import gradio as gr
import numpy as np
import torch

from transformers import pipeline
from transformers import VitsModel, VitsTokenizer, FSMTForConditionalGeneration, FSMTTokenizer, Wav2Vec2ForCTC, Wav2Vec2Processor, MarianMTModel, MarianTokenizer, T5ForConditionalGeneration, T5Tokenizer

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

# Transform audio to en text
asr_pipe = pipeline("automatic-speech-recognition", model="asapp/sew-d-tiny-100k-ft-ls100h", device=device)

# Translate en to rus text
translation_en_to_rus = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ru")

# Create speech from rus text
model = VitsModel.from_pretrained("facebook/mms-tts-rus")
tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-rus")

#model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
#tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ru-en")


def translate(audio):
  en_text = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
  translated_text = translation_en_to_rus(en_text["text"])
  return translated_text[0]['translation_text']

def synthesise(text):
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        speech = model(**inputs).waveform
    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[0]

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")
)

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"]]
)


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

demo.launch()