Update app.py

app.py

@@ -1,14 +1,14 @@
 import gradio as gr
 import torch
 import torchaudio
-from transformers import EncodecModel
+from encodec import EncodecModel
 from encodec.utils import convert_audio
-from encodec.compress import compress_to_file, decompress_from_file # Import from the correct module
+from encodec.compress import compress, decompress
 import io
 
 # Load the Encodec model 
-model = EncodecModel.from_pretrained("facebook/encodec_48khz")
-model.set_target_bandwidth(6.0)  # Set the desired bandwidth (adjust as needed)
+model = EncodecModel.encodec_model_48khz()  # Use the encodec version of the model
+model.set_target_bandwidth(6.0)  # Set the desired bandwidth 
 
 @spaces.GPU
 def encode(audio_file_path):
@@ -16,10 +16,13 @@ def encode(audio_file_path):
         # Load and pre-process the audio waveform
         wav, sr = torchaudio.load(audio_file_path)
         wav = convert_audio(wav, sr, model.sample_rate, model.channels)
+        wav = wav.unsqueeze(0)
 
         # Compress to ecdc file in memory
-        output = io.BytesIO()
-        compress_to_file(model, wav, output) 
+        compressed_audio = compress(model, wav)
+
+        # Save compressed audio to BytesIO
+        output = io.BytesIO(compressed_audio)
         output.seek(0)
 
         return output
@@ -31,8 +34,11 @@ 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_from_file(compressed_audio_file)
+        wav, sr = decompress(compressed_audio)
 
         # Convert the decoded audio to a numpy array for Gradio output
         decoded_audio = wav.cpu().numpy()