import torch import gradio as gr import yt_dlp as youtube_dl from transformers import pipeline from transformers.pipelines.audio_utils import ffmpeg_read import tempfile import os import time MODEL_NAME = "openai/whisper-large-v3" BATCH_SIZE = 8 FILE_LIMIT_MB = 1000 YT_LENGTH_LIMIT_S = 3600 # limit to 1 hour YouTube files device = 0 if torch.cuda.is_available() else "cpu" pipe = pipeline( task="automatic-speech-recognition", model=MODEL_NAME, chunk_length_s=30, device=device, ) def transcribe(inputs, task): 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": task}, return_timestamps=True)["text"] return text def _return_yt_html_embed(yt_url): video_id = yt_url.split("?v=")[-1] HTML_str = ( f'
' "
" ) return HTML_str def download_yt_audio(yt_url, filename): info_loader = youtube_dl.YoutubeDL() try: info = info_loader.extract_info(yt_url, download=False) except youtube_dl.utils.DownloadError as err: raise gr.Error(str(err)) file_length = info["duration"] if file_length > YT_LENGTH_LIMIT_S: yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S)) file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length)) raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.") ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"} with youtube_dl.YoutubeDL(ydl_opts) as ydl: try: ydl.download([yt_url]) except youtube_dl.utils.ExtractorError as err: raise gr.Error(str(err)) def yt_transcribe(yt_url, task): 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} text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"] return html_embed_str, text demo = gr.Blocks(theme="ParityError/Interstellar") mf_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.inputs.Audio(source="microphone", type="filepath", optional=True), gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), ], outputs="text", layout="horizontal", theme="ParityError/Interstellar", title="", description=( "" 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.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"), gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), ], outputs="text", layout="horizontal", theme="ParityError/Interstellar", 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 = gr.Interface( fn=yt_transcribe, 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") ], outputs=["html", "text"], layout="horizontal", theme="ParityError/Interstellar", 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], ["Microphone", "Audio file", "YouTube"]) demo.launch(enable_queue=True)