Update app.py

app.py

@@ -21,67 +21,57 @@ embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validat
 speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
 
 
-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()
-    
-else:        
-
-    def greet(name):
-        return "Hello " + name
-    
-    
-    demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-    
-    demo.launch()
+print("Blocks interface does not work, hmm",gr.__version__)
+
+
+
+
+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()