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| import gradio as gr | |
| from pytube import YouTube | |
| import whisper | |
| # define function for transcription | |
| def whisper_transcript(model_size, url, audio_file): | |
| if url: | |
| link = YouTube(url) | |
| source = link.streams.filter(only_audio=True)[0].download(filename="audio.mp4") | |
| else: | |
| source = audio_file | |
| if model_size.endswith(".en"): | |
| language = "english" | |
| else: | |
| language = None | |
| options = whisper.DecodingOptions(without_timestamps=True) | |
| loaded_model = whisper.load_model(model_size) | |
| transcript = loaded_model.transcribe(source, language=language) | |
| return transcript["text"] | |
| # define Gradio app interface | |
| gradio_ui = gr.Interface( | |
| fn=whisper_transcript, | |
| title="Transcribe multi-lingual audio clips with Whisper", | |
| description="**How to use**: Select a model, paste in a Youtube link or upload an audio clip, then click submit. If your clip is **100% in English, select models ending in ‘.en’**. If the clip is in other languages, or a mix of languages, select models without ‘.en’", | |
| article="**Note**: The larger the model size selected or the longer the audio clip, the more time it would take to process the transcript.", | |
| inputs=[ | |
| gr.Dropdown( | |
| label="Select Model", | |
| choices=[ | |
| "tiny.en", | |
| "base.en", | |
| "small.en", | |
| "medium.en", | |
| "tiny", | |
| "base", | |
| "small", | |
| "medium", | |
| "large", | |
| ], | |
| value="base", | |
| ), | |
| gr.Textbox(label="Paste YouTube link here"), | |
| gr.Audio(label="Upload Audio File", source="upload", type="filepath"), | |
| ], | |
| outputs=gr.Textbox(label="Whisper Transcript"), | |
| ) | |
| gradio_ui.queue().launch() | |