How to Use Kaldi for Speaker Diarization

Before you start

Kaldi speaker diarization works locally from a command-line environment. You do not need to upload a recording to a cloud service. The process separates an audio file into speaker-attributed time segments and does not identify people by name. It decides who is speaking when, using acoustic information.

Use a mono, 16 kHz WAV file for the most predictable results. If the source was recorded on a microphone or another input device, check the file before processing. A different sample rate or a stereo track can still be handled, but conversion may be required. Keep the recording as clean as practical: too much background noise makes speaker boundaries harder to estimate.

Kaldi itself provides the processing tools, but a ready-to-use diarization recipe may not be present in every installation or release. Check the version you have and whether the relevant example scripts are included. If you are following a recipe from the Kaldi project, keep the original script names and directory layout intact.

Step-by-step instructions

1) Place the audio in a working directory and confirm its format with a command that reports channel count and sample rate.

2) Convert the file to mono, 16 kHz WAV if it is not already in that form. Use an audio conversion tool available in the same shell environment, not a generic command copied from another system.

3) Check the input level. Speech should be present at a usable volume without frequent clipping. If the waveform is nearly silent or constantly overloaded, correct that before diarization.

4) Identify the Kaldi diarization recipe that matches your installation and version. The recipe may include stages for feature extraction, preliminary segmentation, and clustering.

5) Set the required path variables for the recipe. Supply the data directory, the WAV list or metadata file, and the output directory expected by that script.

6) Run the recipe from the Kaldi command line. Keep the default output location unless you need to preserve an earlier result. The job produces segment files rather than a single named-speaker transcript.

Check the output

Open the generated segmentation file. A typical result lists each segment with a start time, an end time, and a speaker label such as an anonymous identifier. Confirm that the segments cover the expected speech portions of the recording and do not contain huge silent gaps.

Compare the labeled segments with the original audio. The speaker labels only need to be consistent within the file. The same label should not switch between clearly different voices, while the same voice should not be split into many separate labels without an obvious reason.

Format limitations are normal. Short turns, overlapping speech, similar voices, or contaminated input may produce extra boundaries. Treat the result as an estimate, especially for recordings made in a room with several people.

Common problems and next steps

If a speaker is split into many small segments, reduce the influence of short segments or check whether the recipe exposes a clustering threshold. Version differences matter here: older Kaldi diarization examples may rely on different feature formats or scoring stages than current ones.

If two speakers are merged into one label, the audio may be too noisy or the voices may be acoustically close. Improve the input rather than repeatedly changing the recipe. A quieter recording position, better placement of the input device, or lower background noise can help more than aggressive parameter changes.

If the pipeline fails after generating features, inspect the log rather than rerunning the whole job blindly. The failure may point to an incorrect path, a missing list file, or an unsupported audio encoding. Save the original file before converting it so you can return to the unmodified source if required.