cut to 4s

README.md

@@ -2,7 +2,7 @@
 title: Music Descriptor
 emoji: 🚀
 colorFrom: blue
-colorTo: indigo
+colorTo: green
 sdk: gradio
 sdk_version: 3.29.0
 app_file: app.py
@@ -16,7 +16,6 @@ license: cc-by-nc-4.0
 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 representation.
 
 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
@@ -25,16 +24,11 @@ More models can be referred at the [map organization page](https://huggingface.c
 
 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.
+## Audio Input Length
 
-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.
+Due the **hardware limitation** of the machine hosting this demo (2 CPU and 16GB RAM) only the first 4 seconds of audio are used!
 
-This issue is expected to solve in the future by applying more community-support GPU resources or using other audio encoding strategy.
+This issue is expected to solve in the future by applying more community-support GPU resources or using other audio encoding strategies.
 
-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.
+In the current stage, if you want to directly run the demo with longer audios, you could clone this space and deploy with GPU.
+The code will automatically use GPU for inference if there is GPU that can be detected by `torch.cuda.is_available()`.