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Remove duplicate files. (#236)
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Fangjun Kuang
GitHub
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commit
2f0fbf430c
@ -1,4 +1,4 @@
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Confromer CTC
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Conformer CTC
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=============
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This tutorial shows you how to run a conformer ctc model
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@ -1,156 +0,0 @@
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#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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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 script takes as input lang_dir and generates HLG from
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- H, the ctc topology, built from tokens contained in lang_dir/lexicon.txt
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- L, the lexicon, built from lang_dir/L_disambig.pt
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Caution: We use a lexicon that contains disambiguation symbols
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- G, the LM, built from data/lm/G_3_gram.fst.txt
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The generated HLG is saved in $lang_dir/HLG.pt
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"""
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import argparse
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import logging
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from pathlib import Path
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import k2
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import torch
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from icefall.lexicon import Lexicon
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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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"--lang-dir",
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type=str,
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help="""Input and output directory.
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""",
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)
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return parser.parse_args()
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def compile_HLG(lang_dir: str) -> k2.Fsa:
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"""
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Args:
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lang_dir:
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The language directory, e.g., data/lang_phone or data/lang_bpe_5000.
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Return:
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An FSA representing HLG.
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"""
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lexicon = Lexicon(lang_dir)
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max_token_id = max(lexicon.tokens)
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logging.info(f"Building ctc_topo. max_token_id: {max_token_id}")
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H = k2.ctc_topo(max_token_id)
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L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
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if Path("data/lm/G_3_gram.pt").is_file():
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logging.info("Loading pre-compiled G_3_gram")
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d = torch.load("data/lm/G_3_gram.pt")
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G = k2.Fsa.from_dict(d)
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else:
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logging.info("Loading G_3_gram.fst.txt")
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with open("data/lm/G_3_gram.fst.txt") as f:
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G = k2.Fsa.from_openfst(f.read(), acceptor=False)
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torch.save(G.as_dict(), "data/lm/G_3_gram.pt")
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first_token_disambig_id = lexicon.token_table["#0"]
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first_word_disambig_id = lexicon.word_table["#0"]
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L = k2.arc_sort(L)
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G = k2.arc_sort(G)
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logging.info("Intersecting L and G")
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LG = k2.compose(L, G)
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logging.info(f"LG shape: {LG.shape}")
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logging.info("Connecting LG")
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LG = k2.connect(LG)
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logging.info(f"LG shape after k2.connect: {LG.shape}")
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logging.info(type(LG.aux_labels))
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logging.info("Determinizing LG")
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LG = k2.determinize(LG)
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logging.info(type(LG.aux_labels))
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logging.info("Connecting LG after k2.determinize")
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LG = k2.connect(LG)
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logging.info("Removing disambiguation symbols on LG")
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LG.labels[LG.labels >= first_token_disambig_id] = 0
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assert isinstance(LG.aux_labels, k2.RaggedTensor)
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LG.aux_labels.values[LG.aux_labels.values >= first_word_disambig_id] = 0
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LG = k2.remove_epsilon(LG)
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logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}")
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LG = k2.connect(LG)
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LG.aux_labels = LG.aux_labels.remove_values_eq(0)
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logging.info("Arc sorting LG")
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LG = k2.arc_sort(LG)
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logging.info("Composing H and LG")
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# CAUTION: The name of the inner_labels is fixed
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# to `tokens`. If you want to change it, please
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# also change other places in icefall that are using
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# it.
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HLG = k2.compose(H, LG, inner_labels="tokens")
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logging.info("Connecting LG")
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HLG = k2.connect(HLG)
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logging.info("Arc sorting LG")
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HLG = k2.arc_sort(HLG)
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logging.info(f"HLG.shape: {HLG.shape}")
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return HLG
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def main():
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args = get_args()
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lang_dir = Path(args.lang_dir)
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if (lang_dir / "HLG.pt").is_file():
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logging.info(f"{lang_dir}/HLG.pt already exists - skipping")
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return
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logging.info(f"Processing {lang_dir}")
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HLG = compile_HLG(lang_dir)
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logging.info(f"Saving HLG.pt to {lang_dir}")
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torch.save(HLG.as_dict(), f"{lang_dir}/HLG.pt")
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if __name__ == "__main__":
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formatter = (
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"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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)
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logging.basicConfig(format=formatter, level=logging.INFO)
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main()
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1
egs/aishell/ASR/local/compile_hlg.py
Symbolic link
1
egs/aishell/ASR/local/compile_hlg.py
Symbolic link
@ -0,0 +1 @@
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../../../librispeech/ASR/local/compile_hlg.py
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@ -1,110 +0,0 @@
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#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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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 musan 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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import torch
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from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer, combine
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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_musan(num_mel_bins: int = 80):
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src_dir = Path("data/manifests")
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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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"music",
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"speech",
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"noise",
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)
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manifests = read_manifests_if_cached(
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dataset_parts=dataset_parts, output_dir=src_dir
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)
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assert manifests is not None
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musan_cuts_path = output_dir / "cuts_musan.json.gz"
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if musan_cuts_path.is_file():
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logging.info(f"{musan_cuts_path} already exists - skipping")
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return
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logging.info("Extracting features for Musan")
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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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# create chunks of Musan with duration 5 - 10 seconds
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musan_cuts = (
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CutSet.from_manifests(
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recordings=combine(
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part["recordings"] for part in manifests.values()
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)
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)
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.cut_into_windows(10.0)
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.filter(lambda c: c.duration > 5)
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.compute_and_store_features(
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extractor=extractor,
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storage_path=f"{output_dir}/feats_musan",
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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=LilcomHdf5Writer,
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)
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)
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musan_cuts.to_json(musan_cuts_path)
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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 = (
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"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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)
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logging.basicConfig(format=formatter, level=logging.INFO)
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args = get_args()
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compute_fbank_musan(num_mel_bins=args.num_mel_bins)
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1
egs/aishell/ASR/local/compute_fbank_musan.py
Symbolic link
1
egs/aishell/ASR/local/compute_fbank_musan.py
Symbolic link
@ -0,0 +1 @@
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../../../librispeech/ASR/local/compute_fbank_musan.py
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@ -1,107 +0,0 @@
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#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
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"""
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Convert a transcript file containing words to a corpus file containing tokens
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for LM training with the help of a lexicon.
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If the lexicon contains phones, the resulting LM will be a phone LM; If the
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lexicon contains word pieces, the resulting LM will be a word piece LM.
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If a word has multiple pronunciations, the one that appears first in the lexicon
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is kept; others are removed.
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If the input transcript is:
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hello zoo world hello
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world zoo
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foo zoo world hellO
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and if the lexicon is
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<UNK> SPN
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hello h e l l o 2
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hello h e l l o
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world w o r l d
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zoo z o o
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Then the output is
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h e l l o 2 z o o w o r l d h e l l o 2
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w o r l d z o o
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SPN z o o w o r l d SPN
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"""
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import argparse
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from pathlib import Path
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from typing import Dict, List
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from generate_unique_lexicon import filter_multiple_pronunications
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from icefall.lexicon import read_lexicon
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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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"--transcript",
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type=str,
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help="The input transcript file."
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"We assume that the transcript file consists of "
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"lines. Each line consists of space separated words.",
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)
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parser.add_argument("--lexicon", type=str, help="The input lexicon file.")
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parser.add_argument(
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"--oov", type=str, default="<UNK>", help="The OOV word."
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)
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return parser.parse_args()
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def process_line(
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lexicon: Dict[str, List[str]], line: str, oov_token: str
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) -> None:
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"""
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Args:
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lexicon:
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A dict containing pronunciations. Its keys are words and values
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are pronunciations (i.e., tokens).
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line:
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A line of transcript consisting of space(s) separated words.
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oov_token:
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The pronunciation of the oov word if a word in `line` is not present
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in the lexicon.
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Returns:
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Return None.
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"""
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s = ""
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words = line.strip().split()
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for i, w in enumerate(words):
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tokens = lexicon.get(w, oov_token)
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s += " ".join(tokens)
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s += " "
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print(s.strip())
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def main():
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args = get_args()
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assert Path(args.lexicon).is_file()
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assert Path(args.transcript).is_file()
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assert len(args.oov) > 0
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# Only the first pronunciation of a word is kept
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lexicon = filter_multiple_pronunications(read_lexicon(args.lexicon))
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lexicon = dict(lexicon)
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assert args.oov in lexicon
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oov_token = lexicon[args.oov]
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with open(args.transcript) as f:
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for line in f:
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process_line(lexicon=lexicon, line=line, oov_token=oov_token)
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if __name__ == "__main__":
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main()
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1
egs/aishell/ASR/local/convert_transcript_words_to_tokens.py
Symbolic link
1
egs/aishell/ASR/local/convert_transcript_words_to_tokens.py
Symbolic link
@ -0,0 +1 @@
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../../../librispeech/ASR/local/convert_transcript_words_to_tokens.py
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@ -1,100 +0,0 @@
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#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
|
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#
|
||||
# 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
|
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#
|
||||
# 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.
|
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"""
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This file takes as input a lexicon.txt and output a new lexicon,
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in which each word has a unique pronunciation.
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The way to do this is to keep only the first pronunciation of a word
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in lexicon.txt.
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"""
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import argparse
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import logging
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from pathlib import Path
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from typing import List, Tuple
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from icefall.lexicon import read_lexicon, write_lexicon
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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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"--lang-dir",
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type=str,
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help="""Input and output directory.
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It should contain a file lexicon.txt.
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This file will generate a new file uniq_lexicon.txt
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in it.
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""",
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)
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return parser.parse_args()
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def filter_multiple_pronunications(
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lexicon: List[Tuple[str, List[str]]]
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) -> List[Tuple[str, List[str]]]:
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"""Remove multiple pronunciations of words from a lexicon.
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If a word has more than one pronunciation in the lexicon, only
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the first one is kept, while other pronunciations are removed
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from the lexicon.
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Args:
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lexicon:
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The input lexicon, containing a list of (word, [p1, p2, ..., pn]),
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where "p1, p2, ..., pn" are the pronunciations of the "word".
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Returns:
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Return a new lexicon where each word has a unique pronunciation.
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"""
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seen = set()
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ans = []
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for word, tokens in lexicon:
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if word in seen:
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continue
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seen.add(word)
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ans.append((word, tokens))
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return ans
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def main():
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args = get_args()
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lang_dir = Path(args.lang_dir)
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lexicon_filename = lang_dir / "lexicon.txt"
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in_lexicon = read_lexicon(lexicon_filename)
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out_lexicon = filter_multiple_pronunications(in_lexicon)
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write_lexicon(lang_dir / "uniq_lexicon.txt", out_lexicon)
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logging.info(f"Number of entries in lexicon.txt: {len(in_lexicon)}")
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logging.info(f"Number of entries in uniq_lexicon.txt: {len(out_lexicon)}")
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if __name__ == "__main__":
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formatter = (
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"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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)
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logging.basicConfig(format=formatter, level=logging.INFO)
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main()
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1
egs/aishell/ASR/local/generate_unique_lexicon.py
Symbolic link
1
egs/aishell/ASR/local/generate_unique_lexicon.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/local/generate_unique_lexicon.py
|
||||
@ -1,97 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
|
||||
#
|
||||
# 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.
|
||||
|
||||
|
||||
"""
|
||||
This file computes fbank features of the musan dataset.
|
||||
It looks for manifests in the directory data/manifests.
|
||||
|
||||
The generated fbank features are saved in data/fbank.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer, combine
|
||||
from lhotse.recipes.utils import read_manifests_if_cached
|
||||
|
||||
from icefall.utils import get_executor
|
||||
|
||||
# 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)
|
||||
|
||||
|
||||
def compute_fbank_musan():
|
||||
src_dir = Path("data/manifests")
|
||||
output_dir = Path("data/fbank")
|
||||
num_jobs = min(15, os.cpu_count())
|
||||
num_mel_bins = 80
|
||||
|
||||
dataset_parts = (
|
||||
"music",
|
||||
"speech",
|
||||
"noise",
|
||||
)
|
||||
manifests = read_manifests_if_cached(
|
||||
dataset_parts=dataset_parts, output_dir=src_dir
|
||||
)
|
||||
assert manifests is not None
|
||||
|
||||
musan_cuts_path = output_dir / "cuts_musan.json.gz"
|
||||
|
||||
if musan_cuts_path.is_file():
|
||||
logging.info(f"{musan_cuts_path} already exists - skipping")
|
||||
return
|
||||
|
||||
logging.info("Extracting features for Musan")
|
||||
|
||||
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
|
||||
|
||||
with get_executor() as ex: # Initialize the executor only once.
|
||||
# create chunks of Musan with duration 5 - 10 seconds
|
||||
musan_cuts = (
|
||||
CutSet.from_manifests(
|
||||
recordings=combine(
|
||||
part["recordings"] for part in manifests.values()
|
||||
)
|
||||
)
|
||||
.cut_into_windows(10.0)
|
||||
.filter(lambda c: c.duration > 5)
|
||||
.compute_and_store_features(
|
||||
extractor=extractor,
|
||||
storage_path=f"{output_dir}/feats_musan",
|
||||
num_jobs=num_jobs if ex is None else 80,
|
||||
executor=ex,
|
||||
storage_type=LilcomHdf5Writer,
|
||||
)
|
||||
)
|
||||
musan_cuts.to_json(musan_cuts_path)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = (
|
||||
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
)
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
compute_fbank_musan()
|
||||
1
egs/timit/ASR/local/compute_fbank_musan.py
Symbolic link
1
egs/timit/ASR/local/compute_fbank_musan.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/local/compute_fbank_musan.py
|
||||
@ -1 +0,0 @@
|
||||
../../../icefall/shared/
|
||||
1
egs/timit/ASR/shared
Symbolic link
1
egs/timit/ASR/shared
Symbolic link
@ -0,0 +1 @@
|
||||
../../../icefall/shared
|
||||
Loading…
x
Reference in New Issue
Block a user