mirror of
https://github.com/k2-fsa/icefall.git
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5fe58de43c
* initial commit * support download, data prep, and fbank * on-the-fly feature extraction by default * support BPE based lang * support HLG for BPE * small fix * small fix * chunked feature extraction by default * Compute features for GigaSpeech by splitting the manifest. * Fixes after review. * Split manifests into 2000 pieces. * set audio duration mismatch tolerance to 0.01 * small fix * add conformer training recipe * Add conformer.py without pre-commit checking * lazy loading and use SingleCutSampler * DynamicBucketingSampler * use KaldifeatFbank to compute fbank for musan * use pretrained language model and lexicon * use 3gram to decode, 4gram to rescore * Add decode.py * Update .flake8 * Delete compute_fbank_gigaspeech.py * Use BucketingSampler for valid and test dataloader * Update params in train.py * Use bpe_500 * update params in decode.py * Decrease num_paths while CUDA OOM * Added README * Update RESULTS * black * Decrease num_paths while CUDA OOM * Decode with post-processing * Update results * Remove lazy_load option * Use default `storage_type` * Keep the original tolerance * Use split-lazy * black * Update pretrained model Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
104 lines
2.8 KiB
Python
Executable File
104 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 (
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CutSet,
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KaldifeatFbank,
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KaldifeatFbankConfig,
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combine,
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)
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from lhotse.recipes.utils import read_manifests_if_cached
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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_musan():
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src_dir = Path("data/manifests")
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output_dir = Path("data/fbank")
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# number of workers in dataloader
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num_workers = 10
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# number of seconds in a batch
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batch_duration = 600
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dataset_parts = (
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"music",
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"speech",
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"noise",
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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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musan_cuts_path = output_dir / "cuts_musan.json.gz"
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if musan_cuts_path.is_file():
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logging.info(f"{musan_cuts_path} already exists - skipping")
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return
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logging.info("Extracting features for Musan")
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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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musan_cuts = (
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CutSet.from_manifests(
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recordings=combine(
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part["recordings"] for part in manifests.values()
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)
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)
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.cut_into_windows(10.0)
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.filter(lambda c: c.duration > 5)
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.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=f"{output_dir}/feats_musan",
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num_workers=num_workers,
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batch_duration=batch_duration,
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)
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)
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musan_cuts.to_json(musan_cuts_path)
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def main():
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formatter = (
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"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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)
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logging.basicConfig(format=formatter, level=logging.INFO)
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compute_fbank_musan()
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if __name__ == "__main__":
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main()
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