From e04489ce4c0fd0095d0c79ef8f504f425e0435a8 Mon Sep 17 00:00:00 2001
From: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
Date: 星期三, 13 三月 2024 16:34:42 +0800
Subject: [PATCH] contextual&seaco ONNX export (#1481)
---
funasr/models/bicif_paraformer/model.py | 89 +++-----------------------------------------
1 files changed, 6 insertions(+), 83 deletions(-)
diff --git a/funasr/models/bicif_paraformer/model.py b/funasr/models/bicif_paraformer/model.py
index 9849c8c..6f37dd4 100644
--- a/funasr/models/bicif_paraformer/model.py
+++ b/funasr/models/bicif_paraformer/model.py
@@ -343,86 +343,9 @@
return results, meta_data
- def export(
- self,
- max_seq_len=512,
- **kwargs,
- ):
- self.device = kwargs.get("device")
- is_onnx = kwargs.get("type", "onnx") == "onnx"
- encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
- self.encoder = encoder_class(self.encoder, onnx=is_onnx)
-
- predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
- self.predictor = predictor_class(self.predictor, onnx=is_onnx)
-
- decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
- self.decoder = decoder_class(self.decoder, onnx=is_onnx)
-
- from funasr.utils.torch_function import sequence_mask
-
- self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
-
-
- self.forward = self.export_forward
-
- return self
-
- 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.round().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)
-
- # get predicted timestamps
- us_alphas, us_cif_peak = self.predictor.get_upsample_timestmap(enc, mask, pre_token_length)
-
- return decoder_out, pre_token_length, us_alphas, us_cif_peak
-
- 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', 'us_alphas', 'us_cif_peak']
-
- 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'
- },
- 'us_alphas': {
- 0: 'batch_size',
- 1: 'alphas_length'
- },
- 'us_cif_peak': {
- 0: 'batch_size',
- 1: 'alphas_length'
- },
- }
-
- def export_name(self, ):
- return "model.onnx"
+ def export(self, **kwargs):
+ from .export_meta import export_rebuild_model
+ if 'max_seq_len' not in kwargs:
+ kwargs['max_seq_len'] = 512
+ models = export_rebuild_model(model=self, **kwargs)
+ return models
--
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