Update app.py

app.py

@@ -26,17 +26,26 @@ tangoflux = TangoFluxInference(name="declare-lab/TangoFlux")
 @spaces.GPU(duration=15)
 def gradio_generate(prompt, steps, guidance,duration=10):
 
-    output_wave = tangoflux.generate(prompt,steps=steps,guidance_scale=guidance,duration=duration)
-    output_filename = "temp.wav"
-    torchaudio.save(output_filename, output_wave, sample_rate=44100)
+    output = tangoflux.generate(prompt,steps=steps,guidance_scale=guidance,duration=duration)
+    output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+    
     
     #wavio.write(output_filename, output_wave, rate=44100, sampwidth=2)
+    unique_filename = f"output_{uuid.uuid4().hex}.wav"
+    print(f"Saving audio to file: {unique_filename}")
+
+    # Save to file
+    torchaudio.save(unique_filename, output, sample_rate)
+    print(f"Audio saved: {unique_filename}")
+
+    # Return the path to the generated audio file
+    return unique_filename
 
     #if (output_format == "mp3"):
      #   AudioSegment.from_wav("temp.wav").export("temp.mp3", format = "mp3")
       #  output_filename = "temp.mp3"
 
-    return output_filename
+    #return output_filename
 
 description_text = """
 <p><a href="https://huggingface.co/spaces/declare-lab/tango2/blob/main/app.py?duplicate=true"> <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> For faster inference without waiting in queue, you may duplicate the space and upgrade to a GPU in the settings. <br/><br/>