mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
e04489ce4c
* 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>
85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
#!/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
|
|
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)
|
|
|
|
predictor_class = tables.predictor_classes.get(kwargs["predictor"]+"Export")
|
|
model.predictor = predictor_class(model.predictor, onnx=is_onnx)
|
|
|
|
|
|
decoder_class = tables.decoder_classes.get(kwargs["decoder"]+"Export")
|
|
model.decoder = decoder_class(model.decoder, 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)
|
|
|
|
return model
|
|
|
|
|
|
def export_forward(
|
|
self,
|
|
speech: torch.Tensor,
|
|
speech_lengths: torch.Tensor,
|
|
):
|
|
# a. To device
|
|
batch = {"speech": speech, "speech_lengths": speech_lengths}
|
|
# batch = to_device(batch, device=self.device)
|
|
|
|
enc, enc_len = self.encoder(**batch)
|
|
mask = self.make_pad_mask(enc_len)[:, None, :]
|
|
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask)
|
|
pre_token_length = pre_token_length.floor().type(torch.int32)
|
|
|
|
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
|
|
|
|
def export_dummy_inputs(self):
|
|
speech = torch.randn(2, 30, 560)
|
|
speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
|
|
return (speech, speech_lengths)
|
|
|
|
|
|
def export_input_names(self):
|
|
return ['speech', 'speech_lengths']
|
|
|
|
def export_output_names(self):
|
|
return ['logits', 'token_num']
|
|
|
|
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" |