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| import gradio as gr | |
| import subprocess | |
| import whisper | |
| from googletrans import Translator | |
| import asyncio | |
| import edge_tts | |
| import os | |
| # Extract and Transcribe Audio | |
| def extract_and_transcribe_audio(video_path): | |
| ffmpeg_command = f"ffmpeg -i '{video_path}' -acodec pcm_s24le -ar 48000 -q:a 0 -map a -y 'output_audio.wav'" | |
| subprocess.run(ffmpeg_command, shell=True) | |
| model = whisper.load_model("base") | |
| result = model.transcribe("output_audio.wav") | |
| return result["text"], result['language'] | |
| # Translate Text | |
| def translate_text(whisper_text, whisper_language, target_language): | |
| language_mapping = { | |
| 'English': 'en', | |
| 'Spanish': 'es', | |
| # ... (other mappings) | |
| } | |
| target_language_code = language_mapping[target_language] | |
| translator = Translator() | |
| translated_text = translator.translate(whisper_text, src=whisper_language, dest=target_language_code).text | |
| return translated_text | |
| # Generate Voice | |
| async def generate_voice(translated_text, target_language): | |
| VOICE_MAPPING = { | |
| 'English': 'en-GB-SoniaNeural', | |
| 'Spanish': 'es-ES-PabloNeural', | |
| # ... (other mappings) | |
| } | |
| voice = VOICE_MAPPING[target_language] | |
| communicate = edge_tts.Communicate(translated_text, voice) | |
| await communicate.save("output_synth.wav") | |
| return "output_synth.wav" | |
| # Generate Lip-synced Video (Placeholder) | |
| def generate_lip_synced_video(video_path, output_audio_path): | |
| # Your lip-synced video generation code here | |
| # ... | |
| return "output_high_qual.mp4" | |
| # Main function to be called by Gradio | |
| def process_video(video, target_language): | |
| video_path = "uploaded_video.mp4" | |
| with open(video_path, "wb") as f: | |
| f.write(video.read()) | |
| # Step 1: Extract and Transcribe Audio | |
| whisper_text, whisper_language = extract_and_transcribe_audio(video_path) | |
| # Step 2: Translate Text | |
| translated_text = translate_text(whisper_text, whisper_language, target_language) | |
| # Step 3: Generate Voice | |
| loop = asyncio.get_event_loop() | |
| output_audio_path = loop.run_until_complete(generate_voice(translated_text, target_language)) | |
| # Step 4: Generate Lip-synced Video | |
| output_video_path = generate_lip_synced_video(video_path, output_audio_path) | |
| return output_video_path | |
| # Gradio Interface | |
| iface = gr.Interface( | |
| fn=process_video, | |
| inputs=["file", gr.Interface.Component(type="dropdown", choices=["English", "Spanish"])], | |
| outputs="file", | |
| live=False | |
| ) | |
| iface.launch() | |