import subprocess

# Установка зависимостей внутри пространства
subprocess.run(["pip", "install", "vosk"])
subprocess.run(["pip", "install", "SpeechRecognition"])

import gradio as gr
import numpy as np
import torch
import speech_recognition as sr
from datasets import load_dataset
from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor

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

# Load a lightweight text-to-speech checkpoint and speaker embeddings
processor = SpeechT5Processor.from_pretrained("ttskit/ttskit-tts-ljspeech")

model = SpeechT5ForTextToSpeech.from_pretrained("ttskit/ttskit-tts-ljspeech").to(device)
vocoder = SpeechT5HifiGan.from_pretrained("ljspeech/vocoder-cryptron").to(device)

embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)

def recognize_speech(audio):
    recognizer = sr.Recognizer()
    with sr.AudioFile(audio) as source:
        audio_data = recognizer.record(source)
    return recognizer.recognize_google(audio_data)

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 = recognize_speech(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 cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in English. Demo uses Google Web Speech API for automatic speech recognition, and lightweight text-to-speech and vocoder 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()