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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()
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