app.py

import gradio as gr
import whisper
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from gtts import gTTS
import sounddevice as sd
import scipy.io.wavfile as wav
import os

def translate_speech_to_speech(input_audio):
    # Save the input audio to a temporary file
    input_file = "input_audio" + os.path.splitext(input_audio.name)[1]
    input_audio.save(input_file)
    
    # Language detection and translation code from the first code snippet
    model = whisper.load_model("base")
    audio = whisper.load_audio(input_file)
    audio = whisper.pad_or_trim(audio)
    mel = whisper.log_mel_spectrogram(audio).to(model.device)
    _, probs = model.detect_language(mel)
    
    options = whisper.DecodingOptions()
    result = whisper.decode(model, mel, options)
    
    text = result.text
    lang = max(probs, key=probs.get)
    
    # Translation code from the first code snippet
    to_lang = 'ru'
    tokenizer = AutoTokenizer.from_pretrained("alirezamsh/small100")
    model = AutoModelForSeq2SeqLM.from_pretrained("alirezamsh/small100")
    
    tokenizer.src_lang = lang
    encoded_bg = tokenizer(text, return_tensors="pt")
    generated_tokens = model.generate(**encoded_bg)
    translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
    
    # Text-to-speech (TTS) code from the first code snippet
    tts = gTTS(text=translated_text, lang=to_lang)
    output_file = "translated_speech.mp3"
    tts.save(output_file)
    
    # Load the translated audio and return as an output
    translated_audio = open(output_file, "rb")
    
    return translated_audio

title = "Speech-to-Speech Translator"

input_audio = gr.inputs.Audio(type=["mp3", "wav"])
output_audio = gr.outputs.Audio(type=["mp3", "wav"])

stt_demo = gr.Interface(
    fn=translate_speech_to_speech,
    inputs=input_audio,
    outputs=output_audio,
    title=title,
    description="Speak in any language, and the translator will convert it to speech in the target language.",
)

if __name__ == "__main__":
    stt_demo.launch()