def _maybe_convert_set(extracted_dir, source_csv, target_csv):
print()
if os.path.exists(target_csv):
print('Found CSV file "%s" - not importing "%s".' % (target_csv, source_csv))
return
print('No CSV file "%s" - importing "%s"...' % (target_csv, source_csv))
samples = []
with open(source_csv) as source_csv_file:
reader = csv.DictReader(source_csv_file)
for row in reader:
samples.append((os.path.join(extracted_dir, row["filename"]), row["text"]))
# Mutable counters for the concurrent embedded routine
counter = get_counter()
num_samples = len(samples)
rows = []
print("Importing mp3 files...")
pool = Pool()
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
counter += processed[0]
rows += processed[1]
bar.update(i)
bar.update(num_samples)
pool.close()
pool.join()
print('Writing "%s"...' % target_csv)
with open(target_csv, "w", encoding="utf-8", newline="") as target_csv_file:
writer = csv.DictWriter(target_csv_file, fieldnames=FIELDNAMES)
writer.writeheader()
bar = progressbar.ProgressBar(max_value=len(rows), widgets=SIMPLE_BAR)
for filename, file_size, transcript in bar(rows):
writer.writerow(
{
"wav_filename": filename,
"wav_filesize": file_size,
"transcript": transcript,
}
)
imported_samples = get_imported_samples(counter)
assert counter["all"] == num_samples
assert len(rows) == imported_samples
print_import_report(counter, SAMPLE_RATE, MAX_SECS)
def one_sample(sample):
""" Take a audio file, and optionally convert it to 16kHz WAV """
orig_filename = sample["path"]
# Storing wav files next to the wav ones - just with a different suffix
wav_filename = os.path.splitext(orig_filename)[0] + ".converted.wav"
_maybe_convert_wav(orig_filename, wav_filename)
file_size = -1
frames = 0
if os.path.exists(wav_filename):
file_size = os.path.getsize(wav_filename)
frames = int(
subprocess.check_output(
["soxi", "-s", wav_filename], stderr=subprocess.STDOUT
)
)
label = sample["text"]
rows = []
# Keep track of how many samples are good vs. problematic
counter = get_counter()
if file_size == -1:
# Excluding samples that failed upon conversion
counter["failed"] += 1
elif label is None:
# Excluding samples that failed on label validation
counter["invalid_label"] += 1
elif int(frames / SAMPLE_RATE * 1000 / 10 / 2) < len(str(label)):
# Excluding samples that are too short to fit the transcript
counter["too_short"] += 1
elif frames / SAMPLE_RATE > MAX_SECS:
# Excluding very long samples to keep a reasonable batch-size
counter["too_long"] += 1
else:
# This one is good - keep it for the target CSV
rows.append((wav_filename, file_size, label))
counter["imported_time"] += frames
counter["all"] += 1
counter["total_time"] += frames
return (counter, rows)
def one_sample(sample):
""" Take a audio file, and optionally convert it to 16kHz WAV """
wav_filename = sample[0]
file_size = -1
frames = 0
if os.path.exists(wav_filename):
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
)
os.rename(tmp_filename, wav_filename)
file_size = os.path.getsize(wav_filename)
frames = int(
subprocess.check_output(
["soxi", "-s", wav_filename], stderr=subprocess.STDOUT
)
)
label = label_filter(sample[1])
counter = get_counter()
rows = []
if file_size == -1:
# Excluding samples that failed upon conversion
print("conversion failure", wav_filename)
counter["failed"] += 1
elif label is None:
# Excluding samples that failed on label validation
counter["invalid_label"] += 1
elif int(frames / SAMPLE_RATE * 1000 / 15 / 2) < len(str(label)):
# Excluding samples that are too short to fit the transcript
counter["too_short"] += 1
elif frames / SAMPLE_RATE > MAX_SECS:
# Excluding very long samples to keep a reasonable batch-size
counter["too_long"] += 1
else:
# This one is good - keep it for the target CSV
rows.append((wav_filename, file_size, label))
counter["imported_time"] += frames
counter["all"] += 1
counter["total_time"] += frames
return (counter, rows)
def one_sample(sample):
""" Take an audio file, and optionally convert it to 16kHz WAV """
mp3_filename = sample[0]
if not os.path.splitext(mp3_filename.lower())[1] == ".mp3":
mp3_filename += ".mp3"
# Storing wav files next to the mp3 ones - just with a different suffix
wav_filename = os.path.splitext(mp3_filename)[0] + ".wav"
_maybe_convert_wav(mp3_filename, wav_filename)
file_size = -1
frames = 0
if os.path.exists(wav_filename):
file_size = os.path.getsize(wav_filename)
frames = int(
subprocess.check_output(["soxi", "-s", wav_filename],
stderr=subprocess.STDOUT))
label = FILTER_OBJ.filter(sample[1])
rows = []
counter = get_counter()
if file_size == -1:
# Excluding samples that failed upon conversion
counter["failed"] += 1
elif label is None:
# Excluding samples that failed on label validation
counter["invalid_label"] += 1
elif int(frames / SAMPLE_RATE * 1000 / 10 / 2) < len(str(label)):
# Excluding samples that are too short to fit the transcript
counter["too_short"] += 1
elif frames / SAMPLE_RATE > MAX_SECS:
# Excluding very long samples to keep a reasonable batch-size
counter["too_long"] += 1
else:
# This one is good - keep it for the target CSV
rows.append(
(os.path.split(wav_filename)[-1], file_size, label, sample[2]))
counter["imported_time"] += frames
counter["all"] += 1
counter["total_time"] += frames
return (counter, rows)
def _maybe_convert_sets(target_dir, extracted_data):
extracted_dir = os.path.join(target_dir, extracted_data)
# override existing CSV with normalized one
target_csv_template = os.path.join(
target_dir, ARCHIVE_DIR_NAME + "_" + ARCHIVE_NAME.replace(".zip", "_{}.csv")
)
if os.path.isfile(target_csv_template):
return
ogg_root_dir = os.path.join(extracted_dir, ARCHIVE_NAME.replace(".zip", ""))
# Get audiofile path and transcript for each sentence in tsv
samples = []
glob_dir = os.path.join(ogg_root_dir, "**/*.ogg")
for record in glob(glob_dir, recursive=True):
record_file = record.replace(ogg_root_dir + os.path.sep, "")
if record_filter(record_file):
samples.append(
(
os.path.join(ogg_root_dir, record_file),
os.path.splitext(os.path.basename(record_file))[0],
)
)
counter = get_counter()
num_samples = len(samples)
rows = []
print("Importing ogg files...")
pool = Pool()
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
counter += processed[0]
rows += processed[1]
bar.update(i)
bar.update(num_samples)
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%
train_writer = csv.DictWriter(train_csv_file, fieldnames=FIELDNAMES)
train_writer.writeheader()
dev_writer = csv.DictWriter(dev_csv_file, fieldnames=FIELDNAMES)
dev_writer.writeheader()
test_writer = csv.DictWriter(test_csv_file, fieldnames=FIELDNAMES)
test_writer.writeheader()
for i, item in enumerate(rows):
transcript = validate_label(item[2])
if not transcript:
continue
wav_filename = os.path.join(
ogg_root_dir, item[0].replace(".ogg", ".wav")
)
i_mod = i % 10
if i_mod == 0:
writer = test_writer
elif i_mod == 1:
writer = dev_writer
else:
writer = train_writer
writer.writerow(
dict(
wav_filename=wav_filename,
wav_filesize=os.path.getsize(wav_filename),
transcript=transcript,
)
)
imported_samples = get_imported_samples(counter)
assert counter["all"] == num_samples
assert len(rows) == imported_samples
print_import_report(counter, SAMPLE_RATE, MAX_SECS)
def _maybe_convert_sets(target_dir, extracted_data, english_compatible=False):
extracted_dir = os.path.join(target_dir, extracted_data)
# override existing CSV with normalized one
target_csv_template = os.path.join(target_dir, "ts_" + ARCHIVE_NAME + "_{}.csv")
if os.path.isfile(target_csv_template):
return
path_to_original_csv = os.path.join(extracted_dir, "data.csv")
with open(path_to_original_csv) as csv_f:
data = [
d
for d in csv.DictReader(csv_f, delimiter=",")
if float(d["duration"]) <= MAX_SECS
]
for line in data:
line["path"] = os.path.join(extracted_dir, line["path"])
num_samples = len(data)
rows = []
counter = get_counter()
print("Importing {} wav files...".format(num_samples))
pool = Pool()
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, processed in enumerate(pool.imap_unordered(one_sample, data), start=1):
counter += processed[0]
rows += processed[1]
bar.update(i)
bar.update(num_samples)
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%
train_writer = csv.DictWriter(train_csv_file, fieldnames=FIELDNAMES)
train_writer.writeheader()
dev_writer = csv.DictWriter(dev_csv_file, fieldnames=FIELDNAMES)
dev_writer.writeheader()
test_writer = csv.DictWriter(test_csv_file, fieldnames=FIELDNAMES)
test_writer.writeheader()
for i, item in enumerate(rows):
transcript = validate_label(
cleanup_transcript(
item[2], english_compatible=english_compatible
)
)
if not transcript:
continue
wav_filename = os.path.join(target_dir, extracted_data, item[0])
i_mod = i % 10
if i_mod == 0:
writer = test_writer
elif i_mod == 1:
writer = dev_writer
else:
writer = train_writer
writer.writerow(
dict(
wav_filename=wav_filename,
wav_filesize=os.path.getsize(wav_filename),
transcript=transcript,
)
)
imported_samples = get_imported_samples(counter)
assert counter["all"] == num_samples
assert len(rows) == imported_samples
print_import_report(counter, SAMPLE_RATE, MAX_SECS)
def _maybe_convert_sets(target_dir, extracted_data):
extracted_dir = os.path.join(target_dir, extracted_data)
# override existing CSV with normalized one
target_csv_template = os.path.join(
target_dir, ARCHIVE_DIR_NAME, ARCHIVE_NAME.replace(".tgz", "_{}.csv")
)
if os.path.isfile(target_csv_template):
return
wav_root_dir = os.path.join(extracted_dir)
# Get audiofile path and transcript for each sentence in tsv
samples = []
glob_dir = os.path.join(wav_root_dir, "**/metadata.csv")
for record in glob(glob_dir, recursive=True):
if any(
map(lambda sk: sk in record, SKIP_LIST)
): # pylint: disable=cell-var-from-loop
continue
with open(record, "r") as rec:
for re in rec.readlines():
re = re.strip().split("|")
audio = os.path.join(os.path.dirname(record), "wavs", re[0] + ".wav")
transcript = re[2]
samples.append((audio, transcript))
counter = get_counter()
num_samples = len(samples)
rows = []
print("Importing WAV files...")
pool = Pool()
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
counter += processed[0]
rows += processed[1]
bar.update(i)
bar.update(num_samples)
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%
train_writer = csv.DictWriter(train_csv_file, fieldnames=FIELDNAMES)
train_writer.writeheader()
dev_writer = csv.DictWriter(dev_csv_file, fieldnames=FIELDNAMES)
dev_writer.writeheader()
test_writer = csv.DictWriter(test_csv_file, fieldnames=FIELDNAMES)
test_writer.writeheader()
for i, item in enumerate(rows):
transcript = validate_label(item[2])
if not transcript:
continue
wav_filename = item[0]
i_mod = i % 10
if i_mod == 0:
writer = test_writer
elif i_mod == 1:
writer = dev_writer
else:
writer = train_writer
writer.writerow(
dict(
wav_filename=os.path.relpath(wav_filename, extracted_dir),
wav_filesize=os.path.getsize(wav_filename),
transcript=transcript,
)
)
imported_samples = get_imported_samples(counter)
assert counter["all"] == num_samples
assert len(rows) == imported_samples
print_import_report(counter, SAMPLE_RATE, MAX_SECS)
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:
for sample in exclude:
exclude_transcripts.add(sample[2])
exclude_speakers.add(sample[3])
if rows is None:
rows = []
input_tsv = os.path.join(os.path.abspath(tsv_dir), dataset + ".tsv")
if not os.path.isfile(input_tsv):
return rows
print("Loading TSV file: ", input_tsv)
# Get audiofile path and transcript for each sentence in tsv
samples = []
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"]))
counter = get_counter()
num_samples = len(samples)
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):
counter += processed[0]
rows += processed[1]
bar.update(i)
bar.update(num_samples)
pool.close()
pool.join()
imported_samples = get_imported_samples(counter)
assert counter["all"] == num_samples
assert len(rows) == imported_samples
print_import_report(counter, SAMPLE_RATE, MAX_SECS)
output_csv = os.path.join(os.path.abspath(audio_dir), dataset + ".csv")
print("Saving new DeepSpeech-formatted CSV file to: ", output_csv)
with open(output_csv, "w", encoding="utf-8",
newline="") as output_csv_file:
print("Writing CSV file for DeepSpeech.py as: ", output_csv)
writer = csv.DictWriter(output_csv_file, fieldnames=FIELDNAMES)
writer.writeheader()
bar = progressbar.ProgressBar(max_value=len(rows), widgets=SIMPLE_BAR)
for filename, file_size, transcript, speaker in bar(rows):
if transcript in exclude_transcripts or speaker in exclude_speakers:
continue
if space_after_every_character:
writer.writerow({
"wav_filename": filename,
"wav_filesize": file_size,
"transcript": " ".join(transcript),
})
else:
writer.writerow({
"wav_filename": filename,
"wav_filesize": file_size,
"transcript": transcript,
})
return rows