, stddev=warp, dtype=tf.float32)
return tf.pad(f, tf.constant([[1, 1], [1, 1]]), 'CONSTANT') # zero flow at all edges
warp = (
warp
* tf.cast(size, dtype=tf.float32)
/ tf.cast(2 * (n + 1), dtype=tf.float32)
)
f = tf.random.stateless_normal(
[num_t, num_f], seed, mean=0.0, stddev=warp, dtype=tf.float32
)
return tf.pad(
f, tf.constant([[1, 1], [1, 1]]), "CONSTANT"
) # zero flow at all edges
flows = tf.stack([get_flows(num_t, size_t, self.warp_t), get_flows(num_f, size_f, self.warp_f)], axis=2)
flows = tf.stack(
[
get_flows(num_t, size_t, self.warp_t),
get_flows(num_f, size_f, self.warp_f),
],
axis=2,
)
flows = tf.image.resize_bicubic(tf.expand_dims(flows, 0), [size_t, size_f])
spectrogram_aug = tf.contrib.image.dense_image_warp(tf.expand_dims(tensor, -1), flows)
spectrogram_aug = tf.contrib.image.dense_image_warp(
tf.expand_dims(tensor, -1), flows
)
return tf.reshape(spectrogram_aug, shape=(1, -1, size_f))
class FrequencyMask(GraphAugmentation):
"""See "Frequency mask augmentation" in training documentation"""
def __init__(self, p=1.0, n=3, size=2):
super(FrequencyMask, self).__init__(p, domain='spectrogram')
super(FrequencyMask, self).__init__(p, domain="spectrogram")
self.n = int_range(n) # pylint: disable=invalid-name
self.size = int_range(size)
def apply(self, tensor, transcript=None, clock=0.0):
import tensorflow as tf # pylint: disable=import-outside-toplevel
time_max = tf.shape(tensor)[1]
freq_max = tf.shape(tensor)[2]
n = tf_pick_value_from_range(self.n, clock=clock)
@ -476,10 +599,21 @@ class FrequencyMask(GraphAugmentation):
size = tf_pick_value_from_range(self.size, clock=clock)
size = tf.math.maximum(1, tf.math.minimum(freq_max - 1, size))
seed = tf.cast(clock * tf.int32.max, tf.int32) - i
f0 = tf.random.stateless_uniform((), (-seed, seed), minval=0, maxval=freq_max - size, dtype=tf.dtypes.int32)
freq_mask = tf.concat([tf.ones([1, time_max, f0]),
f0 = tf.random.stateless_uniform(
(),
(-seed, seed),
minval=0,
maxval=freq_max - size,
dtype=tf.dtypes.int32,
)
freq_mask = tf.concat(
[
tf.ones([1, time_max, f0]),
tf.zeros([1, time_max, size]),
tf.ones([1, time_max, freq_max - f0 - size])], axis=2)
tf.ones([1, time_max, freq_max - f0 - size]),
],
axis=2,
)
return i + 1, spectrogram_aug * freq_mask
return tf.while_loop(lambda i, spectrogram_aug: i < n, body, (0, tensor))[1]
@ -487,29 +621,51 @@ class FrequencyMask(GraphAugmentation):
class TimeMask(GraphAugmentation):
"""See "Time mask augmentation" in training documentation"""
def __init__(self, p=1.0, domain='spectrogram', n=3, size=10.0):
def __init__(self, p=1.0, domain="spectrogram", n=3, size=10.0):
super(TimeMask, self).__init__(p, domain=domain)
self.n = int_range(n) # pylint: disable=invalid-name
self.size = float_range(size)
def apply(self, tensor, transcript=None, clock=0.0):
import tensorflow as tf # pylint: disable=import-outside-toplevel
time_max = tf.shape(tensor)[0 if self.domain == 'signal' else 1]
time_max = tf.shape(tensor)[0 if self.domain == "signal" else 1]
n = tf_pick_value_from_range(self.n, clock=clock)
def body(i, augmented):
size = tf.cast(tf_pick_value_from_range(self.size, clock=clock) * self.units_per_ms(), dtype=tf.int32)
size = tf.cast(
tf_pick_value_from_range(self.size, clock=clock) * self.units_per_ms(),
dtype=tf.int32,
)
size = tf.math.maximum(1, tf.math.minimum(time_max - 1, size))
seed = tf.cast(clock * tf.int32.max, tf.int32) - i
t0 = tf.random.stateless_uniform((), (-seed, seed), minval=0, maxval=time_max - size, dtype=tf.dtypes.int32)
t0 = tf.random.stateless_uniform(
(),
(-seed, seed),
minval=0,
maxval=time_max - size,
dtype=tf.dtypes.int32,
)
rest = time_max - t0 - size
if self.domain == 'spectrogram':
if self.domain == "spectrogram":
fm = tf.shape(tensor)[2]
time_mask = tf.concat([tf.ones([1, t0, fm]), tf.zeros([1, size, fm]), tf.ones([1, rest, fm])], axis=1)
elif self.domain == 'signal':
time_mask = tf.concat([tf.ones([t0, 1]), tf.zeros([size, 1]), tf.ones([rest, 1])], axis=0)
time_mask = tf.concat(
[
tf.ones([1, t0, fm]),
tf.zeros([1, size, fm]),
tf.ones([1, rest, fm]),
],
axis=1,
)
elif self.domain == "signal":
time_mask = tf.concat(
[tf.ones([t0, 1]), tf.zeros([size, 1]), tf.ones([rest, 1])], axis=0
)
else:
time_mask = tf.concat([tf.ones([1, t0]), tf.zeros([1, size]), tf.ones([1, rest])], axis=1)
time_mask = tf.concat(
[tf.ones([1, t0]), tf.zeros([1, size]), tf.ones([1, rest])], axis=1
)
return i + 1, augmented * time_mask
return tf.while_loop(lambda i, augmented: i < n, body, (0, tensor))[1]
@ -517,43 +673,55 @@ class TimeMask(GraphAugmentation):
class Dropout(GraphAugmentation):
"""See "Dropout augmentation" in training documentation"""
def __init__(self, p=1.0, domain='spectrogram', rate=0.05):
def __init__(self, p=1.0, domain="spectrogram", rate=0.05):
super(Dropout, self).__init__(p, domain=domain)
self.rate = float_range(rate)
def apply(self, tensor, transcript=None, clock=0.0):
import tensorflow as tf # pylint: disable=import-outside-toplevel
rate = tf_pick_value_from_range(self.rate, clock=clock)
rate = tf.math.maximum(0.0, rate)
factors = tf.random.stateless_uniform(tf.shape(tensor),
factors = tf.random.stateless_uniform(
tf.shape(tensor),
(clock * tf.int32.min, clock * tf.int32.max),
minval=0.0,
maxval=1.0,
dtype=tf.float32)
dtype=tf.float32,
)
return tensor * tf.math.sign(tf.math.floor(factors + rate))
class Add(GraphAugmentation):
"""See "Add augmentation" in training documentation"""
def __init__(self, p=1.0, domain='features', stddev=5):
def __init__(self, p=1.0, domain="features", stddev=5):
super(Add, self).__init__(p, domain=domain)
self.stddev = float_range(stddev)
def apply(self, tensor, transcript=None, clock=0.0):
import tensorflow as tf # pylint: disable=import-outside-toplevel
stddev = tf_pick_value_from_range(self.stddev, clock=clock)
seed = (clock * tf.int32.min, clock * tf.int32.max)
return tensor + tf.random.stateless_normal(tf.shape(tensor), seed, mean=0.0, stddev=stddev)
return tensor + tf.random.stateless_normal(
tf.shape(tensor), seed, mean=0.0, stddev=stddev
)
class Multiply(GraphAugmentation):
"""See "Multiply augmentation" in training documentation"""
def __init__(self, p=1.0, domain='features', stddev=5):
def __init__(self, p=1.0, domain="features", stddev=5):
super(Multiply, self).__init__(p, domain=domain)
self.stddev = float_range(stddev)
def apply(self, tensor, transcript=None, clock=0.0):
import tensorflow as tf # pylint: disable=import-outside-toplevel
stddev = tf_pick_value_from_range(self.stddev, clock=clock)
seed = (clock * tf.int32.min, clock * tf.int32.max)
return tensor * tf.random.stateless_normal(tf.shape(tensor), seed, mean=1.0, stddev=stddev)
return tensor * tf.random.stateless_normal(
tf.shape(tensor), seed, mean=1.0, stddev=stddev
)

40
training/coqui_stt_training/util/check_characters.py
View File

@ -22,14 +22,31 @@ import csv
import os
import sys
import unicodedata
from .io import open_remote
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-csv", "--csv-files", help="Str. Filenames as a comma separated list", required=True)
parser.add_argument("-alpha", "--alphabet-format", help="Bool. Print in format for alphabet.txt", action="store_true")
parser.add_argument("-unicode", "--disable-unicode-variants", help="Bool. DISABLE check for unicode consistency (use with --alphabet-format)", action="store_true")
parser.add_argument(
"-csv",
"--csv-files",
help="Str. Filenames as a comma separated list",
required=True,
)
parser.add_argument(
"-alpha",
"--alphabet-format",
help="Bool. Print in format for alphabet.txt",
action="store_true",
)
parser.add_argument(
"-unicode",
"--disable-unicode-variants",
help="Bool. DISABLE check for unicode consistency (use with --alphabet-format)",
action="store_true",
)
args = parser.parse_args()
in_files = args.csv_files.split(",")
@ -46,11 +63,21 @@ def main():
if not args.disable_unicode_variants:
unicode_transcript = unicodedata.normalize("NFKC", row[2])
if row[2] != unicode_transcript:
print("Your input file", in_file, "contains at least one transript with unicode chars on more than one code-point: '{}'. Consider using NFKC normalization: unicodedata.normalize('NFKC', str).".format(row[2]))
print(
"Your input file",
in_file,
"contains at least one transript with unicode chars on more than one code-point: '{}'. Consider using NFKC normalization: unicodedata.normalize('NFKC', str).".format(
row[2]
),
)
sys.exit(-1)
all_text |= set(row[2])
except IndexError:
print("Your input file", in_file, "is not formatted properly. Check if there are 3 columns with the 3rd containing the transcript")
print(
"Your input file",
in_file,
"is not formatted properly. Check if there are 3 columns with the 3rd containing the transcript",
)
sys.exit(-1)
finally:
csv_file.close()
@ -63,5 +90,6 @@ def main():
else:
print(list(all_text))
if __name__ == '__main__':
if __name__ == "__main__":
main()

93
training/coqui_stt_training/util/checkpoints.py
View File

@ -1,9 +1,10 @@
import sys
import tensorflow as tf
import tensorflow.compat.v1 as tfv1
from .flags import FLAGS
from .logging import log_info, log_error, log_warn
from .logging import log_error, log_info, log_warn
def _load_checkpoint(session, checkpoint_path, allow_drop_layers, allow_lr_init=True):
@ -17,9 +18,11 @@ def _load_checkpoint(session, checkpoint_path, allow_drop_layers, allow_lr_init=
# We explicitly allow the learning rate variable to be missing for backwards
# compatibility with older checkpoints.
lr_var = set(v for v in load_vars if v.op.name == 'learning_rate')
if lr_var and ('learning_rate' not in vars_in_ckpt or
(FLAGS.force_initialize_learning_rate and allow_lr_init)):
lr_var = set(v for v in load_vars if v.op.name == "learning_rate")
if lr_var and (
"learning_rate" not in vars_in_ckpt
or (FLAGS.force_initialize_learning_rate and allow_lr_init)
):
assert len(lr_var) <= 1
load_vars -= lr_var
init_vars |= lr_var
@ -31,7 +34,7 @@ def _load_checkpoint(session, checkpoint_path, allow_drop_layers, allow_lr_init=
missing_vars = set()
for v in load_vars:
if v.op.name not in vars_in_ckpt:
log_warn('CUDNN variable not found: %s' % (v.op.name))
log_warn("CUDNN variable not found: %s" % (v.op.name))
missing_vars.add(v)
init_vars.add(v)
@ -40,10 +43,12 @@ def _load_checkpoint(session, checkpoint_path, allow_drop_layers, allow_lr_init=
# Check that the only missing variables (i.e. those to be initialised)
# are the Adam moment tensors, if they aren't then we have an issue
missing_var_names = [v.op.name for v in missing_vars]
if any('Adam' not in v for v in missing_var_names):
log_error('Tried to load a CuDNN RNN checkpoint but there were '
'more missing variables than just the Adam moment '
'tensors. Missing variables: {}'.format(missing_var_names))
if any("Adam" not in v for v in missing_var_names):
log_error(
"Tried to load a CuDNN RNN checkpoint but there were "
"more missing variables than just the Adam moment "
"tensors. Missing variables: {}".format(missing_var_names)
)
sys.exit(1)
if allow_drop_layers and FLAGS.drop_source_layers > 0:
@ -54,12 +59,16 @@ def _load_checkpoint(session, checkpoint_path, allow_drop_layers, allow_lr_init=
# If we want to use all layers from the source model except
# the last one, we use this: drop_source_layers=1
if FLAGS.drop_source_layers >= 6:
log_warn('The checkpoint only has 6 layers, but you are trying to drop '
'all of them or more than all of them. Continuing and '
'dropping only 5 layers.')
log_warn(
"The checkpoint only has 6 layers, but you are trying to drop "
"all of them or more than all of them. Continuing and "
"dropping only 5 layers."
)
FLAGS.drop_source_layers = 5
dropped_layers = ['2', '3', 'lstm', '5', '6'][-1 * int(FLAGS.drop_source_layers):]
dropped_layers = ["2", "3", "lstm", "5", "6"][
-1 * int(FLAGS.drop_source_layers) :
]
# Initialize all variables needed for DS, but not loaded from ckpt
for v in load_vars:
if any(layer in v.op.name for layer in dropped_layers):
@ -67,16 +76,18 @@ def _load_checkpoint(session, checkpoint_path, allow_drop_layers, allow_lr_init=
load_vars -= init_vars
for v in sorted(load_vars, key=lambda v: v.op.name):
log_info('Loading variable from checkpoint: %s' % (v.op.name))
log_info("Loading variable from checkpoint: %s" % (v.op.name))
v.load(ckpt.get_tensor(v.op.name), session=session)
for v in sorted(init_vars, key=lambda v: v.op.name):
log_info('Initializing variable: %s' % (v.op.name))
log_info("Initializing variable: %s" % (v.op.name))
session.run(v.initializer)
def _checkpoint_path_or_none(checkpoint_filename):
checkpoint = tfv1.train.get_checkpoint_state(FLAGS.load_checkpoint_dir, checkpoint_filename)
checkpoint = tfv1.train.get_checkpoint_state(
FLAGS.load_checkpoint_dir, checkpoint_filename
)
if not checkpoint:
return None
return checkpoint.model_checkpoint_path
@ -91,61 +102,65 @@ def _initialize_all_variables(session):
def _load_or_init_impl(session, method_order, allow_drop_layers, allow_lr_init=True):
for method in method_order:
# Load best validating checkpoint, saved in checkpoint file 'best_dev_checkpoint'
if method == 'best':
ckpt_path = _checkpoint_path_or_none('best_dev_checkpoint')
if method == "best":
ckpt_path = _checkpoint_path_or_none("best_dev_checkpoint")
if ckpt_path:
log_info('Loading best validating checkpoint from {}'.format(ckpt_path))
return _load_checkpoint(session, ckpt_path, allow_drop_layers, allow_lr_init=allow_lr_init)
log_info('Could not find best validating checkpoint.')
log_info("Loading best validating checkpoint from {}".format(ckpt_path))
return _load_checkpoint(
session, ckpt_path, allow_drop_layers, allow_lr_init=allow_lr_init
)
log_info("Could not find best validating checkpoint.")
# Load most recent checkpoint, saved in checkpoint file 'checkpoint'
elif method == 'last':
ckpt_path = _checkpoint_path_or_none('checkpoint')
elif method == "last":
ckpt_path = _checkpoint_path_or_none("checkpoint")
if ckpt_path:
log_info('Loading most recent checkpoint from {}'.format(ckpt_path))
return _load_checkpoint(session, ckpt_path, allow_drop_layers, allow_lr_init=allow_lr_init)
log_info('Could not find most recent checkpoint.')
log_info("Loading most recent checkpoint from {}".format(ckpt_path))
return _load_checkpoint(
session, ckpt_path, allow_drop_layers, allow_lr_init=allow_lr_init
)
log_info("Could not find most recent checkpoint.")
# Initialize all variables
elif method == 'init':
log_info('Initializing all variables.')
elif method == "init":
log_info("Initializing all variables.")
return _initialize_all_variables(session)
else:
log_error('Unknown initialization method: {}'.format(method))
log_error("Unknown initialization method: {}".format(method))
sys.exit(1)
log_error('All initialization methods failed ({}).'.format(method_order))
log_error("All initialization methods failed ({}).".format(method_order))
sys.exit(1)
def reload_best_checkpoint(session):
_load_or_init_impl(session, ['best'], allow_drop_layers=False, allow_lr_init=False)
_load_or_init_impl(session, ["best"], allow_drop_layers=False, allow_lr_init=False)
def load_or_init_graph_for_training(session):
'''
"""
Load variables from checkpoint or initialize variables. By default this will
try to load the best validating checkpoint, then try the last checkpoint,
and finally initialize the weights from scratch. This can be overriden with
the `--load_train` flag. See its documentation for more info.
'''
if FLAGS.load_train == 'auto':
methods = ['best', 'last', 'init']
"""
if FLAGS.load_train == "auto":
methods = ["best", "last", "init"]
else:
methods = [FLAGS.load_train]
_load_or_init_impl(session, methods, allow_drop_layers=True)
def load_graph_for_evaluation(session):
'''
"""
Load variables from checkpoint. Initialization is not allowed. By default
this will try to load the best validating checkpoint, then try the last
checkpoint. This can be overriden with the `--load_evaluate` flag. See its
documentation for more info.
'''
if FLAGS.load_evaluate == 'auto':
methods = ['best', 'last']
"""
if FLAGS.load_evaluate == "auto":
methods = ["best", "last"]
else:
methods = [FLAGS.load_evaluate]
_load_or_init_impl(session, methods, allow_drop_layers=False)

96
training/coqui_stt_training/util/config.py
View File

@ -2,18 +2,20 @@ from __future__ import absolute_import, division, print_function
import os
import sys
import tensorflow.compat.v1 as tfv1
from attrdict import AttrDict
from xdg import BaseDirectory as xdg
from coqui_stt_ctcdecoder import Alphabet, UTF8Alphabet
from xdg import BaseDirectory as xdg
import tensorflow.compat.v1 as tfv1
from .augmentations import NormalizeSampleRate, parse_augmentations
from .flags import FLAGS
from .gpu import get_available_gpus
from .logging import log_error, log_warn
from .helpers import parse_file_size
from .augmentations import parse_augmentations, NormalizeSampleRate
from .io import path_exists_remote
from .logging import log_error, log_warn
class ConfigSingleton:
_config = None
@ -22,28 +24,37 @@ class ConfigSingleton:
if not ConfigSingleton._config:
raise RuntimeError("Global configuration not yet initialized.")
if not hasattr(ConfigSingleton._config, name):
raise RuntimeError("Configuration option {} not found in config.".format(name))
raise RuntimeError(
"Configuration option {} not found in config.".format(name)
)
return ConfigSingleton._config[name]
Config = ConfigSingleton() # pylint: disable=invalid-name
def initialize_globals():
c = AttrDict()
# Augmentations
c.augmentations = parse_augmentations(FLAGS.augment)
if c.augmentations and FLAGS.feature_cache and FLAGS.cache_for_epochs == 0:
log_warn('Due to current feature-cache settings the exact same sample augmentations of the first '
'epoch will be repeated on all following epochs. This could lead to unintended over-fitting. '
'You could use --cache_for_epochs <n_epochs> to invalidate the cache after a given number of epochs.')
log_warn(
"Due to current feature-cache settings the exact same sample augmentations of the first "
"epoch will be repeated on all following epochs. This could lead to unintended over-fitting. "
"You could use --cache_for_epochs <n_epochs> to invalidate the cache after a given number of epochs."
)
if FLAGS.normalize_sample_rate:
c.augmentations = [NormalizeSampleRate(FLAGS.audio_sample_rate)] + c['augmentations']
c.augmentations = [NormalizeSampleRate(FLAGS.audio_sample_rate)] + c[
"augmentations"
]
# Caching
if FLAGS.cache_for_epochs == 1:
log_warn('--cache_for_epochs == 1 is (re-)creating the feature cache on every epoch but will never use it.')
log_warn(
"--cache_for_epochs == 1 is (re-)creating the feature cache on every epoch but will never use it."
)
# Read-buffer
FLAGS.read_buffer = parse_file_size(FLAGS.read_buffer)
@ -58,26 +69,29 @@ def initialize_globals():
# Set default checkpoint dir
if not FLAGS.checkpoint_dir:
FLAGS.checkpoint_dir = xdg.save_data_path(os.path.join('stt', 'checkpoints'))
FLAGS.checkpoint_dir = xdg.save_data_path(os.path.join("stt", "checkpoints"))
if FLAGS.load_train not in ['last', 'best', 'init', 'auto']:
FLAGS.load_train = 'auto'
if FLAGS.load_train not in ["last", "best", "init", "auto"]:
FLAGS.load_train = "auto"
if FLAGS.load_evaluate not in ['last', 'best', 'auto']:
FLAGS.load_evaluate = 'auto'
if FLAGS.load_evaluate not in ["last", "best", "auto"]:
FLAGS.load_evaluate = "auto"
# Set default summary dir
if not FLAGS.summary_dir:
FLAGS.summary_dir = xdg.save_data_path(os.path.join('stt', 'summaries'))
FLAGS.summary_dir = xdg.save_data_path(os.path.join("stt", "summaries"))
# Standard session configuration that'll be used for all new sessions.
c.session_config = tfv1.ConfigProto(allow_soft_placement=True, log_device_placement=FLAGS.log_placement,
c.session_config = tfv1.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_placement,
inter_op_parallelism_threads=FLAGS.inter_op_parallelism_threads,
intra_op_parallelism_threads=FLAGS.intra_op_parallelism_threads,
gpu_options=tfv1.GPUOptions(allow_growth=FLAGS.use_allow_growth))
gpu_options=tfv1.GPUOptions(allow_growth=FLAGS.use_allow_growth),
)
# CPU device
c.cpu_device = '/cpu:0'
c.cpu_device = "/cpu:0"
# Available GPU devices
c.available_devices = get_available_gpus(c.session_config)
@ -123,36 +137,50 @@ def initialize_globals():
# Size of audio window in samples
if (FLAGS.feature_win_len * FLAGS.audio_sample_rate) % 1000 != 0:
log_error('--feature_win_len value ({}) in milliseconds ({}) multiplied '
'by --audio_sample_rate value ({}) must be an integer value. Adjust '
'your --feature_win_len value or resample your audio accordingly.'
''.format(FLAGS.feature_win_len, FLAGS.feature_win_len / 1000, FLAGS.audio_sample_rate))
log_error(
"--feature_win_len value ({}) in milliseconds ({}) multiplied "
"by --audio_sample_rate value ({}) must be an integer value. Adjust "
"your --feature_win_len value or resample your audio accordingly."
"".format(
FLAGS.feature_win_len,
FLAGS.feature_win_len / 1000,
FLAGS.audio_sample_rate,
)
)
sys.exit(1)
c.audio_window_samples = FLAGS.audio_sample_rate * (FLAGS.feature_win_len / 1000)
# Stride for feature computations in samples
if (FLAGS.feature_win_step * FLAGS.audio_sample_rate) % 1000 != 0:
log_error('--feature_win_step value ({}) in milliseconds ({}) multiplied '
'by --audio_sample_rate value ({}) must be an integer value. Adjust '
'your --feature_win_step value or resample your audio accordingly.'
''.format(FLAGS.feature_win_step, FLAGS.feature_win_step / 1000, FLAGS.audio_sample_rate))
log_error(
"--feature_win_step value ({}) in milliseconds ({}) multiplied "
"by --audio_sample_rate value ({}) must be an integer value. Adjust "
"your --feature_win_step value or resample your audio accordingly."
"".format(
FLAGS.feature_win_step,
FLAGS.feature_win_step / 1000,
FLAGS.audio_sample_rate,
)
)
sys.exit(1)
c.audio_step_samples = FLAGS.audio_sample_rate * (FLAGS.feature_win_step / 1000)
if FLAGS.one_shot_infer:
if not path_exists_remote(FLAGS.one_shot_infer):
log_error('Path specified in --one_shot_infer is not a valid file.')
log_error("Path specified in --one_shot_infer is not a valid file.")
sys.exit(1)
if FLAGS.train_cudnn and FLAGS.load_cudnn:
log_error('Trying to use --train_cudnn, but --load_cudnn '
'was also specified. The --load_cudnn flag is only '
'needed when converting a CuDNN RNN checkpoint to '
'a CPU-capable graph. If your system is capable of '
'using CuDNN RNN, you can just specify the CuDNN RNN '
'checkpoint normally with --save_checkpoint_dir.')
log_error(
"Trying to use --train_cudnn, but --load_cudnn "
"was also specified. The --load_cudnn flag is only "
"needed when converting a CuDNN RNN checkpoint to "
"a CPU-capable graph. If your system is capable of "
"using CuDNN RNN, you can just specify the CuDNN RNN "
"checkpoint normally with --save_checkpoint_dir."
)
sys.exit(1)
# If separate save and load flags were not specified, default to load and save

25
training/coqui_stt_training/util/downloader.py
View File

@ -1,10 +1,18 @@
import requests
from os import makedirs, path
import progressbar
import requests
from os import path, makedirs
from .io import open_remote, path_exists_remote, is_remote_path
from .io import is_remote_path, open_remote, path_exists_remote
SIMPLE_BAR = [
"Progress ",
progressbar.Bar(),
" ",
progressbar.Percentage(),
" completed",
]
SIMPLE_BAR = ['Progress ', progressbar.Bar(), ' ', progressbar.Percentage(), ' completed']
def maybe_download(archive_name, target_dir, archive_url):
# If archive file does not exist, download it...
@ -17,10 +25,13 @@ def maybe_download(archive_name, target_dir, archive_url):
if not path_exists_remote(archive_path):
print('No archive "%s" - downloading...' % archive_path)
req = requests.get(archive_url, stream=True)
total_size = int(req.headers.get('content-length', 0))
total_size = int(req.headers.get("content-length", 0))
done = 0
with open_remote(archive_path, 'wb') as f:
bar = progressbar.ProgressBar(max_value=total_size if total_size > 0 else progressbar.UnknownLength, widgets=SIMPLE_BAR)
with open_remote(archive_path, "wb") as f:
bar = progressbar.ProgressBar(
max_value=total_size if total_size > 0 else progressbar.UnknownLength,
widgets=SIMPLE_BAR,
)
for data in req.iter_content(1024 * 1024):
done += len(data)

57
training/coqui_stt_training/util/evaluate_tools.py
View File

@ -9,8 +9,9 @@ import numpy as np
from attrdict import AttrDict
from .flags import FLAGS
from .text import levenshtein
from .io import open_remote
from .text import levenshtein
def pmap(fun, iterable):
pool = Pool()
@ -42,26 +43,28 @@ def process_decode_result(item):
char_length = len(ground_truth)
word_distance = levenshtein(ground_truth.split(), prediction.split())
word_length = len(ground_truth.split())
return AttrDict({
'wav_filename': wav_filename,
'src': ground_truth,
'res': prediction,
'loss': loss,
'char_distance': char_distance,
'char_length': char_length,
'word_distance': word_distance,
'word_length': word_length,
'cer': char_distance / char_length,
'wer': word_distance / word_length,
})
return AttrDict(
{
"wav_filename": wav_filename,
"src": ground_truth,
"res": prediction,
"loss": loss,
"char_distance": char_distance,
"char_length": char_length,
"word_distance": word_distance,
"word_length": word_length,
"cer": char_distance / char_length,
"wer": word_distance / word_length,
}
)
def calculate_and_print_report(wav_filenames, labels, decodings, losses, dataset_name):
r'''
r"""
This routine will calculate and print a WER report.
It'll compute the `mean` WER and create ``Sample`` objects of the ``report_count`` top lowest
loss items from the provided WER results tuple (only items with WER!=0 and ordered by their WER).
'''
"""
samples = pmap(process_decode_result, zip(wav_filenames, labels, decodings, losses))
# Getting the WER and CER from the accumulated edit distances and lengths
@ -88,8 +91,10 @@ def print_report(samples, losses, wer, cer, dataset_name):
# Print summary
mean_loss = np.mean(losses)
print('Test on %s - WER: %f, CER: %f, loss: %f' % (dataset_name, wer, cer, mean_loss))
print('-' * 80)
print(
"Test on %s - WER: %f, CER: %f, loss: %f" % (dataset_name, wer, cer, mean_loss)
)
print("-" * 80)
best_samples = samples[: FLAGS.report_count]
worst_samples = samples[-FLAGS.report_count :]
@ -99,30 +104,30 @@ def print_report(samples, losses, wer, cer, dataset_name):
median_samples = samples[median_index - median_left : median_index + median_right]
def print_single_sample(sample):
print('WER: %f, CER: %f, loss: %f' % (sample.wer, sample.cer, sample.loss))
print(' - wav: file://%s' % sample.wav_filename)
print("WER: %f, CER: %f, loss: %f" % (sample.wer, sample.cer, sample.loss))
print(" - wav: file://%s" % sample.wav_filename)
print(' - src: "%s"' % sample.src)
print(' - res: "%s"' % sample.res)
print('-' * 80)
print("-" * 80)
print('Best WER:', '\n' + '-' * 80)
print("Best WER:", "\n" + "-" * 80)
for s in best_samples:
print_single_sample(s)
print('Median WER:', '\n' + '-' * 80)
print("Median WER:", "\n" + "-" * 80)
for s in median_samples:
print_single_sample(s)
print('Worst WER:', '\n' + '-' * 80)
print("Worst WER:", "\n" + "-" * 80)
for s in worst_samples:
print_single_sample(s)
def save_samples_json(samples, output_path):
''' Save decoded tuples as JSON, converting NumPy floats to Python floats.
"""Save decoded tuples as JSON, converting NumPy floats to Python floats.
We set ensure_ascii=True to prevent json from escaping non-ASCII chars
in the texts.
'''
with open_remote(output_path, 'w') as fout:
"""
with open_remote(output_path, "w") as fout:
json.dump(samples, fout, default=float, ensure_ascii=False, indent=2)

181
training/coqui_stt_training/util/feeding.py
View File

@ -5,73 +5,117 @@ from collections import Counter
from functools import partial
import numpy as np
import tensorflow as tf
import tensorflow as tf
from tensorflow.python.ops import gen_audio_ops as contrib_audio
from .audio import DEFAULT_FORMAT, pcm_to_np, read_frames_from_file, vad_split
from .augmentations import apply_graph_augmentations, apply_sample_augmentations
from .config import Config
from .text import text_to_char_array
from .flags import FLAGS
from .augmentations import apply_sample_augmentations, apply_graph_augmentations
from .audio import read_frames_from_file, vad_split, pcm_to_np, DEFAULT_FORMAT
from .helpers import MEGABYTE, remember_exception
from .sample_collections import samples_from_sources
from .helpers import remember_exception, MEGABYTE
from .text import text_to_char_array
def audio_to_features(audio, sample_rate, transcript=None, clock=0.0, train_phase=False, augmentations=None, sample_id=None):
def audio_to_features(
audio,
sample_rate,
transcript=None,
clock=0.0,
train_phase=False,
augmentations=None,
sample_id=None,
):
if train_phase:
# We need the lambdas to make TensorFlow happy.
# pylint: disable=unnecessary-lambda
tf.cond(tf.math.not_equal(sample_rate, FLAGS.audio_sample_rate),
lambda: tf.print('WARNING: sample rate of sample', sample_id, '(', sample_rate, ') '
'does not match FLAGS.audio_sample_rate. This can lead to incorrect results.'),
tf.cond(
tf.math.not_equal(sample_rate, FLAGS.audio_sample_rate),
lambda: tf.print(
"WARNING: sample rate of sample",
sample_id,
"(",
sample_rate,
") "
"does not match FLAGS.audio_sample_rate. This can lead to incorrect results.",
),
lambda: tf.no_op(),
name='matching_sample_rate')
name="matching_sample_rate",
)
if train_phase and augmentations:
audio = apply_graph_augmentations('signal', audio, augmentations, transcript=transcript, clock=clock)
audio = apply_graph_augmentations(
"signal", audio, augmentations, transcript=transcript, clock=clock
)
spectrogram = contrib_audio.audio_spectrogram(audio,
spectrogram = contrib_audio.audio_spectrogram(
audio,
window_size=Config.audio_window_samples,
stride=Config.audio_step_samples,
magnitude_squared=True)
magnitude_squared=True,
)
if train_phase and augmentations:
spectrogram = apply_graph_augmentations('spectrogram', spectrogram, augmentations, transcript=transcript, clock=clock)
spectrogram = apply_graph_augmentations(
"spectrogram",
spectrogram,
augmentations,
transcript=transcript,
clock=clock,
)
features = contrib_audio.mfcc(spectrogram=spectrogram,
features = contrib_audio.mfcc(
spectrogram=spectrogram,
sample_rate=sample_rate,
dct_coefficient_count=Config.n_input,
upper_frequency_limit=FLAGS.audio_sample_rate / 2)
upper_frequency_limit=FLAGS.audio_sample_rate / 2,
)
features = tf.reshape(features, [-1, Config.n_input])
if train_phase and augmentations:
features = apply_graph_augmentations('features', features, augmentations, transcript=transcript, clock=clock)
features = apply_graph_augmentations(
"features", features, augmentations, transcript=transcript, clock=clock
)
return features, tf.shape(input=features)[0]
def audiofile_to_features(wav_filename, clock=0.0, train_phase=False, augmentations=None):
def audiofile_to_features(
wav_filename, clock=0.0, train_phase=False, augmentations=None
):
samples = tf.io.read_file(wav_filename)
decoded = contrib_audio.decode_wav(samples, desired_channels=1)
return audio_to_features(decoded.audio,
return audio_to_features(
decoded.audio,
decoded.sample_rate,
clock=clock,
train_phase=train_phase,
augmentations=augmentations,
sample_id=wav_filename)
sample_id=wav_filename,
)
def entry_to_features(sample_id, audio, sample_rate, transcript, clock, train_phase=False, augmentations=None):
def entry_to_features(
sample_id,
audio,
sample_rate,
transcript,
clock,
train_phase=False,
augmentations=None,
):
# https://bugs.python.org/issue32117
sparse_transcript = tf.SparseTensor(*transcript)
features, features_len = audio_to_features(audio,
features, features_len = audio_to_features(
audio,
sample_rate,
transcript=sparse_transcript,
clock=clock,
train_phase=train_phase,
augmentations=augmentations,
sample_id=sample_id)
sample_id=sample_id,
)
return sample_id, features, features_len, sparse_transcript
@ -79,12 +123,15 @@ def to_sparse_tuple(sequence):
r"""Creates a sparse representention of ``sequence``.
Returns a tuple with (indices, values, shape)
"""
indices = np.asarray(list(zip([0]*len(sequence), range(len(sequence)))), dtype=np.int64)
indices = np.asarray(
list(zip([0] * len(sequence), range(len(sequence)))), dtype=np.int64
)
shape = np.asarray([1, len(sequence)], dtype=np.int64)
return indices, sequence, shape
def create_dataset(sources,
def create_dataset(
sources,
batch_size,
epochs=1,
augmentations=None,
@ -94,28 +141,39 @@ def create_dataset(sources,
limit=0,
exception_box=None,
process_ahead=None,
buffering=1 * MEGABYTE):
buffering=1 * MEGABYTE,
):
epoch_counter = Counter() # survives restarts of the dataset and its generator
def generate_values():
epoch = epoch_counter['epoch']
epoch = epoch_counter["epoch"]
if train_phase:
epoch_counter['epoch'] += 1
samples = samples_from_sources(sources, buffering=buffering, labeled=True, reverse=reverse)
epoch_counter["epoch"] += 1
samples = samples_from_sources(
sources, buffering=buffering, labeled=True, reverse=reverse
)
num_samples = len(samples)
if limit > 0:
num_samples = min(limit, num_samples)
samples = apply_sample_augmentations(samples,
samples = apply_sample_augmentations(
samples,
augmentations,
buffering=buffering,
process_ahead=2 * batch_size if process_ahead is None else process_ahead,
clock=epoch / epochs,
final_clock=(epoch + 1) / epochs)
final_clock=(epoch + 1) / epochs,
)
for sample_index, sample in enumerate(samples):
if sample_index >= num_samples:
break
clock = (epoch * num_samples + sample_index) / (epochs * num_samples) if train_phase and epochs > 0 else 0.0
transcript = text_to_char_array(sample.transcript, Config.alphabet, context=sample.sample_id)
clock = (
(epoch * num_samples + sample_index) / (epochs * num_samples)
if train_phase and epochs > 0
else 0.0
)
transcript = text_to_char_array(
sample.transcript, Config.alphabet, context=sample.sample_id
)
transcript = to_sparse_tuple(transcript)
yield sample.sample_id, sample.audio, sample.audio_format.rate, transcript, clock
@ -128,31 +186,46 @@ def create_dataset(sources,
def batch_fn(sample_ids, features, features_len, transcripts):
features = tf.data.Dataset.zip((features, features_len))
features = features.padded_batch(batch_size, padded_shapes=([None, Config.n_input], []))
features = features.padded_batch(
batch_size, padded_shapes=([None, Config.n_input], [])
)
transcripts = transcripts.batch(batch_size).map(sparse_reshape)
sample_ids = sample_ids.batch(batch_size)
return tf.data.Dataset.zip((sample_ids, features, transcripts))
process_fn = partial(entry_to_features, train_phase=train_phase, augmentations=augmentations)
process_fn = partial(
entry_to_features, train_phase=train_phase, augmentations=augmentations
)
dataset = (tf.data.Dataset.from_generator(remember_exception(generate_values, exception_box),
output_types=(tf.string, tf.float32, tf.int32,
(tf.int64, tf.int32, tf.int64), tf.float64))
.map(process_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE))
dataset = tf.data.Dataset.from_generator(
remember_exception(generate_values, exception_box),
output_types=(
tf.string,
tf.float32,
tf.int32,
(tf.int64, tf.int32, tf.int64),
tf.float64,
),
).map(process_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
if cache_path:
dataset = dataset.cache(cache_path)
dataset = (dataset.window(batch_size, drop_remainder=train_phase).flat_map(batch_fn)
.prefetch(len(Config.available_devices)))
dataset = (
dataset.window(batch_size, drop_remainder=train_phase)
.flat_map(batch_fn)
.prefetch(len(Config.available_devices))
)
return dataset
def split_audio_file(audio_path,
def split_audio_file(
audio_path,
audio_format=DEFAULT_FORMAT,
batch_size=1,
aggressiveness=3,
outlier_duration_ms=10000,
outlier_batch_size=1,
exception_box=None):
exception_box=None,
):
def generate_values():
frames = read_frames_from_file(audio_path)
segments = vad_split(frames, aggressiveness=aggressiveness)
@ -166,17 +239,23 @@ def split_audio_file(audio_path,
return time_start, time_end, features, features_len
def create_batch_set(bs, criteria):
return (tf.data.Dataset
.from_generator(remember_exception(generate_values, exception_box),
output_types=(tf.int32, tf.int32, tf.float32))
return (
tf.data.Dataset.from_generator(
remember_exception(generate_values, exception_box),
output_types=(tf.int32, tf.int32, tf.float32),
)
.map(to_mfccs, num_parallel_calls=tf.data.experimental.AUTOTUNE)
.filter(criteria)
.padded_batch(bs, padded_shapes=([], [], [None, Config.n_input], [])))
.padded_batch(bs, padded_shapes=([], [], [None, Config.n_input], []))
)
nds = create_batch_set(batch_size,
lambda start, end, f, fl: end - start <= int(outlier_duration_ms))
ods = create_batch_set(outlier_batch_size,
lambda start, end, f, fl: end - start > int(outlier_duration_ms))
nds = create_batch_set(
batch_size, lambda start, end, f, fl: end - start <= int(outlier_duration_ms)
)
ods = create_batch_set(
outlier_batch_size,
lambda start, end, f, fl: end - start > int(outlier_duration_ms),
)
dataset = nds.concatenate(ods)
dataset = dataset.prefetch(len(Config.available_devices))
return dataset

466
training/coqui_stt_training/util/flags.py
View File

@ -1,6 +1,7 @@
from __future__ import absolute_import, division, print_function
import os
import absl.flags
FLAGS = absl.flags.FLAGS
@ -12,179 +13,448 @@ def create_flags():
f = absl.flags
f.DEFINE_string('train_files', '', 'comma separated list of files specifying the dataset used for training. Multiple files will get merged. If empty, training will not be run.')
f.DEFINE_string('dev_files', '', 'comma separated list of files specifying the datasets used for validation. Multiple files will get reported separately. If empty, validation will not be run.')
f.DEFINE_string('test_files', '', 'comma separated list of files specifying the datasets used for testing. Multiple files will get reported separately. If empty, the model will not be tested.')
f.DEFINE_string('metrics_files', '', 'comma separated list of files specifying the datasets used for tracking of metrics (after validation step). Currently the only metric is the CTC loss but without affecting the tracking of best validation loss. Multiple files will get reported separately. If empty, metrics will not be computed.')
f.DEFINE_string(
"train_files",
"",
"comma separated list of files specifying the dataset used for training. Multiple files will get merged. If empty, training will not be run.",
)
f.DEFINE_string(
"dev_files",
"",
"comma separated list of files specifying the datasets used for validation. Multiple files will get reported separately. If empty, validation will not be run.",
)
f.DEFINE_string(
"test_files",
"",
"comma separated list of files specifying the datasets used for testing. Multiple files will get reported separately. If empty, the model will not be tested.",
)
f.DEFINE_string(
"metrics_files",
"",
"comma separated list of files specifying the datasets used for tracking of metrics (after validation step). Currently the only metric is the CTC loss but without affecting the tracking of best validation loss. Multiple files will get reported separately. If empty, metrics will not be computed.",
)
f.DEFINE_string('read_buffer', '1MB', 'buffer-size for reading samples from datasets (supports file-size suffixes KB, MB, GB, TB)')
f.DEFINE_string('feature_cache', '', 'cache MFCC features to disk to speed up future training runs on the same data. This flag specifies the path where cached features extracted from --train_files will be saved. If empty, or if online augmentation flags are enabled, caching will be disabled.')
f.DEFINE_integer('cache_for_epochs', 0, 'after how many epochs the feature cache is invalidated again - 0 for "never"')
f.DEFINE_string(
"read_buffer",
"1MB",
"buffer-size for reading samples from datasets (supports file-size suffixes KB, MB, GB, TB)",
)
f.DEFINE_string(
"feature_cache",
"",
"cache MFCC features to disk to speed up future training runs on the same data. This flag specifies the path where cached features extracted from --train_files will be saved. If empty, or if online augmentation flags are enabled, caching will be disabled.",
)
f.DEFINE_integer(
"cache_for_epochs",
0,
'after how many epochs the feature cache is invalidated again - 0 for "never"',
)
f.DEFINE_integer('feature_win_len', 32, 'feature extraction audio window length in milliseconds')
f.DEFINE_integer('feature_win_step', 20, 'feature extraction window step length in milliseconds')
f.DEFINE_integer('audio_sample_rate', 16000, 'sample rate value expected by model')
f.DEFINE_boolean('normalize_sample_rate', True, 'normalize sample rate of all train_files to --audio_sample_rate')
f.DEFINE_integer(
"feature_win_len", 32, "feature extraction audio window length in milliseconds"
)
f.DEFINE_integer(
"feature_win_step", 20, "feature extraction window step length in milliseconds"
)
f.DEFINE_integer("audio_sample_rate", 16000, "sample rate value expected by model")
f.DEFINE_boolean(
"normalize_sample_rate",
True,
"normalize sample rate of all train_files to --audio_sample_rate",
)
# Data Augmentation
# ================
f.DEFINE_multi_string('augment', None, 'specifies an augmentation of the training samples. Format is "--augment operation[param1=value1, ...]"')
f.DEFINE_multi_string(
"augment",
None,
'specifies an augmentation of the training samples. Format is "--augment operation[param1=value1, ...]"',
)
# Global Constants
# ================
f.DEFINE_integer('epochs', 75, 'how many epochs (complete runs through the train files) to train for')
f.DEFINE_integer(
"epochs",
75,
"how many epochs (complete runs through the train files) to train for",
)
f.DEFINE_float('dropout_rate', 0.05, 'dropout rate for feedforward layers')
f.DEFINE_float('dropout_rate2', -1.0, 'dropout rate for layer 2 - defaults to dropout_rate')
f.DEFINE_float('dropout_rate3', -1.0, 'dropout rate for layer 3 - defaults to dropout_rate')
f.DEFINE_float('dropout_rate4', 0.0, 'dropout rate for layer 4 - defaults to 0.0')
f.DEFINE_float('dropout_rate5', 0.0, 'dropout rate for layer 5 - defaults to 0.0')
f.DEFINE_float('dropout_rate6', -1.0, 'dropout rate for layer 6 - defaults to dropout_rate')
f.DEFINE_float("dropout_rate", 0.05, "dropout rate for feedforward layers")
f.DEFINE_float(
"dropout_rate2", -1.0, "dropout rate for layer 2 - defaults to dropout_rate"
)
f.DEFINE_float(
"dropout_rate3", -1.0, "dropout rate for layer 3 - defaults to dropout_rate"
)
f.DEFINE_float("dropout_rate4", 0.0, "dropout rate for layer 4 - defaults to 0.0")
f.DEFINE_float("dropout_rate5", 0.0, "dropout rate for layer 5 - defaults to 0.0")
f.DEFINE_float(
"dropout_rate6", -1.0, "dropout rate for layer 6 - defaults to dropout_rate"
)
f.DEFINE_float('relu_clip', 20.0, 'ReLU clipping value for non-recurrent layers')
f.DEFINE_float("relu_clip", 20.0, "ReLU clipping value for non-recurrent layers")
# Adam optimizer(http://arxiv.org/abs/1412.6980) parameters
f.DEFINE_float('beta1', 0.9, 'beta 1 parameter of Adam optimizer')
f.DEFINE_float('beta2', 0.999, 'beta 2 parameter of Adam optimizer')
f.DEFINE_float('epsilon', 1e-8, 'epsilon parameter of Adam optimizer')
f.DEFINE_float('learning_rate', 0.001, 'learning rate of Adam optimizer')
f.DEFINE_float("beta1", 0.9, "beta 1 parameter of Adam optimizer")
f.DEFINE_float("beta2", 0.999, "beta 2 parameter of Adam optimizer")
f.DEFINE_float("epsilon", 1e-8, "epsilon parameter of Adam optimizer")
f.DEFINE_float("learning_rate", 0.001, "learning rate of Adam optimizer")
# Batch sizes
f.DEFINE_integer('train_batch_size', 1, 'number of elements in a training batch')
f.DEFINE_integer('dev_batch_size', 1, 'number of elements in a validation batch')
f.DEFINE_integer('test_batch_size', 1, 'number of elements in a test batch')
f.DEFINE_integer("train_batch_size", 1, "number of elements in a training batch")
f.DEFINE_integer("dev_batch_size", 1, "number of elements in a validation batch")
f.DEFINE_integer("test_batch_size", 1, "number of elements in a test batch")
f.DEFINE_integer('export_batch_size', 1, 'number of elements per batch on the exported graph')
f.DEFINE_integer(
"export_batch_size", 1, "number of elements per batch on the exported graph"
)
# Performance
f.DEFINE_integer('inter_op_parallelism_threads', 0, 'number of inter-op parallelism threads - see tf.ConfigProto for more details. USE OF THIS FLAG IS UNSUPPORTED')
f.DEFINE_integer('intra_op_parallelism_threads', 0, 'number of intra-op parallelism threads - see tf.ConfigProto for more details. USE OF THIS FLAG IS UNSUPPORTED')
f.DEFINE_boolean('use_allow_growth', False, 'use Allow Growth flag which will allocate only required amount of GPU memory and prevent full allocation of available GPU memory')
f.DEFINE_boolean('load_cudnn', False, 'Specifying this flag allows one to convert a CuDNN RNN checkpoint to a checkpoint capable of running on a CPU graph.')
f.DEFINE_boolean('train_cudnn', False, 'use CuDNN RNN backend for training on GPU. Note that checkpoints created with this flag can only be used with CuDNN RNN, i.e. fine tuning on a CPU device will not work')
f.DEFINE_boolean('automatic_mixed_precision', False, 'whether to allow automatic mixed precision training. USE OF THIS FLAG IS UNSUPPORTED. Checkpoints created with automatic mixed precision training will not be usable without mixed precision.')
f.DEFINE_integer(
"inter_op_parallelism_threads",
0,
"number of inter-op parallelism threads - see tf.ConfigProto for more details. USE OF THIS FLAG IS UNSUPPORTED",
)
f.DEFINE_integer(
"intra_op_parallelism_threads",
0,
"number of intra-op parallelism threads - see tf.ConfigProto for more details. USE OF THIS FLAG IS UNSUPPORTED",
)
f.DEFINE_boolean(
"use_allow_growth",
False,
"use Allow Growth flag which will allocate only required amount of GPU memory and prevent full allocation of available GPU memory",
)
f.DEFINE_boolean(
"load_cudnn",
False,
"Specifying this flag allows one to convert a CuDNN RNN checkpoint to a checkpoint capable of running on a CPU graph.",
)
f.DEFINE_boolean(
"train_cudnn",
False,
"use CuDNN RNN backend for training on GPU. Note that checkpoints created with this flag can only be used with CuDNN RNN, i.e. fine tuning on a CPU device will not work",
)
f.DEFINE_boolean(
"automatic_mixed_precision",
False,
"whether to allow automatic mixed precision training. USE OF THIS FLAG IS UNSUPPORTED. Checkpoints created with automatic mixed precision training will not be usable without mixed precision.",
)
# Sample limits
f.DEFINE_integer('limit_train', 0, 'maximum number of elements to use from train set - 0 means no limit')
f.DEFINE_integer('limit_dev', 0, 'maximum number of elements to use from validation set - 0 means no limit')
f.DEFINE_integer('limit_test', 0, 'maximum number of elements to use from test set - 0 means no limit')
f.DEFINE_integer(
"limit_train",
0,
"maximum number of elements to use from train set - 0 means no limit",
)
f.DEFINE_integer(
"limit_dev",
0,
"maximum number of elements to use from validation set - 0 means no limit",
)
f.DEFINE_integer(
"limit_test",
0,
"maximum number of elements to use from test set - 0 means no limit",
)
# Sample order
f.DEFINE_boolean('reverse_train', False, 'if to reverse sample order of the train set')
f.DEFINE_boolean('reverse_dev', False, 'if to reverse sample order of the dev set')
f.DEFINE_boolean('reverse_test', False, 'if to reverse sample order of the test set')
f.DEFINE_boolean(
"reverse_train", False, "if to reverse sample order of the train set"
)
f.DEFINE_boolean("reverse_dev", False, "if to reverse sample order of the dev set")
f.DEFINE_boolean(
"reverse_test", False, "if to reverse sample order of the test set"
)
# Checkpointing
f.DEFINE_string('checkpoint_dir', '', 'directory from which checkpoints are loaded and to which they are saved - defaults to directory "stt/checkpoints" within user\'s data home specified by the XDG Base Directory Specification')
f.DEFINE_string('load_checkpoint_dir', '', 'directory in which checkpoints are stored - defaults to directory "stt/checkpoints" within user\'s data home specified by the XDG Base Directory Specification')
f.DEFINE_string('save_checkpoint_dir', '', 'directory to which checkpoints are saved - defaults to directory "stt/checkpoints" within user\'s data home specified by the XDG Base Directory Specification')
f.DEFINE_integer('checkpoint_secs', 600, 'checkpoint saving interval in seconds')
f.DEFINE_integer('max_to_keep', 5, 'number of checkpoint files to keep - default value is 5')
f.DEFINE_string('load_train', 'auto', 'what checkpoint to load before starting the training process. "last" for loading most recent epoch checkpoint, "best" for loading best validation loss checkpoint, "init" for initializing a new checkpoint, "auto" for trying several options.')
f.DEFINE_string('load_evaluate', 'auto', 'what checkpoint to load for evaluation tasks (test epochs, model export, single file inference, etc). "last" for loading most recent epoch checkpoint, "best" for loading best validation loss checkpoint, "auto" for trying several options.')
f.DEFINE_string(
"checkpoint_dir",
"",
'directory from which checkpoints are loaded and to which they are saved - defaults to directory "stt/checkpoints" within user\'s data home specified by the XDG Base Directory Specification',
)
f.DEFINE_string(
"load_checkpoint_dir",
"",
'directory in which checkpoints are stored - defaults to directory "stt/checkpoints" within user\'s data home specified by the XDG Base Directory Specification',
)
f.DEFINE_string(
"save_checkpoint_dir",
"",
'directory to which checkpoints are saved - defaults to directory "stt/checkpoints" within user\'s data home specified by the XDG Base Directory Specification',
)
f.DEFINE_integer("checkpoint_secs", 600, "checkpoint saving interval in seconds")
f.DEFINE_integer(
"max_to_keep", 5, "number of checkpoint files to keep - default value is 5"
)
f.DEFINE_string(
"load_train",
"auto",
'what checkpoint to load before starting the training process. "last" for loading most recent epoch checkpoint, "best" for loading best validation loss checkpoint, "init" for initializing a new checkpoint, "auto" for trying several options.',
)
f.DEFINE_string(
"load_evaluate",
"auto",
'what checkpoint to load for evaluation tasks (test epochs, model export, single file inference, etc). "last" for loading most recent epoch checkpoint, "best" for loading best validation loss checkpoint, "auto" for trying several options.',
)
# Transfer Learning
f.DEFINE_integer('drop_source_layers', 0, 'single integer for how many layers to drop from source model (to drop just output == 1, drop penultimate and output ==2, etc)')
f.DEFINE_integer(
"drop_source_layers",
0,
"single integer for how many layers to drop from source model (to drop just output == 1, drop penultimate and output ==2, etc)",
)
# Exporting
f.DEFINE_string('export_dir', '', 'directory in which exported models are stored - if omitted, the model won\'t get exported')
f.DEFINE_boolean('remove_export', False, 'whether to remove old exported models')
f.DEFINE_boolean('export_tflite', False, 'export a graph ready for TF Lite engine')
f.DEFINE_integer('n_steps', 16, 'how many timesteps to process at once by the export graph, higher values mean more latency')
f.DEFINE_boolean('export_zip', False, 'export a TFLite model and package with LM and info.json')
f.DEFINE_string('export_file_name', 'output_graph', 'name for the exported model file name')
f.DEFINE_integer('export_beam_width', 500, 'default beam width to embed into exported graph')
f.DEFINE_string(
"export_dir",
"",
"directory in which exported models are stored - if omitted, the model won't get exported",
)
f.DEFINE_boolean("remove_export", False, "whether to remove old exported models")
f.DEFINE_boolean("export_tflite", False, "export a graph ready for TF Lite engine")
f.DEFINE_integer(
"n_steps",
16,
"how many timesteps to process at once by the export graph, higher values mean more latency",
)
f.DEFINE_boolean(
"export_zip", False, "export a TFLite model and package with LM and info.json"
)
f.DEFINE_string(
"export_file_name", "output_graph", "name for the exported model file name"
)
f.DEFINE_integer(
"export_beam_width", 500, "default beam width to embed into exported graph"
)
# Model metadata
f.DEFINE_string('export_author_id', 'author', 'author of the exported model. GitHub user or organization name used to uniquely identify the author of this model')
f.DEFINE_string('export_model_name', 'model', 'name of the exported model. Must not contain forward slashes.')
f.DEFINE_string('export_model_version', '0.0.1', 'semantic version of the exported model. See https://semver.org/. This is fully controlled by you as author of the model and has no required connection with Coqui STT versions')
f.DEFINE_string(
"export_author_id",
"author",
"author of the exported model. GitHub user or organization name used to uniquely identify the author of this model",
)
f.DEFINE_string(
"export_model_name",
"model",
"name of the exported model. Must not contain forward slashes.",
)
f.DEFINE_string(
"export_model_version",
"0.0.1",
"semantic version of the exported model. See https://semver.org/. This is fully controlled by you as author of the model and has no required connection with Coqui STT versions",
)
def str_val_equals_help(name, val_desc):
f.DEFINE_string(name, '<{}>'.format(val_desc), val_desc)
f.DEFINE_string(name, "<{}>".format(val_desc), val_desc)
str_val_equals_help('export_contact_info', 'public contact information of the author. Can be an email address, or a link to a contact form, issue tracker, or discussion forum. Must provide a way to reach the model authors')
str_val_equals_help('export_license', 'SPDX identifier of the license of the exported model. See https://spdx.org/licenses/. If the license does not have an SPDX identifier, use the license name.')
str_val_equals_help('export_language', 'language the model was trained on - IETF BCP 47 language tag including at least language, script and region subtags. E.g. "en-Latn-UK" or "de-Latn-DE" or "cmn-Hans-CN". Include as much info as you can without loss of precision. For example, if a model is trained on Scottish English, include the variant subtag: "en-Latn-GB-Scotland".')
str_val_equals_help('export_min_stt_version', 'minimum Coqui STT version (inclusive) the exported model is compatible with')
str_val_equals_help('export_max_stt_version', 'maximum Coqui STT version (inclusive) the exported model is compatible with')
str_val_equals_help('export_description', 'Freeform description of the model being exported. Markdown accepted. You can also leave this flag unchanged and edit the generated .md file directly. Useful things to describe are demographic and acoustic characteristics of the data used to train the model, any architectural changes, names of public datasets that were used when applicable, hyperparameters used for training, evaluation results on standard benchmark datasets, etc.')
str_val_equals_help(
"export_contact_info",
"public contact information of the author. Can be an email address, or a link to a contact form, issue tracker, or discussion forum. Must provide a way to reach the model authors",
)
str_val_equals_help(
"export_license",
"SPDX identifier of the license of the exported model. See https://spdx.org/licenses/. If the license does not have an SPDX identifier, use the license name.",
)
str_val_equals_help(
"export_language",
'language the model was trained on - IETF BCP 47 language tag including at least language, script and region subtags. E.g. "en-Latn-UK" or "de-Latn-DE" or "cmn-Hans-CN". Include as much info as you can without loss of precision. For example, if a model is trained on Scottish English, include the variant subtag: "en-Latn-GB-Scotland".',
)
str_val_equals_help(
"export_min_stt_version",
"minimum Coqui STT version (inclusive) the exported model is compatible with",
)
str_val_equals_help(
"export_max_stt_version",
"maximum Coqui STT version (inclusive) the exported model is compatible with",
)
str_val_equals_help(
"export_description",
"Freeform description of the model being exported. Markdown accepted. You can also leave this flag unchanged and edit the generated .md file directly. Useful things to describe are demographic and acoustic characteristics of the data used to train the model, any architectural changes, names of public datasets that were used when applicable, hyperparameters used for training, evaluation results on standard benchmark datasets, etc.",
)
# Reporting
f.DEFINE_integer('log_level', 1, 'log level for console logs - 0: DEBUG, 1: INFO, 2: WARN, 3: ERROR')
f.DEFINE_boolean('show_progressbar', True, 'Show progress for training, validation and testing processes. Log level should be > 0.')
f.DEFINE_integer(
"log_level",
1,
"log level for console logs - 0: DEBUG, 1: INFO, 2: WARN, 3: ERROR",
)
f.DEFINE_boolean(
"show_progressbar",
True,
"Show progress for training, validation and testing processes. Log level should be > 0.",
)
f.DEFINE_boolean('log_placement', False, 'whether to log device placement of the operators to the console')
f.DEFINE_integer('report_count', 5, 'number of phrases for each of best WER, median WER and worst WER to print out during a WER report')
f.DEFINE_boolean(
"log_placement",
False,
"whether to log device placement of the operators to the console",
)
f.DEFINE_integer(
"report_count",
5,
"number of phrases for each of best WER, median WER and worst WER to print out during a WER report",
)
f.DEFINE_string('summary_dir', '', 'target directory for TensorBoard summaries - defaults to directory "stt/summaries" within user\'s data home specified by the XDG Base Directory Specification')
f.DEFINE_string(
"summary_dir",
"",
'target directory for TensorBoard summaries - defaults to directory "stt/summaries" within user\'s data home specified by the XDG Base Directory Specification',
)
f.DEFINE_string('test_output_file', '', 'path to a file to save all src/decoded/distance/loss tuples generated during a test epoch')
f.DEFINE_string(
"test_output_file",
"",
"path to a file to save all src/decoded/distance/loss tuples generated during a test epoch",
)
# Geometry
f.DEFINE_integer('n_hidden', 2048, 'layer width to use when initialising layers')
f.DEFINE_boolean('layer_norm', False, 'wether to use layer-normalization after each fully-connected layer (except the last one)')
f.DEFINE_integer("n_hidden", 2048, "layer width to use when initialising layers")
f.DEFINE_boolean(
"layer_norm",
False,
"wether to use layer-normalization after each fully-connected layer (except the last one)",
)
# Initialization
f.DEFINE_integer('random_seed', 4568, 'default random seed that is used to initialize variables')
f.DEFINE_integer(
"random_seed", 4568, "default random seed that is used to initialize variables"
)
# Early Stopping
f.DEFINE_boolean('early_stop', False, 'Enable early stopping mechanism over validation dataset. If validation is not being run, early stopping is disabled.')
f.DEFINE_integer('es_epochs', 25, 'Number of epochs with no improvement after which training will be stopped. Loss is not stored in the checkpoint so when checkpoint is revived it starts the loss calculation from start at that point')
f.DEFINE_float('es_min_delta', 0.05, 'Minimum change in loss to qualify as an improvement. This value will also be used in Reduce learning rate on plateau')
f.DEFINE_boolean(
"early_stop",
False,
"Enable early stopping mechanism over validation dataset. If validation is not being run, early stopping is disabled.",
)
f.DEFINE_integer(
"es_epochs",
25,
"Number of epochs with no improvement after which training will be stopped. Loss is not stored in the checkpoint so when checkpoint is revived it starts the loss calculation from start at that point",
)
f.DEFINE_float(
"es_min_delta",
0.05,
"Minimum change in loss to qualify as an improvement. This value will also be used in Reduce learning rate on plateau",
)
# Reduce learning rate on plateau
f.DEFINE_boolean('reduce_lr_on_plateau', False, 'Enable reducing the learning rate if a plateau is reached. This is the case if the validation loss did not improve for some epochs.')
f.DEFINE_integer('plateau_epochs', 10, 'Number of epochs to consider for RLROP. Has to be smaller than es_epochs from early stopping')
f.DEFINE_float('plateau_reduction', 0.1, 'Multiplicative factor to apply to the current learning rate if a plateau has occurred.')
f.DEFINE_boolean('force_initialize_learning_rate', False, 'Force re-initialization of learning rate which was previously reduced.')
f.DEFINE_boolean(
"reduce_lr_on_plateau",
False,
"Enable reducing the learning rate if a plateau is reached. This is the case if the validation loss did not improve for some epochs.",
)
f.DEFINE_integer(
"plateau_epochs",
10,
"Number of epochs to consider for RLROP. Has to be smaller than es_epochs from early stopping",
)
f.DEFINE_float(
"plateau_reduction",
0.1,
"Multiplicative factor to apply to the current learning rate if a plateau has occurred.",
)
f.DEFINE_boolean(
"force_initialize_learning_rate",
False,
"Force re-initialization of learning rate which was previously reduced.",
)
# Decoder
f.DEFINE_boolean('bytes_output_mode', False, 'enable Bytes Output Mode mode. When this is used the model outputs UTF-8 byte values directly rather than using an alphabet mapping. The --alphabet_config_path option will be ignored. See the training documentation for more details.')
f.DEFINE_string('alphabet_config_path', 'data/alphabet.txt', 'path to the configuration file specifying the alphabet used by the network. See the comment in data/alphabet.txt for a description of the format.')
f.DEFINE_string('scorer_path', '', 'path to the external scorer file.')
f.DEFINE_alias('scorer', 'scorer_path')
f.DEFINE_integer('beam_width', 1024, 'beam width used in the CTC decoder when building candidate transcriptions')
f.DEFINE_float('lm_alpha', 0.931289039105002, 'the alpha hyperparameter of the CTC decoder. Language Model weight.')
f.DEFINE_float('lm_beta', 1.1834137581510284, 'the beta hyperparameter of the CTC decoder. Word insertion weight.')
f.DEFINE_float('cutoff_prob', 1.0, 'only consider characters until this probability mass is reached. 1.0 = disabled.')
f.DEFINE_integer('cutoff_top_n', 300, 'only process this number of characters sorted by probability mass for each time step. If bigger than alphabet size, disabled.')
f.DEFINE_boolean(
"bytes_output_mode",
False,
"enable Bytes Output Mode mode. When this is used the model outputs UTF-8 byte values directly rather than using an alphabet mapping. The --alphabet_config_path option will be ignored. See the training documentation for more details.",
)
f.DEFINE_string(
"alphabet_config_path",
"data/alphabet.txt",
"path to the configuration file specifying the alphabet used by the network. See the comment in data/alphabet.txt for a description of the format.",
)
f.DEFINE_string("scorer_path", "", "path to the external scorer file.")
f.DEFINE_alias("scorer", "scorer_path")
f.DEFINE_integer(
"beam_width",
1024,
"beam width used in the CTC decoder when building candidate transcriptions",
)
f.DEFINE_float(
"lm_alpha",
0.931289039105002,
"the alpha hyperparameter of the CTC decoder. Language Model weight.",
)
f.DEFINE_float(
"lm_beta",
1.1834137581510284,
"the beta hyperparameter of the CTC decoder. Word insertion weight.",
)
f.DEFINE_float(
"cutoff_prob",
1.0,
"only consider characters until this probability mass is reached. 1.0 = disabled.",
)
f.DEFINE_integer(
"cutoff_top_n",
300,
"only process this number of characters sorted by probability mass for each time step. If bigger than alphabet size, disabled.",
)
# Inference mode
f.DEFINE_string('one_shot_infer', '', 'one-shot inference mode: specify a wav file and the script will load the checkpoint and perform inference on it.')
f.DEFINE_string(
"one_shot_infer",
"",
"one-shot inference mode: specify a wav file and the script will load the checkpoint and perform inference on it.",
)
# Optimizer mode
f.DEFINE_float('lm_alpha_max', 5, 'the maximum of the alpha hyperparameter of the CTC decoder explored during hyperparameter optimization. Language Model weight.')
f.DEFINE_float('lm_beta_max', 5, 'the maximum beta hyperparameter of the CTC decoder explored during hyperparameter optimization. Word insertion weight.')
f.DEFINE_integer('n_trials', 2400, 'the number of trials to run during hyperparameter optimization.')
f.DEFINE_float(
"lm_alpha_max",
5,
"the maximum of the alpha hyperparameter of the CTC decoder explored during hyperparameter optimization. Language Model weight.",
)
f.DEFINE_float(
"lm_beta_max",
5,
"the maximum beta hyperparameter of the CTC decoder explored during hyperparameter optimization. Word insertion weight.",
)
f.DEFINE_integer(
"n_trials",
2400,
"the number of trials to run during hyperparameter optimization.",
)
# Register validators for paths which require a file to be specified
f.register_validator('alphabet_config_path',
f.register_validator(
"alphabet_config_path",
os.path.isfile,
message='The file pointed to by --alphabet_config_path must exist and be readable.')
message="The file pointed to by --alphabet_config_path must exist and be readable.",
)
f.register_validator('one_shot_infer',
f.register_validator(
"one_shot_infer",
lambda value: not value or os.path.isfile(value),
message='The file pointed to by --one_shot_infer must exist and be readable.')
message="The file pointed to by --one_shot_infer must exist and be readable.",
)
# sphinx-doc: training_ref_flags_end

2
training/coqui_stt_training/util/gpu.py
View File

@ -6,4 +6,4 @@ def get_available_gpus(config):
Returns the number of GPUs available on this system.
"""
local_device_protos = device_lib.list_local_devices(session_config=config)
return [x.name for x in local_device_protos if x.device_type == 'GPU']
return [x.name for x in local_device_protos if x.device_type == "GPU"]

96
training/coqui_stt_training/util/helpers.py
View File

@ -1,21 +1,21 @@
import heapq
import os
import random
import sys
import time
import heapq
import semver
import random
from multiprocessing import Pool
from collections import namedtuple
from multiprocessing import Pool
import semver
KILO = 1024
KILOBYTE = 1 * KILO
MEGABYTE = KILO * KILOBYTE
GIGABYTE = KILO * MEGABYTE
TERABYTE = KILO * GIGABYTE
SIZE_PREFIX_LOOKUP = {'k': KILOBYTE, 'm': MEGABYTE, 'g': GIGABYTE, 't': TERABYTE}
SIZE_PREFIX_LOOKUP = {"k": KILOBYTE, "m": MEGABYTE, "g": GIGABYTE, "t": TERABYTE}
ValueRange = namedtuple('ValueRange', 'start end r')
ValueRange = namedtuple("ValueRange", "start end r")
def parse_file_size(file_size):
@ -23,39 +23,49 @@ def parse_file_size(file_size):
if len(file_size) == 0:
return 0
n = int(keep_only_digits(file_size))
if file_size[-1] == 'b':
if file_size[-1] == "b":
file_size = file_size[:-1]
e = file_size[-1]
return SIZE_PREFIX_LOOKUP[e] * n if e in SIZE_PREFIX_LOOKUP else n
def keep_only_digits(txt):
return ''.join(filter(str.isdigit, txt))
return "".join(filter(str.isdigit, txt))
def secs_to_hours(secs):
hours, remainder = divmod(secs, 3600)
minutes, seconds = divmod(remainder, 60)
return '%d:%02d:%02d' % (hours, minutes, seconds)
return "%d:%02d:%02d" % (hours, minutes, seconds)
def check_ctcdecoder_version():
ds_version_s = open(os.path.join(os.path.dirname(__file__), '../VERSION')).read().strip()
ds_version_s = (
open(os.path.join(os.path.dirname(__file__), "../VERSION")).read().strip()
)
try:
# pylint: disable=import-outside-toplevel
from coqui_stt_ctcdecoder import __version__ as decoder_version
except ImportError as e:
if e.msg.find('__version__') > 0:
print("Coqui STT version ({ds_version}) requires CTC decoder to expose __version__. "
"Please upgrade the coqui_stt_ctcdecoder package to version {ds_version}".format(ds_version=ds_version_s))
if e.msg.find("__version__") > 0:
print(
"Coqui STT version ({ds_version}) requires CTC decoder to expose __version__. "
"Please upgrade the coqui_stt_ctcdecoder package to version {ds_version}".format(
ds_version=ds_version_s
)
)
sys.exit(1)
raise e
rv = semver.compare(ds_version_s, decoder_version)
if rv != 0:
print("Coqui STT version ({}) and CTC decoder version ({}) do not match. "
"Please ensure matching versions are in use.".format(ds_version_s, decoder_version))
print(
"Coqui STT version ({}) and CTC decoder version ({}) do not match. "
"Please ensure matching versions are in use.".format(
ds_version_s, decoder_version
)
)
sys.exit(1)
return rv
@ -65,6 +75,7 @@ class Interleaved:
"""Collection that lazily combines sorted collections in an interleaving fashion.
During iteration the next smallest element from all the sorted collections is always picked.
The collections must support iter() and len()."""
def __init__(self, *iterables, key=lambda obj: obj, reverse=False):
self.iterables = iterables
self.key = key
@ -83,6 +94,7 @@ class LenMap:
Wrapper around python map() output object that preserves the original collection length
by implementing __len__.
"""
def __init__(self, fn, iterable):
try:
self.length = len(iterable)
@ -108,11 +120,21 @@ class LimitingPool:
"""Limits unbound ahead-processing of multiprocessing.Pool's imap method
before items get consumed by the iteration caller.
This prevents OOM issues in situations where items represent larger memory allocations."""
def __init__(self, processes=None, initializer=None, initargs=None, process_ahead=None, sleeping_for=0.1):
def __init__(
self,
processes=None,
initializer=None,
initargs=None,
process_ahead=None,
sleeping_for=0.1,
):
self.process_ahead = os.cpu_count() if process_ahead is None else process_ahead
self.sleeping_for = sleeping_for
self.processed = 0
self.pool = Pool(processes=processes, initializer=initializer, initargs=initargs)
self.pool = Pool(
processes=processes, initializer=initializer, initargs=initargs
)
def __enter__(self):
return self
@ -139,6 +161,7 @@ class LimitingPool:
class ExceptionBox:
"""Helper class for passing-back and re-raising an exception from inside a TensorFlow dataset generator.
Used in conjunction with `remember_exception`."""
def __init__(self):
self.exception = None
@ -152,6 +175,7 @@ class ExceptionBox:
def remember_exception(iterable, exception_box=None):
"""Wraps a TensorFlow dataset generator for catching its actual exceptions
that would otherwise just interrupt iteration w/o bubbling up."""
def do_iterate():
try:
yield from iterable()
@ -159,6 +183,7 @@ def remember_exception(iterable, exception_box=None):
return
except Exception as ex: # pylint: disable = broad-except
exception_box.exception = ex
return iterable if exception_box is None else do_iterate
@ -174,30 +199,34 @@ def get_value_range(value, target_type):
Any "missing" values are filled so that ValueRange always includes [start,end,r].
"""
if isinstance(value, str):
if '~' in value:
parts = value.split('~')
if "~" in value:
parts = value.split("~")
if len(parts) != 2:
raise ValueError('Cannot parse value range')
raise ValueError("Cannot parse value range")
value = parts[0]
r = parts[1]
else:
r = 0 # if no <r> supplied, use 0
parts = value.split(':')
parts = value.split(":")
if len(parts) == 1:
parts.append(parts[0]) # only one <value> given, so double it
if len(parts) != 2:
raise ValueError('Cannot parse value range')
raise ValueError("Cannot parse value range")
return ValueRange(target_type(parts[0]), target_type(parts[1]), target_type(r))
if isinstance(value, tuple):
if len(value) == 2:
return ValueRange(target_type(value[0]), target_type(value[1]), target_type(0))
return ValueRange(
target_type(value[0]), target_type(value[1]), target_type(0)
)
if len(value) == 3:
return ValueRange(target_type(value[0]), target_type(value[1]), target_type(value[2]))
return ValueRange(
target_type(value[0]), target_type(value[1]), target_type(value[2])
)
else:
raise ValueError('Cannot convert to ValueRange: Wrong tuple size')
raise ValueError("Cannot convert to ValueRange: Wrong tuple size")
if isinstance(value, int) or isinstance(value, float):
return ValueRange(target_type(value), target_type(value), target_type(0))
raise ValueError('Cannot convert to ValueRange: Wrong tuple size')
raise ValueError("Cannot convert to ValueRange: Wrong tuple size")
def int_range(value):
@ -217,20 +246,25 @@ def pick_value_from_range(value_range, clock=None):
def tf_pick_value_from_range(value_range, clock=None, double_precision=False):
import tensorflow as tf # pylint: disable=import-outside-toplevel
if clock is None:
clock = tf.random.stateless_uniform([], seed=(-1, 1), dtype=tf.float64)
else:
clock = tf.maximum(tf.constant(0.0, dtype=tf.float64),
tf.minimum(tf.constant(1.0, dtype=tf.float64), clock))
clock = tf.maximum(
tf.constant(0.0, dtype=tf.float64),
tf.minimum(tf.constant(1.0, dtype=tf.float64), clock),
)
value = value_range.start + clock * (value_range.end - value_range.start)
if value_range.r:
# if the option <r> (<value>~<r>, randomization radius) is supplied,
# sample the value from a uniform distribution with "radius" <r>
value = tf.random.stateless_uniform([],
value = tf.random.stateless_uniform(
[],
minval=value - value_range.r,
maxval=value + value_range.r,
seed=(clock * tf.int32.min, clock * tf.int32.max),
dtype=tf.float64)
dtype=tf.float64,
)
if isinstance(value_range.start, int):
return tf.cast(tf.math.round(value), tf.int64 if double_precision else tf.int32)
return tf.cast(value, tf.float64 if double_precision else tf.float32)

82
training/coqui_stt_training/util/importers.py
View File

@ -3,33 +3,72 @@ import importlib
import os
import re
import sys
from .helpers import secs_to_hours
from collections import Counter
from .helpers import secs_to_hours
def get_counter():
return Counter({'all': 0, 'failed': 0, 'invalid_label': 0, 'too_short': 0, 'too_long': 0, 'imported_time': 0, 'total_time': 0})
return Counter(
{
"all": 0,
"failed": 0,
"invalid_label": 0,
"too_short": 0,
"too_long": 0,
"imported_time": 0,
"total_time": 0,
}
)
def get_imported_samples(counter):
return counter['all'] - counter['failed'] - counter['too_short'] - counter['too_long'] - counter['invalid_label']
return (
counter["all"]
- counter["failed"]
- counter["too_short"]
- counter["too_long"]
- counter["invalid_label"]
)
def print_import_report(counter, sample_rate, max_secs):
print('Imported %d samples.' % (get_imported_samples(counter)))
if counter['failed'] > 0:
print('Skipped %d samples that failed upon conversion.' % counter['failed'])
if counter['invalid_label'] > 0:
print('Skipped %d samples that failed on transcript validation.' % counter['invalid_label'])
if counter['too_short'] > 0:
print('Skipped %d samples that were too short to match the transcript.' % counter['too_short'])
if counter['too_long'] > 0:
print('Skipped %d samples that were longer than %d seconds.' % (counter['too_long'], max_secs))
print('Final amount of imported audio: %s from %s.' % (secs_to_hours(counter['imported_time'] / sample_rate), secs_to_hours(counter['total_time'] / sample_rate)))
print("Imported %d samples." % (get_imported_samples(counter)))
if counter["failed"] > 0:
print("Skipped %d samples that failed upon conversion." % counter["failed"])
if counter["invalid_label"] > 0:
print(
"Skipped %d samples that failed on transcript validation."
% counter["invalid_label"]
)
if counter["too_short"] > 0:
print(
"Skipped %d samples that were too short to match the transcript."
% counter["too_short"]
)
if counter["too_long"] > 0:
print(
"Skipped %d samples that were longer than %d seconds."
% (counter["too_long"], max_secs)
)
print(
"Final amount of imported audio: %s from %s."
% (
secs_to_hours(counter["imported_time"] / sample_rate),
secs_to_hours(counter["total_time"] / sample_rate),
)
)
def get_importers_parser(description):
parser = argparse.ArgumentParser(description=description)
parser.add_argument('--validate_label_locale', help='Path to a Python file defining a |validate_label| function for your locale. WARNING: THIS WILL ADD THIS FILE\'s DIRECTORY INTO PYTHONPATH.')
parser.add_argument(
"--validate_label_locale",
help="Path to a Python file defining a |validate_label| function for your locale. WARNING: THIS WILL ADD THIS FILE's DIRECTORY INTO PYTHONPATH.",
)
return parser
def get_validate_label(args):
"""
Expects an argparse.Namespace argument to search for validate_label_locale parameter.
@ -43,19 +82,22 @@ def get_validate_label(args):
:type: function
"""
# Python 3.5 does not support passing a pathlib.Path to os.path.* methods
if 'validate_label_locale' not in args or (args.validate_label_locale is None):
print('WARNING: No --validate_label_locale specified, your might end with inconsistent dataset.')
if "validate_label_locale" not in args or (args.validate_label_locale is None):
print(
"WARNING: No --validate_label_locale specified, your might end with inconsistent dataset."
)
return validate_label_eng
validate_label_locale = str(args.validate_label_locale)
if not os.path.exists(os.path.abspath(validate_label_locale)):
print('ERROR: Inexistent --validate_label_locale specified. Please check.')
print("ERROR: Inexistent --validate_label_locale specified. Please check.")
return None
module_dir = os.path.abspath(os.path.dirname(validate_label_locale))
sys.path.insert(1, module_dir)
fname = os.path.basename(validate_label_locale).replace('.py', '')
fname = os.path.basename(validate_label_locale).replace(".py", "")
locale_module = importlib.import_module(fname, package=None)
return locale_module.validate_label
# Validate and normalize transcriptions. Returns a cleaned version of the label
# or None if it's invalid.
def validate_label_eng(label):
@ -72,7 +114,7 @@ def validate_label_eng(label):
label = label.replace("?", "")
label = label.replace("!", "")
label = label.replace(":", "")
label = label.replace("\"", "")
label = label.replace('"', "")
label = label.strip()
label = label.lower()

17
training/coqui_stt_training/util/io.py
View File

@ -4,6 +4,7 @@ into HDFS storage using Tensorflow's C++ FileStream API.
Currently only includes wrappers for Google's GCS, but this can easily be expanded for AWS S3 buckets.
"""
import os
from tensorflow.io import gfile
@ -12,7 +13,7 @@ def is_remote_path(path):
Returns True iff the path is one of the remote formats that this
module supports
"""
return path.startswith('gs://') or path.startswith('hdfs://')
return path.startswith("gs://") or path.startswith("hdfs://")
def path_exists_remote(path):
@ -32,7 +33,9 @@ def copy_remote(src, dst, overwrite=False):
return gfile.copy(src, dst, overwrite)
def open_remote(path, mode='r', buffering=-1, encoding=None, newline=None, closefd=True, opener=None):
def open_remote(
path, mode="r", buffering=-1, encoding=None, newline=None, closefd=True, opener=None
):
"""
Wrapper around open() method that can handle remote paths like `gs://...`
off Google Cloud using Tensorflow's IO helpers.
@ -45,7 +48,15 @@ def open_remote(path, mode='r', buffering=-1, encoding=None, newline=None, close
"""
if is_remote_path(path):
return gfile.GFile(path, mode=mode)
return open(path, mode, buffering=buffering, encoding=encoding, newline=newline, closefd=closefd, opener=opener)
return open(
path,
mode,
buffering=buffering,
encoding=encoding,
newline=newline,
closefd=closefd,
opener=opener,
)
def isdir_remote(path):

19
training/coqui_stt_training/util/logging.py
View File

@ -1,42 +1,43 @@
from __future__ import print_function
import progressbar
import sys
from .flags import FLAGS
import progressbar
from .flags import FLAGS
# Logging functions
# =================
def prefix_print(prefix, message):
print(prefix + ('\n' + prefix).join(message.split('\n')))
print(prefix + ("\n" + prefix).join(message.split("\n")))
def log_debug(message):
if FLAGS.log_level == 0:
prefix_print('D ', message)
prefix_print("D ", message)
def log_info(message):
if FLAGS.log_level <= 1:
prefix_print('I ', message)
prefix_print("I ", message)
def log_warn(message):
if FLAGS.log_level <= 2:
prefix_print('W ', message)
prefix_print("W ", message)
def log_error(message):
if FLAGS.log_level <= 3:
prefix_print('E ', message)
prefix_print("E ", message)
def create_progressbar(*args, **kwargs):
# Progress bars in stdout by default
if 'fd' not in kwargs:
kwargs['fd'] = sys.stdout
if "fd" not in kwargs:
kwargs["fd"] = sys.stdout
if FLAGS.show_progressbar:
return progressbar.ProgressBar(*args, **kwargs)

231
training/coqui_stt_training/util/sample_collections.py
View File

@ -1,45 +1,47 @@
# -*- coding: utf-8 -*-
import os
import io
import csv
import io
import json
import os
import tarfile
from pathlib import Path
from functools import partial
from pathlib import Path
from .helpers import KILOBYTE, MEGABYTE, GIGABYTE, Interleaved, LenMap
from .audio import (
Sample,
AUDIO_TYPE_PCM,
AUDIO_TYPE_OPUS,
AUDIO_TYPE_PCM,
SERIALIZABLE_AUDIO_TYPES,
Sample,
get_loadable_audio_type_from_extension,
write_wav
write_wav,
)
from .io import open_remote, is_remote_path
from .helpers import GIGABYTE, KILOBYTE, MEGABYTE, Interleaved, LenMap
from .io import is_remote_path, open_remote
BIG_ENDIAN = 'big'
BIG_ENDIAN = "big"
INT_SIZE = 4
BIGINT_SIZE = 2 * INT_SIZE
MAGIC = b'SAMPLEDB'
MAGIC = b"SAMPLEDB"
BUFFER_SIZE = 1 * MEGABYTE
REVERSE_BUFFER_SIZE = 16 * KILOBYTE
CACHE_SIZE = 1 * GIGABYTE
SCHEMA_KEY = 'schema'
CONTENT_KEY = 'content'
MIME_TYPE_KEY = 'mime-type'
MIME_TYPE_TEXT = 'text/plain'
CONTENT_TYPE_SPEECH = 'speech'
CONTENT_TYPE_TRANSCRIPT = 'transcript'
SCHEMA_KEY = "schema"
CONTENT_KEY = "content"
MIME_TYPE_KEY = "mime-type"
MIME_TYPE_TEXT = "text/plain"
CONTENT_TYPE_SPEECH = "speech"
CONTENT_TYPE_TRANSCRIPT = "transcript"
class LabeledSample(Sample):
"""In-memory labeled audio sample representing an utterance.
Derived from util.audio.Sample and used by sample collection readers and writers."""
def __init__(self, audio_type, raw_data, transcript, audio_format=None, sample_id=None):
def __init__(
self, audio_type, raw_data, transcript, audio_format=None, sample_id=None
):
"""
Parameters
----------
@ -55,7 +57,9 @@ class LabeledSample(Sample):
Tracking ID - should indicate sample's origin as precisely as possible.
It is typically assigned by collection readers.
"""
super().__init__(audio_type, raw_data, audio_format=audio_format, sample_id=sample_id)
super().__init__(
audio_type, raw_data, audio_format=audio_format, sample_id=sample_id
)
self.transcript = transcript
@ -65,13 +69,14 @@ class PackedSample:
have the child process do the loading/unpacking of the sample, allowing for parallel file
I/O.
"""
def __init__(self, filename, audio_type, label):
self.filename = filename
self.audio_type = audio_type
self.label = label
def unpack(self):
with open_remote(self.filename, 'rb') as audio_file:
with open_remote(self.filename, "rb") as audio_file:
data = audio_file.read()
if self.label is None:
s = Sample(self.audio_type, data, sample_id=self.filename)
@ -83,7 +88,7 @@ def unpack_maybe(sample):
"""
Loads the supplied sample from disk (or the network) if the audio isn't loaded in to memory already.
"""
if hasattr(sample, 'unpack'):
if hasattr(sample, "unpack"):
realized_sample = sample.unpack()
else:
realized_sample = sample
@ -117,13 +122,16 @@ def load_sample(filename, label=None):
class DirectSDBWriter:
"""Sample collection writer for creating a Sample DB (SDB) file"""
def __init__(self,
def __init__(
self,
sdb_filename,
buffering=BUFFER_SIZE,
audio_type=AUDIO_TYPE_OPUS,
bitrate=None,
id_prefix=None,
labeled=True):
labeled=True,
):
"""
Parameters
----------
@ -148,7 +156,7 @@ class DirectSDBWriter:
raise ValueError('Audio type "{}" not supported'.format(audio_type))
self.audio_type = audio_type
self.bitrate = bitrate
self.sdb_file = open_remote(sdb_filename, 'wb', buffering=buffering)
self.sdb_file = open_remote(sdb_filename, "wb", buffering=buffering)
self.offsets = []
self.num_samples = 0
@ -156,7 +164,9 @@ class DirectSDBWriter:
schema_entries = [{CONTENT_KEY: CONTENT_TYPE_SPEECH, MIME_TYPE_KEY: audio_type}]
if self.labeled:
schema_entries.append({CONTENT_KEY: CONTENT_TYPE_TRANSCRIPT, MIME_TYPE_KEY: MIME_TYPE_TEXT})
schema_entries.append(
{CONTENT_KEY: CONTENT_TYPE_TRANSCRIPT, MIME_TYPE_KEY: MIME_TYPE_TEXT}
)
meta_data = {SCHEMA_KEY: schema_entries}
meta_data = json.dumps(meta_data).encode()
self.write_big_int(len(meta_data))
@ -177,20 +187,23 @@ class DirectSDBWriter:
def add(self, sample):
def to_bytes(n):
return n.to_bytes(INT_SIZE, BIG_ENDIAN)
sample.change_audio_type(self.audio_type, bitrate=self.bitrate)
opus = sample.audio.getbuffer()
opus_len = to_bytes(len(opus))
if self.labeled:
transcript = sample.transcript.encode()
transcript_len = to_bytes(len(transcript))
entry_len = to_bytes(len(opus_len) + len(opus) + len(transcript_len) + len(transcript))
buffer = b''.join([entry_len, opus_len, opus, transcript_len, transcript])
entry_len = to_bytes(
len(opus_len) + len(opus) + len(transcript_len) + len(transcript)
)
buffer = b"".join([entry_len, opus_len, opus, transcript_len, transcript])
else:
entry_len = to_bytes(len(opus_len) + len(opus))
buffer = b''.join([entry_len, opus_len, opus])
buffer = b"".join([entry_len, opus_len, opus])
self.offsets.append(self.sdb_file.tell())
self.sdb_file.write(buffer)
sample.sample_id = '{}:{}'.format(self.id_prefix, self.num_samples)
sample.sample_id = "{}:{}".format(self.id_prefix, self.num_samples)
self.num_samples += 1
return sample.sample_id
@ -221,12 +234,15 @@ class DirectSDBWriter:
class SDB: # pylint: disable=too-many-instance-attributes
"""Sample collection reader for reading a Sample DB (SDB) file"""
def __init__(self,
def __init__(
self,
sdb_filename,
buffering=BUFFER_SIZE,
id_prefix=None,
labeled=True,
reverse=False):
reverse=False,
):
"""
Parameters
----------
@ -244,30 +260,36 @@ class SDB: # pylint: disable=too-many-instance-attributes
"""
self.sdb_filename = sdb_filename
self.id_prefix = sdb_filename if id_prefix is None else id_prefix
self.sdb_file = open_remote(sdb_filename, 'rb', buffering=REVERSE_BUFFER_SIZE if reverse else buffering)
self.sdb_file = open_remote(
sdb_filename, "rb", buffering=REVERSE_BUFFER_SIZE if reverse else buffering
)
self.offsets = []
if self.sdb_file.read(len(MAGIC)) != MAGIC:
raise RuntimeError('No Sample Database')
raise RuntimeError("No Sample Database")
meta_chunk_len = self.read_big_int()
self.meta = json.loads(self.sdb_file.read(meta_chunk_len).decode())
if SCHEMA_KEY not in self.meta:
raise RuntimeError('Missing schema')
raise RuntimeError("Missing schema")
self.schema = self.meta[SCHEMA_KEY]
speech_columns = self.find_columns(content=CONTENT_TYPE_SPEECH, mime_type=SERIALIZABLE_AUDIO_TYPES)
speech_columns = self.find_columns(
content=CONTENT_TYPE_SPEECH, mime_type=SERIALIZABLE_AUDIO_TYPES
)
if not speech_columns:
raise RuntimeError('No speech data (missing in schema)')
raise RuntimeError("No speech data (missing in schema)")
self.speech_index = speech_columns[0]
self.audio_type = self.schema[self.speech_index][MIME_TYPE_KEY]
self.transcript_index = None
if labeled is not False:
transcript_columns = self.find_columns(content=CONTENT_TYPE_TRANSCRIPT, mime_type=MIME_TYPE_TEXT)
transcript_columns = self.find_columns(
content=CONTENT_TYPE_TRANSCRIPT, mime_type=MIME_TYPE_TEXT
)
if transcript_columns:
self.transcript_index = transcript_columns[0]
else:
if labeled is True:
raise RuntimeError('No transcript data (missing in schema)')
raise RuntimeError("No transcript data (missing in schema)")
sample_chunk_len = self.read_big_int()
self.sdb_file.seek(sample_chunk_len + BIGINT_SIZE, 1)
@ -290,12 +312,16 @@ class SDB: # pylint: disable=too-many-instance-attributes
if mime_type is not None:
criteria.append((MIME_TYPE_KEY, mime_type))
if len(criteria) == 0:
raise ValueError('At least one of "content" or "mime-type" has to be provided')
raise ValueError(
'At least one of "content" or "mime-type" has to be provided'
)
matches = []
for index, column in enumerate(self.schema):
matched = 0
for field, value in criteria:
if column[field] == value or (isinstance(value, list) and column[field] in value):
if column[field] == value or (
isinstance(value, list) and column[field] in value
):
matched += 1
if matched == len(criteria):
matches.append(index)
@ -306,8 +332,11 @@ class SDB: # pylint: disable=too-many-instance-attributes
column_data = [None] * len(columns)
found = 0
if not 0 <= row_index < len(self.offsets):
raise ValueError('Wrong sample index: {} - has to be between 0 and {}'
.format(row_index, len(self.offsets) - 1))
raise ValueError(
"Wrong sample index: {} - has to be between 0 and {}".format(
row_index, len(self.offsets) - 1
)
)
self.sdb_file.seek(self.offsets[row_index] + INT_SIZE)
for index in range(len(self.schema)):
chunk_len = self.read_int()
@ -321,13 +350,17 @@ class SDB: # pylint: disable=too-many-instance-attributes
return tuple(column_data)
def __getitem__(self, i):
sample_id = '{}:{}'.format(self.id_prefix, i)
sample_id = "{}:{}".format(self.id_prefix, i)
if self.transcript_index is None:
[audio_data] = self.read_row(i, self.speech_index)
return Sample(self.audio_type, audio_data, sample_id=sample_id)
audio_data, transcript = self.read_row(i, self.speech_index, self.transcript_index)
audio_data, transcript = self.read_row(
i, self.speech_index, self.transcript_index
)
transcript = transcript.decode()
return LabeledSample(self.audio_type, audio_data, transcript, sample_id=sample_id)
return LabeledSample(
self.audio_type, audio_data, transcript, sample_id=sample_id
)
def __iter__(self):
for i in range(len(self.offsets)):
@ -346,10 +379,8 @@ class SDB: # pylint: disable=too-many-instance-attributes
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):
def __init__(self, csv_filename, absolute_paths=False, labeled=True):
"""
Parameters
----------
@ -372,11 +403,11 @@ class CSVWriter: # pylint: disable=too-many-instance-attributes
raise RuntimeError('"{}" already existing'.format(self.csv_dir))
os.mkdir(str(self.csv_dir))
self.absolute_paths = absolute_paths
fieldnames = ['wav_filename', 'wav_filesize']
fieldnames = ["wav_filename", "wav_filesize"]
self.labeled = labeled
if labeled:
fieldnames.append('transcript')
self.csv_file = open_remote(csv_filename, 'w', encoding='utf-8', newline='')
fieldnames.append("transcript")
self.csv_file = open_remote(csv_filename, "w", encoding="utf-8", newline="")
self.csv_writer = csv.DictWriter(self.csv_file, fieldnames=fieldnames)
self.csv_writer.writeheader()
self.counter = 0
@ -385,17 +416,19 @@ class CSVWriter: # pylint: disable=too-many-instance-attributes
return self
def add(self, sample):
sample_filename = self.csv_dir / 'sample{0:08d}.wav'.format(self.counter)
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
"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
row["transcript"] = sample.transcript
self.csv_writer.writerow(row)
return sample.sample_id
@ -412,11 +445,8 @@ class CSVWriter: # pylint: disable=too-many-instance-attributes
class TarWriter: # pylint: disable=too-many-instance-attributes
"""Sample collection writer for writing a CSV data-set and all its referenced WAV samples to a tar file."""
def __init__(self,
tar_filename,
gz=False,
labeled=True,
include=None):
def __init__(self, tar_filename, gz=False, labeled=True, include=None):
"""
Parameters
----------
@ -432,17 +462,19 @@ class TarWriter: # pylint: disable=too-many-instance-attributes
Currently only works with local files (not gs:// or hdfs://...)
"""
self.tar = tarfile.open(tar_filename, 'w:gz' if gz else 'w')
samples_dir = tarfile.TarInfo('samples')
self.tar = tarfile.open(tar_filename, "w:gz" if gz else "w")
samples_dir = tarfile.TarInfo("samples")
samples_dir.type = tarfile.DIRTYPE
self.tar.addfile(samples_dir)
if include:
for include_path in include:
self.tar.add(include_path, recursive=False, arcname=Path(include_path).name)
fieldnames = ['wav_filename', 'wav_filesize']
self.tar.add(
include_path, recursive=False, arcname=Path(include_path).name
)
fieldnames = ["wav_filename", "wav_filesize"]
self.labeled = labeled
if labeled:
fieldnames.append('transcript')
fieldnames.append("transcript")
self.csv_file = io.StringIO()
self.csv_writer = csv.DictWriter(self.csv_file, fieldnames=fieldnames)
self.csv_writer.writeheader()
@ -452,7 +484,7 @@ class TarWriter: # pylint: disable=too-many-instance-attributes
return self
def add(self, sample):
sample_filename = 'samples/sample{0:08d}.wav'.format(self.counter)
sample_filename = "samples/sample{0:08d}.wav".format(self.counter)
self.counter += 1
sample.change_audio_type(AUDIO_TYPE_PCM)
sample_file = io.BytesIO()
@ -462,21 +494,18 @@ class TarWriter: # pylint: disable=too-many-instance-attributes
sample_tar = tarfile.TarInfo(sample_filename)
sample_tar.size = sample_size
self.tar.addfile(sample_tar, sample_file)
row = {
'wav_filename': sample_filename,
'wav_filesize': sample_size
}
row = {"wav_filename": sample_filename, "wav_filesize": sample_size}
if self.labeled:
row['transcript'] = sample.transcript
row["transcript"] = sample.transcript
self.csv_writer.writerow(row)
return sample_filename
def close(self):
if self.csv_file and self.tar:
csv_tar = tarfile.TarInfo('samples.csv')
csv_tar = tarfile.TarInfo("samples.csv")
csv_tar.size = self.csv_file.tell()
self.csv_file.seek(0)
self.tar.addfile(csv_tar, io.BytesIO(self.csv_file.read().encode('utf8')))
self.tar.addfile(csv_tar, io.BytesIO(self.csv_file.read().encode("utf8")))
if self.tar:
self.tar.close()
@ -489,6 +518,7 @@ class TarWriter: # pylint: disable=too-many-instance-attributes
class SampleList:
"""Sample collection base class with samples loaded from a list of in-memory paths."""
def __init__(self, samples, labeled=True, reverse=False):
"""
Parameters
@ -507,7 +537,9 @@ class SampleList:
def __getitem__(self, i):
sample_spec = self.samples[i]
return load_sample(sample_spec[0], label=sample_spec[2] if self.labeled else None)
return load_sample(
sample_spec[0], label=sample_spec[2] if self.labeled else None
)
def __len__(self):
return len(self.samples)
@ -516,6 +548,7 @@ class SampleList:
class CSV(SampleList):
"""Sample collection reader for reading a Coqui STT CSV file
Automatically orders samples by CSV column wav_filesize (if available)."""
def __init__(self, csv_filename, labeled=None, reverse=False):
"""
Parameters
@ -531,30 +564,34 @@ class CSV(SampleList):
If the order of the samples should be reversed
"""
rows = []
with open_remote(csv_filename, 'r', encoding='utf8') as csv_file:
with open_remote(csv_filename, "r", encoding="utf8") as csv_file:
reader = csv.DictReader(csv_file)
if 'transcript' in reader.fieldnames:
if "transcript" in reader.fieldnames:
if labeled is None:
labeled = True
elif labeled:
raise RuntimeError('No transcript data (missing CSV column)')
raise RuntimeError("No transcript data (missing CSV column)")
for row in reader:
wav_filename = Path(row['wav_filename'])
if not wav_filename.is_absolute() and not is_remote_path(row['wav_filename']):
wav_filename = Path(row["wav_filename"])
if not wav_filename.is_absolute() and not is_remote_path(
row["wav_filename"]
):
wav_filename = Path(csv_filename).parent / wav_filename
wav_filename = str(wav_filename)
else:
# Pathlib otherwise removes a / from filenames like hdfs://
wav_filename = row['wav_filename']
wav_filesize = int(row['wav_filesize']) if 'wav_filesize' in row else 0
wav_filename = row["wav_filename"]
wav_filesize = int(row["wav_filesize"]) if "wav_filesize" in row else 0
if labeled:
rows.append((wav_filename, wav_filesize, row['transcript']))
rows.append((wav_filename, wav_filesize, row["transcript"]))
else:
rows.append((wav_filename, wav_filesize))
super(CSV, self).__init__(rows, labeled=labeled, reverse=reverse)
def samples_from_source(sample_source, buffering=BUFFER_SIZE, labeled=None, reverse=False):
def samples_from_source(
sample_source, buffering=BUFFER_SIZE, labeled=None, reverse=False
):
"""
Loads samples from a sample source file.
@ -577,14 +614,16 @@ def samples_from_source(sample_source, buffering=BUFFER_SIZE, labeled=None, reve
iterable of util.sample_collections.LabeledSample or util.audio.Sample instances supporting len.
"""
ext = os.path.splitext(sample_source)[1].lower()
if ext == '.sdb':
if ext == ".sdb":
return SDB(sample_source, buffering=buffering, labeled=labeled, reverse=reverse)
if ext == '.csv':
if ext == ".csv":
return CSV(sample_source, labeled=labeled, reverse=reverse)
raise ValueError('Unknown file type: "{}"'.format(ext))
def samples_from_sources(sample_sources, buffering=BUFFER_SIZE, labeled=None, reverse=False):
def samples_from_sources(
sample_sources, buffering=BUFFER_SIZE, labeled=None, reverse=False
):
"""
Loads and combines samples from a list of source files. Sources are combined in an interleaving way to
keep default sample order from shortest to longest.
@ -616,14 +655,22 @@ def samples_from_sources(sample_sources, buffering=BUFFER_SIZE, labeled=None, re
"""
sample_sources = list(sample_sources)
if len(sample_sources) == 0:
raise ValueError('No files')
raise ValueError("No files")
if len(sample_sources) == 1:
return samples_from_source(sample_sources[0], buffering=buffering, labeled=labeled, reverse=reverse)
return samples_from_source(
sample_sources[0], buffering=buffering, labeled=labeled, reverse=reverse
)
# If we wish to interleave based on duration, we have to unpack the audio. Note that this unpacking should
# be done lazily onn the fly so that it respects the LimitingPool logic used in the feeding code.
cols = [LenMap(
unpack_maybe, samples_from_source(source, buffering=buffering, labeled=labeled, reverse=reverse))
for source in sample_sources]
cols = [
LenMap(
unpack_maybe,
samples_from_source(
source, buffering=buffering, labeled=labeled, reverse=reverse
),
)
for source in sample_sources
]
return Interleaved(*cols, key=lambda s: s.duration, reverse=reverse)

9
training/coqui_stt_training/util/stm.py
View File

@ -1,10 +1,12 @@
import codecs
import unicodedata
class STMSegment(object):
r"""
Representation of an individual segment in an STM file.
"""
def __init__(self, stm_line):
tokens = stm_line.split()
self._filename = tokens[0]
@ -19,9 +21,11 @@ class STMSegment(object):
# We need to do the encode-decode dance here because encode
# returns a bytes() object on Python 3, and text_to_char_array
# expects a string.
self._transcript = unicodedata.normalize("NFKD", self._transcript.strip()) \
.encode("ascii", "ignore") \
self._transcript = (
unicodedata.normalize("NFKD", self._transcript.strip())
.encode("ascii", "ignore")
.decode("ascii", "ignore")
)
@property
def filename(self):
@ -51,6 +55,7 @@ class STMSegment(object):
def transcript(self):
return self._transcript
def parse_stm_file(stm_file):
r"""
Parses an STM file at ``stm_file`` into a list of :class:`STMSegment`.

24
training/coqui_stt_training/util/text.py
View File

@ -1,9 +1,11 @@
from __future__ import absolute_import, division, print_function
import numpy as np
import struct
def text_to_char_array(transcript, alphabet, context=''):
import numpy as np
def text_to_char_array(transcript, alphabet, context=""):
r"""
Given a transcript string, map characters to
integers and return a numpy array representing the processed string.
@ -13,15 +15,20 @@ def text_to_char_array(transcript, alphabet, context=''):
# Provide the row context (especially wav_filename) for alphabet errors
raise ValueError(
'Alphabet cannot encode transcript "{}" while processing sample "{}", '
'check that your alphabet contains all characters in the training corpus. '
'Missing characters are: {}.'
.format(transcript, context, list(ch for ch in transcript if not alphabet.CanEncodeSingle(ch))))
"check that your alphabet contains all characters in the training corpus. "
"Missing characters are: {}.".format(
transcript,
context,
list(ch for ch in transcript if not alphabet.CanEncodeSingle(ch)),
)
)
encoded = alphabet.Encode(transcript)
if len(encoded) == 0:
raise ValueError('While processing {}: Found an empty transcript! '
'You must include a transcript for all training data.'
.format(context))
raise ValueError(
"While processing {}: Found an empty transcript! "
"You must include a transcript for all training data.".format(context)
)
return encoded
@ -35,6 +42,7 @@ def text_to_char_array(transcript, alphabet, context=''):
# version 1.0. This software is distributed without any warranty. For more
# information, see <http://creativecommons.org/publicdomain/zero/1.0>
def levenshtein(a, b):
"Calculates the Levenshtein distance between a and b."
n, m = len(a), len(b)

191
transcribe.py
View File

@ -2,24 +2,32 @@
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import json
import os
import sys
import json
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import tensorflow as tf
import tensorflow.compat.v1.logging as tflogging
tflogging.set_verbosity(tflogging.ERROR)
import logging
logging.getLogger('sox').setLevel(logging.ERROR)
import glob
logging.getLogger("sox").setLevel(logging.ERROR)
import glob
from multiprocessing import Process, cpu_count
from coqui_stt_ctcdecoder import Scorer, ctc_beam_search_decoder_batch
from coqui_stt_training.util.audio import AudioFile
from coqui_stt_training.util.config import Config, initialize_globals
from coqui_stt_training.util.feeding import split_audio_file
from coqui_stt_training.util.flags import create_flags, FLAGS
from coqui_stt_training.util.logging import log_error, log_info, log_progress, create_progressbar
from coqui_stt_ctcdecoder import ctc_beam_search_decoder_batch, Scorer
from multiprocessing import Process, cpu_count
from coqui_stt_training.util.flags import FLAGS, create_flags
from coqui_stt_training.util.logging import (
create_progressbar,
log_error,
log_info,
log_progress,
)
def fail(message, code=1):
@ -28,8 +36,11 @@ def fail(message, code=1):
def transcribe_file(audio_path, tlog_path):
from coqui_stt_training.train import create_model # pylint: disable=cyclic-import,import-outside-toplevel
from coqui_stt_training.train import ( # pylint: disable=cyclic-import,import-outside-toplevel
create_model,
)
from coqui_stt_training.util.checkpoints import load_graph_for_evaluation
initialize_globals()
scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.scorer_path, Config.alphabet)
try:
@ -37,16 +48,23 @@ def transcribe_file(audio_path, tlog_path):
except NotImplementedError:
num_processes = 1
with AudioFile(audio_path, as_path=True) as wav_path:
data_set = split_audio_file(wav_path,
data_set = split_audio_file(
wav_path,
batch_size=FLAGS.batch_size,
aggressiveness=FLAGS.vad_aggressiveness,
outlier_duration_ms=FLAGS.outlier_duration_ms,
outlier_batch_size=FLAGS.outlier_batch_size)
iterator = tf.data.Iterator.from_structure(data_set.output_types, data_set.output_shapes,
output_classes=data_set.output_classes)
outlier_batch_size=FLAGS.outlier_batch_size,
)
iterator = tf.data.Iterator.from_structure(
data_set.output_types,
data_set.output_shapes,
output_classes=data_set.output_classes,
)
batch_time_start, batch_time_end, batch_x, batch_x_len = iterator.get_next()
no_dropout = [None] * 6
logits, _ = create_model(batch_x=batch_x, seq_length=batch_x_len, dropout=no_dropout)
logits, _ = create_model(
batch_x=batch_x, seq_length=batch_x_len, dropout=no_dropout
)
transposed = tf.nn.softmax(tf.transpose(logits, [1, 0, 2]))
tf.train.get_or_create_global_step()
with tf.Session(config=Config.session_config) as session:
@ -55,30 +73,43 @@ def transcribe_file(audio_path, tlog_path):
transcripts = []
while True:
try:
starts, ends, batch_logits, batch_lengths = \
session.run([batch_time_start, batch_time_end, transposed, batch_x_len])
starts, ends, batch_logits, batch_lengths = session.run(
[batch_time_start, batch_time_end, transposed, batch_x_len]
)
except tf.errors.OutOfRangeError:
break
decoded = ctc_beam_search_decoder_batch(batch_logits, batch_lengths, Config.alphabet, FLAGS.beam_width,
decoded = ctc_beam_search_decoder_batch(
batch_logits,
batch_lengths,
Config.alphabet,
FLAGS.beam_width,
num_processes=num_processes,
scorer=scorer)
scorer=scorer,
)
decoded = list(d[0][1] for d in decoded)
transcripts.extend(zip(starts, ends, decoded))
transcripts.sort(key=lambda t: t[0])
transcripts = [{'start': int(start),
'end': int(end),
'transcript': transcript} for start, end, transcript in transcripts]
with open(tlog_path, 'w') as tlog_file:
transcripts = [
{"start": int(start), "end": int(end), "transcript": transcript}
for start, end, transcript in transcripts
]
with open(tlog_path, "w") as tlog_file:
json.dump(transcripts, tlog_file, default=float)
def transcribe_many(src_paths, dst_paths):
pbar = create_progressbar(prefix='Transcribing files | ', max_value=len(src_paths)).start()
pbar = create_progressbar(
prefix="Transcribing files | ", max_value=len(src_paths)
).start()
for i in range(len(src_paths)):
p = Process(target=transcribe_file, args=(src_paths[i], dst_paths[i]))
p.start()
p.join()
log_progress('Transcribed file {} of {} from "{}" to "{}"'.format(i + 1, len(src_paths), src_paths[i], dst_paths[i]))
log_progress(
'Transcribed file {} of {} from "{}" to "{}"'.format(
i + 1, len(src_paths), src_paths[i], dst_paths[i]
)
)
pbar.update(i)
pbar.finish()
@ -99,70 +130,116 @@ def resolve(base_path, spec_path):
def main(_):
if not FLAGS.src or not os.path.exists(FLAGS.src):
# path not given or non-existant
fail('You have to specify which file or catalog to transcribe via the --src flag.')
fail(
"You have to specify which file or catalog to transcribe via the --src flag."
)
else:
# path given and exists
src_path = os.path.abspath(FLAGS.src)
if os.path.isfile(src_path):
if src_path.endswith('.catalog'):
if src_path.endswith(".catalog"):
# Transcribe batch of files via ".catalog" file (from DSAlign)
if FLAGS.dst:
fail('Parameter --dst not supported if --src points to a catalog')
fail("Parameter --dst not supported if --src points to a catalog")
catalog_dir = os.path.dirname(src_path)
with open(src_path, 'r') as catalog_file:
with open(src_path, "r") as catalog_file:
catalog_entries = json.load(catalog_file)
catalog_entries = [(resolve(catalog_dir, e['audio']), resolve(catalog_dir, e['tlog'])) for e in catalog_entries]
catalog_entries = [
(resolve(catalog_dir, e["audio"]), resolve(catalog_dir, e["tlog"]))
for e in catalog_entries
]
if any(map(lambda e: not os.path.isfile(e[0]), catalog_entries)):
fail('Missing source file(s) in catalog')
if not FLAGS.force and any(map(lambda e: os.path.isfile(e[1]), catalog_entries)):
fail('Destination file(s) from catalog already existing, use --force for overwriting')
if any(map(lambda e: not os.path.isdir(os.path.dirname(e[1])), catalog_entries)):
fail('Missing destination directory for at least one catalog entry')
fail("Missing source file(s) in catalog")
if not FLAGS.force and any(
map(lambda e: os.path.isfile(e[1]), catalog_entries)
):
fail(
"Destination file(s) from catalog already existing, use --force for overwriting"
)
if any(
map(
lambda e: not os.path.isdir(os.path.dirname(e[1])),
catalog_entries,
)
):
fail("Missing destination directory for at least one catalog entry")
src_paths, dst_paths = zip(*paths)
transcribe_many(src_paths, dst_paths)
else:
# Transcribe one file
dst_path = os.path.abspath(FLAGS.dst) if FLAGS.dst else os.path.splitext(src_path)[0] + '.tlog'
dst_path = (
os.path.abspath(FLAGS.dst)
if FLAGS.dst
else os.path.splitext(src_path)[0] + ".tlog"
)
if os.path.isfile(dst_path):
if FLAGS.force:
transcribe_one(src_path, dst_path)
else:
fail('Destination file "{}" already existing - use --force for overwriting'.format(dst_path), code=0)
fail(
'Destination file "{}" already existing - use --force for overwriting'.format(
dst_path
),
code=0,
)
elif os.path.isdir(os.path.dirname(dst_path)):
transcribe_one(src_path, dst_path)
else:
fail('Missing destination directory')
fail("Missing destination directory")
elif os.path.isdir(src_path):
# Transcribe all files in dir
print("Transcribing all WAV files in --src")
if FLAGS.dst:
fail('Destination file not supported for batch decoding jobs.')
fail("Destination file not supported for batch decoding jobs.")
else:
if not FLAGS.recursive:
print("If you wish to recursively scan --src, then you must use --recursive")
print(
"If you wish to recursively scan --src, then you must use --recursive"
)
wav_paths = glob.glob(src_path + "/*.wav")
else:
wav_paths = glob.glob(src_path + "/**/*.wav")
dst_paths = [path.replace('.wav','.tlog') for path in wav_paths]
dst_paths = [path.replace(".wav", ".tlog") for path in wav_paths]
transcribe_many(wav_paths, dst_paths)
if __name__ == '__main__':
if __name__ == "__main__":
create_flags()
tf.app.flags.DEFINE_string('src', '', 'Source path to an audio file or directory or catalog file.'
'Catalog files should be formatted from DSAlign. A directory will'
'be recursively searched for audio. If --dst not set, transcription logs (.tlog) will be '
'written in-place using the source filenames with '
'suffix ".tlog" instead of ".wav".')
tf.app.flags.DEFINE_string('dst', '', 'path for writing the transcription log or logs (.tlog). '
'If --src is a directory, this one also has to be a directory '
'and the required sub-dir tree of --src will get replicated.')
tf.app.flags.DEFINE_boolean('recursive', False, 'scan dir of audio recursively')
tf.app.flags.DEFINE_boolean('force', False, 'Forces re-transcribing and overwriting of already existing '
'transcription logs (.tlog)')
tf.app.flags.DEFINE_integer('vad_aggressiveness', 3, 'How aggressive (0=lowest, 3=highest) the VAD should '
'split audio')
tf.app.flags.DEFINE_integer('batch_size', 40, 'Default batch size')
tf.app.flags.DEFINE_float('outlier_duration_ms', 10000, 'Duration in ms after which samples are considered outliers')
tf.app.flags.DEFINE_integer('outlier_batch_size', 1, 'Batch size for duration outliers (defaults to 1)')
tf.app.flags.DEFINE_string(
"src",
"",
"Source path to an audio file or directory or catalog file."
"Catalog files should be formatted from DSAlign. A directory will"
"be recursively searched for audio. If --dst not set, transcription logs (.tlog) will be "
"written in-place using the source filenames with "
'suffix ".tlog" instead of ".wav".',
)
tf.app.flags.DEFINE_string(
"dst",
"",
"path for writing the transcription log or logs (.tlog). "
"If --src is a directory, this one also has to be a directory "
"and the required sub-dir tree of --src will get replicated.",
)
tf.app.flags.DEFINE_boolean("recursive", False, "scan dir of audio recursively")
tf.app.flags.DEFINE_boolean(
"force",
False,
"Forces re-transcribing and overwriting of already existing "
"transcription logs (.tlog)",
)
tf.app.flags.DEFINE_integer(
"vad_aggressiveness",
3,
"How aggressive (0=lowest, 3=highest) the VAD should " "split audio",
)
tf.app.flags.DEFINE_integer("batch_size", 40, "Default batch size")
tf.app.flags.DEFINE_float(
"outlier_duration_ms",
10000,
"Duration in ms after which samples are considered outliers",
)
tf.app.flags.DEFINE_integer(
"outlier_batch_size", 1, "Batch size for duration outliers (defaults to 1)"
)
tf.app.run(main)