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Test pre-trained model in CI (#80)
* Add CI to run pre-trained models. * Minor fixes. * Install kaldifeat * Install a CPU version of PyTorch. * Fix CI errors. * Disable decoder layers in pretrained.py if it is not used. * Clone pre-trained model from GitHub. * Minor fixes. * Minor fixes. * Minor fixes.
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Fangjun Kuang
GitHub
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106
.github/workflows/run-pretrained.yml
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.github/workflows/run-pretrained.yml
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# Copyright 2021 Fangjun Kuang (csukuangfj@gmail.com)
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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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name: run-pre-trained-conformer-ctc
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on:
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push:
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branches:
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- master
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pull_request:
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types: [labeled]
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jobs:
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run_pre_trained_conformer_ctc:
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if: github.event.label.name == 'ready' || github.event_name == 'push'
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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os: [ubuntu-18.04]
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python-version: [3.6, 3.7, 3.8, 3.9]
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torch: ["1.8.1"]
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k2-version: ["1.9.dev20210919"]
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fail-fast: false
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steps:
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- uses: actions/checkout@v2
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with:
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fetch-depth: 0
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- name: Setup Python ${{ matrix.python-version }}
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uses: actions/setup-python@v1
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install Python dependencies
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run: |
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python3 -m pip install --upgrade pip pytest
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pip install torch==${{ matrix.torch }}+cpu -f https://download.pytorch.org/whl/torch_stable.html
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pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
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python3 -m pip install git+https://github.com/lhotse-speech/lhotse
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python3 -m pip install kaldifeat
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# We are in ./icefall and there is a file: requirements.txt in it
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pip install -r requirements.txt
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- name: Install graphviz
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shell: bash
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run: |
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python3 -m pip install -qq graphviz
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sudo apt-get -qq install graphviz
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- name: Download pre-trained model
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shell: bash
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run: |
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sudo apt-get -qq install git-lfs tree sox
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cd egs/librispeech/ASR
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mkdir tmp
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cd tmp
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git lfs install
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git clone https://github.com/csukuangfj/icefall-asr-conformer-ctc-bpe-500
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cd ..
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tree tmp
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soxi tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/*.flac
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ls -lh tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/*.flac
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- name: Run CTC decoding
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shell: bash
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run: |
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export PYTHONPATH=$PWD:PYTHONPATH
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cd egs/librispeech/ASR
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./conformer_ctc/pretrained.py \
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--num-classes 500 \
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--checkpoint ./tmp/icefall-asr-conformer-ctc-bpe-500/exp/pretrained.pt \
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--bpe-model ./tmp/icefall-asr-conformer-ctc-bpe-500/data/lang_bpe_500/bpe.model \
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--method ctc-decoding \
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./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1089-134686-0001.flac \
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./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0001.flac \
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./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0002.flac
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- name: Run HLG decoding
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shell: bash
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run: |
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export PYTHONPATH=$PWD:$PYTHONPATH
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cd egs/librispeech/ASR
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./conformer_ctc/pretrained.py \
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--num-classes 500 \
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--checkpoint ./tmp/icefall-asr-conformer-ctc-bpe-500/exp/pretrained.pt \
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--words-file ./tmp/icefall-asr-conformer-ctc-bpe-500/data/lang_bpe_500/words.txt \
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--HLG ./tmp/icefall-asr-conformer-ctc-bpe-500/data/lang_bpe_500/HLG.pt \
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./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1089-134686-0001.flac \
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./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0001.flac \
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./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0002.flac
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5
.github/workflows/test.yml
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5
.github/workflows/test.yml
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echo "lib_path: $lib_path"
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export DYLD_LIBRARY_PATH=$lib_path:$DYLD_LIBRARY_PATH
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pytest ./test
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# runt tests for conformer ctc
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cd egs/librispeech/ASR/conformer_ctc
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pytest
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@ -166,6 +166,15 @@ def get_parser():
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""",
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)
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parser.add_argument(
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"--num-classes",
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type=int,
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default=5000,
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help="""
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Vocab size in the BPE model.
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""",
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)
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parser.add_argument(
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"--eos-id",
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type=int,
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@ -199,7 +208,6 @@ def get_params() -> AttributeDict:
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"use_feat_batchnorm": True,
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"feature_dim": 80,
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"nhead": 8,
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"num_classes": 5000,
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"attention_dim": 512,
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"num_decoder_layers": 6,
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# parameters for decoding
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@ -242,7 +250,13 @@ def main():
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args = parser.parse_args()
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params = get_params()
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if args.method != "attention-decoder":
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# to save memory as the attention decoder
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# will not be used
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params.num_decoder_layers = 0
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params.update(vars(args))
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logging.info(f"{params}")
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device = torch.device("cpu")
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@ -264,7 +278,7 @@ def main():
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)
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checkpoint = torch.load(args.checkpoint, map_location="cpu")
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model.load_state_dict(checkpoint["model"])
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model.load_state_dict(checkpoint["model"], strict=False)
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model.to(device)
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model.eval()
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logging.info("Use CTC decoding")
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bpe_model = spm.SentencePieceProcessor()
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bpe_model.load(params.bpe_model)
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max_token_id = bpe_model.get_piece_size() - 1
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max_token_id = params.num_classes - 1
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H = k2.ctc_topo(
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max_token=max_token_id,
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@ -96,6 +96,26 @@ def get_parser():
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""",
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)
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="conformer_ctc/exp",
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help="""The experiment dir.
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It specifies the directory where all training related
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files, e.g., checkpoints, log, etc, are saved
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""",
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)
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parser.add_argument(
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"--lang-dir",
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type=str,
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default="data/lang_bpe",
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help="""The lang dir
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It contains language related input files such as
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"lexicon.txt"
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""",
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)
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return parser
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@ -110,12 +130,6 @@ def get_params() -> AttributeDict:
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Explanation of options saved in `params`:
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- exp_dir: It specifies the directory where all training related
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files, e.g., checkpoints, log, etc, are saved
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- lang_dir: It contains language related input files such as
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"lexicon.txt"
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- best_train_loss: Best training loss so far. It is used to select
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the model that has the lowest training loss. It is
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updated during the training.
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@ -166,8 +180,6 @@ def get_params() -> AttributeDict:
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"""
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params = AttributeDict(
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{
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"exp_dir": Path("conformer_ctc/exp"),
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"lang_dir": Path("data/lang_bpe"),
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"best_train_loss": float("inf"),
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"best_valid_loss": float("inf"),
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"best_train_epoch": -1,
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@ -638,6 +650,8 @@ def main():
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parser = get_parser()
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LibriSpeechAsrDataModule.add_arguments(parser)
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args = parser.parse_args()
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args.exp_dir = Path(args.exp_dir)
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args.lang_dir = Path(args.lang_dir)
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world_size = args.world_size
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assert world_size >= 1
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