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| import gradio as gr | |
| from gradio import components | |
| import whisper | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| from gtts import gTTS | |
| import soundfile as sf | |
| import os | |
| import numpy as np | |
| def translate_speech_to_speech(input_tuple): | |
| input_audio, sample_rate = input_tuple | |
| # Save the input audio to a temporary file | |
| input_file = "input_audio.wav" | |
| sf.write(input_file, input_audio, sample_rate) # use the sample rate from Gradio | |
| # 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.wav" | |
| tts.save(output_file) | |
| # Load the translated audio and return as an output | |
| translated_audio, _ = sf.read(output_file, dtype="int16") | |
| return translated_audio | |
| title = "Speech-to-Speech Translator" | |
| input_audio = gr.inputs.Audio(source="microphone") | |
| output_audio = gr.outputs.Audio(type="numpy") | |
| 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() |