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Initial release
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@QubitPi
QubitPi committed Oct 5, 2024
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Showing 1 changed file with 114 additions and 130 deletions.
244 changes: 114 additions & 130 deletions app.py
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import whisper
from pytubefix import YouTube
from pytubefix.cli import on_progress
import requests
import time
import streamlit as st
from streamlit_lottie import st_lottie
import numpy as np
import os
from typing import Iterator
from io import StringIO
from utils import write_vtt, write_srt
import ffmpeg
from languages import LANGUAGES
import torch
from zipfile import ZipFile
import base64
import requests
from typing import Iterator
from io import StringIO
import numpy as np
import pathlib
import re
import os

st.set_page_config(page_title="Auto Subtitled Video Generator", page_icon=":movie_camera:", layout="wide")

torch.cuda.is_available()
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# Model options: tiny, base, small, medium, large
loaded_model = whisper.load_model("small", device=DEVICE)
current_size = "None"

# Define a function that we can use to load lottie files from a link.
@st.cache(allow_output_mutation=True)
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()


APP_DIR = pathlib.Path(__file__).parent.absolute()

LOCAL_DIR = APP_DIR / "local_youtube"
LOCAL_DIR = APP_DIR / "local"
LOCAL_DIR.mkdir(exist_ok=True)
save_dir = LOCAL_DIR / "output"
save_dir.mkdir(exist_ok=True)


loaded_model = whisper.load_model("base")
current_size = "None"

col1, col2 = st.columns([1, 3])
with col1:
lottie = load_lottieurl("https://assets8.lottiefiles.com/packages/lf20_jh9gfdye.json")
lottie = load_lottieurl("https://assets1.lottiefiles.com/packages/lf20_HjK9Ol.json")
st_lottie(lottie)

with col2:
st.write("""
## Auto Subtitled Video Generator
##### Input a YouTube video link and get a video with subtitles.
##### Upload a video file and get a video with subtitles.
###### ➠ If you want to transcribe the video in its original language, select the task as "Transcribe"
###### ➠ If you want to translate the subtitles to English, select the task as "Translate"
###### I recommend starting with the base model and then experimenting with the larger models, the small and medium models often work well. """)


def download_video(link):
yt = YouTube(link, on_progress_callback=on_progress)
ys = yt.streams.get_highest_resolution()
video = ys.download(filename=f"{save_dir}/youtube_video.mp4")
return video


def convert(seconds):
return time.strftime("%H:%M:%S", time.gmtime(seconds))


@st.cache(allow_output_mutation=True)
def change_model(current_size, size):
if current_size != size:
loaded_model = whisper.load_model(size)
Expand All @@ -75,20 +55,23 @@ def change_model(current_size, size):
raise Exception("Model size is the same as the current size.")


def inference(link, loaded_model, task):
yt = YouTube(link, on_progress_callback=on_progress)
ys = yt.streams.get_audio_only()
path = ys.download(filename=f"{save_dir}/audio.mp3", mp3=True)
@st.cache(allow_output_mutation=True)
def inferecence(loaded_model, uploaded_file, task):
with open(f"{save_dir}/input.mp4", "wb") as f:
f.write(uploaded_file.read())
audio = ffmpeg.input(f"{save_dir}/input.mp4")
audio = ffmpeg.output(audio, f"{save_dir}/output.wav", acodec="pcm_s16le", ac=1, ar="16k")
ffmpeg.run(audio, overwrite_output=True)
if task == "Transcribe":
options = dict(task="transcribe", best_of=5)
results = loaded_model.transcribe(path, **options)
results = loaded_model.transcribe(f"{save_dir}/output.wav", **options)
vtt = getSubs(results["segments"], "vtt", 80)
srt = getSubs(results["segments"], "srt", 80)
lang = results["language"]
return results["text"], vtt, srt, lang
elif task == "Translate":
options = dict(task="translate", best_of=5)
results = loaded_model.transcribe(path, **options)
results = loaded_model.transcribe(f"{save_dir}/output.wav", **options)
vtt = getSubs(results["segments"], "vtt", 80)
srt = getSubs(results["segments"], "srt", 80)
lang = results["language"]
Expand All @@ -111,145 +94,146 @@ def getSubs(segments: Iterator[dict], format: str, maxLineWidth: int) -> str:
return segmentStream.read()


def get_language_code(language):
if language in LANGUAGES.keys():
detected_language = LANGUAGES[language]
return detected_language
else:
raise ValueError("Language not supported")


def generate_subtitled_video(video, audio, transcript):
video_file = ffmpeg.input(video)
audio_file = ffmpeg.input(audio)
ffmpeg.concat(video_file.filter("subtitles", transcript), audio_file, v=1, a=1).output("youtube_sub.mp4").run(quiet=True, overwrite_output=True)
video_with_subs = open("youtube_sub.mp4", "rb")
return video_with_subs

ffmpeg.concat(video_file.filter("subtitles", transcript), audio_file, v=1, a=1).output("final.mp4").run(quiet=True,
overwrite_output=True)
video_with_subs = open("final.mp4", "rb")
return video_with_subs


def main():
size = st.selectbox("Select Model Size (The larger the model, the more accurate the transcription will be, but it will take longer)", ["tiny", "base", "small", "medium", "large-v3"], index=1)
size = st.selectbox(
"Select Model Size (The larger the model, the more accurate the transcription will be, but it will take longer)",
["tiny", "base", "small", "medium", "large"], index=1)
loaded_model = change_model(current_size, size)
st.write(f"Model is {'multilingual' if loaded_model.is_multilingual else 'English-only'} "
f"and has {sum(np.prod(p.shape) for p in loaded_model.parameters()):,} parameters.")
link = st.text_input("YouTube Link (The longer the video, the longer the processing time)", placeholder="Input YouTube link and press enter")
f"and has {sum(np.prod(p.shape) for p in loaded_model.parameters()):,} parameters.")
input_file = st.file_uploader("File", type=["mp4", "avi", "mov", "mkv"])
# get the name of the input_file
if input_file is not None:
filename = input_file.name[:-4]
else:
filename = None
task = st.selectbox("Select Task", ["Transcribe", "Translate"], index=0)
if task == "Transcribe":
if st.button("Transcribe"):
with st.spinner("Transcribing the video..."):
results = inference(link, loaded_model, task)
video = download_video(link)
lang = results[3]
detected_language = get_language_code(lang)

results = inferecence(loaded_model, input_file, task)
col3, col4 = st.columns(2)
col5, col6, col7, col8 = st.columns(4)
col9, col10 = st.columns(2)
with col3:
st.video(video)

# Split result["text"] on !,? and . , but save the punctuation
sentences = re.split("([!?.])", results[0])
# Join the punctuation back to the sentences
sentences = ["".join(i) for i in zip(sentences[0::2], sentences[1::2])]
text = "\n\n".join(sentences)
st.video(input_file)

with open("transcript.txt", "w+", encoding='utf8') as f:
f.writelines(text)
f.writelines(results[0])
f.close()
with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
datatxt = f.read()
with open("transcript.vtt", "w+",encoding='utf8') as f:

with open("transcript.vtt", "w+", encoding='utf8') as f:
f.writelines(results[1])
f.close()
with open(os.path.join(os.getcwd(), "transcript.vtt"), "rb") as f:
datavtt = f.read()
with open("transcript.srt", "w+",encoding='utf8') as f:

with open("transcript.srt", "w+", encoding='utf8') as f:
f.writelines(results[2])
f.close()
with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
datasrt = f.read()


with col5:
st.download_button(label="Download Transcript (.txt)",
data=datatxt,
file_name="transcript.txt")
with col6:
st.download_button(label="Download Transcript (.vtt)",
data=datavtt,
file_name="transcript.vtt")
with col7:
st.download_button(label="Download Transcript (.srt)",
data=datasrt,
file_name="transcript.srt")
with col9:
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.")
with col10:
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.")

with col4:
with st.spinner("Generating Subtitled Video"):
video_with_subs = generate_subtitled_video(video, f"{save_dir}/audio.mp3", "transcript.srt")
video_with_subs = generate_subtitled_video(f"{save_dir}/input.mp4", f"{save_dir}/output.wav",
"transcript.srt")
st.video(video_with_subs)
st.balloons()

zipObj = ZipFile("YouTube_transcripts_and_video.zip", "w")
zipObj.write("transcript.txt")
zipObj.write("transcript.vtt")
zipObj.write("transcript.srt")
zipObj.write("youtube_sub.mp4")
zipObj.close()
ZipfileDotZip = "YouTube_transcripts_and_video.zip"
with open(ZipfileDotZip, "rb") as f:
datazip = f.read()
b64 = base64.b64encode(datazip).decode()
href = f"<a href=\"data:file/zip;base64,{b64}\" download='{ZipfileDotZip}'>\
Download Transcripts and Video\
</a>"
st.markdown(href, unsafe_allow_html=True)

st.snow()
with col8:
st.download_button(label="Download Video with Subtitles",
data=video_with_subs,
file_name=f"{filename}_with_subs.mp4")
elif task == "Translate":
if st.button("Translate to English"):
with st.spinner("Translating to English..."):
results = inference(link, loaded_model, task)
video = download_video(link)
lang = results[3]
detected_language = get_language_code(lang)

results = inferecence(loaded_model, input_file, task)
col3, col4 = st.columns(2)
col5, col6, col7, col8 = st.columns(4)
col9, col10 = st.columns(2)
with col3:
st.video(video)

# Split result["text"] on !,? and . , but save the punctuation
sentences = re.split("([!?.])", results[0])
# Join the punctuation back to the sentences
sentences = ["".join(i) for i in zip(sentences[0::2], sentences[1::2])]
text = "\n\n".join(sentences)
st.video(input_file)

with open("transcript.txt", "w+", encoding='utf8') as f:
f.writelines(text)
f.writelines(results[0])
f.close()
with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
datatxt = f.read()
with open("transcript.vtt", "w+",encoding='utf8') as f:

with open("transcript.vtt", "w+", encoding='utf8') as f:
f.writelines(results[1])
f.close()
with open(os.path.join(os.getcwd(), "transcript.vtt"), "rb") as f:
datavtt = f.read()
with open("transcript.srt", "w+",encoding='utf8') as f:

with open("transcript.srt", "w+", encoding='utf8') as f:
f.writelines(results[2])
f.close()
with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
datasrt = f.read()


with col5:
st.download_button(label="Download Transcript (.txt)",
data=datatxt,
file_name="transcript.txt")
with col6:
st.download_button(label="Download Transcript (.vtt)",
data=datavtt,
file_name="transcript.vtt")
with col7:
st.download_button(label="Download Transcript (.srt)",
data=datasrt,
file_name="transcript.srt")
with col9:
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.")
with col10:
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.")

with col4:
with st.spinner("Generating Subtitled Video"):
video_with_subs = generate_subtitled_video(video, f"{save_dir}/audio.mp3", "transcript.srt")
video_with_subs = generate_subtitled_video(f"{save_dir}/input.mp4", f"{save_dir}/output.wav",
"transcript.srt")
st.video(video_with_subs)
st.balloons()

zipObj = ZipFile("YouTube_transcripts_and_video.zip", "w")
zipObj.write("transcript.txt")
zipObj.write("transcript.vtt")
zipObj.write("transcript.srt")
zipObj.write("youtube_sub.mp4")
zipObj.close()
ZipfileDotZip = "YouTube_transcripts_and_video.zip"
with open(ZipfileDotZip, "rb") as f:
datazip = f.read()
b64 = base64.b64encode(datazip).decode()
href = f"<a href=\"data:file/zip;base64,{b64}\" download='{ZipfileDotZip}'>\
Download Transcripts and Video\
</a>"
st.markdown(href, unsafe_allow_html=True)

st.snow()
with col8:
st.download_button(label="Download Video with Subtitles ",
data=video_with_subs,
file_name=f"{filename}_with_subs.mp4")
else:
st.info("Please select a task.")
st.error("Please select a task.")


if __name__ == "__main__":
main()

st.markdown(
"###### Made with :heart: by [@BatuhanYılmaz](https://github.com/BatuhanYilmaz26) [![this is an image link](https://i.imgur.com/thJhzOO.png)](https://www.buymeacoffee.com/batuhanylmz)")

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