Spaces:
Running
Running
| import torch | |
| import gradio as gr | |
| import pytube as pt | |
| from transformers import pipeline | |
| from transformers.pipelines.audio_utils import ffmpeg_read | |
| import tempfile | |
| MODEL_NAME = "openai/whisper-large-v2" | |
| 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(microphone, file_upload, task): | |
| 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): | |
| raise gr.Error("You have to either use the microphone or upload an audio file") | |
| file_size_mb = os.stat(inputs).st_size / (1024 * 1024) | |
| if file_size_mb > FILE_LIMIT_MB: | |
| raise gr.Error( | |
| f"File size exceeds file size limit. Got file of size {file_size_mb:.2f}MB for a limit of {FILE_LIMIT_MB}MB." | |
| ) | |
| file = microphone if microphone is not None else file_upload | |
| text = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task})["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 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_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)) | |
| def yt_transcribe(yt_url, task, max_filesize=75.0): | |
| yt = pt.YouTube(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, pipeline.feature_extractor.sampling_rate) | |
| inputs = {"array": inputs, "sampling_rate": pipeline.feature_extractor.sampling_rate} | |
| text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task})["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), | |
| gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), | |
| ], | |
| outputs="text", | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="Whisper Large V2: 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="huggingface", | |
| title="Whisper Large V2: 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, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"]) | |
| demo.launch(enable_queue=True) | |