CSJ Data Preparation (#617)

* workspace setup

* csj prepare done

* Change compute_fbank_musan.py t soft link

* add description

* change lhotse prepare csj command

* split train-dev here

* Add header

* remove debug

* save manifest_statistics

* generate transcript in Lhotse

* update comments in config file

egs/csj/ASR/.gitignore

@@ -0,0 +1,7 @@
+librispeech_*.*
+todelete*
+lang*
+notify_tg.py
+finetune_*
+misc.ini
+.vscode/*

egs/csj/ASR/local/compute_fbank_csj.py

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+#!/usr/bin/env python3
+# Copyright    2022  The University of Electro-Communications  (Author: Teo Wen Shen)  # noqa
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import logging
+import os
+from itertools import islice
+from pathlib import Path
+from random import Random
+from typing import List, Tuple
+
+import torch
+from lhotse import (
+    CutSet,
+    Fbank,
+    FbankConfig,
+    # fmt: off
+    # See the following for why LilcomChunkyWriter is preferred
+    # https://github.com/k2-fsa/icefall/pull/404
+    # https://github.com/lhotse-speech/lhotse/pull/527
+    # fmt: on
+    LilcomChunkyWriter,
+    RecordingSet,
+    SupervisionSet,
+)
+
+ARGPARSE_DESCRIPTION = """
+This script follows the espnet method of splitting the remaining core+noncore
+utterances into valid and train cutsets at an index which is by default 4000.
+
+In other words, the core+noncore utterances are shuffled, where 4000 utterances
+of the shuffled set go to the `valid` cutset and are not subject to speed
+perturbation. The remaining utterances become the `train` cutset and are speed-
+perturbed (0.9x, 1.0x, 1.1x).
+
+"""
+
+# Torch's multithreaded behavior needs to be disabled or
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+RNG_SEED = 42
+
+
+def make_cutset_blueprints(
+    manifest_dir: Path,
+    split: int,
+) -> List[Tuple[str, CutSet]]:
+
+    cut_sets = []
+    # Create eval datasets
+    logging.info("Creating eval cuts.")
+    for i in range(1, 4):
+        cut_set = CutSet.from_manifests(
+            recordings=RecordingSet.from_file(
+                manifest_dir / f"csj_recordings_eval{i}.jsonl.gz"
+            ),
+            supervisions=SupervisionSet.from_file(
+                manifest_dir / f"csj_supervisions_eval{i}.jsonl.gz"
+            ),
+        )
+        cut_set = cut_set.trim_to_supervisions(keep_overlapping=False)
+        cut_sets.append((f"eval{i}", cut_set))
+
+    # Create train and valid cuts
+    logging.info(
+        "Loading, trimming, and shuffling the remaining core+noncore cuts."
+    )
+    recording_set = RecordingSet.from_file(
+        manifest_dir / "csj_recordings_core.jsonl.gz"
+    ) + RecordingSet.from_file(manifest_dir / "csj_recordings_noncore.jsonl.gz")
+    supervision_set = SupervisionSet.from_file(
+        manifest_dir / "csj_supervisions_core.jsonl.gz"
+    ) + SupervisionSet.from_file(
+        manifest_dir / "csj_supervisions_noncore.jsonl.gz"
+    )
+
+    cut_set = CutSet.from_manifests(
+        recordings=recording_set,
+        supervisions=supervision_set,
+    )
+    cut_set = cut_set.trim_to_supervisions(keep_overlapping=False)
+    cut_set = cut_set.shuffle(Random(RNG_SEED))
+
+    logging.info(
+        "Creating valid and train cuts from core and noncore,"
+        f"split at {split}."
+    )
+    valid_set = CutSet.from_cuts(islice(cut_set, 0, split))
+
+    train_set = CutSet.from_cuts(islice(cut_set, split, None))
+    train_set = (
+        train_set + train_set.perturb_speed(0.9) + train_set.perturb_speed(1.1)
+    )
+
+    cut_sets.extend([("valid", valid_set), ("train", train_set)])
+
+    return cut_sets
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        description=ARGPARSE_DESCRIPTION,
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser.add_argument(
+        "--manifest-dir", type=Path, help="Path to save manifests"
+    )
+    parser.add_argument(
+        "--fbank-dir", type=Path, help="Path to save fbank features"
+    )
+    parser.add_argument(
+        "--split", type=int, default=4000, help="Split at this index"
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+
+    extractor = Fbank(FbankConfig(num_mel_bins=80))
+    num_jobs = min(16, os.cpu_count())
+
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    if (args.fbank_dir / ".done").exists():
+        logging.info(
+            "Previous fbank computed for CSJ found. "
+            f"Delete {args.fbank_dir / '.done'} to allow recomputing fbank."
+        )
+        return
+    else:
+        cut_sets = make_cutset_blueprints(args.manifest_dir, args.split)
+        for part, cut_set in cut_sets:
+            logging.info(f"Processing {part}")
+            cut_set = cut_set.compute_and_store_features(
+                extractor=extractor,
+                num_jobs=num_jobs,
+                storage_path=(args.fbank_dir / f"feats_{part}").as_posix(),
+                storage_type=LilcomChunkyWriter,
+            )
+            cut_set.to_file(args.manifest_dir / f"csj_cuts_{part}.jsonl.gz")
+
+        logging.info("All fbank computed for CSJ.")
+        (args.fbank_dir / ".done").touch()
+
+
+if __name__ == "__main__":
+    main()

egs/csj/ASR/local/compute_fbank_musan.py

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+../../../librispeech/ASR/local/compute_fbank_musan.py