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6f71981667
* mgb2 * mgb2 * adding pruned transducer stateless to mgb2 * update display_manifest_statistics.py * . * stateless transducer MGB-2 * Update README.md * Update RESULTS.md * Update prepare_lang_bpe.py * Update asr_datamodule.py * .nfs removed * Adding symlink * . * resolving conflicts * Update .gitignore * black formatting * Update compile_hlg.py * Update compute_fbank_musan.py * Update convert_transcript_words_to_tokens.py * Update download_lm.py * Update generate_unique_lexicon.py * adding simlinks * fixing symbolic links
102 lines
3.4 KiB
Python
Executable File
102 lines
3.4 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2022 Johns Hopkins University (Amir Hussein)
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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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"""
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This file computes fbank features of the MGB2 dataset.
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It looks for manifests in the directory data/manifests.
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The generated fbank features are saved in data/fbank.
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"""
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import logging
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import os
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from pathlib import Path
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import torch
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from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
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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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# 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_mgb2():
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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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"train",
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"test",
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"dev",
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)
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manifests = read_manifests_if_cached(
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prefix="mgb2", 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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assert len(manifests) == len(dataset_parts), (
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len(manifests),
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len(dataset_parts),
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list(manifests.keys()),
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dataset_parts,
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)
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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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logging.info(f"{partition} already exists - skipping.")
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continue
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logging.info(f"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 + cut_set.perturb_speed(0.9) + 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=LilcomChunkyWriter,
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)
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logging.info("About to split cuts into smaller chunks.")
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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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cut_set.to_file(output_dir / f"cuts_{partition}.jsonl.gz")
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if __name__ == "__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_mgb2()
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