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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 load_cmvn():
neg_mean = None
inv_stddev = None
with open("am.mvn") as f:
for line in f:
if not line.startswith("<LearnRateCoef>"):
continue
t = line.split()[3:-1]
if neg_mean is None:
neg_mean = ",".join(t)
else:
inv_stddev = ",".join(t)
return neg_mean, inv_stddev
def load_lfr_params():
with open("config.yaml") as f:
for line in f:
if "lfr_m" in line:
lfr_m = int(line.split()[-1])
elif "lfr_n" in line:
lfr_n = int(line.split()[-1])
break
lfr_window_size = lfr_m
lfr_window_shift = lfr_n
return lfr_window_size, lfr_window_shift
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():
lfr_window_size, lfr_window_shift = load_lfr_params()
neg_mean, inv_stddev = load_cmvn()
vocab_size = get_vocab_size()
meta_data = {
"lfr_window_size": str(lfr_window_size),
"lfr_window_shift": str(lfr_window_shift),
"neg_mean": neg_mean,
"inv_stddev": inv_stddev,
"model_type": "paraformer",
"version": "1",
"model_author": "damo",
"vocab_size": str(vocab_size),
"comment": "speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
}
add_meta_data("model.onnx", meta_data)
if __name__ == "__main__":
main()
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