Create export_onnx.py

export_onnx.py

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+#!/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")