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| import torch | |
| import os | |
| import gradio as gr | |
| import pytube as pt | |
| from asr_diarizer import ASRDiarizationPipeline # TODO: speechbox import | |
| MODEL_NAME = "openai/whisper-tiny" | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| pipe = ASRDiarizationPipeline.from_pretrained( | |
| asr_model=MODEL_NAME, | |
| device=device, | |
| use_auth_token=HF_TOKEN, | |
| ) | |
| def tuple_to_string(start_end_tuple, ndigits=1): | |
| return str((round(start_end_tuple[0], ndigits), round(start_end_tuple[1], ndigits))) | |
| def format_as_transcription(raw_segments, with_timestamps=False): | |
| if with_timestamps: | |
| return "\n\n".join([chunk["speaker"] + " " + tuple_to_string(chunk["timestamp"]) + chunk["text"] for chunk in raw_segments]) | |
| else: | |
| return "\n\n".join([chunk["speaker"] + chunk["text"] for chunk in raw_segments]) | |
| def transcribe(file_upload, with_timestamps): | |
| raw_segments = pipe(file_upload) | |
| transcription = format_as_transcription(raw_segments, with_timestamps=with_timestamps) | |
| return transcription | |
| def _return_yt_html_embed(yt_url): | |
| video_id = yt_url.split("?v=")[-1] | |
| HTML_str = ( | |
| f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>' | |
| " </center>" | |
| ) | |
| return HTML_str | |
| def yt_transcribe(yt_url, with_timestamps): | |
| yt = pt.YouTube(yt_url) | |
| html_embed_str = _return_yt_html_embed(yt_url) | |
| stream = yt.streams.filter(only_audio=True)[0] | |
| stream.download(filename="audio.mp3") | |
| text = pipe("audio.mp3") | |
| return html_embed_str, format_as_transcription(text, with_timestamps=with_timestamps) | |
| demo = gr.Blocks() | |
| mf_transcribe = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.inputs.Audio(source="upload", type="filepath"), | |
| gr.Checkbox(label="With timestamps?", value=True), | |
| ], | |
| outputs="text", | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="Whisper Demo: Transcribe Audio", | |
| description=( | |
| "Transcribe audio files with speaker diarization using 🤗 Speechbox. Demo uses the pre-trained checkpoint" | |
| f" [Whisper Tiny](https://huggingface.co/openai/whisper-tiny) for the ASR transcriptions and" | |
| f" [PyAnnote Audio](https://huggingface.co/pyannote/speaker-diarization) to label the speakers." | |
| ), | |
| examples=[ | |
| ["./processed.wav", True], | |
| ["./processed.wav", False], | |
| ], | |
| allow_flagging="never", | |
| ) | |
| yt_transcribe = gr.Interface( | |
| fn=yt_transcribe, | |
| inputs=[ | |
| gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), | |
| gr.Checkbox(label="With timestamps?", value=True), | |
| ], | |
| outputs=["html", "text"], | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="Whisper Speaker Diarization Demo: Transcribe YouTube", | |
| description=( | |
| "Transcribe YouTube videos with speaker diarization using 🤗 Speechbox. Demo uses the pre-trained checkpoint" | |
| f" [Whisper Tiny](https://huggingface.co/openai/whisper-tiny) for the ASR transcriptions and" | |
| f" [PyAnnote Audio](https://huggingface.co/pyannote/speaker-diarization) to label the speakers." | |
| ), | |
| examples=[ | |
| ["https://www.youtube.com/watch?v=9dAWIPixYxc", True], | |
| ["https://www.youtube.com/watch?v=9dAWIPixYxc", False], | |
| ], | |
| allow_flagging="never", | |
| ) | |
| with demo: | |
| gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"]) | |
| demo.launch(enable_queue=True) | |