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egs/ami/ASR/local/compute_fbank_ami.py
Executable file
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egs/ami/ASR/local/compute_fbank_ami.py
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#!/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 computes fbank features of the AMI dataset.
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For the training data, we pool together IHM, reverberated IHM, and GSS-enhanced
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audios. For the test data, we separately prepare IHM, SDM, and GSS-enhanced
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parts (which are the 3 evaluation settings).
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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 math
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from pathlib import Path
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import torch
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import torch.multiprocessing
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from lhotse import CutSet, LilcomChunkyWriter
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from lhotse.features.kaldifeat import (
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KaldifeatFbank,
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KaldifeatFbankConfig,
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KaldifeatFrameOptions,
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KaldifeatMelOptions,
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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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torch.multiprocessing.set_sharing_strategy("file_system")
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def compute_fbank_ami():
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src_dir = Path("data/manifests")
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output_dir = Path("data/fbank")
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sampling_rate = 16000
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num_mel_bins = 80
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extractor = KaldifeatFbank(
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KaldifeatFbankConfig(
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frame_opts=KaldifeatFrameOptions(sampling_rate=sampling_rate),
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mel_opts=KaldifeatMelOptions(num_bins=num_mel_bins),
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device="cuda",
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)
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)
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logging.info("Reading manifests")
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manifests_ihm = read_manifests_if_cached(
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dataset_parts=["train", "dev", "test"],
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output_dir=src_dir,
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prefix="ami-ihm",
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suffix="jsonl.gz",
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)
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manifests_sdm = read_manifests_if_cached(
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dataset_parts=["train", "dev", "test"],
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output_dir=src_dir,
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prefix="ami-sdm",
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suffix="jsonl.gz",
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)
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# For GSS we already have cuts so we read them directly.
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manifests_gss = read_manifests_if_cached(
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dataset_parts=["train", "dev", "test"],
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output_dir=src_dir,
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prefix="ami-gss",
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suffix="jsonl.gz",
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)
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def _extract_feats(cuts: CutSet, storage_path: Path, manifest_path: Path) -> None:
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cuts = cuts + cuts.perturb_speed(0.9) + cuts.perturb_speed(1.1)
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_ = cuts.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=storage_path,
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manifest_path=manifest_path,
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batch_duration=5000,
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num_workers=8,
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storage_type=LilcomChunkyWriter,
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)
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logging.info(
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"Preparing training cuts: IHM + reverberated IHM + SDM + GSS (optional)"
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)
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logging.info("Processing train split IHM")
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cuts_ihm = (
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CutSet.from_manifests(**manifests_ihm["train"])
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.trim_to_supervisions(keep_overlapping=False, keep_all_channels=False)
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.modify_ids(lambda x: x + "-ihm")
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)
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_extract_feats(
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cuts_ihm,
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output_dir / "feats_train_ihm",
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src_dir / "cuts_train_ihm.jsonl.gz",
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)
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logging.info("Processing train split IHM + reverberated IHM")
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cuts_ihm_rvb = cuts_ihm.reverb_rir()
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_extract_feats(
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cuts_ihm_rvb,
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output_dir / "feats_train_ihm_rvb",
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src_dir / "cuts_train_ihm_rvb.jsonl.gz",
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)
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logging.info("Processing train split SDM")
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cuts_sdm = (
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CutSet.from_manifests(**manifests_sdm["train"])
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.trim_to_supervisions(keep_overlapping=False)
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.modify_ids(lambda x: x + "-sdm")
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)
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_extract_feats(
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cuts_sdm,
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output_dir / "feats_train_sdm",
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src_dir / "cuts_train_sdm.jsonl.gz",
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)
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logging.info("Processing train split GSS")
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cuts_gss = (
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CutSet.from_manifests(**manifests_gss["train"])
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.trim_to_supervisions(keep_overlapping=False)
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.modify_ids(lambda x: x + "-gss")
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)
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_extract_feats(
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cuts_gss,
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output_dir / "feats_train_gss",
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src_dir / "cuts_train_gss.jsonl.gz",
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)
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logging.info("Preparing test cuts: IHM, SDM, GSS (optional)")
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for split in ["dev", "test"]:
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logging.info(f"Processing {split} IHM")
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cuts_ihm = (
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CutSet.from_manifests(**manifests_ihm[split])
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.trim_to_supervisions(keep_overlapping=False, keep_all_channels=False)
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.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=output_dir / f"feats_{split}_ihm",
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manifest_path=src_dir / f"cuts_{split}_ihm.jsonl.gz",
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batch_duration=5000,
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num_workers=8,
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storage_type=LilcomChunkyWriter,
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)
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)
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logging.info(f"Processing {split} SDM")
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cuts_sdm = (
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CutSet.from_manifests(**manifests_sdm[split])
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.trim_to_supervisions(keep_overlapping=False)
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.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=output_dir / f"feats_{split}_sdm",
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manifest_path=src_dir / f"cuts_{split}_sdm.jsonl.gz",
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batch_duration=500,
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num_workers=4,
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storage_type=LilcomChunkyWriter,
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)
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)
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logging.info(f"Processing {split} GSS")
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cuts_gss = (
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CutSet.from_manifests(**manifests_gss[split])
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.trim_to_supervisions(keep_overlapping=False)
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.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=output_dir / f"feats_{split}_gss",
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manifest_path=src_dir / f"cuts_{split}_gss.jsonl.gz",
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batch_duration=500,
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num_workers=4,
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storage_type=LilcomChunkyWriter,
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)
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)
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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_ami()
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144
egs/ami/ASR/prepare.sh
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144
egs/ami/ASR/prepare.sh
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#!/usr/bin/env bash
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set -eou pipefail
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stage=-1
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stop_stage=100
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use_gss=true # Use GSS-based enhancement with MDM setting
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# We assume dl_dir (download dir) contains the following
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# directories and files. If not, they will be downloaded
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# by this script automatically.
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#
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# - $dl_dir/amicorpus
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# You can find audio and transcripts in this path.
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#
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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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# http://www.openslr.org/17/
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#
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# - music
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# - noise
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# - speech
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#
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# - $dl_dir/{LDC2004S13,LDC2005S13,LDC2004T19,LDC2005T19}
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# These contain the Fisher English audio and transcripts. We will
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# only use the transcripts as extra LM training data (similar to Kaldi).
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#
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dl_dir=$PWD/download
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. shared/parse_options.sh || exit 1
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# All files generated by this script are saved in "data".
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# You can safely remove "data" and rerun this script to regenerate it.
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mkdir -p data
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vocab_size=500
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log() {
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# This function is from espnet
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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log "dl_dir: $dl_dir"
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download data"
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# If you have pre-downloaded it to /path/to/amicorpus,
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# you can create a symlink
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#
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# ln -sfv /path/to/amicorpus $dl_dir/amicorpus
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#
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if [ ! -d $dl_dir/amicorpus ]; then
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lhotse download ami --mic ihm $dl_dir/amicorpus
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lhotse download ami --mic mdm $dl_dir/amicorpus
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fi
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# If you have pre-downloaded it to /path/to/musan,
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# you can create a symlink
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#
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# ln -sfv /path/to/musan $dl_dir/
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#
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if [ ! -d $dl_dir/musan ]; then
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lhotse download musan $dl_dir
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fi
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fi
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare AMI manifests"
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# We assume that you have downloaded the AMI corpus
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# to $dl_dir/amicorpus. We perform text normalization for the transcripts.
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mkdir -p data/manifests
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for mic in ihm sdm mdm; do
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lhotse prepare ami --mic $mic --partition full-corpus-asr --normalize-text kaldi \
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--max-words-per-segment 30 $dl_dir/amicorpus data/manifests/
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done
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fi
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: Prepare musan manifest"
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# We assume that you have downloaded the musan corpus
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# to $dl_dir/musan
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mkdir -p data/manifests
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lhotse prepare musan $dl_dir/musan data/manifests
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ] && [ $use_gss = true ]; then
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log "Stage 3: Apply GSS enhancement on MDM data (this stage requires a GPU)"
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# We assume that you have installed the GSS package: https://github.com/desh2608/gss
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local/prepare_ami_gss.sh data/manifests exp/ami_gss
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fi
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if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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log "Stage 4: Compute fbank features for AMI"
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mkdir -p data/fbank
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python local/compute_fbank_ami.py
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log "Combine features from train splits"
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lhotse combine data/manifests/cuts_train_{ihm,ihm_rvb,sdm,gss}.jsonl.gz - | shuf |\
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gzip -c > data/manifests/cuts_train_all.jsonl.gz
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fi
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if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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log "Stage 5: Compute fbank features for musan"
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mkdir -p data/fbank
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python local/compute_fbank_musan.py
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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Dump transcripts for BPE model training."
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mkdir -p data/lm
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cat <(gunzip -c data/manifests/ami-sdm_supervisions_train.jsonl.gz | jq '.text' | sed 's:"::g')> data/lm/transcript_words.txt
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fi
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Prepare BPE based lang"
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lang_dir=data/lang_bpe_${vocab_size}
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mkdir -p $lang_dir
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# Add special words to words.txt
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echo "<eps> 0" > $lang_dir/words.txt
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echo "!SIL 1" >> $lang_dir/words.txt
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echo "<UNK> 2" >> $lang_dir/words.txt
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# Add regular words to words.txt
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cat data/lm/transcript_words.txt | grep -o -E '\w+' | sort -u | awk '{print $0,NR+2}' >> $lang_dir/words.txt
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# Add remaining special word symbols expected by LM scripts.
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num_words=$(cat $lang_dir/words.txt | wc -l)
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echo "<s> ${num_words}" >> $lang_dir/words.txt
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num_words=$(cat $lang_dir/words.txt | wc -l)
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echo "</s> ${num_words}" >> $lang_dir/words.txt
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num_words=$(cat $lang_dir/words.txt | wc -l)
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echo "#0 ${num_words}" >> $lang_dir/words.txt
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./local/train_bpe_model.py \
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--lang-dir $lang_dir \
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--vocab-size $vocab_size \
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--transcript data/lm/transcript_words.txt
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if [ ! -f $lang_dir/L_disambig.pt ]; then
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./local/prepare_lang_bpe.py --lang-dir $lang_dir
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fi
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fi
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