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!pip install -U openai-whisper
!pip install -U git+https://github.com/linto-ai/whisper-timestamped
!pip install gradio moviepy whisper-timestamped
import os
import datetime as dt
import json
import whisper_timestamped as whisper
from moviepy.video.io.VideoFileClip import VideoFileClip
import gradio as gr
# Helper functions and global variables
outdir = dt.datetime.now().strftime("%Y%m%d%H%M")
if os.path.exists(outdir):
random_digits = str(random.randint(1000, 9999))
new_outdir = outdir + random_digits
os.mkdir(new_outdir)
outdir = new_outdir
print("Created new output directory:", new_outdir)
else:
os.system(f"mkdir {outdir}")
print("date time now:" + outdir)
model = whisper.load_model("base")
def generate_timestamps(vidname):
audio = whisper.load_audio(vidname)
result = whisper.transcribe(model, audio, language="en")
return result
def get_segment_info(data):
new_list = []
for segment in data.get("segments", []):
if "id" in segment and "start" in segment and "end" in segment and "text" in segment:
new_item = {
"id": segment["id"],
"start": segment["start"],
"end": segment["end"],
"text": segment["text"]
}
new_list.append(new_item)
return new_list
def combine_entries(entries):
combined_entries = []
current_entry = None
total_duration = 0
for entry in entries:
entry_duration = entry["end"] - entry["start"]
if total_duration + entry_duration > 30:
if current_entry:
current_entry["end"] = entry["end"]
combined_entries.append(current_entry)
current_entry = {
"start": entry["start"],
"end": entry["end"],
"text": entry["text"]
}
total_duration = entry_duration
else:
if current_entry:
current_entry["end"] = entry["end"]
current_entry["text"] += " " + entry["text"]
total_duration += entry_duration
else:
current_entry = {
"start": entry["start"],
"end": entry["end"],
"text": entry["text"]
}
total_duration = entry_duration
if current_entry:
combined_entries.append(current_entry)
return combined_entries
def extract_video_segment(input_video, output_video, start_time, end_time):
video_clip = VideoFileClip(input_video).subclip(start_time, end_time)
video_clip.write_videofile(output_video, codec="libx264", audio_codec="aac")
video_clip.close()
def save_segments(outdir, name, combined_entries):
segments = combined_entries
input_video = name
for i, segment in enumerate(segments):
start_time = segment['start']
end_time = segment['end']
output_video_file = f'{outdir}/output_segment_{i + 1}.mp4'
extract_video_segment(input_video, output_video_file, start_time, end_time)
def split_up_video(video_path, output_dir):
result = generate_timestamps(video_path)
combined_entries = combine_entries(get_segment_info(result))
scribeout = open(f"{output_dir}/transcript.txt", "w")
scribeout.write(json.dumps(combined_entries, indent=2, ensure_ascii=False))
scribeout.close()
save_segments(output_dir, video_path, combined_entries)
filename, extension = os.path.splitext(video_path)
os.system(f"zip -r {filename}.zip {output_dir}")
return f"{filename}.zip"
# Gradio interface
def process_video(video):
output_dir = dt.datetime.now().strftime("%Y%m%d%H%M")
os.mkdir(output_dir)
video_path = video.name
output_zip = split_up_video(video_path, output_dir)
return output_zip
iface = gr.Interface(
fn=process_video,
inputs=gr.File(file_count="single", type="filepath", label="Upload a Video"),
outputs=gr.File(label="Download Zipped Segments and Transcript"),
title="Video Splitter",
description="Upload a video and get a zipped file with segmented videos and a transcript."
)
iface.launch()
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