import torch import spaces import gradio as gr from pytube import YouTube from transformers import pipeline, WhisperProcessor, WhisperForConditionalGeneration from transformers.pipelines.audio_utils import ffmpeg_read import tempfile import os MODEL_NAME = "MohamedRashad/Arabic-Whisper-CodeSwitching-Edition" BATCH_SIZE = 8 FILE_LIMIT_MB = 1000*3 YT_LENGTH_LIMIT_S = 60*60*3 # limit to 3 hour YouTube files device = 0 if torch.cuda.is_available() else "cpu" processor = WhisperProcessor.from_pretrained(MODEL_NAME) model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16) pipe = pipeline( task="automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, chunk_length_s=30, device=device, ) @spaces.GPU(120) def transcribe(inputs): if inputs is None: raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.") text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe", "language": "arabic"}, return_timestamps=True)["text"] return text def _return_yt_html_embed(yt_url): video_id = YouTube(yt_url).video_id HTML_str = ( f'
' "
" ) return HTML_str def download_yt_audio(yt_url, filename): yt = YouTube(yt_url) if yt.length > YT_LENGTH_LIMIT_S: raise gr.Error("YouTube video is too long! Please upload a video that is less than 1 hour long.") stream = yt.streams.filter(only_audio=True).first() stream.download(filename=filename) def seconds_to_timestamp(seconds): total_seconds = int(seconds) hours = total_seconds // 3600 minutes = (total_seconds % 3600) // 60 remaining_seconds = seconds % 60 return f"{hours:02d}:{minutes:02d}:{remaining_seconds:06.3f}" def chunks_to_subtitle(chunks): subtitle = "" for chunk in chunks: start = seconds_to_timestamp(chunk["timestamp"][0]) end = seconds_to_timestamp(chunk["timestamp"][1]) text = chunk["text"] subtitle += f"{start} --> {end}\n{text}\n\n" return subtitle @spaces.GPU(120) def yt_transcribe(yt_url): html_embed_str = _return_yt_html_embed(yt_url) with tempfile.TemporaryDirectory() as tmpdirname: filepath = os.path.join(tmpdirname, "video.mp4") download_yt_audio(yt_url, filepath) with open(filepath, "rb") as f: inputs = f.read() inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate) inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate} output = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe", "language": "arabic"}, return_timestamps=True) subtitle = chunks_to_subtitle(output["chunks"]) return html_embed_str, subtitle demo = gr.Blocks() mf_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.Audio(sources="microphone", type="filepath"), ], outputs="text", title="Whisper Large V3: Transcribe Audio", description=( "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the" f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" " of arbitrary length." ), allow_flagging="never", ) file_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.Audio(sources="upload", type="filepath", label="Audio file"), ], outputs="text", title="Whisper Large V3: Transcribe Audio", description=( "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the" f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" " of arbitrary length." ), allow_flagging="never", ) yt_transcribe_demo = gr.Interface( fn=yt_transcribe, inputs=[ gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), ], outputs=["html", "text"], title="Whisper Large V3: 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", ) with demo: gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe_demo], ["Microphone", "Audio file", "YouTube"]) demo.queue().launch(share=True)