BatuhanYilmaz
commited on
Commit
β’
f13254e
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Parent(s):
0a8bf2e
Update 01_π₯_Input_YouTube_Link.py
Browse files- 01_π₯_Input_YouTube_Link.py +92 -92
01_π₯_Input_YouTube_Link.py
CHANGED
@@ -1,5 +1,6 @@
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import whisper
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from
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import requests
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import time
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import streamlit as st
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from utils import write_vtt, write_srt
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import ffmpeg
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from languages import LANGUAGES
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st.set_page_config(page_title="Auto Subtitled Video Generator ", page_icon=":movie_camera:", layout="wide")
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# Define a function that we can use to load lottie files from a link.
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@st.cache()
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def load_lottieurl(url: str):
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r = requests.get(url)
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if r.status_code != 200:
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return None
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return r.json()
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col1, col2 = st.columns([1, 3])
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with col1:
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lottie = load_lottieurl("https://assets8.lottiefiles.com/packages/lf20_jh9gfdye.json")
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###### β If you want to transcribe the video in its original language, select the task as "Transcribe"
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###### β If you want to translate the subtitles to English, select the task as "Translate"
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###### I recommend starting with the base model and then experimenting with the larger models, the small and medium models often work well. """)
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@st.cache(allow_output_mutation=True)
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def populate_metadata(link):
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yt = YouTube(link)
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author = yt.author
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title = yt.title
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description = yt.description
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thumbnail = yt.thumbnail_url
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length = yt.length
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views = yt.views
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return author, title, description, thumbnail, length, views
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@st.cache(allow_output_mutation=True)
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def download_video(link):
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yt = YouTube(link)
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return video
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return time.strftime("%H:%M:%S", time.gmtime(seconds))
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loaded_model = whisper.load_model("base")
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current_size = "None"
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@st.cache(allow_output_mutation=True)
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def change_model(current_size, size):
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if current_size != size:
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loaded_model = whisper.load_model(size)
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raise Exception("Model size is the same as the current size.")
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@st.cache(allow_output_mutation=True)
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def inference(link, loaded_model, task):
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yt = YouTube(link)
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if task == "Transcribe":
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options = dict(task="transcribe", best_of=5)
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results = loaded_model.transcribe(path, **options)
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raise ValueError("Task not supported")
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@st.cache(allow_output_mutation=True)
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def getSubs(segments: Iterator[dict], format: str, maxLineWidth: int) -> str:
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segmentStream = StringIO()
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def generate_subtitled_video(video, audio, transcript):
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video_file = ffmpeg.input(video)
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audio_file = ffmpeg.input(audio)
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ffmpeg.concat(video_file.filter("subtitles", transcript), audio_file, v=1, a=1).output("
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video_with_subs = open("
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return video_with_subs
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loaded_model = change_model(current_size, size)
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st.write(f"Model is {'multilingual' if loaded_model.is_multilingual else 'English-only'} "
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f"and has {sum(np.prod(p.shape) for p in loaded_model.parameters()):,} parameters.")
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link = st.text_input("YouTube Link (The longer the video, the longer the processing time)")
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task = st.selectbox("Select Task", ["Transcribe", "Translate"], index=0)
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if task == "Transcribe":
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if st.button("Transcribe"):
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video = download_video(link)
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lang = results[3]
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detected_language = get_language_code(lang)
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col3, col4 = st.columns(2)
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col5, col6, col7, col8 = st.columns(4)
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col9, col10 = st.columns(2)
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with col3:
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st.video(video)
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#
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with open("transcript.txt", "w+", encoding='utf8') as f:
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f.writelines(
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f.close()
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with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
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datatxt = f.read()
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f.close()
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with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
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datasrt = f.read()
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with col5:
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st.download_button(label="Download Transcript (.txt)",
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data=datatxt,
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file_name="transcript.txt")
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with col6:
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st.download_button(label="Download Transcript (.vtt)",
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data=datavtt,
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file_name="transcript.vtt")
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with col7:
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st.download_button(label="Download Transcript (.srt)",
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data=datasrt,
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file_name="transcript.srt")
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with col9:
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st.success("You can download the transcript in .srt format, edit it (if you need to) and upload it to YouTube to create subtitles for your video.")
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with col10:
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st.info("Streamlit refreshes after the download button is clicked. The data is cached so you can download the transcript again without having to transcribe the video again.")
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with col4:
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with st.spinner("Generating Subtitled Video
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video_with_subs = generate_subtitled_video(video, "audio.mp3", "transcript.srt")
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st.video(video_with_subs)
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st.balloons()
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elif task == "Translate":
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if st.button("Translate to English"):
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video = download_video(link)
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lang = results[3]
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detected_language = get_language_code(lang)
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col3, col4 = st.columns(2)
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col5, col6, col7, col8 = st.columns(4)
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col9, col10 = st.columns(2)
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with col3:
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st.video(video)
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#
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with open("transcript.txt", "w+", encoding='utf8') as f:
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f.writelines(
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f.close()
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with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
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datatxt = f.read()
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f.close()
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with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
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datasrt = f.read()
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st.download_button(label="Download Transcript (.txt)",
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data=datatxt,
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file_name="transcript.txt")
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with col6:
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st.download_button(label="Download Transcript (.vtt)",
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data=datavtt,
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file_name="transcript.vtt")
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with col7:
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st.download_button(label="Download Transcript (.srt)",
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data=datasrt,
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file_name="transcript.srt")
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with col9:
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st.success("You can download the transcript in .srt format, edit it (if you need to) and upload it to YouTube to create subtitles for your video.")
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with col10:
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st.info("Streamlit refreshes after the download button is clicked. The data is cached so you can download the transcript again without having to transcribe the video again.")
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with col4:
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with st.spinner("Generating Subtitled Video
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video_with_subs = generate_subtitled_video(video, "audio.mp3", "transcript.srt")
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st.video(video_with_subs)
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st.balloons()
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else:
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st.
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if __name__ == "__main__":
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main()
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import whisper
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from pytubefix import YouTube
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from pytubefix.cli import on_progress
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import requests
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import time
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import streamlit as st
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from utils import write_vtt, write_srt
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import ffmpeg
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from languages import LANGUAGES
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import torch
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from zipfile import ZipFile
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from io import BytesIO
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import base64
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import pathlib
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import re
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st.set_page_config(page_title="Auto Subtitled Video Generator", page_icon=":movie_camera:", layout="wide")
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torch.cuda.is_available()
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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# Model options: tiny, base, small, medium, large
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loaded_model = whisper.load_model("small", device=DEVICE)
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current_size = "None"
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# Define a function that we can use to load lottie files from a link.
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def load_lottieurl(url: str):
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r = requests.get(url)
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if r.status_code != 200:
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return None
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return r.json()
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APP_DIR = pathlib.Path(__file__).parent.absolute()
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LOCAL_DIR = APP_DIR / "local_youtube"
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LOCAL_DIR.mkdir(exist_ok=True)
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save_dir = LOCAL_DIR / "output"
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save_dir.mkdir(exist_ok=True)
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col1, col2 = st.columns([1, 3])
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with col1:
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lottie = load_lottieurl("https://assets8.lottiefiles.com/packages/lf20_jh9gfdye.json")
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###### β If you want to transcribe the video in its original language, select the task as "Transcribe"
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###### β If you want to translate the subtitles to English, select the task as "Translate"
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###### I recommend starting with the base model and then experimenting with the larger models, the small and medium models often work well. """)
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def download_video(link):
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yt = YouTube(link, on_progress_callback=on_progress)
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ys = yt.streams.get_highest_resolution()
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video = ys.download(filename=f"{save_dir}/youtube_video.mp4")
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return video
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return time.strftime("%H:%M:%S", time.gmtime(seconds))
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def change_model(current_size, size):
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if current_size != size:
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loaded_model = whisper.load_model(size)
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raise Exception("Model size is the same as the current size.")
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def inference(link, loaded_model, task):
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yt = YouTube(link, on_progress_callback=on_progress)
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ys = yt.streams.get_audio_only()
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path = ys.download(filename=f"{save_dir}/audio.mp3", mp3=True)
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if task == "Transcribe":
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options = dict(task="transcribe", best_of=5)
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results = loaded_model.transcribe(path, **options)
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raise ValueError("Task not supported")
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def getSubs(segments: Iterator[dict], format: str, maxLineWidth: int) -> str:
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segmentStream = StringIO()
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def generate_subtitled_video(video, audio, transcript):
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video_file = ffmpeg.input(video)
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audio_file = ffmpeg.input(audio)
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ffmpeg.concat(video_file.filter("subtitles", transcript), audio_file, v=1, a=1).output("youtube_sub.mp4").run(quiet=True, overwrite_output=True)
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video_with_subs = open("youtube_sub.mp4", "rb")
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return video_with_subs
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loaded_model = change_model(current_size, size)
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st.write(f"Model is {'multilingual' if loaded_model.is_multilingual else 'English-only'} "
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f"and has {sum(np.prod(p.shape) for p in loaded_model.parameters()):,} parameters.")
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link = st.text_input("YouTube Link (The longer the video, the longer the processing time)", placeholder="Input YouTube link and press enter")
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task = st.selectbox("Select Task", ["Transcribe", "Translate"], index=0)
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if task == "Transcribe":
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if st.button("Transcribe"):
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with st.spinner("Transcribing the video..."):
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results = inference(link, loaded_model, task)
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video = download_video(link)
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lang = results[3]
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detected_language = get_language_code(lang)
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col3, col4 = st.columns(2)
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with col3:
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st.video(video)
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# Split result["text"] on !,? and . , but save the punctuation
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sentences = re.split("([!?.])", results[0])
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# Join the punctuation back to the sentences
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sentences = ["".join(i) for i in zip(sentences[0::2], sentences[1::2])]
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text = "\n\n".join(sentences)
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with open("transcript.txt", "w+", encoding='utf8') as f:
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f.writelines(text)
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f.close()
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with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
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datatxt = f.read()
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f.close()
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with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
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datasrt = f.read()
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with col4:
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with st.spinner("Generating Subtitled Video"):
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video_with_subs = generate_subtitled_video(video, f"{save_dir}/audio.mp3", "transcript.srt")
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st.video(video_with_subs)
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st.balloons()
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zipObj = ZipFile("YouTube_transcripts_and_video.zip", "w")
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zipObj.write("transcript.txt")
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zipObj.write("transcript.vtt")
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zipObj.write("transcript.srt")
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zipObj.write("youtube_sub.mp4")
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zipObj.close()
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ZipfileDotZip = "YouTube_transcripts_and_video.zip"
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with open(ZipfileDotZip, "rb") as f:
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datazip = f.read()
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b64 = base64.b64encode(datazip).decode()
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href = f"<a href=\"data:file/zip;base64,{b64}\" download='{ZipfileDotZip}'>\
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Download Transcripts and Video\
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</a>"
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st.markdown(href, unsafe_allow_html=True)
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elif task == "Translate":
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if st.button("Translate to English"):
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with st.spinner("Translating to English..."):
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results = inference(link, loaded_model, task)
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video = download_video(link)
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lang = results[3]
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detected_language = get_language_code(lang)
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col3, col4 = st.columns(2)
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with col3:
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st.video(video)
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# Split result["text"] on !,? and . , but save the punctuation
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sentences = re.split("([!?.])", results[0])
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# Join the punctuation back to the sentences
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sentences = ["".join(i) for i in zip(sentences[0::2], sentences[1::2])]
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text = "\n\n".join(sentences)
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with open("transcript.txt", "w+", encoding='utf8') as f:
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f.writelines(text)
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f.close()
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with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
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datatxt = f.read()
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f.close()
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with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
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datasrt = f.read()
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with col4:
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with st.spinner("Generating Subtitled Video"):
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video_with_subs = generate_subtitled_video(video, f"{save_dir}/audio.mp3", "transcript.srt")
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st.video(video_with_subs)
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st.balloons()
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|
237 |
+
zipObj = ZipFile("YouTube_transcripts_and_video.zip", "w")
|
238 |
+
zipObj.write("transcript.txt")
|
239 |
+
zipObj.write("transcript.vtt")
|
240 |
+
zipObj.write("transcript.srt")
|
241 |
+
zipObj.write("youtube_sub.mp4")
|
242 |
+
zipObj.close()
|
243 |
+
ZipfileDotZip = "YouTube_transcripts_and_video.zip"
|
244 |
+
with open(ZipfileDotZip, "rb") as f:
|
245 |
+
datazip = f.read()
|
246 |
+
b64 = base64.b64encode(datazip).decode()
|
247 |
+
href = f"<a href=\"data:file/zip;base64,{b64}\" download='{ZipfileDotZip}'>\
|
248 |
+
Download Transcripts and Video\
|
249 |
+
</a>"
|
250 |
+
st.markdown(href, unsafe_allow_html=True)
|
251 |
+
|
252 |
else:
|
253 |
+
st.info("Please select a task.")
|
254 |
|
255 |
|
256 |
if __name__ == "__main__":
|
257 |
main()
|
258 |
+
|