update description

README.md

@@ -10,4 +10,31 @@ pinned: true
 license: cc-by-nc-4.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<!-- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference -->
+
+# Demo Introduction
+This is an example of using the [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M) model as backbone to conduct multiple music understanding tasks with the universal represenation.
+
+The tasks include EMO, GS, MTGInstrument, MTGGenre, MTGTop50, MTGMood, NSynthI, NSynthP, VocalSetS, VocalSetT. 
+
+More models can be referred at the [map organization page](https://huggingface.co/m-a-p).
+
+# Known Issues
+
+## Audio Format Support
+
+Theorectically, all the audio formats supported by [torchaudio.load()](https://pytorch.org/audio/stable/torchaudio.html#torchaudio.load) can be used in the demo. Theese should include but not limited to `WAV, AMB, MP3, FLAC`.
+
+## Error Output
+
+Due the **hardware limitation** of the machine hosting our demospecification (2 CPU and 16GB RAM), there might be `Error` output when uploading long audios. 
+
+Unfortunately, we couldn't fix this in a short time since our team are all volunteer researchers.
+
+We recommend to test audios less than 30 seconds or using the live mode if you are trying the [Music Descriptor demo](https://huggingface.co/spaces/m-a-p/Music-Descriptor) hosted online at HuggingFace Space.
+
+This issue is expected to solve in the future by applying more community-support GPU resources or using other audio encoding strategy.
+
+In the current stage, if you want to directly run the demo with longer audios, you could:
+* clone this space `git clone https://huggingface.co/spaces/m-a-p/Music-Descriptor` and deploy the demo on your own machine with higher performance following the [official instruction](https://huggingface.co/docs/hub/spaces). The code will automatically use GPU for inference if there is GPU that can be detected by `torch.cuda.is_available()`. 
+* develop your own application with the MERT models if you have the experience of machine learning.

app.py

@@ -42,7 +42,18 @@ live_inputs = [
 
 title = "One Model for All Music Understanding Tasks"
 description = "An example of using the [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M) model as backbone to conduct multiple music understanding tasks with the universal represenation."
-article = "The tasks include EMO, GS, MTGInstrument, MTGGenre, MTGTop50, MTGMood, NSynthI, NSynthP, VocalSetS, VocalSetT. \n\n More models can be referred at the [map organization page](https://huggingface.co/m-a-p)."
+# article = "The tasks include EMO, GS, MTGInstrument, MTGGenre, MTGTop50, MTGMood, NSynthI, NSynthP, VocalSetS, VocalSetT. \n\n More models can be referred at the [map organization page](https://huggingface.co/m-a-p)."
+with open('./README.md', 'r') as f:
+    # skip the header
+    header_count = 0
+    for line in f:
+        if '---' in line:
+            header_count += 1
+        if header_count >= 2:
+            break
+    # read the rest conent
+    article = f.read()
+    
 audio_examples = [
     # ["input/example-1.wav"],
     # ["input/example-2.wav"],