export model

游雁

2023-02-07 88c4f4a25df3c171dc0d07efc400f73e6a09e165

export model

 funasr/export/export_model.py

@@ -42,10 +42,10 @@
            self.export_config,
        )
        self._export_onnx(model, verbose, export_dir)
        # if self.onnx:
        #     self._export_onnx(model, verbose, export_dir)
        # else:
        #     self._export_torchscripts(model, verbose, export_dir)
        if self.onnx:
            self._export_onnx(model, verbose, export_dir)
        else:
            self._export_torchscripts(model, verbose, export_dir)

        logging.info("output dir: {}".format(export_dir))

@@ -54,7 +54,7 @@
        if enc_size:
            dummy_input = model.get_dummy_inputs(enc_size)
        else:
            dummy_input = model.get_dummy_inputs()
            dummy_input = model.get_dummy_inputs_txt()

        # model_script = torch.jit.script(model)
        model_script = torch.jit.trace(model, dummy_input)

 funasr/export/models/e2e_asr_paraformer.py

@@ -63,8 +63,9 @@

        decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
        decoder_out = torch.log_softmax(decoder_out, dim=-1)
        sample_ids = decoder_out.argmax(dim=-1)

        return decoder_out, pre_token_length
        return decoder_out, sample_ids
    
    # def get_output_size(self):
    #     return self.model.encoders[0].size
@@ -74,6 +75,14 @@
        speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
        return (speech, speech_lengths)

    def get_dummy_inputs_txt(self, txt_file: str = "/mnt/workspace/data_fbank/0207/12345.wav.fea.txt"):
        import numpy as np
        fbank = np.loadtxt(txt_file)
        fbank_lengths = np.array([fbank.shape[0], ], dtype=np.int32)
        speech = torch.from_numpy(fbank[None, :, :].astype(np.float32))
        speech_lengths = torch.from_numpy(fbank_lengths.astype(np.int32))
        return (speech, speech_lengths)

    def get_input_names(self):
        return ['speech', 'speech_lengths']


 funasr/export/test_onnx.py

@@ -3,13 +3,13 @@


if __name__ == '__main__':
    onnx_path = "/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/torchscripts/model.onnx"
    onnx_path = "/Users/zhifu/Downloads/model.onnx"
    sess = onnxruntime.InferenceSession(onnx_path)
    input_name = [nd.name for nd in sess.get_inputs()]
    output_name = [nd.name for nd in sess.get_outputs()]

    def _get_feed_dict(feats_length):
        return {'speech': np.zeros((1, feats_length, 560), dtype=np.float32), 'speech_lengths': np.array([feats_length,], dtype=np.int32)}
        return {'speech': np.zeros((1, feats_length, 560), dtype=np.float32), 'speech_lengths': np.array([feats_length,], dtype=np.int64)}

    def _run(feed_dict):
        output = sess.run(output_name, input_feed=feed_dict)