From 7ae979bc5e9a9a09236848cc879c2cbd2bfa0837 Mon Sep 17 00:00:00 2001
From: ShiLiang Zhang <sly.zsl@alibaba-inc.com>
Date: 星期三, 08 五月 2024 17:17:38 +0800
Subject: [PATCH] Update README.md
---
funasr/models/fsmn_vad_streaming/export_meta.py | 30 ++++++++++++++++++------------
1 files changed, 18 insertions(+), 12 deletions(-)
diff --git a/funasr/models/fsmn_vad_streaming/export_meta.py b/funasr/models/fsmn_vad_streaming/export_meta.py
index 7183026..f760169 100644
--- a/funasr/models/fsmn_vad_streaming/export_meta.py
+++ b/funasr/models/fsmn_vad_streaming/export_meta.py
@@ -12,7 +12,7 @@
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)
-
+
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)
@@ -22,38 +22,44 @@
return model
+
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
-
+ 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']
+ 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']
+ return ["logits", "out_cache0", "out_cache1", "out_cache2", "out_cache3"]
+
def export_dynamic_axes(self):
return {
- 'speech': {
- 1: 'feats_length'
- },
+ "speech": {1: "feats_length"},
}
-def export_name(self, ):
+
+def export_name(
+ self,
+):
return "model.onnx"
--
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