# -*- coding: utf-8 -*-
"""HW3_ml.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1z4ht7K9pttbgWmDDnrQhqoZ6SYAiaeUe
"""

# !pip -q uninstall gradio -y
# !pip -q install gradio==3.50.2

# !pip -q install datasets

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

from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline, WhisperProcessor

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

# load speech translation checkpoint
asr_pipe = pipeline("automatic-speech-recognition", model="voidful/wav2vec2-xlsr-multilingual-56", device=device)

# !pip -q install sentencepiece
# load text-to-speech checkpoint and speaker embeddings
# processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
processor = WhisperProcessor.from_pretrained(
    "openai/whisper-small")

translator1 = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")
translator2 = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ru")

from transformers import VitsModel, VitsTokenizer

# model = pipeline("text-to-speech", model="suno/bark-small")

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

def translator_mul_ru(text):

    translation  = translator2(translator1(text)[0]['translation_text'])
    return translation[0]['translation_text']

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


def synthesise(text):
    translated_text = translator_mul_ru(text)
    inputs = tokenizer(translated_text, return_tensors="pt")
    input_ids = inputs["input_ids"]

    with torch.no_grad():
        outputs = model(input_ids)
    speech = outputs["waveform"]
    return speech.cpu()


def speech_to_speech_translation(audio):
    translated_text = translate(audio)
    print(translated_text)
    synthesised_speech = synthesise(translated_text)
    synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
    return 16000, synthesised_speech[0]

title = "Cascaded STST"
description = """
* Сначала модель распознает речь с помощью voidful/wav2vec2-xlsr-multilingual-56 и возвращает текст на любом из 56 языков.
* Далее происходит перевод текста с любого на английский с помощью Helsinki-NLP/opus-mt-mul-en, а затем с английского на русский также с помощью Helsinki-NLP/opus-mt-en-ru
* В конце осуществляется воспроизведение русского текста моделью facebook/mms-tts-rus


Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Russian. Demo uses facebook/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"),
    title=title,
    description=description,
)

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

demo.launch()