From 9d48230c4f8f25bf88c5d6105f97370a36c9cf43 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 11 三月 2024 10:48:50 +0800
Subject: [PATCH] export onnx (#1457)
---
funasr/models/bicif_paraformer/model.py | 88 +++++++++++++++++++++++++++++++++++++++++++
1 files changed, 87 insertions(+), 1 deletions(-)
diff --git a/funasr/models/bicif_paraformer/model.py b/funasr/models/bicif_paraformer/model.py
index 696cd56..eb7318b 100644
--- a/funasr/models/bicif_paraformer/model.py
+++ b/funasr/models/bicif_paraformer/model.py
@@ -341,4 +341,90 @@
result_i = {"key": key[i], "token_int": token_int}
results.append(result_i)
- return results, meta_data
\ No newline at end of file
+ return results, meta_data
+
+ def export(
+ self,
+ max_seq_len=512,
+ **kwargs,
+ ):
+ 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 MakePadMask
+ from funasr.utils.torch_function import sequence_mask
+
+ if is_onnx:
+ self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
+ else:
+ 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"
--
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