#!/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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import torch.nn as nn
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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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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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model.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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language: torch.Tensor,
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textnorm: torch.Tensor,
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**kwargs,
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):
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speech = speech.to(device=kwargs["device"])
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speech_lengths = speech_lengths.to(device=kwargs["device"])
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language_query = self.embed(language).to(speech.device)
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textnorm_query = self.embed(textnorm).to(speech.device)
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speech = torch.cat((textnorm_query, speech), dim=1)
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speech_lengths += 1
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event_emo_query = self.embed(torch.LongTensor([[1, 2]]).to(speech.device)).repeat(
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speech.size(0), 1, 1
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)
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input_query = torch.cat((language_query, event_emo_query), dim=1)
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speech = torch.cat((input_query, speech), dim=1)
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speech_lengths += 3
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# Encoder
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encoder_out, encoder_out_lens = self.encoder(speech, speech_lengths)
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if isinstance(encoder_out, tuple):
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encoder_out = encoder_out[0]
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# c. Passed the encoder result and the beam search
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ctc_logits = self.ctc.log_softmax(encoder_out)
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return ctc_logits, encoder_out_lens
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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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language = torch.tensor([0, 0], dtype=torch.int32)
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textnorm = torch.tensor([15, 15], dtype=torch.int32)
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return (speech, speech_lengths, language, textnorm)
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def export_input_names(self):
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return ["speech", "speech_lengths", "language", "textnorm"]
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def export_output_names(self):
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return ["ctc_logits", "encoder_out_lens"]
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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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