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https://github.com/k2-fsa/icefall.git
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added support for stcmds and primewords
This commit is contained in:
zr_jin
parent
0098d5eeb6
commit
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114
egs/multi_zh-hans/ASR/local/compute_fbank_primewords.py
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114
egs/multi_zh-hans/ASR/local/compute_fbank_primewords.py
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@ -0,0 +1,114 @@
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#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang
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# Zengrui Jin)
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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 Primewords dataset.
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It looks for manifests in the directory data/manifests/primewords.
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The generated fbank features are saved in data/fbank.
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"""
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import argparse
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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_thchs30(num_mel_bins: int = 80):
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src_dir = Path("data/manifests/primewords")
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output_dir = Path("data/fbank")
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num_jobs = min(15, os.cpu_count())
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dataset_parts = ("train",)
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prefix = "primewords"
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suffix = "jsonl.gz"
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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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prefix=prefix,
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suffix=suffix,
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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"{prefix}_cuts_{partition}.{suffix}").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}/{prefix}_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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cut_set.to_file(output_dir / f"{prefix}_cuts_{partition}.{suffix}")
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--num-mel-bins",
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type=int,
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default=80,
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help="""The number of mel bins for Fbank""",
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)
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return parser.parse_args()
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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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args = get_args()
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compute_fbank_thchs30(num_mel_bins=args.num_mel_bins)
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118
egs/multi_zh-hans/ASR/local/compute_fbank_stcmds.py
Normal file
118
egs/multi_zh-hans/ASR/local/compute_fbank_stcmds.py
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@ -0,0 +1,118 @@
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#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang
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# Zengrui Jin)
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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 ST-CMDS dataset.
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It looks for manifests in the directory data/manifests/stcmds.
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The generated fbank features are saved in data/fbank.
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"""
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import argparse
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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_thchs30(num_mel_bins: int = 80):
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src_dir = Path("data/manifests/stcmds")
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output_dir = Path("data/fbank")
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num_jobs = min(15, os.cpu_count())
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dataset_parts = (
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"train",
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"dev",
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"test",
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)
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prefix = "stcmds"
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suffix = "jsonl.gz"
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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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prefix=prefix,
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suffix=suffix,
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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"{prefix}_cuts_{partition}.{suffix}").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}/{prefix}_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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cut_set.to_file(output_dir / f"{prefix}_cuts_{partition}.{suffix}")
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--num-mel-bins",
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type=int,
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default=80,
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help="""The number of mel bins for Fbank""",
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)
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return parser.parse_args()
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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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args = get_args()
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compute_fbank_thchs30(num_mel_bins=args.num_mel_bins)
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@ -101,14 +101,76 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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if [ -e ../../aishell/ASR/data/fbank/.aishell2.done ]; then
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cd data/fbank
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ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell2_feats_train) .
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ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell_feats_dev) .
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ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell_feats_test) .
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ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell_cuts_train.jsonl.gz) .
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ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell_cuts_dev.jsonl.gz) .
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ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell_cuts_test.jsonl.gz) .
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ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell2_feats_dev) .
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ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell2_feats_test) .
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ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell2_cuts_train.jsonl.gz) .
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ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell2_cuts_dev.jsonl.gz) .
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ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell2_cuts_test.jsonl.gz) .
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cd ../..
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else
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log "Abort! Please run ../../aishell2/ASR/prepare.sh --stage 3 --stop-stage 3"
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exit 1
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fi
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fi
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log "Dataset: AISHELL-4"
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if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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log "Stage 5: Prepare AISHELL-4"
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if [ -e ../../aishell/ASR/data/fbank/.aishell4.done ]; then
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cd data/fbank
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ln -svf $(realpath ../../../../aishell4/ASR/data/fbank/aishell4_feats_train) .
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ln -svf $(realpath ../../../../aishell4/ASR/data/fbank/aishell4_feats_dev) .
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ln -svf $(realpath ../../../../aishell4/ASR/data/fbank/aishell4_feats_test) .
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ln -svf $(realpath ../../../../aishell4/ASR/data/fbank/aishell4_cuts_train.jsonl.gz) .
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ln -svf $(realpath ../../../../aishell4/ASR/data/fbank/aishell4_cuts_dev.jsonl.gz) .
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ln -svf $(realpath ../../../../aishell4/ASR/data/fbank/aishell4_cuts_test.jsonl.gz) .
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cd ../..
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else
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log "Abort! Please run ../../aishell4/ASR/prepare.sh --stage 3 --stop-stage 3"
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exit 1
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fi
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fi
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log "Dataset: ST-CMDS"
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Prepare ST-CMDS"
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if [ ! -f $dl_dir/stcmds/ST-CMDS-20170001_1-OS.tar.gz ]; then
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log "Downloading ST-CMDS"
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lhotse download stcmds $dl_dir/stcmds
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fi
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if [ ! -f data/manifests/.stcmds.done ]; then
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mkdir -p data/manifests
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lhotse prepare stcmds $dl_dir/stcmds data/manifests/stcmds
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touch data/manifests/.stcmds.done
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fi
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if [ ! -f data/fbank/.stcmds.done ]; then
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mkdir -p data/fbank
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./local/compute_fbank_stcmds.py
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touch data/fbank/.stcmds.done
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fi
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fi
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log "Dataset: Primewords"
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 6: Prepare Primewords"
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if [ ! -f $dl_dir/primewords/primewords_md_2018_set1.tar.gz ]; then
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log "Downloading Primewords"
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lhotse download primewords $dl_dir/primewords
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fi
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if [ ! -f data/manifests/.stcmds.done ]; then
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mkdir -p data/manifests
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lhotse prepare stcmds $dl_dir/primewords data/manifests/primewords
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touch data/manifests/.primewords.done
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fi
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if [ ! -f data/fbank/.primewords.done ]; then
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mkdir -p data/fbank
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./local/compute_fbank_primewords.py
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touch data/fbank/.primewords.done
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fi
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fi
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