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| import gradio as gr | |
| from dataset import TranscriptDataset | |
| from downloader import WhisperPP, YoutubeDownloader | |
| from interpreter import WhisperInterpreter | |
| model_size = "base" | |
| mode = "transcribe" | |
| write = False | |
| download_path = "tmp/" | |
| def dataset(url, name, token): | |
| ds = TranscriptDataset(name) | |
| data = [] | |
| #whisper_options = dict( | |
| # model_size=model_size, mode=mode, write=write, number_videos=500) | |
| #whisperPP = WhisperPP(data,name, **whisper_options) | |
| #downloader = YoutubeDownloader(download_path) | |
| #downloader.download(url, whisperPP) | |
| params = dict(model_size=model_size,write=write, number_videos=500) | |
| overwrite = True | |
| ds.generate_dataset(url, download_path, overwrite, params) | |
| ds.upload(token) | |
| return "Dataset created at : " + "https://huggingface.co/datasets/"+ name | |
| yt_input = gr.Textbox(label = 'Youtube Link') | |
| name_input = gr.Textbox(label = 'Dataset Name',placeholder = "Enter in the format username/repo_name") | |
| token_input = gr.Textbox(label = "HF Token", placeholder="Write access token") | |
| repo_output = gr.Textbox(label = "Outcome") | |
| iface = gr.Interface(fn=dataset, inputs=[yt_input, name_input, token_input], outputs=repo_output, title="Create Transcription Dataset for Youtube using OpenAI Whisper !", | |
| description="Create a HuggingFace repository for Youtube Transcripts! You need to specify a write token obtained in https://hf.co/settings/token. This Space is a an experimental demo.", | |
| article="<p>Find your write token at <a href='https://huggingface.co/settings/token' target='_blank'>token settings</a></p>") | |
| iface.launch() |