import gradio as gr
import numpy as np
import torch

from transformers import AutoTokenizer, VitsModel
from transformers import pipeline

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

# Translate audio to russian text
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny", device=device)
translator_to_ru = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ru")

def translate(audio, translator_to_ru: pipeline = translator_to_ru):
    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
    return translator_to_ru(outputs['text'])[0]['translation_text']

# Text to russian speech
model = VitsModel.from_pretrained("facebook/mms-tts-rus")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-rus")

def synthesise(text: str, tokenizer: AutoTokenizer = tokenizer, model: VitsModel = model):
    inputs = tokenizer(text, return_tensors="pt")
    # print(inputs)
    with torch.no_grad():
        output = model(**inputs).waveform
    return output.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[0]


title = "Cascaded STST"
description = """
Demo for cascaded speech-to-speech translation (STST), mapping from source speech in multi language to target speech in Russian. Demo uses OpenAI's [Whisper Tiny](https://huggingface.co/openai/whisper-tiny) model for speech translation, and Facebook's
[mms-tts-rus](https://huggingface.co/acebook/mms-tts-rus) 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=[["./test_2.wav"]],
    title=title,
    description=description,
)

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

demo.launch()