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96 lines
2.8 KiB
Python
Executable File
96 lines
2.8 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 Johns Hopkins University (Piotr Żelasko)
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# Copyright 2021 Xiaomi Corp. (Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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from pathlib import Path
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import torch
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from lhotse import CutSet, KaldifeatFbank, KaldifeatFbankConfig
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# Torch's multithreaded behavior needs to be disabled or
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# it wastes a lot of CPU and slow things down.
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# Do this outside of main() in case it needs to take effect
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# even when we are not invoking the main (e.g. when spawning subprocesses).
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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def compute_fbank_gigaspeech():
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in_out_dir = Path("data/fbank")
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# number of workers in dataloader
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num_workers = 20
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# number of seconds in a batch
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batch_duration = 1000
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subsets = (
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"DEV",
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"TEST",
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# "L",
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# "M",
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# "S",
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# "XS",
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)
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device = torch.device("cpu")
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if torch.cuda.is_available():
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device = torch.device("cuda", 0)
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extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device))
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logging.info(f"device: {device}")
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for partition in subsets:
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cuts_path = in_out_dir / f"gigaspeech_cuts_{partition}.jsonl.gz"
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if cuts_path.is_file():
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logging.info(f"{cuts_path} exists - skipping")
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continue
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raw_cuts_path = in_out_dir / f"gigaspeech_cuts_{partition}_raw.jsonl.gz"
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logging.info(f"Loading {raw_cuts_path}")
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cut_set = CutSet.from_file(raw_cuts_path)
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logging.info("Computing features")
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cut_set = cut_set.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=f"{in_out_dir}/gigaspeech_feats_{partition}",
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num_workers=num_workers,
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batch_duration=batch_duration,
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overwrite=True,
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)
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cut_set = cut_set.trim_to_supervisions(
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keep_overlapping=False, min_duration=None
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)
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logging.info(f"Saving to {cuts_path}")
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cut_set.to_file(cuts_path)
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logging.info(f"Saved to {cuts_path}")
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def main():
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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logging.basicConfig(format=formatter, level=logging.INFO)
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compute_fbank_gigaspeech()
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if __name__ == "__main__":
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main()
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