Update app.py

app.py

@@ -21,52 +21,55 @@ embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validat
 speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
 
 
-def translate(audio):
-    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
-    return outputs["text"]
-
-
-def synthesise(text):
-    inputs = processor(text=text, return_tensors="pt")
-    speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
-    return speech.cpu()
-
-
-def speech_to_speech_translation(audio):
-    translated_text = translate(audio)
-    synthesised_speech = synthesise(translated_text)
-    synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
-    return 16000, synthesised_speech
-
-
-title = "Cascaded STST"
-description = """
-Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in English. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Microsoft's
-[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech:
-
-![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagram of cascaded speech to speech translation")
-"""
-
-demo = gr.Blocks()
-
-mic_translate = gr.Interface(
-    fn=speech_to_speech_translation,
-    inputs=gr.Audio(source="microphone", type="filepath"),
-    outputs=gr.Audio(label="Generated Speech", type="numpy"),
-    title=title,
-    description=description,
-)
-
-file_translate = gr.Interface(
-    fn=speech_to_speech_translation,
-    inputs=gr.Audio(source="upload", type="filepath"),
-    outputs=gr.Audio(label="Generated Speech", type="numpy"),
-    examples=[["./example.wav"]],
-    title=title,
-    description=description,
-)
-
-with demo:
-    gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
-
-demo.launch()
+print(speaker_embeddings.shape)
+
+if 0:
+    def translate(audio):
+        outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
+        return outputs["text"]
+    
+    
+    def synthesise(text):
+        inputs = processor(text=text, return_tensors="pt")
+        speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
+        return speech.cpu()
+    
+    
+    def speech_to_speech_translation(audio):
+        translated_text = translate(audio)
+        synthesised_speech = synthesise(translated_text)
+        synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
+        return 16000, synthesised_speech
+    
+    
+    title = "Cascaded STST"
+    description = """
+    Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in English. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Microsoft's
+    [SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech:
+    
+    ![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagram of cascaded speech to speech translation")
+    """
+    
+    demo = gr.Blocks()
+    
+    mic_translate = gr.Interface(
+        fn=speech_to_speech_translation,
+        inputs=gr.Audio(source="microphone", type="filepath"),
+        outputs=gr.Audio(label="Generated Speech", type="numpy"),
+        title=title,
+        description=description,
+    )
+    
+    file_translate = gr.Interface(
+        fn=speech_to_speech_translation,
+        inputs=gr.Audio(source="upload", type="filepath"),
+        outputs=gr.Audio(label="Generated Speech", type="numpy"),
+        examples=[["./example.wav"]],
+        title=title,
+        description=description,
+    )
+    
+    with demo:
+        gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
+    
+    demo.launch()