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69 lines
2.1 KiB
Python
Executable File
69 lines
2.1 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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This file computes fbank features of the librispeech dataset.
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Its looks for manifests in the directory data/manifests
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and generated fbank features are saved in data/fbank.
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"""
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import os
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from pathlib import Path
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from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer
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from lhotse.recipes.utils import read_manifests_if_cached
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from icefall.utils import get_executor
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def compute_fbank_librispeech():
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src_dir = Path("data/manifests")
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output_dir = Path("data/fbank")
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num_jobs = min(15, os.cpu_count())
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num_mel_bins = 80
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dataset_parts = (
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"dev-clean",
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"dev-other",
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"test-clean",
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"test-other",
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"train-clean-100",
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"train-clean-360",
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"train-other-500",
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)
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manifests = read_manifests_if_cached(
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dataset_parts=dataset_parts, output_dir=src_dir
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)
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assert manifests is not None
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extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
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with get_executor() as ex: # Initialize the executor only once.
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for partition, m in manifests.items():
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if (output_dir / f"cuts_{partition}.json.gz").is_file():
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print(f"{partition} already exists - skipping.")
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continue
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print("Processing", partition)
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cut_set = CutSet.from_manifests(
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recordings=m["recordings"],
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supervisions=m["supervisions"],
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)
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if "train" in partition:
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cut_set = (
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cut_set
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+ cut_set.perturb_speed(0.9)
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+ cut_set.perturb_speed(1.1)
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)
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cut_set = cut_set.compute_and_store_features(
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extractor=extractor,
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storage_path=f"{output_dir}/feats_{partition}",
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# when an executor is specified, make more partitions
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num_jobs=num_jobs if ex is None else 80,
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executor=ex,
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storage_type=LilcomHdf5Writer,
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)
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cut_set.to_json(output_dir / f"cuts_{partition}.json.gz")
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if __name__ == "__main__":
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compute_fbank_librispeech()
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