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

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

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

def translate(audio):
    outputs = pipe(
        audio,
        max_new_tokens=256,
        generate_kwargs={"task": "transcribe", "language": "es"}
    )

model = VitsModel.from_pretrained("facebook/mms-tts-spa")
tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-spa")

def synthesise(text):
    inputs=tokenizer(text, return_tensors="pt")
    input_ids = inputs["input_ids"]
    with torch.no_grad():
        outputs = model(input_ids)
    return outputs["waveform"]

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

demo = gr.Blocks()

mic_translate = gr.Interface(
    fn=speech_to_speech_translation,
    inputs=gr.Audio(sources="microphone", type="filepath"),
    outputs=gr.Audio(label="Generated Speech", type="numpy"),
)

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

with demo:
    gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])

demo.launch()