export_onnx.py

#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)

import types
import torch
import torch.nn as nn
from funasr.register import tables


def export_rebuild_model(model, **kwargs):
 model.device = kwargs.get("device")
 is_onnx = kwargs.get("type", "onnx") == "onnx"
 # encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
 # model.encoder = encoder_class(model.encoder, onnx=is_onnx)



 from funasr.utils.torch_function import sequence_mask

 # model.make_pad_mask = sequence_mask(kwargs["max_seq_len"], flip=False)

 model.forward = types.MethodType(export_forward, model)
 model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
 model.export_input_names = types.MethodType(export_input_names, model)
 model.export_output_names = types.MethodType(export_output_names, model)
 model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
 model.export_name = types.MethodType(export_name, model)

 model.export_name = "model"
 return model


def export_forward(
 self,
 speech: torch.Tensor,
 speech_lengths: torch.Tensor,
 language: torch.Tensor,
 textnorm: torch.Tensor,
 **kwargs,
):
 speech = speech.to(device='cuda')
 speech_lengths = speech_lengths.to(device='cuda')
 
 language_query = self.embed(language.to(speech.device)).unsqueeze(1)
 
 textnorm_query = self.embed(textnorm.to(speech.device)).unsqueeze(1)
 print(textnorm_query.shape, speech.shape)
 speech = torch.cat((textnorm_query, speech), dim=1)
 speech_lengths += 1
 
 event_emo_query = self.embed(torch.LongTensor([[1, 2]]).to(speech.device)).repeat(
 speech.size(0), 1, 1
 )
 input_query = torch.cat((language_query, event_emo_query), dim=1)
 speech = torch.cat((input_query, speech), dim=1)
 speech_lengths += 3
 
 # Encoder
 encoder_out, encoder_out_lens = self.encoder(speech, speech_lengths)
 if isinstance(encoder_out, tuple):
 encoder_out = encoder_out[0]
 
 # c. Passed the encoder result and the beam search
 # ctc_logits = self.ctc.log_softmax(encoder_out)
 ctc_logits = self.ctc.ctc_lo(encoder_out)
 
 
 return ctc_logits, encoder_out_lens


def export_dummy_inputs(self):
 speech = torch.randn(2, 30, 560)
 speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
 language = torch.tensor([0, 0], dtype=torch.int32)
 textnorm = torch.tensor([15, 15], dtype=torch.int32)
 return (speech, speech_lengths, language, textnorm)


def export_input_names(self):
 return ["speech", "speech_lengths", "language", "textnorm"]


def export_output_names(self):
 return ["ctc_logits", "encoder_out_lens"]


def export_dynamic_axes(self):
 return {
 "speech": {0: "batch_size", 1: "feats_length"},
 "speech_lengths": {
 0: "batch_size",
 },
 "language": {0: "batch_size"},
 "textnorm": {0: "batch_size"},
 "ctc_logits": {0: "batch_size", 1: "logits_length"},
 }


def export_name(
 self,
):
 return "model.onnx"



if __name__ == "__main__":
 from model import SenseVoiceSmall

 model_dir = "iic/SenseVoiceSmall"
 #model_dir = "./SenseVoiceSmall"
 model, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir)
 # model = model.to("cpu")
 model = export_rebuild_model(model, max_seq_len=512, device="cuda")
 # model.export()
 print("Export Done.")

 dummy_inputs = model.export_dummy_inputs()

 # Export the model
 torch.onnx.export(
 model,
 dummy_inputs,
 "model.onnx",
 input_names=model.export_input_names(),
 output_names=model.export_output_names(),
 dynamic_axes=model.export_dynamic_axes(),
 opset_version=18
 )
 # import os
 # import onnxmltools
 # from onnxmltools.utils.float16_converter import (
 # convert_float_to_float16)
 # decoder_onnx_model = onnxmltools.utils.load_model("model.onnx")
 # decoder_onnx_model = convert_float_to_float16(decoder_onnx_model)
 # decoder_onnx_path = "model_fp16.onnx"
 # onnxmltools.utils.save_model(decoder_onnx_model, decoder_onnx_path)
 # print("Model has been successfully exported to model.onnx")