mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
675b4605e8
* update * update * update * update onnx * update with main (#1492) * contextual&seaco ONNX export (#1481) * contextual&seaco ONNX export * update ContextualEmbedderExport2 * update ContextualEmbedderExport2 * update code * onnx (#1482) * qwenaudio qwenaudiochat * qwenaudio qwenaudiochat * whisper * whisper * llm * llm * llm * llm * llm * llm * llm * llm * export onnx * export onnx * export onnx * dingding * dingding * llm * doc * onnx * onnx * onnx * onnx * onnx * onnx * v1.0.15 * qwenaudio * qwenaudio * issue doc * update * update * bugfix * onnx * update export calling * update codes * remove useless code * update code --------- Co-authored-by: zhifu gao <zhifu.gzf@alibaba-inc.com> * acknowledge --------- Co-authored-by: Shi Xian <40013335+R1ckShi@users.noreply.github.com> * update onnx * update onnx * train update * train update * train update * train update * punc update --------- Co-authored-by: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
#!/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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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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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(self, inputs: torch.Tensor, text_lengths: torch.Tensor):
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"""Compute loss value from buffer sequences.
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Args:
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input (torch.Tensor): Input ids. (batch, len)
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hidden (torch.Tensor): Target ids. (batch, len)
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"""
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x = self.embed(inputs)
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h, _ = self.encoder(x, text_lengths)
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y = self.decoder(h)
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return y
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def export_dummy_inputs(self):
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length = 120
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text_indexes = torch.randint(0, self.embed.num_embeddings, (2, length)).type(torch.int32)
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text_lengths = torch.tensor([length-20, length], dtype=torch.int32)
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return (text_indexes, text_lengths)
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def export_input_names(self):
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return ['inputs', 'text_lengths']
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def export_output_names(self):
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return ['logits']
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def export_dynamic_axes(self):
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return {
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'inputs': {
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0: 'batch_size',
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1: 'feats_length'
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},
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'text_lengths': {
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0: 'batch_size',
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},
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'logits': {
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0: 'batch_size',
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1: 'logits_length'
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},
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}
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def export_name(self):
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return "model.onnx" |