app.py

import gradio as gr
import numpy as np
import torch
from datasets import load_dataset

from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, 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)

# load text-to-speech checkpoint and speaker embeddings
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")

model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)

embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
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()