mirror of
https://github.com/k2-fsa/icefall.git
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a4402b88e6
* merge upstream * add SURT model and training * add libricss decoding * add chunk width randomization * decode SURT with libricss * initial commit for zipformer_ctc * remove unwanted changes * remove changes to other recipe * fix zipformer softlink * fix for JIT export * add missing file * fix symbolic links * update results * clean commit for SURT recipe * training libricss surt model * remove unwanted files * remove unwanted changes * remove changes in librispeech * change some files to symlinks * remove unwanted changes in utils * add export script * add README * minor fix in README * add assets for README * replace some files with symlinks * remove unused decoding methods * fix symlink * address comments from @csukuangfj
86 lines
3.5 KiB
Python
Executable File
86 lines
3.5 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2022 Johns Hopkins University (authors: Desh Raj)
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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 adds source features as temporal arrays to the mixture manifests.
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It looks for manifests in the directory data/manifests.
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"""
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import logging
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from pathlib import Path
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import numpy as np
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from lhotse import CutSet, LilcomChunkyWriter, load_manifest, load_manifest_lazy
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from tqdm import tqdm
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def add_source_feats(num_jobs=1):
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src_dir = Path("data/manifests")
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output_dir = Path("data/fbank")
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for type_affix in ["full", "ov40"]:
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logging.info(f"Adding source features for {type_affix}")
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mixed_name_clean = f"train_clean_{type_affix}"
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mixed_name_rvb = f"train_rvb_{type_affix}"
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logging.info("Reading mixed cuts")
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mixed_cuts_clean = load_manifest_lazy(
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src_dir / f"cuts_{mixed_name_clean}.jsonl.gz"
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)
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mixed_cuts_rvb = load_manifest_lazy(src_dir / f"cuts_{mixed_name_rvb}.jsonl.gz")
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logging.info("Reading source cuts")
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source_cuts = load_manifest(src_dir / "librispeech_cuts_train_trimmed.jsonl.gz")
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logging.info("Adding source features to the mixed cuts")
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with tqdm() as pbar, CutSet.open_writer(
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src_dir / f"cuts_{mixed_name_clean}_sources.jsonl.gz"
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) as cut_writer_clean, CutSet.open_writer(
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src_dir / f"cuts_{mixed_name_rvb}_sources.jsonl.gz"
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) as cut_writer_rvb, LilcomChunkyWriter(
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output_dir / f"feats_train_{type_affix}_sources"
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) as source_feat_writer:
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for cut_clean, cut_rvb in zip(mixed_cuts_clean, mixed_cuts_rvb):
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assert cut_rvb.id == cut_clean.id + "_rvb"
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# Create source_feats and source_feat_offsets
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# (See `lhotse.datasets.K2SurtDataset` for details)
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source_feats = []
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source_feat_offsets = []
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cur_offset = 0
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for sup in sorted(
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cut_clean.supervisions, key=lambda s: (s.start, s.speaker)
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):
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source_cut = source_cuts[sup.id]
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source_feats.append(source_cut.load_features())
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source_feat_offsets.append(cur_offset)
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cur_offset += source_cut.num_frames
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cut_clean.source_feats = source_feat_writer.store_array(
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cut_clean.id, np.concatenate(source_feats, axis=0)
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)
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cut_clean.source_feat_offsets = source_feat_offsets
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cut_writer_clean.write(cut_clean)
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cut_rvb.source_feats = cut_clean.source_feats
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cut_rvb.source_feat_offsets = cut_clean.source_feat_offsets
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cut_writer_rvb.write(cut_rvb)
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pbar.update(1)
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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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add_source_feats()
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