From 750e7b583ca3eae4e725c478514e3e0945fa2bdc Mon Sep 17 00:00:00 2001
From: Heinrich Dinkel <RicherMans@users.noreply.github.com>
Date: Wed, 14 Aug 2024 07:55:34 +0800
Subject: [PATCH] Update README.md

#7 , Added Finetuned Dasheng on Audioset.
---
 README.md | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)

diff --git a/README.md b/README.md
index d4af660..538ba50 100644
--- a/README.md
+++ b/README.md
@@ -151,6 +151,46 @@ MultiGPU support is realized using [Accelerate](https://huggingface.co/docs/acce
 accelerate launch --mixed_precision='bf16' dasheng/train/train.py dasheng/train/config/dasheng_base.yaml
 ```
 
+## FAQ
+
+### Is there an Audioset-finetuned Dasheng?
+
+Yes, the performance for the base model is 49.7 mAP. One can use it as follows:
+
+```python
+from typing import Any, Mapping
+import dasheng
+import torch
+
+class DashengAudiosetClassifier(torch.nn.Module):
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.dashengmodel = dasheng.dasheng_base()
+        self.classifier = torch.nn.Sequential(torch.nn.LayerNorm(self.dashengmodel.embed_dim), torch.nn.Linear(self.dashengmodel.embed_dim, 527))
+
+    def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False):
+        self.dashengmodel.load_state_dict(state_dict, strict=False)
+        for_classifier_dict = {}
+        for k,v in state_dict.items():
+            if 'outputlayer' in k:
+                for_classifier_dict[k.replace('outputlayer.','')]  = v
+        self.classifier.load_state_dict(for_classifier_dict)
+        return self
+
+    def forward(self, x):
+        x = self.dashengmodel(x).mean(1)
+        return self.classifier(x).sigmoid()
+
+
+mdl = DashengAudiosetClassifier()
+check = torch.hub.load_state_dict_from_url('https://zenodo.org/records/13315686/files/dasheng_audioset_mAP497.pt?download=1',map_location='cpu')
+mdl.load_state_dict(check)
+
+prediction = mdl(torch.randn(1,16000))
+```
+
+
 ## Citation
 
 ```bibtex