For your requirement of identifying silence breaks in an audio file, especially in a Linux environment or from a Python project, there are several tools and libraries you might find useful. Here are some recommendations:

1. SoX - Sound eXchange: SoX is a command-line utility that can process audio files in various formats. It includes a silence detection feature that allows you to identify periods of silence in an audio file. You can use it directly from the command line in Linux.

2. Pydub: Pydub is a Python library that can be used for manipulating audio. It has a feature to detect silence which can be used to find silent segments in an audio file. It's straightforward to use and integrates well with Python projects.

3. Audiolab: This is another Python library which is part of the scikits suite. It can be used for audio processing and analysis, and while it doesn't have built-in silence detection, you can use it to analyze the audio and implement your own silence detection logic.

4. FFmpeg: This is a very powerful multimedia framework that can handle a wide range of audio and video processing tasks. FFmpeg can be used to detect silence in audio files through its command-line interface.

5. LibROSA: A Python package for music and audio analysis. It provides the building blocks necessary to create music information retrieval systems. While it doesn't have direct silence detection, it can be used to analyze audio files and detect segments with low amplitude that might correspond to silence.

Each of these tools has its own strengths and can be used depending on the specific requirements of your project. For example, if you're looking for something that integrates easily with a Python project, Pydub or LibROSA might be more suitable. On the other hand, for a more general command-line tool, SoX or FFmpeg would be appropriate.

Remember to install the necessary dependencies and codecs for handling different audio formats, especially when working with tools like FFmpeg or SoX.

Given your requirement for processing long recordings efficiently and programmatically, I suggest you consider the following tools, which are known for their performance and flexibility:

1. SoX (Sound eXchange): SoX is highly efficient for audio processing tasks and can be used via the command line. It includes a silence detection feature that can identify periods of silence in an audio file. You can automate SoX in a script to process large audio files.

2. FFmpeg: FFmpeg is renowned for its processing speed and versatility. It can be used to detect silence in audio files through its command-line interface. FFmpeg supports various audio formats and can be integrated into scripts for batch processing.

3. Python with librosa and numpy: If you prefer a more customizable approach and are comfortable with Python coding, you can use the librosa library in combination with numpy. This approach allows you to analyze the audio file and implement a custom silence detection algorithm optimized for speed and tailored to your specific requirements.

4. Julius: Julius is a high-performance, two-pass large vocabulary continuous speech recognition (LVCSR) decoder software for speech-related researchers and developers. While primarily used for speech recognition, it can be adapted for silence detection in audio files, especially if you are dealing with speech data.

5. Praat Scripting: Praat is another tool used in phonetics, but it also offers scripting capabilities. You can write scripts to automate the analysis of audio files, including silence detection.

Given your expertise in machine learning and IT, you might find the Python-based solution (using librosa and numpy) particularly appealing as it offers flexibility and the possibility of integrating machine learning techniques for enhanced performance. However, for sheer speed and efficiency in handling large files, SoX and FFmpeg are excellent choices, especially when used as part of a scripted solution.

When implementing these solutions, you'll want to pay attention to the definition of 'silence' (e.g., threshold levels, duration) and test the tool to ensure it meets your accuracy requirements for silence detection in speech recordings.

Using FFmpeg for silence detection in an audio file is a powerful choice, especially for long recordings. FFmpeg provides a filter called silencedetect that you can use to identify periods of silence. Here is a basic example of how you can use it:

1. Basic Command: The following command will analyze an audio file and print the moments where silence starts and ends.

   bash
   ffmpeg -i input_audio_file.mp3 -af silencedetect=noise=-30dB:d=0.5 -f null -
   

   In this command:

   • -i input_audio_file.mp3 specifies the input file.
   • -af silencedetect=noise=-30dB:d=0.5 adds the silencedetect filter, where noise=-30dB is the noise threshold for silence (you can adjust this value) and d=0.5 is the minimum duration of silence to be detected (in seconds).
   • -f null - directs FFmpeg to discard the output rather than saving it to a file.

2. Interpreting the Output: FFmpeg will print messages to the console like silence_start: <timestamp> and silence_end: <timestamp> | silence_duration: <duration>. These indicate the start and end of detected silence periods.

3. Customizing for Your Needs: You might need to adjust the noise threshold and d duration parameters based on your specific audio characteristics. The values -30dB and 0.5 are just starting points.

4. Automating the Process: You can write a script to parse these timestamps from FFmpeg's output and use them as needed in your project.

5. Batch Processing: If you have multiple files to process, you can script the execution of FFmpeg to handle each file in turn.

Remember that detecting silence accurately can depend a lot on the nature of your audio. You might need to experiment with different threshold levels to find what works best for your specific audio files.

Yes, you can use FFmpeg to automatically split an input audio file at points of silence. This process involves using the silencedetect filter to identify the silence, and then splitting the file based on these points. However, FFmpeg alone doesn't directly split files based on silencedetect output. Instead, you'll typically need a two-step process:

1. Detect Silence Periods: Use FFmpeg with the silencedetect filter to identify the timestamps of silence in the audio.

2. Split the File: Use the silence timestamps to split the file into segments. This can be done with additional FFmpeg commands in a script.

Here's an example of how you might do this:

Step 1: Detect Silence Periods

Run FFmpeg with silencedetect to get the timestamps:

bash
ffmpeg -i input_audio_file.mp3 -af silencedetect=noise=-30dB:d=1 -f null - 2>&1 | grep 'silence_end' > silence.txt

This command analyzes input_audio_file.mp3, using a silence threshold of -30dB and a minimum duration of 1 second for silence. It outputs the results to a file called silence.txt. The grep command filters the output to only include lines that contain 'silence_end', which also include the duration of the silence.

Step 2: Split the File

You would then write a script to parse the silence.txt file and use the timestamps to split the audio file with FFmpeg. Here's a pseudocode outline of what that script might do:

• Read the silence.txt file and extract the timestamps.
• Use these timestamps to define the start and end points of each audio segment.
• For each segment, run a command like:

  bash
  ffmpeg -i input_audio_file.mp3 -ss start_time -to end_time -c copy output_segment.mp3
  

  where start_time and end_time are the calculated start and end points for each segment, and output_segment.mp3 is the file for that segment.

This process requires some scripting, as FFmpeg doesn't provide a direct way to split files based on silence detection without additional processing. The exact details of the script will depend on your specific requirements and your comfort with scripting. For batch processing and more complex requirements, you might consider using a programming language like Python to handle the parsing and automate the FFmpeg commands.

Yes, you can perform the splitting in one pass with FFmpeg, but it requires a bit of manual setup if you already have a file with the detected silences. FFmpeg doesn't directly read from a file to split based on silence timestamps, so you'll need to construct an FFmpeg command manually using the timestamps from your file.

Here's a general approach to do this:

1. Prepare the Timestamps: First, you'll need to extract the silence end times from your file and convert them into a format suitable for FFmpeg. This typically involves transforming the timestamps into a list of start and end times for each segment you want to extract.

2. Construct the FFmpeg Command: You'll then construct an FFmpeg command that uses the -ss (start time) and -t (duration) options to extract each segment. You can use the concat demuxer in FFmpeg to do this in one command.

   An FFmpeg command using the concat demuxer looks like this:

   bash
   ffmpeg -f concat -safe 0 -i mylist.txt -c copy output.mp3
   

   Here, mylist.txt contains lines in the following format:

   arduino
   file 'input_audio_file.mp3'
   inpoint start_time
   outpoint end_time
   

   Repeat the above three lines for each segment you want to extract.

3. Automate the Process: Ideally, you'll want to automate the process of creating mylist.txt from your silence detection file. This can be done with a simple script in a language like Python or Bash.

4. Run the FFmpeg Command: Once you have your mylist.txt file ready, you can run the FFmpeg command to perform the splitting.

This process is a bit complex because FFmpeg doesn't natively support reading silence timestamps directly to split files. However, with a bit of scripting, you can automate most of it. Remember that the exact details will depend on the format of your silence detection file and your specific requirements.

To use FFmpeg with the concat demuxer for splitting your audio file based on the silence timestamps, you'll need to slightly modify the format of your input script and follow a specific process. The file you're referring to, with the inpoint and outpoint specifications, doesn't directly match the format FFmpeg expects for the concat demuxer. Instead, the concat demuxer uses a different format, but we can still achieve your goal by a slightly different method.

Here's how you can do it:

1. Create a Text File for Each Segment: Instead of using inpoint and outpoint, you'll create a series of text files where each file specifies a segment of the original audio to be included in the final output. Your script will generate these text files.

   For example, if you want to split the file into two parts at timestamp 5930.9 seconds, you'll create two text files:

   segment1.txt:

   arduino
   file 'input_audio_file.mp3'
   inpoint 0
   outpoint 5930.9
   

   segment2.txt:

   arduino
   file 'input_audio_file.mp3'
   inpoint 5930.9
   

2. Use FFmpeg with the Concat Demuxer: For each segment, you'll use FFmpeg with the concat demuxer. The command for each segment will look like this:

   For the first segment:

   bash
   ffmpeg -f concat -safe 0 -i segment1.txt -c copy output_segment1.mp3
   

   And for the second segment:

   bash
   ffmpeg -f concat -safe 0 -i segment2.txt -c copy output_segment2.mp3
   

3. Automate the Process: You can automate the creation of these segment text files and the corresponding FFmpeg commands using a script. The script would read your original list of inpoint and outpoint values, create the necessary text files for each segment, and then run the FFmpeg commands to produce the output files.

This process allows you to use FFmpeg to split the audio at the silence points you've detected, but it requires an intermediate step of creating the correct input format for FFmpeg. The advantage of this method is that it's very flexible and can be adapted for any number of splits.