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https://github.com/k2-fsa/icefall.git
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50d2281524
* Add modified transducer for aishell. * Minor fixes. * Add extra data in transducer training. The extra data is from http://www.openslr.org/62/ * Update export.py and pretrained.py * Update CI to install pretrained models with aishell. * Update results. * Update results. * Update README. * Use symlinks to avoid copies.
73 lines
2.1 KiB
Python
Executable File
73 lines
2.1 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2022 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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from lhotse import CutSet
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from lhotse.recipes.utils import read_manifests_if_cached
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def preprocess_aidatatang_200zh():
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src_dir = Path("data/manifests/aidatatang_200zh")
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output_dir = Path("data/fbank/aidatatang_200zh")
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output_dir.mkdir(exist_ok=True, parents=True)
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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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logging.info("Loading manifest")
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manifests = read_manifests_if_cached(
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dataset_parts=dataset_parts,
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output_dir=src_dir,
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)
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assert len(manifests) > 0
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for partition, m in manifests.items():
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logging.info(f"Processing {partition}")
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raw_cuts_path = output_dir / f"cuts_{partition}_raw.jsonl.gz"
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if raw_cuts_path.is_file():
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logging.info(f"{partition} already exists - skipping")
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continue
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for sup in m["supervisions"]:
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sup.custom = {"origin": "aidatatang_200zh"}
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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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logging.info(f"Saving to {raw_cuts_path}")
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cut_set.to_file(raw_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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preprocess_aidatatang_200zh()
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if __name__ == "__main__":
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main()
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