Export NeMo FastConformer Hybrid Transducer-CTC Large Streaming to ON…

…NX. (#843)

.github/workflows/export-nemo-fast-conformer-hybrid-transducer-ctc.yaml

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+name: export-nemo-speaker-verification-to-onnx
+
+on:
+  workflow_dispatch:
+
+concurrency:
+  group: export-nemo-fast-conformer-hybrid-transducer-ctc-to-onnx-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  export-nemo-fast-conformer-hybrid-transducer-ctc-to-onnx:
+    if: github.repository_owner == 'k2-fsa' || github.repository_owner == 'csukuangfj'
+    name: export NeMo fast conformer
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [macos-latest]
+        python-version: ["3.10"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install NeMo
+        shell: bash
+        run: |
+          BRANCH='main'
+          pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[asr]
+          pip install onnxruntime
+          pip install kaldi-native-fbank
+          pip install soundfile librosa
+
+      - name: Run
+        shell: bash
+        run: |
+          cd scripts/nemo/fast-conformer-hybrid-transducer-ctc
+          ./run-ctc.sh
+
+          mv -v sherpa-onnx-nemo* ../../..
+
+      - name: Download test waves
+        shell: bash
+        run: |
+          mkdir test_wavs
+          pushd test_wavs
+          curl -SL -O https://hf-mirror.com/csukuangfj/sherpa-onnx-nemo-ctc-en-conformer-small/resolve/main/test_wavs/0.wav
+          curl -SL -O https://hf-mirror.com/csukuangfj/sherpa-onnx-nemo-ctc-en-conformer-small/resolve/main/test_wavs/1.wav
+          curl -SL -O https://hf-mirror.com/csukuangfj/sherpa-onnx-nemo-ctc-en-conformer-small/resolve/main/test_wavs/8k.wav
+          curl -SL -O https://hf-mirror.com/csukuangfj/sherpa-onnx-nemo-ctc-en-conformer-small/resolve/main/test_wavs/trans.txt
+          popd
+
+          cp -av test_wavs ./sherpa-onnx-nemo-streaming-fast-conformer-ctc-80ms
+          cp -av test_wavs ./sherpa-onnx-nemo-streaming-fast-conformer-ctc-480ms
+          cp -av test_wavs ./sherpa-onnx-nemo-streaming-fast-conformer-ctc-1040ms
+
+          tar cjvf sherpa-onnx-nemo-streaming-fast-conformer-ctc-80ms.tar.bz2 sherpa-onnx-nemo-streaming-fast-conformer-ctc-80ms
+          tar cjvf sherpa-onnx-nemo-streaming-fast-conformer-ctc-480ms.tar.bz2 sherpa-onnx-nemo-streaming-fast-conformer-ctc-480ms
+          tar cjvf sherpa-onnx-nemo-streaming-fast-conformer-ctc-1040ms.tar.bz2 sherpa-onnx-nemo-streaming-fast-conformer-ctc-1040ms
+
+      - name: Release
+        uses: svenstaro/upload-release-action@v2
+        with:
+          file_glob: true
+          file: ./*.tar.bz2
+          overwrite: true
+          repo_name: k2-fsa/sherpa-onnx
+          repo_token: ${{ secrets.UPLOAD_GH_SHERPA_ONNX_TOKEN }}
+          tag: asr-models

scripts/nemo/fast-conformer-hybrid-transducer-ctc/README.md

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+# Introduction
+
+This folder contains scripts for exporting models from
+
+  - https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_streaming_80ms
+  - https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_streaming_480ms
+  - https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_streaming_1040ms
+
+to `sherpa-onnx`.

scripts/nemo/fast-conformer-hybrid-transducer-ctc/export-onnx-ctc.py

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+#!/usr/bin/env python3
+import argparse
+from typing import Dict
+
+import nemo.collections.asr as nemo_asr
+import onnx
+import torch
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model",
+        type=str,
+        required=True,
+        choices=["80", "480", "1040"],
+    )
+    return parser.parse_args()
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    model = onnx.load(filename)
+    while len(model.metadata_props):
+        model.metadata_props.pop()
+
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = str(value)
+
+    onnx.save(model, filename)
+
+
+@torch.no_grad()
+def main():
+    args = get_args()
+    model_name = f"stt_en_fastconformer_hybrid_large_streaming_{args.model}ms"
+
+    asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name=model_name)
+
+    with open("./tokens.txt", "w", encoding="utf-8") as f:
+        for i, s in enumerate(asr_model.joint.vocabulary):
+            f.write(f"{s} {i}\n")
+        f.write(f"<blk> {i+1}\n")
+        print("Saved to tokens.txt")
+
+    decoder_type = "ctc"
+    asr_model.change_decoding_strategy(decoder_type=decoder_type)
+    asr_model.eval()
+
+    assert asr_model.encoder.streaming_cfg is not None
+    if isinstance(asr_model.encoder.streaming_cfg.chunk_size, list):
+        chunk_size = asr_model.encoder.streaming_cfg.chunk_size[1]
+    else:
+        chunk_size = asr_model.encoder.streaming_cfg.chunk_size
+
+    if isinstance(asr_model.encoder.streaming_cfg.pre_encode_cache_size, list):
+        pre_encode_cache_size = asr_model.encoder.streaming_cfg.pre_encode_cache_size[1]
+    else:
+        pre_encode_cache_size = asr_model.encoder.streaming_cfg.pre_encode_cache_size
+    window_size = chunk_size + pre_encode_cache_size
+
+    print("chunk_size", chunk_size)
+    print("pre_encode_cache_size", pre_encode_cache_size)
+    print("window_size", window_size)
+
+    chunk_shift = chunk_size
+
+    # cache_last_channel: (batch_size, dim1, dim2, dim3)
+    cache_last_channel_dim1 = len(asr_model.encoder.layers)
+    cache_last_channel_dim2 = asr_model.encoder.streaming_cfg.last_channel_cache_size
+    cache_last_channel_dim3 = asr_model.encoder.d_model
+
+    # cache_last_time: (batch_size, dim1, dim2, dim3)
+    cache_last_time_dim1 = len(asr_model.encoder.layers)
+    cache_last_time_dim2 = asr_model.encoder.d_model
+    cache_last_time_dim3 = asr_model.encoder.conv_context_size[0]
+
+    asr_model.set_export_config({"decoder_type": "ctc", "cache_support": True})
+
+    filename = "model.onnx"
+
+    asr_model.export(filename)
+
+    meta_data = {
+        "vocab_size": asr_model.decoder.vocab_size,
+        "window_size": window_size,
+        "chunk_shift": chunk_shift,
+        "normalize_type": "None",
+        "cache_last_channel_dim1": cache_last_channel_dim1,
+        "cache_last_channel_dim2": cache_last_channel_dim2,
+        "cache_last_channel_dim3": cache_last_channel_dim3,
+        "cache_last_time_dim1": cache_last_time_dim1,
+        "cache_last_time_dim2": cache_last_time_dim2,
+        "cache_last_time_dim3": cache_last_time_dim3,
+        "subsampling_factor": 8,
+        "model_type": "EncDecHybridRNNTCTCBPEModel",
+        "version": "1",
+        "model_author": "NeMo",
+        "url": f"https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/{model_name}",
+        "comment": "Only the CTC branch is exported",
+    }
+    add_meta_data(filename, meta_data)
+
+    print(meta_data)
+
+
+if __name__ == "__main__":
+    main()

scripts/nemo/fast-conformer-hybrid-transducer-ctc/run-ctc.sh

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+#!/usr/bin/env bash
+
+set -ex
+
+if [ ! -e ./0.wav ]; then
+  # curl -SL -O https://hf-mirror.com/csukuangfj/icefall-asr-librispeech-streaming-zipformer-small-2024-03-18/resolve/main/test_wavs/0.wav
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-asr-librispeech-streaming-zipformer-small-2024-03-18/resolve/main/test_wavs/0.wav
+fi
+
+ms=(
+80
+480
+1040
+)
+
+for m in ${ms[@]}; do
+  ./export-onnx-ctc.py --model $m
+  d=sherpa-onnx-nemo-streaming-fast-conformer-ctc-${m}ms
+  if [ ! -f $d/model.onnx ]; then
+    mkdir -p $d
+    mv -v model.onnx $d/
+    mv -v tokens.txt $d/
+    ls -lh $d
+  fi
+done
+
+# Now test the exported models
+
+for m in ${ms[@]}; do
+  d=sherpa-onnx-nemo-streaming-fast-conformer-ctc-${m}ms
+  python3 ./test-onnx-ctc.py \
+    --model $d/model.onnx \
+    --tokens $d/tokens.txt \
+    --wav ./0.wav
+done