Update app.py

app.py

@@ -1,29 +1,30 @@
 import gradio as gr
-import spaces
 import torch
 import torchaudio
 from encodec import EncodecModel
 from encodec.utils import convert_audio
-from encodec.compress import compress, decompress
+from encodec.compress import compress_to_file, decompress_from_file
 import io
 
 # Load the Encodec model 
-model = EncodecModel.encodec_model_48khz()  # Use the encodec version of the model
-model.set_target_bandwidth(6.0)  # Set the desired bandwidth 
+model = EncodecModel.encodec_model_48khz()
+model.set_target_bandwidth(6.0)
 
 @spaces.GPU
 def encode(audio_file_path):
     try:
         # Load and pre-process the audio waveform
         wav, sr = torchaudio.load(audio_file_path)
+
+        # Convert to mono if necessary
+        if wav.shape[0] > 1:
+            wav = torch.mean(wav, dim=0, keepdim=True)
+
         wav = convert_audio(wav, sr, model.sample_rate, model.channels)
-        wav = wav.unsqueeze(0)
 
         # Compress to ecdc file in memory
-        compressed_audio = compress(model, wav)
-
-        # Save compressed audio to BytesIO
-        output = io.BytesIO(compressed_audio)
+        output = io.BytesIO()
+        compress_to_file(model, wav, output) 
         output.seek(0)
 
         return output
@@ -35,11 +36,8 @@ def encode(audio_file_path):
 @spaces.GPU
 def decode(compressed_audio_file):
     try:
-        # Load compressed audio
-        compressed_audio = compressed_audio_file.read()
-
         # Decompress audio
-        wav, sr = decompress(compressed_audio)
+        wav, sr = decompress_from_file(compressed_audio_file)
 
         # Convert the decoded audio to a numpy array for Gradio output
         decoded_audio = wav.cpu().numpy()