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| import os | |
| import torch | |
| import gradio as gr | |
| import pytube as pt | |
| import spaces | |
| from transformers import pipeline | |
| from huggingface_hub import model_info | |
| MODEL_NAME = "NbAiLab/whisper-large-sme" #this always needs to stay in line 8 :D sorry for the hackiness | |
| lang = "fi" | |
| share = (os.environ.get("SHARE", "False")[0].lower() in "ty1") or None | |
| auth_token = os.environ.get("AUTH_TOKEN") or True | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| print(f"Using device: {device}") | |
| def pipe(file, return_timestamps=False): | |
| asr = pipeline( | |
| task="automatic-speech-recognition", | |
| model=MODEL_NAME, | |
| chunk_length_s=30, | |
| device=device, | |
| token=auth_token, | |
| ) | |
| asr.model.config.forced_decoder_ids = asr.tokenizer.get_decoder_prompt_ids( | |
| language=lang, | |
| task="transcribe", | |
| no_timestamps=not return_timestamps, | |
| ) | |
| # asr.model.config.no_timestamps_token_id = asr.tokenizer.encode("<|notimestamps|>", add_special_tokens=False)[0] | |
| return asr(file, return_timestamps=return_timestamps) | |
| def transcribe(file, return_timestamps=False): | |
| if not return_timestamps: | |
| text = pipe(file)["text"] | |
| else: | |
| chunks = pipe(file, return_timestamps=True)["chunks"] | |
| text = [] | |
| for chunk in chunks: | |
| start_time = time.strftime('%H:%M:%S', time.gmtime(chunk["timestamp"][0])) if chunk["timestamp"][0] is not None else "??:??:??" | |
| end_time = time.strftime('%H:%M:%S', time.gmtime(chunk["timestamp"][1])) if chunk["timestamp"][1] is not None else "??:??:??" | |
| line = f"[{start_time} -> {end_time}] {chunk['text']}" | |
| text.append(line) | |
| text = "\n".join(text) | |
| return text | |
| 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, return_timestamps=False): | |
| 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 = transcribe("audio.mp3", return_timestamps=return_timestamps) | |
| return html_embed_str, text | |
| demo = gr.Blocks() | |
| mf_transcribe = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.components.Audio(sources=['upload', 'microphone'], type="filepath"), | |
| # gr.components.Checkbox(label="Return timestamps"), | |
| ], | |
| outputs="text", | |
| theme="huggingface", | |
| title="Whisper Demo: Transcribe Audio", | |
| description=( | |
| "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned" | |
| f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" | |
| " of arbitrary length." | |
| ), | |
| allow_flagging="never", | |
| ) | |
| yt_transcribe = gr.Interface( | |
| fn=yt_transcribe, | |
| inputs=[ | |
| gr.components.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), | |
| # gr.components.Checkbox(label="Return timestamps"), | |
| ], | |
| examples=[["https://www.youtube.com/watch?v=mukeSSa5GKo"]], | |
| outputs=["html", "text"], | |
| theme="huggingface", | |
| title="Whisper Demo: Transcribe YouTube", | |
| description=( | |
| "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:" | |
| f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of" | |
| " arbitrary length." | |
| ), | |
| allow_flagging="never", | |
| ) | |
| with demo: | |
| gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"]) | |
| demo.launch(share=True).queue() | |