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AI Prompt

I would like to split a large video in two parts using python, while preserving the format and quality. What are my options?

AI Response

I’d be happy to help you split a video using Python. First, I’d like to understand a few things about your needs:

  1. What format is your video file (e.g., MP4, AVI, MOV)?
  2. Do you have a specific time or frame where you want to split it?
  3. Are you comfortable installing Python packages?
  4. What’s your experience level with Python?

Once you answer these questions, I can suggest the most appropriate approach for your situation.

AI Prompt

I am quite experienced with python and have no problem installing libraries. the video is an mp4 downloaded from youtube. It’s a recording of a video lesson and Ideally I would like to split it at the lesson break, which is a section of several minutes of near silence

AI Response

Ah, that’s an interesting challenge! Given your experience level, we could approach this in two steps:

  1. First, we can analyze the audio track to detect the period of silence
  2. Then split the video at that point

For this task, we could use: - moviepy for video manipulation - librosa or pydub for audio analysis

Would you like me to explain how we can detect the silence first, or would you prefer to see a complete solution right away?

AI Prompt

is there something related to ffmpeg?

AI Response

Yes, absolutely! FFmpeg is actually an excellent choice for this task. There are several ways to use FFmpeg with Python:

  1. python-ffmpeg - A pure Python binding for FFmpeg
  2. ffmpeg-python - A more Pythonic wrapper for FFmpeg
  3. Direct FFmpeg command-line calls using Python’s subprocess

FFmpeg would be more efficient than moviepy (which actually uses FFmpeg under the hood). Would you like to explore one of these approaches? Which one interests you most?

Also, FFmpeg has built-in silence detection capabilities that might be perfect for your use case.

AI Prompt

that’s great. What are differences between 1 and 2? If they are basically equivalent, I do prefer pythonic wrappers

AI Response

The main differences are:

Given your preference for Pythonic code, let’s go with ffmpeg-python. Would you like to see how to use it to detect silence and split your video?

AI Prompt

ok. Let’s go with ffmpeg-python then

AI Response

Great choice! First, let’s install the package:

!pip install ffmpeg-python

Before we proceed with the code, I should mention we’ll need two steps: 1. First detect the silence period to find our split point 2. Then split the video at that point

Would you like me to show you how to detect the silence first? We can use FFmpeg’s silencedetect filter for this.

AI Prompt

I assume I need to install ffmpeg first?

AI Response

Yes, you’ll need FFmpeg installed on your system first! The method depends on your operating system:

Which operating system are you using? I can provide more specific installation instructions if needed.

AI Prompt

ok. I have everything installed and ready to go. What’s the plan?

AI Response

Here’s our plan to split your video:

  1. First, we’ll use the silencedetect filter to find periods of silence in your video. We’ll need to set two parameters:
    • noise tolerance (in dB)
    • minimum silence duration (in seconds)

Would you like me to show you how to detect silence periods first? We can then adjust these parameters based on your specific video’s characteristics.

Before we start, could you tell me the path/filename of your video so we can use it in our code?

AI Prompt

yes please. I know absolutely nothing about the library, so I’ll need examples of everything

AI Response

I’ll show you how to detect silence using ffmpeg-python. Here’s a basic example to get us started:

import ffmpeg
import json

def detect_silence(input_file, noise_db="-30dB", duration=1):
    """
    Detect silence in a video file
    noise_db: noise tolerance level (e.g., "-30dB")
    duration: minimum silence duration in seconds
    """
    try:
        # Run silence detection
        out = (
            ffmpeg
            .input(input_file)
            .filter('silencedetect', n=noise_db, d=duration)
            .output('pipe:', format='null')
            .run(capture_stderr=True)
        )
        
        # Print the output to see what we get
        print(out[1].decode())
        
    except ffmpeg.Error as e:
        print('Error:', e.stderr.decode())

Would you like to try this with your video file? We can then adjust the noise_db and duration parameters based on the results we get.

AI Prompt

Two things: 1. The break is not perfectly silent. Nobody talks, but there are still some minor noises in the background. Not sure what noise level would be appropriate 2. How can I test this?

AI Response

Good questions! Let’s address both:

  1. For background noise, we typically start with a higher threshold like “-30dB” (less sensitive) and adjust if needed. We can try different values:
    • “-30dB” (very tolerant of noise)
    • “-40dB” (moderately sensitive)
    • “-50dB” (more sensitive)
  2. Let’s test it! You can call the function like this:
# Example usage with your video file
video_path = "path/to/your/video.mp4"
detect_silence(video_path, noise_db="-30dB", duration=3)

Would you like to try this with your video? We can start with these parameters and adjust them based on what we find. The duration parameter (set to 3 seconds here) helps avoid detecting brief pauses in speech.

What’s the path to your video file?

AI Prompt

Ok, I run the function and it seemed to have worked. It printed to the stdout the following:

[silencedetect @ 0x6000021dd860] silence_start: 4913.747007
[silencedetect @ 0x6000021dd860] silence_end: 4976.207098 | silence_duration: 62.460091
[out#0/null @ 0x6000030d83c0] video:0KiB audio:1223096KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: unknown
size=N/A time=01:58:20.05 bitrate=N/A speed=1.44e+03x

What now? Should the function return something or should I add to the pipe?

AI Response

Great! We’ve found a silence period of about 62 seconds starting at around 1:22:13 (4913 seconds). Let’s modify the function to parse this output into something more usable. Here’s how we can improve it:

def detect_silence(input_file, noise_db="-30dB", duration=3):
    """
    Detect silence in a video file and return the silence periods
    Returns: list of tuples (start_time, end_time, duration)
    """
    try:
        out = (
            ffmpeg
            .input(input_file)
            .filter('silencedetect', n=noise_db, d=duration)
            .output('pipe:', format='null')
            .run(capture_stderr=True)
        )
        
        # Parse the output
        silence_periods = []
        stderr = out[1].decode()
        
        start_times = [float(x.split('silence_start: ')[1]) 
                      for x in stderr.split('\n') if 'silence_start' in x]
        end_times = [float(x.split('silence_end: ')[1].split(' |')[0]) 
                    for x in stderr.split('\n') if 'silence_end' in x]
        
        for start, end in zip(start_times, end_times):
            duration = end - start
            silence_periods.append((start, end, duration))
            
        return silence_periods
        
    except ffmpeg.Error as e:
        print('Error:', e.stderr.decode())
        return []

Would you like to try this improved version? It will return a list of tuples containing the start time, end time, and duration of each silence period.

AI Prompt

whoa! There’s no better way than parsing the stderr?

AI Response

Unfortunately, no! This is one of FFmpeg’s quirks - the silence detection information is only available through stderr. Even the official FFmpeg tool outputs this way.

There are a few alternatives we could consider: 1. Use a different library like librosa for audio analysis (but this would be slower) 2. Use FFmpeg’s JSON output format for some operations (but silence detection doesn’t support it) 3. Write the data to a file and read it back (but that would be even more complicated)

Would you like to proceed with the stderr parsing approach (it’s actually quite reliable despite looking hacky), or would you prefer to explore one of the alternatives?

AI Prompt

Ok. Stderror parsing it is then. I have saved the results in a variable which is a list of tuples. Containing exactly one tuple with start, end and duration of the silence. What now? How do I use this to split the file?

AI Response

Now that we have the split point, we can use FFmpeg to split the video without re-encoding (which preserves quality and is fast). We’ll use the start time from your first tuple for the split.

Would you like me to show you how to split the video at that point? We’ll need to: 1. Extract the first part (from start to silence_start) 2. Extract the second part (from silence_end to the end)

Or would you prefer to split at the middle of the silence period? Let me know which approach you’d prefer and I’ll show you the code for it.

AI Prompt

Let’s split in the middle. should be easier

AI Response

You’re right! Let’s split in the middle of the silence period. Here’s how we can do it with ffmpeg-python:

def split_video(input_file, split_time, output_part1, output_part2):
    """
    Split video at specified time (in seconds)
    """
    try:
        # Extract first part
        (
            ffmpeg
            .input(input_file, ss=0, t=split_time)
            .output(output_part1, c='copy')
            .overwrite_output()
            .run()
        )
        
        # Extract second part
        (
            ffmpeg
            .input(input_file, ss=split_time)
            .output(output_part2, c='copy')
            .overwrite_output()
            .run()
        )
    except ffmpeg.Error as e:
        print('Error:', e.stderr.decode())

Would you like me to explain how to use this function with your silence period data? We’ll need to calculate the middle point from your tuple first.

AI Prompt

Quick aside: I want to write a function that takes the file path (as a “Pathlib” Path) as a string and appends _part1 and _part2 to the file name (before the extension)

AI Response

Here’s a function to help with that using pathlib.Path:

def get_output_paths(input_path: str | Path) -> tuple[Path, Path]:
    path = Path(input_path)
    stem = path.stem  # gets filename without extension
    suffix = path.suffix  # gets extension including dot
    return (
        path.with_name(f"{stem}_part1{suffix}"),
        path.with_name(f"{stem}_part2{suffix}")
    )

Would you like to test this function with your video path before we integrate it with the splitting code?

AI Prompt

I can do it myself, thanks

AI Response

Great! Once you’ve tested the path handling, would you like to see how to calculate the middle point of the silence period and put everything together?

AI Prompt

how can I add leading zeroes to an integer? I have a tuple of integers like (1, 32, 5) and I want to represent it as 01:32:05

AI Response

You can use Python’s string formatting with the format specifier :02d for 2-digit zero-padded integers:

f"{hours:02d}:{minutes:02d}:{seconds:02d}"

Or alternatively using str.zfill():

":".join(str(n).zfill(2) for n in (1, 32, 5))

Would you like me to explain either of these approaches in more detail?

AI Prompt

can you remind me what is the difference between writelines and write for a file? I have a string containing newline charachters and I want to save it into a file

AI Response

The key differences are:

For your case with a single string containing newlines, write() is the simpler choice since it will preserve the newlines exactly as they are in your string. Like this:

with open(filename, 'w') as f:
    f.write(my_string)

Would you like me to explain more about when you might want to use writelines instead?