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

from transformers import VitsModel, VitsTokenizer, pipeline


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

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, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "nl"})
    return outputs["text"]


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

    speech = outputs.audio[0]
    return speech


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 Italian to Dutch speech translation using OpenAI Whisper and MMS models
"""

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()