Create export_onnx.py

export_onnx.py

@@ -0,0 +1,139 @@
+#!/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")