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.

.github/workflows/run-pretrained.yml

@@ -0,0 +1,106 @@
+# Copyright      2021  Fangjun Kuang (csukuangfj@gmail.com)
+
+# 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.
+
+name: run-pre-trained-conformer-ctc
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [labeled]
+
+jobs:
+  run_pre_trained_conformer_ctc:
+    if: github.event.label.name == 'ready' || github.event_name == 'push'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-18.04]
+        python-version: [3.6, 3.7, 3.8, 3.9]
+        torch: ["1.8.1"]
+        k2-version: ["1.9.dev20210919"]
+
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v1
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Python dependencies
+        run: |
+          python3 -m pip install --upgrade pip pytest
+          pip install torch==${{ matrix.torch }}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+          pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
+
+          python3 -m pip install git+https://github.com/lhotse-speech/lhotse
+          python3 -m pip install kaldifeat
+          # We are in ./icefall and there is a file: requirements.txt in it
+          pip install -r requirements.txt
+
+      - name: Install graphviz
+        shell: bash
+        run: |
+          python3 -m pip install -qq graphviz
+          sudo apt-get -qq install graphviz
+
+      - name: Download pre-trained model
+        shell: bash
+        run: |
+          sudo apt-get -qq install git-lfs tree sox
+          cd egs/librispeech/ASR
+          mkdir tmp
+          cd tmp
+          git lfs install
+          git clone https://github.com/csukuangfj/icefall-asr-conformer-ctc-bpe-500
+          cd ..
+          tree tmp
+          soxi tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/*.flac
+          ls -lh tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/*.flac
+
+      - name: Run CTC decoding
+        shell: bash
+        run: |
+          export PYTHONPATH=$PWD:PYTHONPATH
+          cd egs/librispeech/ASR
+          ./conformer_ctc/pretrained.py \
+            --num-classes 500 \
+            --checkpoint ./tmp/icefall-asr-conformer-ctc-bpe-500/exp/pretrained.pt \
+            --bpe-model ./tmp/icefall-asr-conformer-ctc-bpe-500/data/lang_bpe_500/bpe.model \
+            --method ctc-decoding \
+            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1089-134686-0001.flac \
+            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0001.flac \
+            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0002.flac
+
+      - name: Run HLG decoding
+        shell: bash
+        run: |
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          cd egs/librispeech/ASR
+          ./conformer_ctc/pretrained.py \
+            --num-classes 500 \
+            --checkpoint ./tmp/icefall-asr-conformer-ctc-bpe-500/exp/pretrained.pt \
+            --words-file ./tmp/icefall-asr-conformer-ctc-bpe-500/data/lang_bpe_500/words.txt \
+            --HLG ./tmp/icefall-asr-conformer-ctc-bpe-500/data/lang_bpe_500/HLG.pt \
+            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1089-134686-0001.flac \
+            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0001.flac \
+            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0002.flac

.github/workflows/test.yml

@@ -84,3 +84,8 @@ jobs:
           echo "lib_path: $lib_path"
           export DYLD_LIBRARY_PATH=$lib_path:$DYLD_LIBRARY_PATH
           pytest ./test
+
+          # runt tests for conformer ctc
+          cd egs/librispeech/ASR/conformer_ctc
+          pytest
+

egs/librispeech/ASR/conformer_ctc/pretrained.py

@@ -166,6 +166,15 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--num-classes",
+        type=int,
+        default=5000,
+        help="""
+        Vocab size in the BPE model.
+        """,
+    )
+
     parser.add_argument(
         "--eos-id",
         type=int,
@@ -199,7 +208,6 @@ def get_params() -> AttributeDict:
             "use_feat_batchnorm": True,
             "feature_dim": 80,
             "nhead": 8,
-            "num_classes": 5000,
             "attention_dim": 512,
             "num_decoder_layers": 6,
             # parameters for decoding
@@ -242,7 +250,13 @@ def main():
     args = parser.parse_args()
 
     params = get_params()
+    if args.method != "attention-decoder":
+        # to save memory as the attention decoder
+        # will not be used
+        params.num_decoder_layers = 0
+
     params.update(vars(args))
+
     logging.info(f"{params}")
 
     device = torch.device("cpu")
@@ -264,7 +278,7 @@ def main():
     )
 
     checkpoint = torch.load(args.checkpoint, map_location="cpu")
-    model.load_state_dict(checkpoint["model"])
+    model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
 
@@ -305,7 +319,7 @@ def main():
         logging.info("Use CTC decoding")
         bpe_model = spm.SentencePieceProcessor()
         bpe_model.load(params.bpe_model)
-        max_token_id = bpe_model.get_piece_size() - 1
+        max_token_id = params.num_classes - 1
 
         H = k2.ctc_topo(
             max_token=max_token_id,

egs/librispeech/ASR/conformer_ctc/train.py

@@ -96,6 +96,26 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="conformer_ctc/exp",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        default="data/lang_bpe",
+        help="""The lang dir
+        It contains language related input files such as
+        "lexicon.txt"
+        """,
+    )
+
     return parser
 
 
@@ -110,12 +130,6 @@ def get_params() -> AttributeDict:
 
     Explanation of options saved in `params`:
 
-        - exp_dir: It specifies the directory where all training related
-                   files, e.g., checkpoints, log, etc, are saved
-
-        - lang_dir: It contains language related input files such as
-                    "lexicon.txt"
-
         - best_train_loss: Best training loss so far. It is used to select
                            the model that has the lowest training loss. It is
                            updated during the training.
@@ -166,8 +180,6 @@ def get_params() -> AttributeDict:
     """
     params = AttributeDict(
         {
-            "exp_dir": Path("conformer_ctc/exp"),
-            "lang_dir": Path("data/lang_bpe"),
             "best_train_loss": float("inf"),
             "best_valid_loss": float("inf"),
             "best_train_epoch": -1,
@@ -638,6 +650,8 @@ def main():
     parser = get_parser()
     LibriSpeechAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+    args.lang_dir = Path(args.lang_dir)
 
     world_size = args.world_size
     assert world_size >= 1