#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import types
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import torch
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from funasr.register import tables
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def export_rebuild_model(model, **kwargs):
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model.device = kwargs.get("device")
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is_onnx = kwargs.get("type", "onnx") == "onnx"
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encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
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model.encoder = encoder_class(model.encoder, onnx=is_onnx)
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predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
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model.predictor = predictor_class(model.predictor, onnx=is_onnx)
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decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
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model.decoder = decoder_class(model.decoder, onnx=is_onnx)
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from funasr.utils.torch_function import sequence_mask
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model.make_pad_mask = sequence_mask(kwargs["max_seq_len"], flip=False)
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model.forward = types.MethodType(export_forward, model)
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model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
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model.export_input_names = types.MethodType(export_input_names, model)
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model.export_output_names = types.MethodType(export_output_names, model)
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model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
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model.export_name = types.MethodType(export_name, model)
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return model
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def export_forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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):
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# a. To device
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batch = {"speech": speech, "speech_lengths": speech_lengths}
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# batch = to_device(batch, device=self.device)
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enc, enc_len = self.encoder(**batch)
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mask = self.make_pad_mask(enc_len)[:, None, :]
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pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask)
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pre_token_length = pre_token_length.floor().type(torch.int32)
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decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
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decoder_out = torch.log_softmax(decoder_out, dim=-1)
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# sample_ids = decoder_out.argmax(dim=-1)
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return decoder_out, pre_token_length
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def export_dummy_inputs(self):
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speech = torch.randn(2, 30, 560)
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speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
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return (speech, speech_lengths)
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def export_input_names(self):
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return ["speech", "speech_lengths"]
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def export_output_names(self):
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return ["logits", "token_num"]
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def export_dynamic_axes(self):
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return {
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"speech": {0: "batch_size", 1: "feats_length"},
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"speech_lengths": {
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0: "batch_size",
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},
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"logits": {0: "batch_size", 1: "logits_length"},
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}
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def export_name(
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self,
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):
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return "model.onnx"
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