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| import gradio as gr | |
| from typing import Any | |
| import math | |
| import torch | |
| from transformers import pipeline | |
| from diffusers import StableDiffusionPipeline | |
| from TTS.api import TTS | |
| import whisper | |
| import utils | |
| from youtubeaudioextractor import PytubeAudioExtractor | |
| from transcriber import SpanishTranscriber, WhisperTranscriber | |
| from textprocessor import TextProcessor | |
| from videocreator import VideoCreator | |
| from share_btn import community_icon_html, loading_icon_html, share_js | |
| MAX_NUM_WORDS = 20000 | |
| MAX_CHUNK_LENGTH = 1000 | |
| spanish_transcribe_model = "juancopi81/whisper-medium-es" | |
| languages = {"Spanish": "es", "English": "en"} | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| device_dict = {"cuda": 0, "cpu": -1} | |
| dtype = torch.float16 if device == "cuda" else torch.float32 | |
| # Detect if code is running in Colab | |
| is_colab = utils.is_google_colab() | |
| colab_instruction = "" if is_colab else """ | |
| <p>You can skip the queue using Colab: | |
| <a href=""> | |
| <img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p>""" | |
| device_print = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" | |
| # Initialize components | |
| audio_extractor = PytubeAudioExtractor() | |
| es_transcription_pipe = pipeline( | |
| task="automatic-speech-recognition", | |
| model=spanish_transcribe_model, | |
| chunk_length_s=30, | |
| device=device_dict[device], | |
| ) | |
| es_transcription_pipe.model.config.forced_decoder_ids = es_transcription_pipe.tokenizer.get_decoder_prompt_ids(language="es", | |
| task="transcribe") | |
| es_audio_transcriber = SpanishTranscriber(es_transcription_pipe) | |
| en_transcription_pipe = whisper.load_model("base") | |
| en_audio_transcriber = WhisperTranscriber(en_transcription_pipe) | |
| openai_model = "text-davinci-003" | |
| text_processor = TextProcessor(openai_model) | |
| image_model_id = "runwayml/stable-diffusion-v1-5" | |
| image_pipeline = StableDiffusionPipeline.from_pretrained(image_model_id, | |
| torch_dtype=dtype, | |
| revision="fp16") | |
| image_pipeline = image_pipeline.to(device) | |
| es_vo_model_name = TTS.list_models()[22] | |
| en_vo_model_name = TTS.list_models()[8] | |
| # Init TTS | |
| es_tts = TTS(es_vo_model_name) | |
| en_tts = TTS(en_vo_model_name) | |
| def datapipeline(url: str, | |
| video_language: str, | |
| summary_language: str, | |
| video_styles: str) -> Any: | |
| audio_path_file = audio_extractor.extract(url) | |
| print(f"Audio file created at: {audio_path_file}") | |
| # Select transcriber | |
| if video_language == "Spanish": | |
| audio_transcriber = es_audio_transcriber | |
| elif video_language == "English": | |
| audio_transcriber = en_audio_transcriber | |
| else: | |
| return "Language not supported" | |
| if summary_language == "Spanish": | |
| video_creator = VideoCreator(es_tts, image_pipeline) | |
| elif summary_language == "English": | |
| video_creator = VideoCreator(en_tts, image_pipeline) | |
| else: | |
| return "Language not supported" | |
| transcribed_text = audio_transcriber.transcribe(audio_path_file) | |
| print("Audio transcription ready!") | |
| # Get total number of words in text | |
| num_words_transcription = len(transcribed_text.split()) | |
| if num_words_transcription > MAX_NUM_WORDS: | |
| print("to add return here") | |
| if num_words_transcription > MAX_CHUNK_LENGTH: | |
| num_chunks = math.ceil(num_words_transcription / MAX_CHUNK_LENGTH) | |
| num_words_per_chunk = num_words_transcription // num_chunks | |
| chunks = utils.splitter(num_words_per_chunk, transcribed_text) | |
| json_scenes = {} | |
| for chunk in chunks: | |
| if len(chunk.split()) > 50: | |
| max_key = max(json_scenes.keys(), default=0) | |
| chunk_scenes = text_processor.get_json_scenes(chunk, | |
| summary_language) | |
| chunk_scenes = {k+max_key: v for k, v in chunk_scenes.items()} | |
| json_scenes.update(chunk_scenes) | |
| else: | |
| json_scenes = text_processor.get_json_scenes(transcribed_text, | |
| summary_language) | |
| print("Scenes ready") | |
| video = video_creator.create_video(json_scenes, video_styles) | |
| print("Video at", video) | |
| return video, video | |
| css = """ | |
| a { | |
| color: inherit; | |
| text-decoration: underline; | |
| } | |
| .gradio-container { | |
| font-family: 'IBM Plex Sans', sans-serif; | |
| } | |
| .gr-button { | |
| color: white; | |
| border-color: #000000; | |
| background: #000000; | |
| } | |
| input[type='range'] { | |
| accent-color: #000000; | |
| } | |
| .dark input[type='range'] { | |
| accent-color: #dfdfdf; | |
| } | |
| .container { | |
| max-width: 730px; | |
| margin: auto; | |
| padding-top: 1.5rem; | |
| } | |
| #gallery { | |
| min-height: 22rem; | |
| margin-bottom: 15px; | |
| margin-left: auto; | |
| margin-right: auto; | |
| border-bottom-right-radius: .5rem !important; | |
| border-bottom-left-radius: .5rem !important; | |
| } | |
| #gallery>div>.h-full { | |
| min-height: 20rem; | |
| } | |
| .details:hover { | |
| text-decoration: underline; | |
| } | |
| .gr-button { | |
| white-space: nowrap; | |
| } | |
| .gr-button:focus { | |
| border-color: rgb(147 197 253 / var(--tw-border-opacity)); | |
| outline: none; | |
| box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); | |
| --tw-border-opacity: 1; | |
| --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); | |
| --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); | |
| --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); | |
| --tw-ring-opacity: .5; | |
| } | |
| #advanced-btn { | |
| font-size: .7rem !important; | |
| line-height: 19px; | |
| margin-top: 12px; | |
| margin-bottom: 12px; | |
| padding: 2px 8px; | |
| border-radius: 14px !important; | |
| } | |
| #advanced-options { | |
| margin-bottom: 20px; | |
| } | |
| .footer { | |
| margin-bottom: 45px; | |
| margin-top: 35px; | |
| text-align: center; | |
| border-bottom: 1px solid #e5e5e5; | |
| } | |
| .footer>p { | |
| font-size: .8rem; | |
| display: inline-block; | |
| padding: 0 10px; | |
| transform: translateY(10px); | |
| background: white; | |
| } | |
| .dark .footer { | |
| border-color: #303030; | |
| } | |
| .dark .footer>p { | |
| background: #0b0f19; | |
| } | |
| .acknowledgments h4{ | |
| margin: 1.25em 0 .25em 0; | |
| font-weight: bold; | |
| font-size: 115%; | |
| } | |
| #container-advanced-btns{ | |
| display: flex; | |
| flex-wrap: wrap; | |
| justify-content: space-between; | |
| align-items: center; | |
| } | |
| .animate-spin { | |
| animation: spin 1s linear infinite; | |
| } | |
| @keyframes spin { | |
| from { | |
| transform: rotate(0deg); | |
| } | |
| to { | |
| transform: rotate(360deg); | |
| } | |
| } | |
| #share-btn-container { | |
| display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; | |
| margin-top: 10px; | |
| margin-left: auto; | |
| } | |
| #share-btn { | |
| all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0; | |
| } | |
| #share-btn * { | |
| all: unset; | |
| } | |
| #share-btn-container div:nth-child(-n+2){ | |
| width: auto !important; | |
| min-height: 0px !important; | |
| } | |
| #share-btn-container .wrap { | |
| display: none !important; | |
| } | |
| .gr-form{ | |
| flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0; | |
| } | |
| #prompt-container{ | |
| gap: 0; | |
| } | |
| #generated_id{ | |
| min-height: 700px | |
| } | |
| #setting_id{ | |
| margin-bottom: 12px; | |
| text-align: center; | |
| font-weight: 900; | |
| } | |
| """ | |
| block = gr.Blocks(css=css) | |
| with block as demo: | |
| gr.HTML( | |
| f""" | |
| <div style="text-align: center; max-width: 650px; margin: 0 auto;"> | |
| <div | |
| style=" | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 0.8rem; | |
| font-size: 1.75rem; | |
| " | |
| > | |
| <h1 style="font-weight: 900; margin-bottom: 7px;"> | |
| YouTube to Video Summary | |
| </h1> | |
| </div> | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| Enter the URL of a YouTube video (in Spanish or English) and you'll recieve a video with an illustraded summary (in Spanish or English, it works as translator). | |
| It works for audio books, history lessons, etc. Try it out with a short video (less than 4 minutes). SEE SOME EXAMPLES AT THE BOTTOM. | |
| </p> | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| Running on <b>{device_print}</b> | |
| </p> | |
| </p> | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| You can buy me a coffee to support this space: | |
| <span style="display: flex;align-items: center;justify-content: center;height: 30px;"> | |
| <a href="https://www.buymeacoffee.com/juancopi81j"> | |
| <img src="https://badgen.net/badge/icon/Buy%20Me%20A%20Coffee?icon=buymeacoffee&label" alt="Buy me a coffee"></a>. | |
| </span> | |
| </p> | |
| </div> | |
| """ | |
| ) | |
| with gr.Group(): | |
| with gr.Box(): | |
| with gr.Row(elem_id="setting_id").style(mobile_collapse=False, equal_height=True): | |
| gr.HTML("<h1>Settings</h1>") | |
| with gr.Row(): | |
| with gr.Column(): | |
| video_language = gr.Radio(choices=["Spanish", "English"], | |
| label="Language of your input video:", | |
| value="Spanish") | |
| with gr.Column(): | |
| summary_language = gr.Radio(choices=["Spanish", "English"], | |
| label="Language of your output video:", | |
| value="Spanish") | |
| with gr.Row(): | |
| video_styles = gr.Textbox(label="(OPTIONAL) Enter the styles for your ouput video", | |
| value="", | |
| placeholder="illustration, highly detailed, digital painting, concept art, matte, art by wlop and artgerm and greg rutkowski and alphonse mucha, masterpiece") | |
| with gr.Group(): | |
| with gr.Box(): | |
| with gr.Row().style(mobile_collapse=False, equal_height=True): | |
| url = gr.Textbox( | |
| label="Enter the URL of the YouTubeVideo", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="YouTube URL", | |
| elem_id="prompt-in" | |
| ).style( | |
| border=(True, False, True, True), | |
| rounded=(True, False, False, True), | |
| container=False, | |
| ) | |
| btn = gr.Button("Run").style( | |
| margin=False, | |
| rounded=(False, True, True, False), | |
| ) | |
| video_output = gr.Video(elem_id="output-video") | |
| file_output = gr.File() | |
| btn.click(datapipeline, | |
| inputs=[url, | |
| video_language, | |
| summary_language, | |
| video_styles], | |
| outputs=[video_output, file_output]) | |
| with gr.Group(elem_id="share-btn-container"): | |
| community_icon = gr.HTML(community_icon_html) | |
| loading_icon = gr.HTML(loading_icon_html) | |
| share_button = gr.Button("Share to community", elem_id="share-btn") | |
| share_button.click(None, [], [], _js=share_js) | |
| gr.Examples( | |
| examples=[["https://www.youtube.com/watch?v=c0i5016pB2Y", "English", "Spanish", "oil on painting"], | |
| ["https://www.youtube.com/watch?v=Hk5evm1NgzA", "Spanish", "English", "trending on artstation pixiv makoto shinkai"], | |
| ["https://www.youtube.com/watch?v=sRmmQBBln9Q", "Spanish", "Spanish", "Hyper real, 4k"], | |
| ["https://www.youtube.com/watch?v=qz4Wc48KITA", "Spanish", "English", "detailed art by kay nielsen and walter crane, illustration style, watercolor"]], | |
| inputs=[url, video_language, summary_language, video_styles], | |
| outputs=[video_output, file_output], | |
| fn=datapipeline, | |
| cache_examples=True | |
| ) | |
| gr.HTML( | |
| """ | |
| <div class="footer"> | |
| <p>This demos is part of the Whisper Sprint (Dec. 2022).</a> | |
| </p> | |
| </div> | |
| """ | |
| ) | |
| gr.Markdown(''' | |
| [](https://twitter.com/juancopi81) | |
|  | |
| ''') | |
| if not is_colab: | |
| demo.queue(concurrency_count=1) | |
| demo.launch(debug=is_colab, share=is_colab) |