Tone: Cross platform audio tagger and metadata editor (github.com)

Picard.

Check out Whisper, it is surprisingly good and several of the output formats include time codes, works with multiple languages.

https://github.com/openai/whisper

That is funny. For audio books I'm currently working on an `epub` command for `tone` which will be able to extract text from `epub` files, e.g.:

  tone epub --format="markdown" --extract-sentences --one-file-per-chapter output-path/

As a result, you can use https://github.com/readbeyond/aeneas with the generated text / markdown files to create a json mapping file looking like this:

  {
   "fragments": [
    {
     "begin": "0.000",
     "children": [], 
     "end": "7.920",
     "id": "f000001",
     "language": "eng",
     "lines": [
      "This is the first sentence of the audio book."
     ]   
    }
  }

Since aeneas is a bit inaccurate, I'm also working on an improvement with silence detection for these mapping files.

If you are looking for something that is "ready to use", you could check out https://github.com/r4victor/syncabook or the according library https://github.com/r4victor/afaligner

If you have audio files, that are NOT audio books, the epub approach will not help you and the other comments are more helpful.

See my comment showing how to transcribe using Whisper:

https://news.ycombinator.com/item?id=35366778

You could try to perform a silence detection, if there is a silence after every song.

https://stackoverflow.com/questions/42507879/how-to-detect-t...

  ffmpeg -i audio.mp3 -af silencedetect=n=-50dB:d=0.5 -f null - 2>&1

  # -50dB is the threshold for detecting audio as a silence
  # 0.5 means the silences must at least have a duration of 0.5 seconds

In your case I would try:

  ffmpeg -i audio.mp3 -af silencedetect=n=-30dB:d=2.5 -f null - 2>&1

That should give you a pretty accurate list of where the songs begin and end... that way, you can extract the songs and run fingerprinting.

After this, you could use the ability of `tone` to add `chapters` to audio files (for mp3 it is id3v2 chapter addendum, see https://mutagen-specs.readthedocs.io/en/latest/id3/id3v2-cha...) to have at least marks for each track as title. MP3 chapters will not be recognized by most players, but at least it is within the specs.

That may be impossible because in the mix, songs may be played with different speed so no detection app that I know will find them.

Otherwise, you could play a mixtape and put shazam in the background to see if it will find the songs (it can do that continiously). If it can, then you can easily automate file splitting using ffmpeg and powershell and grabbing a list using GUI/CLI automation via shazam desktop or somethig like https://audd.io.

`ffmpeg` is great, but it needs improvements for some tags / formats (that is why I developed tone in the first place).

Examples:

  - Duration detection is inaccurate
  - Tags `MovementName` and `MovementIndex` (for series) are not supported for some formats
  - etc.

Another disadvantage is, that (afaik) `ffmpeg` needs to reprocess the file (audio wise) to write tags instead of just changing the metadata inplace.