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import os
import tempfile
import gradio as gr
import whisper
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from gtts import gTTS
from IPython.display import Audio
# Load the models and tokenizer
whisper_model = whisper.load_model("base")
tokenizer = AutoTokenizer.from_pretrained("alirezamsh/small100")
model = AutoModelForSeq2SeqLM.from_pretrained("alirezamsh/small100")
def translate_audio(input_file, to_lang):
# Load the audio file
audio = whisper.load_audio(input_file)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
# Detect language using Whisper
_, probs = whisper_model.detect_language(mel)
lang = max(probs, key=probs.get)
# Convert audio to text
options = whisper.DecodingOptions()
result = whisper.decode(whisper_model, mel, options)
text = result.text
# Translate the text
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]
# Convert translated text to audio
tts = gTTS(text=translated_text, lang=to_lang)
temp_output_file = tempfile.NamedTemporaryFile(suffix=".mp3").name
tts.save(temp_output_file)
# Load audio data from file
audio_data = open(temp_output_file, "rb").read()
return Audio(audio_data)
def translate_audio_interface(input_file, to_lang):
return translate_audio(input_file, to_lang)
iface = gr.Interface(
fn=translate_audio_interface,
inputs=["file", "text"],
outputs="audio",
title="Audio Translation",
description="Upload an MP3 file and select the target language for translation.",
examples=[
["audio_example.mp3", "en"],
["speech_sample.mp3", "fr"],
]
)
iface.launch(debug = True)
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