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import gradio as gr
import os
import whisper
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from gtts import gTTS
import IPython.display as ipd
# Load Whisper STT model
whisper_model = whisper.load_model("base")
# Load translation models
tokenizer = AutoTokenizer.from_pretrained("alirezamsh/small100")
model = AutoModelForSeq2SeqLM.from_pretrained("alirezamsh/small100")
def translate_speech(audio_file, target_lang):
# Load audio
audio = whisper.load_audio(audio_file)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
# Detect language
_, probs = whisper_model.detect_language(mel)
lang = max(probs, key=probs.get)
# Decode audio into text
options = whisper.DecodingOptions()
result = whisper.decode(whisper_model, mel, options)
text = result.text
# Translate text
tokenizer.src_lang = lang
encoded_text = tokenizer(text, return_tensors="pt")
generated_tokens = model.generate(**encoded_text)
translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
# Text-to-speech (TTS)
tts = gTTS(text=translated_text, lang=target_lang)
audio_path = "translated_audio.mp3"
tts.save(audio_path)
return audio_path
def translate_speech_interface(audio, target_lang):
audio_path = "recorded_audio.wav"
with open(audio_path, "wb") as f:
f.write(audio.read())
translated_audio = translate_speech(audio_path, target_lang)
translated_audio = open(translated_audio, "rb")
return translated_audio
# Define the Gradio interface
audio_recording = gr.inputs.Audio(source="microphone", type="wav", label="Record your speech")
target_language = gr.inputs.Dropdown(["en", "ru", "fr"], label="Target Language")
output_audio = gr.outputs.Audio(type="audio/mpeg", label="Translated Audio")
gr.Interface(fn=translate_speech_interface, inputs=[audio_recording, target_language], outputs=output_audio, title="Speech Translator").launch()
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