import torch
from transformers import pipeline

device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipe = pipeline(
    "automatic-speech-recognition", model="openai/whisper-base", device=device
)

from datasets import load_dataset

# dataset = load_dataset("facebook/voxpopuli", "en", split="validation", streaming=True, trust_remote_code=True)  
# sample = next(iter(dataset))

def translate(audio):
    outputs = pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "fr"}) # "language": "fr"
    return outputs["text"]

from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan

processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")

model = SpeechT5ForTextToSpeech.from_pretrained("ccourc23/fine_tuned_SpeechT5") 
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)

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()

import numpy as np

target_dtype = np.int16
max_range = np.iinfo(target_dtype).max


def speech_to_speech_translation(audio):
    translated_text = translate(audio)
    synthesised_speech = synthesise(translated_text)
    synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
    return 16000, synthesised_speech

import gradio as gr


file_translate = gr.Interface(
    fn=speech_to_speech_translation,
    inputs=gr.Audio(sources="upload", type="filepath"),
    outputs=gr.Audio(label="Generated Speech"),
)

file_translate.launch(share=True)