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#!/usr/bin/env python3
# Copyright (c) 2023 Xiaomi Corporation
# Author: Fangjun Kuang
from typing import Dict
import numpy as np
import onnx
def get_vocab_size():
with open("tokens.txt") as f:
return len(f.readlines())
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)
for key, value in meta_data.items():
meta = model.metadata_props.add()
meta.key = key
meta.value = value
onnx.save(model, filename)
print(f"Updated {filename}")
def main():
vocab_size = get_vocab_size()
# 8 for citrinet
# 4 for conformer ctc
subsampling_factor = 4
meta_data = {
"vocab_size": str(vocab_size),
"normalize_type": "per_feature",
"subsampling_factor": str(subsampling_factor),
"model_type": "EncDecCTCModelBPE",
"version": "1",
"model_author": "nemo",
"comment": "https://registry.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_small",
}
add_meta_data("model.onnx", meta_data)
if __name__ == "__main__":
main()
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