Spaces:
Sleeping
Sleeping
| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Mon Dec 9 16:43:31 2024 | |
| @author: Pradeep Kumar | |
| """ | |
| import whisper | |
| import torch | |
| import os | |
| import gradio as gr | |
| from deep_translator import GoogleTranslator | |
| # Check if NVIDIA GPU is available | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Directories for transcripts | |
| BASE_DIR = os.getcwd() | |
| TRANSCRIPTS_FOLDER = os.path.join(BASE_DIR, 'transcripts') | |
| # Ensure transcripts directory exists | |
| def check_directory(path): | |
| if not os.path.exists(path): | |
| os.makedirs(path) | |
| check_directory(TRANSCRIPTS_FOLDER) | |
| def transcribe_and_translate(audio_file, selected_language, model_type="base"): | |
| """ | |
| Transcribe audio using Whisper and translate it into English if required. | |
| :param audio_file: Path to the uploaded audio file | |
| :param selected_language: Language code for transcription | |
| :param model_type: Whisper model type (default is 'base') | |
| :return: Transcription and translation | |
| """ | |
| temp_audio_path = os.path.join(BASE_DIR, audio_file.name) | |
| # Save the uploaded file to a temporary location | |
| with open(temp_audio_path, "wb") as f: | |
| f.write(audio_file.read()) | |
| try: | |
| # Load the Whisper model based on user selection | |
| model = whisper.load_model(model_type, device=DEVICE) | |
| except Exception as e: | |
| return f"Failed to load Whisper model ({model_type}): {e}" | |
| try: | |
| # Transcribe with the user-selected language | |
| if selected_language: | |
| result = model.transcribe(temp_audio_path, language=selected_language, verbose=False) | |
| else: | |
| return "Language selection is required." | |
| # Save the transcription with timestamps | |
| transcript_file = os.path.join(TRANSCRIPTS_FOLDER, f"{audio_file.name}_transcript.txt") | |
| translated_text = [] | |
| with open(transcript_file, 'w', encoding='utf-8') as text_file: | |
| for segment in result['segments']: | |
| start_time = segment['start'] | |
| end_time = segment['end'] | |
| text = segment['text'] | |
| text_file.write(f"[{start_time:.2f} - {end_time:.2f}] {text}\n") | |
| if selected_language in ['Dutch', 'English']: | |
| text_en = GoogleTranslator(source='auto', target='en').translate(text) | |
| translated_text.append(f"[{start_time:.2f} - {end_time:.2f}] {text_en}") | |
| text_file.write(f"[{start_time:.2f} - {end_time:.2f}] {text_en}\n") | |
| # Return the transcription and translation | |
| return "\n".join(translated_text) if translated_text else result['text'] | |
| except Exception as e: | |
| return f"Failed to process the audio file: {e}" | |
| finally: | |
| # Clean up temporary audio file | |
| if os.path.exists(temp_audio_path): | |
| os.remove(temp_audio_path) | |
| # Define the Gradio interface | |
| interface = gr.Interface( | |
| fn=transcribe_and_translate, | |
| inputs=[ | |
| gr.Audio(source="upload", type="file", label="Upload Audio"), | |
| gr.Dropdown(label="Select Language", choices=["nl","en"], value="mai"), | |
| gr.Dropdown(label="Select Model Type", choices=["tiny", "base", "small", "medium", "large"], value="base") | |
| ], | |
| outputs="text", | |
| title="Transcription and Translation" | |
| ) | |
| if __name__ == '__main__': | |
| # Launch the Gradio interface | |
| interface.launch() | |