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Add GigaSpeech 2 recipe #1365
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Add GigaSpeech 2 recipe #1365
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Thanks!! The recipe looks good to me, although I have one suggestion. If you could re-use the streaming manifest writing mechanism from GigaSpeech 1 recipe, it would allow users to prepare this dataset with minimal memory usage. As-is, it will take a lot of CPU memory to hold the entire manifest in memory before writing it to disk. See:
lhotse/lhotse/recipes/gigaspeech.py
Lines 96 to 129 in da4d70d
with RecordingSet.open_writer( | |
output_dir / f"gigaspeech_recordings_{part}.jsonl.gz" | |
) as rec_writer, SupervisionSet.open_writer( | |
output_dir / f"gigaspeech_supervisions_{part}.jsonl.gz" | |
) as sup_writer, CutSet.open_writer( | |
output_dir / f"gigaspeech_cuts_{part}.jsonl.gz" | |
) as cut_writer: | |
for recording, segments in tqdm( | |
parallel_map( | |
parse_utterance, | |
gigaspeech.audios("{" + part + "}"), | |
repeat(gigaspeech.gigaspeech_dataset_dir), | |
num_jobs=num_jobs, | |
), | |
desc="Processing GigaSpeech JSON entries", | |
): | |
# Fix and validate the recording + supervisions | |
recordings, segments = fix_manifests( | |
recordings=RecordingSet.from_recordings([recording]), | |
supervisions=SupervisionSet.from_segments(segments), | |
) | |
validate_recordings_and_supervisions( | |
recordings=recordings, supervisions=segments | |
) | |
# Create the cut since most users will need it anyway. | |
# There will be exactly one cut since there's exactly one recording. | |
cuts = CutSet.from_manifests( | |
recordings=recordings, supervisions=segments | |
) | |
# Write the manifests | |
rec_writer.write(recordings[0]) | |
for s in segments: | |
sup_writer.write(s) | |
cut_writer.write(cuts[0]) |
Sure, I will implement this later. |
This PR adds a recipe for GigaSpeech 2.
GigaSpeech 2 raw comprises about 30,000 hours of automatically transcribed speech across Thai, Indonesian, and Vietnamese. GigaSpeech 2 refined consists of 10,000 hours of Thai, 6,000 hours each for Indonesian and Vietnamese. GigaSpeech 2 test sets more realistically reflect speech recognition scenarios and mirror the real performance of an ASR system for low-resource languages.
For more details, please visit:
Dataset: https://huggingface.co/datasets/speechcolab/gigaspeech2
Preprint paper: https://arxiv.org/pdf/2406.11546