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https://github.com/k2-fsa/icefall.git
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140 lines
4.3 KiB
Python
Executable File
140 lines
4.3 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# Modified 2023 The Chinese University of Hong Kong (author: 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 SwitchBoard dataset.
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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 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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from typing import Optional
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import sentencepiece as spm
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import torch
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from filter_cuts import filter_cuts
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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, str2bool
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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 get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--bpe-model",
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type=str,
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help="""Path to the bpe.model. If not None, we will remove short and
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long utterances before extracting features""",
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)
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parser.add_argument(
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"--dataset",
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type=str,
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help="""Dataset parts to compute fbank. If None, we will use all""",
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)
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parser.add_argument(
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"--perturb-speed",
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type=str2bool,
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default=False,
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help="""Perturb speed with factor 0.9 and 1.1 on train subset.""",
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)
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return parser.parse_args()
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def compute_fbank_switchboard(
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dir_name: str,
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bpe_model: Optional[str] = None,
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dataset: Optional[str] = None,
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perturb_speed: Optional[bool] = True,
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):
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src_dir = Path(f"data/manifests/{dir_name}")
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output_dir = Path(f"data/fbank_nb/{dir_name}")
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num_jobs = min(1, os.cpu_count())
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num_mel_bins = 80
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if bpe_model:
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logging.info(f"Loading {bpe_model}")
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sp = spm.SentencePieceProcessor()
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sp.load(bpe_model)
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if dataset is None:
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dataset_parts = ("all",)
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else:
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dataset_parts = dataset.split(" ", -1)
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prefix = dir_name
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suffix = "jsonl.gz"
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manifests = {
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"eval2000": "data/manifests/eval2000/eval2000_cuts_all_trimmed.jsonl.gz",
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}
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assert manifests is not None
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extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins, sampling_rate=8000))
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with get_executor() as ex: # Initialize the executor only once.
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partition = "all"
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cuts_filename = f"{prefix}_cuts_{partition}.{suffix}"
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print(cuts_filename)
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if (output_dir / cuts_filename).is_file():
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logging.info(f"{prefix} already exists - skipping.")
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return
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logging.info(f"Processing {prefix}")
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cut_set = CutSet.from_file(manifests[prefix])
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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 = cut_set.trim_to_supervisions(keep_overlapping=False)
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cut_set.to_file(output_dir / cuts_filename)
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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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logging.info(vars(args))
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compute_fbank_switchboard(
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dir_name="eval2000",
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bpe_model=args.bpe_model,
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dataset=args.dataset,
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perturb_speed=args.perturb_speed,
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)
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