| import torch | |
| from scipy.io.wavfile import write | |
| from main_pipeline import CleaningPipeline | |
| import gradio as gr | |
| title = "Audio denoising and speaker diarization " | |
| example_list = [ | |
| ["dialog.mp3"] | |
| ] | |
| def app_pipeline(audio): | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| cleaning_pipeline = CleaningPipeline(device) | |
| audio_path = 'test.wav' | |
| write(audio_path, audio[0], audio[1]) | |
| result = cleaning_pipeline(audio_path) | |
| if result != []: | |
| return result | |
| app = gr.Interface( | |
| app_pipeline, | |
| gr.Audio(type="numpy", label="Input_audio"), | |
| [gr.Audio(visible=True, label='denoised_audio' if i == 0 else f'speaker{i}') for i in range(20)], | |
| title=title, | |
| examples=example_list, | |
| cache_examples=False, | |
| ) | |
| app.launch(debug=True, enable_queue=True, | |
| ) | |