110 lines
3.2 KiB
Python
Executable File
110 lines
3.2 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2025 Xiaomi Corp. (authors: Fangjun Kuang)
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from pathlib import Path
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from typing import Dict
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import os
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import nemo.collections.asr as nemo_asr
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import onnx
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import onnxmltools
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import torch
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from onnxmltools.utils.float16_converter import (
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convert_float_to_float16,
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convert_float_to_float16_model_path,
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)
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from onnxruntime.quantization import QuantType, quantize_dynamic
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def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
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onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
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onnx_fp16_model = convert_float_to_float16(onnx_fp32_model, keep_io_types=True)
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onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
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def export_onnx_fp16_large_2gb(onnx_fp32_path, onnx_fp16_path):
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onnx_fp16_model = convert_float_to_float16_model_path(
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onnx_fp32_path, keep_io_types=True
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)
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onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
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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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asr_model = nemo_asr.models.ASRModel.from_pretrained(
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model_name="nvidia/parakeet-tdt-0.6b-v2"
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)
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asr_model.eval()
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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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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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os.system("ls -lh *.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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"normalize_type": normalize_type,
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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": "EncDecRNNTBPEModel",
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"version": "2",
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"model_author": "NeMo",
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"url": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2",
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"comment": "Only the transducer branch is exported",
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"feat_dim": 128,
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}
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for m in ["encoder", "decoder", "joiner"]:
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quantize_dynamic(
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model_input=f"./{m}.onnx",
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model_output=f"./{m}.int8.onnx",
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weight_type=QuantType.QUInt8 if m == "encoder" else QuantType.QInt8,
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)
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os.system("ls -lh *.onnx")
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if m == "encoder":
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export_onnx_fp16_large_2gb(f"{m}.onnx", f"{m}.fp16.onnx")
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else:
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export_onnx_fp16(f"{m}.onnx", f"{m}.fp16.onnx")
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add_meta_data("encoder.int8.onnx", meta_data)
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add_meta_data("encoder.fp16.onnx", meta_data)
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print("meta_data", meta_data)
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if __name__ == "__main__":
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main()
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