def convert_chapter(path: pathlib.Path, sink: pathlib.Path, prefix: str,
transformer: sox.Transformer):
transcriptions = locate_transcriptions(path)
if not sink.is_dir():
os.makedirs(sink)
if transcriptions:
with open(str(transcriptions), "r") as transcriptions:
for line in transcriptions.readlines():
line = line.strip()
end_of_index = line.find(" ")
file_name = line[:end_of_index].strip()
file_name_with_extension = file_name + ".flac"
audio_input_file = path / file_name_with_extension
audio_output_file = sink / f"{prefix}-{file_name}.wav"
label_output_file = sink / f"{prefix}-{file_name}.lab"
label = line[end_of_index:].strip()
transformer.build_file(input_filepath=str(audio_input_file),
output_filepath=str(audio_output_file))
with open(str(label_output_file), "w") as label_file:
label_file.write(label)
def convert_cv(transformer: sox.Transformer, max_per_speaker: int,
tsv: pathlib.Path, clips: pathlib.Path, sink: pathlib.Path,
prefix: str):
meta = pd.read_csv(tsv, delimiter="\t")
for client, df in tqdm(meta.groupby(by="client_id")):
df = df.tail(max_per_speaker)
speaker_sink = sink / client
if not speaker_sink.is_dir():
os.makedirs(speaker_sink)
for audio, transcription in zip(df["path"], df["sentence"]):
try:
audio_without_stem = audio.split(".")[0]
input_audio_file = clips / audio
output_audio_file = speaker_sink / f"{prefix}-{audio_without_stem}.wav"
output_transcription_file = speaker_sink / f"{prefix}-{audio_without_stem}.lab"
with open(str(output_transcription_file), "w") as o:
transcription = normalize_transcription(transcription)
o.write(transcription)
transformer.build_file(input_filepath=str(input_audio_file),
output_filepath=str(output_audio_file))
except Exception as e:
print(
f"Failed to convert audio {audio} with sentence {transcription} reason: {str(e)}"
)
