Export NeMo FastConformer Hybrid Transducer Large Streaming to ONNX (#844)

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

@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+# Copyright      2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+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 = "rnnt"
+    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": "rnnt", "cache_support": True})
+
+    # asr_model.export("model.onnx")
+    asr_model.encoder.export("encoder.onnx")
+    asr_model.decoder.export("decoder.onnx")
+    asr_model.joint.export("joiner.onnx")
+    # model.onnx is a suffix.
+    # It will generate two files:
+    # encoder-model.onnx
+    # decoder_joint-model.onnx
+
+    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,
+        "pred_rnn_layers": asr_model.decoder.pred_rnn_layers,
+        "pred_hidden": asr_model.decoder.pred_hidden,
+        "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 transducer branch is exported",
+    }
+    add_meta_data("encoder.onnx", meta_data)
+
+    print(meta_data)
+
+
+if __name__ == "__main__":
+    main()