add new ckpt

app.py

@@ -11,22 +11,36 @@ model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True)
 
 @spaces.GPU()
 def restore_audio(input_audio):
-    # load the audio file
+    # Load the audio file
+    waveform, sample_rate = torchaudio.load(input_audio)
+    
+    # Calculate the duration of the audio (in seconds)
+    duration = waveform.shape[1] / sample_rate
+
+    # Output file path
     output_path = "restored_output.wav"
-    model(input_audio, output_path)
+    
+    if duration > 10:
+        model(input_audio, output_path, short=False)
+    else:
+        model(input_audio, output_path) # short=True by default
+
     return output_path
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("<h1 style='text-align: center;'>🔊 Voice Restoration with Transformer-based Model</h1>")
+    gr.Markdown("# 🔊 Voice Restoration with Transformer-based Model")
     gr.Markdown(
         """
-        <p style='text-align: center;'>Upload a degraded audio file or select an example, and the space will restore it using the <b>VoiceRestore</b> model!<br>
-        Based on this <a href='https://github.com/skirdey/voicerestore' target='_blank'>repo</a> by <a href='https://github.com/skirdey' target='_blank'>@Stan Kirdey</a>,<br>
-        and the HF Transformers 🤗 model by <a href='https://github.com/jadechoghari' target='_blank'>@jadechoghari</a>.
-        </p>
+        Upload a degraded audio file or select an example, and the space will restore it using the **VoiceRestore** model!
+
+        Based on this [repo](https://github.com/skirdey/voicerestore) by [@Stan Kirdey](https://github.com/skirdey),
+        and the HF Transformers 🤗 [Model](https://huggingface.co/jadechoghari/VoiceRestore) by [@jadechoghari](https://github.com/jadechoghari).
+
+        The model returns optimized results for audio less than 10 seconds, however, it supports unlimited duration!
         """
     )
+
     
     with gr.Row():
         with gr.Column():

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:556a83f119c828d0a6ee47bc84149403fd4428eb755bcc222169a68eaa4443dd
+oid sha256:4d8ebdc57b002abd36d71ea2d5d0955380b46f283b7953baaf3bcb09f67ad135
 size 1204445470