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/fsmn_vad_streaming/model.py | 48 +++++-------------------------------------------
1 files changed, 5 insertions(+), 43 deletions(-)
diff --git a/funasr/models/fsmn_vad_streaming/model.py b/funasr/models/fsmn_vad_streaming/model.py
index 602cf23..59536bb 100644
--- a/funasr/models/fsmn_vad_streaming/model.py
+++ b/funasr/models/fsmn_vad_streaming/model.py
@@ -644,49 +644,11 @@
return results, meta_data
def export(self, **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)
- self.forward = self.export_forward
-
- return self
-
- def export_forward(self, feats: torch.Tensor, *args, **kwargs):
-
- scores, out_caches = self.encoder(feats, *args)
-
- return scores, out_caches
-
- def export_dummy_inputs(self, data_in=None, frame=30):
- if data_in is None:
- speech = torch.randn(1, frame, self.encoder_conf.get("input_dim"))
- else:
- speech = None # Undo
-
- cache_frames = self.encoder_conf.get("lorder") + self.encoder_conf.get("rorder") - 1
- in_cache0 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
- in_cache1 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
- in_cache2 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
- in_cache3 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
-
- return (speech, in_cache0, in_cache1, in_cache2, in_cache3)
-
- def export_input_names(self):
- return ['speech', 'in_cache0', 'in_cache1', 'in_cache2', 'in_cache3']
-
- def export_output_names(self):
- return ['logits', 'out_cache0', 'out_cache1', 'out_cache2', 'out_cache3']
-
- def export_dynamic_axes(self):
- return {
- 'speech': {
- 1: 'feats_length'
- },
- }
-
- def export_name(self, ):
- return "model.onnx"
-
+
+ from .export_meta import export_rebuild_model
+ models = export_rebuild_model(model=self, **kwargs)
+ return models
+
def DetectCommonFrames(self, cache: dict = {}) -> int:
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
return 0
--
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