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import torch |
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import gradio as gr |
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import yt_dlp as youtube_dl |
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from transformers import pipeline |
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from transformers.pipelines.audio_utils import ffmpeg_read |
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import tempfile |
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import os |
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import time |
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MODEL_NAME = "openai/whisper-large-v3" |
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BATCH_SIZE = 8 |
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FILE_LIMIT_MB = 1000 |
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YT_LENGTH_LIMIT_S = 3600 |
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device = 0 if torch.cuda.is_available() else "cpu" |
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pipe = pipeline( |
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task="automatic-speech-recognition", |
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model=MODEL_NAME, |
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chunk_length_s=30, |
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device=device, |
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) |
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def transcribe(inputs, task): |
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if inputs is None: |
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raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.") |
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text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"] |
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return text |
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def _return_yt_html_embed(yt_url): |
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video_id = yt_url.split("?v=")[-1] |
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HTML_str = ( |
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f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>' |
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" </center>" |
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) |
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return HTML_str |
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def download_yt_audio(yt_url, filename): |
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info_loader = youtube_dl.YoutubeDL() |
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try: |
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info = info_loader.extract_info(yt_url, download=False) |
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except youtube_dl.utils.DownloadError as err: |
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raise gr.Error(str(err)) |
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file_length = info["duration"] |
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file_length_s = int(file_length) |
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if file_length_s > YT_LENGTH_LIMIT_S: |
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yt_length_limit_hms = time.strftime("%H:%M:%S", time.gmtime(YT_LENGTH_LIMIT_S)) |
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file_length_hms = time.strftime("%H:%M:%S", time.gmtime(file_length_s)) |
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raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.") |
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ydl_opts = {"outtmpl": filename, "format": "bestaudio/best"} |
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with youtube_dl.YoutubeDL(ydl_opts) as ydl: |
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try: |
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ydl.download([yt_url]) |
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except youtube_dl.utils.ExtractorError as err: |
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raise gr.Error(str(err)) |
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def yt_transcribe(yt_url, task, max_filesize=75.0): |
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html_embed_str = _return_yt_html_embed(yt_url) |
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with tempfile.TemporaryDirectory() as tmpdirname: |
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filepath = os.path.join(tmpdirname, "audio.m4a") |
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download_yt_audio(yt_url, filepath) |
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with open(filepath, "rb") as f: |
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inputs = f.read() |
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inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate) |
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inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate} |
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text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"] |
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return html_embed_str, text |
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description = """ |
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Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the checkpoint openai/whisper-large-v3 and Transformers to transcribe audio files of arbitrary length.<br> |
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<a href='https://www.twman.org' target='_blank'>TonTon Huang Ph.D. </a><br> |
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<a href='https://blog.twman.org/2021/04/ASR.html' target='_blank'>那些語音處理 (Speech Processing) 踩的坑</a> | <a href='https://blog.twman.org/2021/04/NLP.html' target='_blank'>那些自然語言處理 (Natural Language Processing, NLP) 踩的坑</a><br> |
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<a href='https://blog.twman.org/2024/02/asr-tts.html' target='_blank'>那些ASR和TTS可能會踩的坑</a> | <a href='https://blog.twman.org/2024/02/LLM.html' target='_blank'>那些大模型開發會踩的坑</a><br> |
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<a href='https://blog.twman.org/2023/04/GPT.html' target='_blank'>什麼是大語言模型，它是什麼？想要嗎？</a><br> |
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<a href='https://blog.twman.org/2023/07/wsl.html' target='_blank'>用PaddleOCR的PPOCRLabel來微調醫療診斷書和收據</a> | <a href='https://blog.twman.org/2023/07/HugIE.html' target='_blank'>基於機器閱讀理解和指令微調的統一信息抽取框架之診斷書醫囑資訊擷取分析</a><br> |
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""" |
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file_transcribe = gr.Interface( |
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fn=transcribe, |
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inputs=[ |
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gr.Audio(type="filepath"), |
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gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), |
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], |
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outputs="text", |
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title="Whisper Large V3: Transcribe Audio", |
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description=description, |
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allow_flagging="never", |
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) |
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yt_description = """ |
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Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint openai/whisper-large-v3 and Transformers to transcribe audio files of arbitrary length.<br> |
|
<a href='https://www.twman.org' target='_blank'>TonTon Huang Ph.D. </a><br> |
|
<a href='https://blog.twman.org/2021/04/ASR.html' target='_blank'>那些語音處理 (Speech Processing) 踩的坑</a> | <a href='https://blog.twman.org/2021/04/NLP.html' target='_blank'>那些自然語言處理 (Natural Language Processing, NLP) 踩的坑</a><br> |
|
<a href='https://blog.twman.org/2024/02/asr-tts.html' target='_blank'>那些ASR和TTS可能會踩的坑</a> | <a href='https://blog.twman.org/2024/02/LLM.html' target='_blank'>那些大模型開發會踩的坑</a><br> |
|
<a href='https://blog.twman.org/2023/04/GPT.html' target='_blank'>什麼是大語言模型，它是什麼？想要嗎？</a><br> |
|
<a href='https://blog.twman.org/2023/07/wsl.html' target='_blank'>用PaddleOCR的PPOCRLabel來微調醫療診斷書和收據</a> | <a href='https://blog.twman.org/2023/07/HugIE.html' target='_blank'>基於機器閱讀理解和指令微調的統一信息抽取框架之診斷書醫囑資訊擷取分析</a><br> |
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""" |
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yt_transcribe = gr.Interface( |
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fn=yt_transcribe, |
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inputs=[ |
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gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), |
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gr.Radio(["transcribe", "translate"], label="Task", value="transcribe") |
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], |
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outputs=["html", "text"], |
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title="Whisper Large V3: Transcribe YouTube", |
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description=yt_description, |
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allow_flagging="never", |
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) |
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with gr.Blocks() as demo: |
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gr.TabbedInterface([file_transcribe, yt_transcribe], ["Audio file", "YouTube"]) |
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demo.launch(debug=True) |
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