Update app.py

app.py

@@ -22,87 +22,6 @@ model = VitsModel.from_pretrained("Matthijs/mms-tts-deu")
 tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-deu")
 
 
-def translate(audio):
-    outputs = asr_pipe(
-        audio,
-        max_new_tokens=256,
-        generate_kwargs={"task": "transcribe", "language": "de"}
-    )
-    return outputs["text"]
-
-
-def synthesise(text):
-    if len(text.strip()) == 0:
-        return (16000, np.zeros(0).astype(np.int16))
-
-    inputs = tokenizer(text, return_tensors="pt")
-    input_ids = inputs["input_ids"]
-
-    with torch.no_grad():
-        outputs = model(input_ids)
-
-    speech = outputs.audio[0]
-    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()import gradio as gr
-import numpy as np
-import torch
-
-from transformers import (
-    VitsModel,
-    VitsTokenizer,
-    pipeline
-)
-
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-# load speech translation checkpoint
-asr_pipe = pipeline(
-    "automatic-speech-recognition",
-    model="openai/whisper-base",
-    device=device
-)
-
-model = VitsModel.from_pretrained("Matthijs/mms-tts-deu")
-tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-deu")
-
-
 def translate(audio):
     outputs = asr_pipe(
         audio,