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import gradio as gr
import moviepy.editor as mp
import librosa
from transformers import pipeline
# Load Whisper model for speech-to-text
asr = pipeline("automatic-speech-recognition", model="openai/whisper-large")
# MarianMT or M2M100 for translation (multi-language)
translator = pipeline("translation", model="facebook/m2m100_418M")
# Supported languages with their codes
languages = {
"Persian (fa)": "fa",
"French (fr)": "fr",
"Spanish (es)": "es",
"German (de)": "de",
"Chinese (zh)": "zh",
"Arabic (ar)": "ar",
"Hindi (hi)": "hi",
"Russian (ru)": "ru"
}
def generate_subtitles(video_file, language_name):
try:
# Extract the target language code from the selected language name
target_language = languages[language_name]
# Check if video_file is a file object or a file path string
if isinstance(video_file, str):
video_path = video_file # It's a file path
else:
video_path = video_file.name # It's a file object
print(f"Processing video from path: {video_path}")
# Extract audio from video using moviepy
video = mp.VideoFileClip(video_path)
audio_path = "temp_audio.wav"
audio = video.audio
audio.write_audiofile(audio_path, codec='pcm_s16le')
print("Starting speech-to-text transcription")
# Load the audio file as a waveform using librosa
waveform, sr = librosa.load(audio_path, sr=16000) # sr=16000 for Whisper
# Pass the waveform (NumPy array) directly to Whisper's ASR model
transcription = asr(waveform)["text"]
print("Starting translation")
# Translate transcription to the target language using M2M100
translation_pipeline = pipeline('translation', model='facebook/m2m100_418M')
translated_subtitles = translation_pipeline(
transcription,
forced_bos_token_id=translation_pipeline.tokenizer.get_lang_id(target_language)
)[0]["translation_text"]
# Return subtitles
subtitles = f"Original: {transcription}\nTranslated: {translated_subtitles}"
return subtitles
except Exception as e:
# Catch and log the error
print(f"Error occurred: {e}")
return f"Error occurred: {e}"
# Define Gradio interface
def subtitle_video(video_file, language_name):
try:
# Handle both file-like objects and file paths
return generate_subtitles(video_file, language_name)
except Exception as e:
print(f"Error in processing video: {e}")
return f"Error in processing video: {e}"
# Gradio app layout
interface = gr.Interface(
fn=subtitle_video,
inputs=[
gr.Video(label="Upload Video"),
gr.Dropdown( # Dropdown for language selection
label="Choose Target Language",
choices=list(languages.keys()), # Display language names in the dropdown
value="Persian (fa)" # Default language
)
],
outputs="text",
title="Automatic Video Subtitler & Translator"
)
interface.launch()
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