130 lines
4.2 KiB
Python
Executable File
130 lines
4.2 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
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import argparse
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from typing import Dict
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import nemo.collections.asr as nemo_asr
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import onnx
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import torch
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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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"--model",
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type=str,
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required=True,
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choices=["80", "480", "1040"],
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)
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return parser.parse_args()
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def add_meta_data(filename: str, meta_data: Dict[str, str]):
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"""Add meta data to an ONNX model. It is changed in-place.
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Args:
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filename:
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Filename of the ONNX model to be changed.
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meta_data:
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Key-value pairs.
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"""
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model = onnx.load(filename)
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while len(model.metadata_props):
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model.metadata_props.pop()
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for key, value in meta_data.items():
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meta = model.metadata_props.add()
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meta.key = key
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meta.value = str(value)
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onnx.save(model, filename)
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@torch.no_grad()
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def main():
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args = get_args()
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model_name = f"stt_en_fastconformer_hybrid_large_streaming_{args.model}ms"
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asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name=model_name)
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with open("./tokens.txt", "w", encoding="utf-8") as f:
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for i, s in enumerate(asr_model.joint.vocabulary):
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f.write(f"{s} {i}\n")
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f.write(f"<blk> {i+1}\n")
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print("Saved to tokens.txt")
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decoder_type = "rnnt"
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asr_model.change_decoding_strategy(decoder_type=decoder_type)
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asr_model.eval()
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assert asr_model.encoder.streaming_cfg is not None
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if isinstance(asr_model.encoder.streaming_cfg.chunk_size, list):
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chunk_size = asr_model.encoder.streaming_cfg.chunk_size[1]
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else:
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chunk_size = asr_model.encoder.streaming_cfg.chunk_size
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if isinstance(asr_model.encoder.streaming_cfg.pre_encode_cache_size, list):
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pre_encode_cache_size = asr_model.encoder.streaming_cfg.pre_encode_cache_size[1]
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else:
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pre_encode_cache_size = asr_model.encoder.streaming_cfg.pre_encode_cache_size
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window_size = chunk_size + pre_encode_cache_size
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print("chunk_size", chunk_size)
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print("pre_encode_cache_size", pre_encode_cache_size)
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print("window_size", window_size)
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chunk_shift = chunk_size
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# cache_last_channel: (batch_size, dim1, dim2, dim3)
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cache_last_channel_dim1 = len(asr_model.encoder.layers)
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cache_last_channel_dim2 = asr_model.encoder.streaming_cfg.last_channel_cache_size
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cache_last_channel_dim3 = asr_model.encoder.d_model
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# cache_last_time: (batch_size, dim1, dim2, dim3)
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cache_last_time_dim1 = len(asr_model.encoder.layers)
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cache_last_time_dim2 = asr_model.encoder.d_model
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cache_last_time_dim3 = asr_model.encoder.conv_context_size[0]
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asr_model.set_export_config({"decoder_type": "rnnt", "cache_support": True})
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# asr_model.export("model.onnx")
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asr_model.encoder.export("encoder.onnx")
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asr_model.decoder.export("decoder.onnx")
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asr_model.joint.export("joiner.onnx")
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# model.onnx is a suffix.
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# It will generate two files:
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# encoder-model.onnx
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# decoder_joint-model.onnx
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normalize_type = asr_model.cfg.preprocessor.normalize
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if normalize_type == "NA":
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normalize_type = ""
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meta_data = {
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"vocab_size": asr_model.decoder.vocab_size,
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"window_size": window_size,
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"chunk_shift": chunk_shift,
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"normalize_type": normalize_type,
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"cache_last_channel_dim1": cache_last_channel_dim1,
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"cache_last_channel_dim2": cache_last_channel_dim2,
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"cache_last_channel_dim3": cache_last_channel_dim3,
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"cache_last_time_dim1": cache_last_time_dim1,
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"cache_last_time_dim2": cache_last_time_dim2,
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"cache_last_time_dim3": cache_last_time_dim3,
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"pred_rnn_layers": asr_model.decoder.pred_rnn_layers,
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"pred_hidden": asr_model.decoder.pred_hidden,
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"subsampling_factor": 8,
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"model_type": "EncDecHybridRNNTCTCBPEModel",
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"version": "1",
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"model_author": "NeMo",
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"url": f"https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/{model_name}",
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"comment": "Only the transducer branch is exported",
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}
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add_meta_data("encoder.onnx", meta_data)
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print(meta_data)
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if __name__ == "__main__":
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main()
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