export-onnx-rnnt-v2.py
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#!/usr/bin/env python3
# Copyright 2025 Xiaomi Corp. (authors: Fangjun Kuang)
import os
import gigaam
import onnx
import torch
from gigaam.utils import onnx_converter
from onnxruntime.quantization import QuantType, quantize_dynamic
from torch import Tensor
"""
==========Input==========
NodeArg(name='audio_signal', type='tensor(float)', shape=['batch_size', 64, 'seq_len'])
NodeArg(name='length', type='tensor(int64)', shape=['batch_size'])
==========Output==========
NodeArg(name='encoded', type='tensor(float)', shape=['batch_size', 768, 'Transposeencoded_dim_2'])
NodeArg(name='encoded_len', type='tensor(int32)', shape=['batch_size'])
==========Input==========
NodeArg(name='x', type='tensor(int32)', shape=[1, 1])
NodeArg(name='unused_x_len.1', type='tensor(int32)', shape=[1])
NodeArg(name='h.1', type='tensor(float)', shape=[1, 1, 320])
NodeArg(name='c.1', type='tensor(float)', shape=[1, 1, 320])
==========Output==========
NodeArg(name='dec', type='tensor(float)', shape=[1, 320, 1])
NodeArg(name='unused_x_len', type='tensor(int32)', shape=[1])
NodeArg(name='h', type='tensor(float)', shape=[1, 1, 320])
NodeArg(name='c', type='tensor(float)', shape=[1, 1, 320])
==========Input==========
NodeArg(name='enc', type='tensor(float)', shape=[1, 768, 1])
NodeArg(name='dec', type='tensor(float)', shape=[1, 320, 1])
==========Output==========
NodeArg(name='joint', type='tensor(float)', shape=[1, 1, 1, 34])
"""
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)
class EncoderWrapper(torch.nn.Module):
def __init__(self, m):
super().__init__()
self.m = m
def forward(self, audio_signal: Tensor, length: Tensor):
# https://github.com/salute-developers/GigaAM/blob/main/gigaam/encoder.py#L499
out, out_len = self.m.encoder(audio_signal, length)
return out, out_len.to(torch.int64)
def to_onnx(self, dir_path: str = "."):
onnx_converter(
model_name=f"{self.m.cfg.model_name}_encoder",
out_dir=dir_path,
module=self.m.encoder,
dynamic_axes=self.m.encoder.dynamic_axes(),
)
class DecoderWrapper(torch.nn.Module):
def __init__(self, m):
super().__init__()
self.m = m
def forward(self, x: Tensor, unused_x_len: Tensor, h: Tensor, c: Tensor):
# https://github.com/salute-developers/GigaAM/blob/main/gigaam/decoder.py#L110C17-L110C54
emb = self.m.head.decoder.embed(x)
g, (h, c) = self.m.head.decoder.lstm(emb.transpose(0, 1), (h, c))
return g.permute(1, 2, 0), unused_x_len + 1, h, c
def to_onnx(self, dir_path: str = "."):
label, hidden_h, hidden_c = self.m.head.decoder.input_example()
label = label.to(torch.int32)
label_len = torch.zeros(1, dtype=torch.int32)
onnx_converter(
model_name=f"{self.m.cfg.model_name}_decoder",
out_dir=dir_path,
module=self,
dynamic_axes=self.m.encoder.dynamic_axes(),
inputs=(label, label_len, hidden_h, hidden_c),
input_names=["x", "unused_x_len.1", "h.1", "c.1"],
output_names=["dec", "unused_x_len", "h", "c"],
)
def main() -> None:
model_name = "v2_rnnt"
model = gigaam.load_model(
model_name, fp16_encoder=False, use_flash=False, download_root="."
)
# use characters
# space is 0
# <blk> is the last token
with open("./tokens.txt", "w", encoding="utf-8") as f:
for i, s in enumerate(model.cfg["labels"]):
f.write(f"{s} {i}\n")
f.write(f"<blk> {i+1}\n")
print("Saved to tokens.txt")
EncoderWrapper(model).to_onnx(".")
DecoderWrapper(model).to_onnx(".")
onnx_converter(
model_name=f"{model.cfg.model_name}_joint",
out_dir=".",
module=model.head.joint,
)
meta_data = {
# vocab_size does not include the blank
# we will increase vocab_size by 1 in the c++ code
"vocab_size": model.cfg["head"]["decoder"]["num_classes"] - 1,
"pred_rnn_layers": model.cfg["head"]["decoder"]["pred_rnn_layers"],
"pred_hidden": model.cfg["head"]["decoder"]["pred_hidden"],
"normalize_type": "",
"subsampling_factor": 4,
"model_type": "EncDecRNNTBPEModel",
"version": "2",
"model_author": "https://github.com/salute-developers/GigaAM",
"license": "https://github.com/salute-developers/GigaAM/blob/main/LICENSE",
"language": "Russian",
"is_giga_am": 1,
}
add_meta_data(f"./{model_name}_encoder.onnx", meta_data)
quantize_dynamic(
model_input=f"./{model_name}_encoder.onnx",
model_output="./encoder.int8.onnx",
weight_type=QuantType.QUInt8,
)
os.rename(f"./{model_name}_decoder.onnx", "decoder.onnx")
os.rename(f"./{model_name}_joint.onnx", "joiner.onnx")
os.remove(f"./{model_name}_encoder.onnx")
if __name__ == "__main__":
main()