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"

# carguemos el checkpoint para translación del habla
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)

# carguemos el checkpoint texto-a-habla (text-to-speech) así como el speaker embeddings
processor = SpeechT5Processor.from_pretrained("jjsprockel/speecht5_finetuned_voxpopuli_it")

model = SpeechT5ForTextToSpeech.from_pretrained("jjsprockel/speecht5_finetuned_voxpopuli_it").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)


def translate(audio):
    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "it"})
    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 = "Traductor al Italiano de Voz a Voz en Cascada"
description = """
Demostración de un traductor de voz a voz en cascada (cascaded speech-to-speech translation o STST), funciona mapeando de una fuente de voz en cualquier lenguaje a una meta de texto en Italiano. Se usa el modelo de OpenAI [Whisper Base](https://huggingface.co/openai/whisper-base) para la traducción, y el modelo de Microsoft
[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) sometido a un ajuse fino para pasar de texto-a-voz:

![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagrama de la traducción de voz a voz en cascada")
"""

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()