import gradio as gr import os from gradio_client import Client def transcribe_audio(youtube_url: str, task: str = "transcribe", return_timestamps: bool = False, api_name: str = "/predict_2") -> dict: """ Transcribe audio from a given YouTube URL using a specified model. Parameters: - youtube_url (str): The YouTube URL to transcribe. - task (str, optional): The task to perform. Default is "transcribe". - return_timestamps (bool, optional): Whether to return timestamps. Default is True. - api_name (str, optional): The API endpoint to use. Default is "/predict_2". Returns: - dict: The transcription result. """ client = Client("https://sanchit-gandhi-whisper-jax.hf.space/") result = client.predict(youtube_url, task, return_timestamps, fn_index=7) return result MODEL_NAME = "openai/whisper-large-v2" demo = gr.Blocks() EXAMPLES = [ ["https://www.youtube.com/watch?v=H1YoNlz2LxA", "translate",False], ] yt_transcribe = gr.Interface( fn=transcribe_audio, inputs=[ gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), gr.inputs.Checkbox(label="Return timestamps") ], outputs=[gr.outputs.HTML(label="Video"), gr.outputs.Textbox(label="Transcription").style(show_copy_button=True)], layout="horizontal", theme=gr.themes.Base(), title="Whisper Large V2: Transcribe YouTube", description=( "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint" f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of" " arbitrary length." ), allow_flagging="never", examples=EXAMPLES, cache_examples=False ) with demo: gr.DuplicateButton() gr.TabbedInterface([yt_transcribe], [ "YouTube"]) demo.launch(enable_queue=True)