import spaces 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 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, ) @spaces.GPU def transcribe(inputs, task): """ Transcribe or translate an audio input using the Whisper Large V3 model. This function runs automatic speech recognition on an uploaded or recorded audio file using a Hugging Face Transformers pipeline. It supports both transcription (same-language) and translation (to English). Args: inputs (str or dict): The audio input, typically a filepath to an audio file provided by Gradio, or a dictionary containing audio array and sampling rate. task (str): The task to perform, either "transcribe" or "translate". Returns: str: The transcribed or translated text output from the Whisper model. """ 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): """ Generate an HTML iframe embed for a YouTube video. This helper function extracts the video ID from a YouTube URL and returns a simple HTML string for embedding the video in the UI. Args: yt_url (str): The full URL of the YouTube video. Returns: str: An HTML string containing an iframe embedding the YouTube video. """ video_id = yt_url.split("?v=")[-1] HTML_str = ( f'
' "
" ) return HTML_str def download_yt_audio(yt_url, filename): """ Download a YouTube video's audio to a local file. This function validates the video duration against a maximum length, then downloads the video (with audio) to a specified local filepath using yt-dlp. Args: yt_url (str): The URL of the YouTube video to download. filename (str): The destination filepath where the video/audio will be saved. Raises: gr.Error: If the video cannot be downloaded or exceeds the maximum allowed duration. """ 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_string"] file_h_m_s = file_length.split(":") file_h_m_s = [int(sub_length) for sub_length in file_h_m_s] if len(file_h_m_s) == 1: file_h_m_s.insert(0, 0) if len(file_h_m_s) == 2: file_h_m_s.insert(0, 0) file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2] if file_length_s > 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_s)) 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)) @spaces.GPU def yt_transcribe(yt_url, task, max_filesize=75.0): """ Transcribe or translate audio from a YouTube video. This function downloads a YouTube video, extracts its audio, converts it to the appropriate format, and runs Whisper-based automatic speech recognition on the content. Args: yt_url (str): The URL of the YouTube video to transcribe. task (str): The task to perform, either "transcribe" or "translate". max_filesize (float, optional): Maximum allowed file size in MB. Currently unused but kept for interface compatibility. Returns: tuple: - html_embed_str (str): An HTML iframe embedding the YouTube video. - text (str): The transcribed or translated text output. """ 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() mf_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.Audio(sources="microphone", type="filepath"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), ], 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." ), ) file_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.Audio(sources="upload", type="filepath", label="Audio file"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), ], 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." ), ) yt_transcribe = gr.Interface( fn=yt_transcribe, inputs=[ gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe") ], 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." ), ) with demo: gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"]) demo.launch(mcp_server=True)