import torch import gradio as gr import pytube as pt from transformers import pipeline from huggingface_hub import model_info MODEL_NAME = "BlueRaccoon/whisper-small-uz" #this always needs to stay in line 8 :D sorry for the hackiness lang = "uz" device = 0 if torch.cuda.is_available() else "cpu" pipe = pipeline( task="automatic-speech-recognition", model=MODEL_NAME, chunk_length_s=30, device=device, ) pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe") def transcribe(microphone, file_upload): warn_output = "" if (microphone is not None) and (file_upload is not None): warn_output = ( "WARNING: You've uploaded an audio file and used the microphone. " "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" ) elif (microphone is None) and (file_upload is None): return "ERROR: You have to either use the microphone or upload an audio file" file = microphone if microphone is not None else file_upload text = pipe(file)["text"] return warn_output + 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): 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")["text"] return html_embed_str, text demo = gr.Blocks() mf_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.inputs.Audio(source="microphone", type="filepath", optional=True), gr.inputs.Audio(source="upload", type="filepath", optional=True), ], outputs="text", layout="horizontal", 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.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")], outputs=["html", "text"], layout="horizontal", 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(enable_queue=True)