app.py

import torch
from transformers import pipeline, VitsModel, VitsTokenizer
import numpy as np
import gradio as gr

device = "cuda:0" if torch.cuda.is_available() else "cpu"

# Load Whisper-small
pipe = pipeline("automatic-speech-recognition",
                model="openai/whisper-small",
                device=device
)

# Load the model checkpoint and tokenizer
#model = VitsModel.from_pretrained("Matthijs/mms-tts-fra")
#tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-fra")
model = VitsModel.from_pretrained("facebook/mms-tts-fra")
tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-fra")


# Define a function to translate an audio, in French here
def translate(audio):
    outputs = pipe(audio, max_new_tokens=256,
                   generate_kwargs={"task": "transcribe", "language": "fr"})
    return outputs["text"]


# Define function to generate the waveform output
def synthesise(text):
    inputs = tokenizer(text, return_tensors="pt")
    input_ids = inputs["input_ids"]

    with torch.no_grad(): 
      outputs = model(input_ids)
    
    return outputs.audio[0]


# Define the pipeline
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


# Define the title etc
title = "Cascaded STST"
description = """
Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in French. Demo uses OpenAI's [Whisper Small](https://huggingface.co/openai/whisper-small) model for speech translation, and Facebook's
[MMS TTS](https://huggingface.co/facebook/mms-tts) model, finetuned by [Matthijs](https://huggingface.co/Matthijs), 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()