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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=kwargs["device"])
- speech_lengths = speech_lengths.to(device=kwargs["device"])
-
- language_query = self.embed(language).to(speech.device)
-
- textnorm_query = self.embed(textnorm).to(speech.device)
- 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)
-
-
- 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",
- },
- "logits": {0: "batch_size", 1: "logits_length"},
- }
- def export_name(
- self,
- ):
- return "model.onnx"
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