How to Run pyannote Speaker Diarization Offline

Before you start

Check your audio source before running the model. Diarization depends on distinguishable voices and clean recording conditions. If you plan to use a microphone, confirm that it is selected as the input device in your operating system. Keep the mic at a moderate distance and monitor the input level so speech is audible without clipping. A sample rate of 16 kHz is commonly used for speech processing, but verify the requirements for your installed version instead of assuming one fixed value. Background noise, overlapping speech, music, or low-quality compressed audio can make speaker boundaries harder to detect.

Offline in this context means running the diarization pipeline on a local machine rather than sending audio to a hosted service. The model files still need to be available locally or downloaded through the package's normal model-loading process. You should confirm that your Python environment, compatible package versions, and any required model access are ready before processing a long recording.

Step-by-step instructions

Use a current Python environment and install the diarization package according to the documentation for your platform. Keep the installation isolated in a virtual environment where possible. The exact package name, dependency versions, and model loading behavior can differ between releases, so follow the instructions matching the version you install.

Once the package is installed, prepare a short test file with clear speech from two or more people. Use WAV or another supported uncompressed or lightly compressed format. A short clip is easier to inspect and faster to process than a long meeting recording.

Run the diarization pipeline on the local audio file. In the standard usage pattern, you load a pretrained pipeline and pass the file path to it. The output is an annotation object that contains speaker labels and time segments. Save the result in a structured text format so you can review it without rerunning the model.

from pyannote.audio import Pipeline

pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")
diarization = pipeline("meeting.wav")

for turn, _, speaker in diarization.itertracks(yield_label=True):
    print(f"{turn.start:.1f}-{turn.end:.1f} {speaker}")

Expected output is a sequence of turns with start time, end time, and a speaker label such as SPEAKER_00 or SPEAKER_01. Labels are assigned per recording and do not identify a person by name. Check the package documentation for the current API and any required token or accepted-use confirmation for downloading model files.

Check the output

Open the result and compare a few segments against the audio. Play the moments where a speaker label changes and confirm that the boundary sounds correct. In quiet recordings, short pauses and breaths should not create many extra segments. In noisy recordings, expect some fragmentation, especially when people speak over each other or the input device captures room echo.

Diarization output has practical limits. It reports who spoke when, not what was said, and it does not assign real names automatically. Speaker labels can change between separate runs or files. The main quality indicators are whether the number of detected speakers is reasonable and whether long continuous speech is not split into many small pieces.

Common problems and next steps

If the output contains one speaker instead of several, reduce background noise and avoid a single input device picking up one voice much louder than the others. Check that the recording is not mono-compressed to the point where speaker differences are lost. A cleaner recording is usually more effective than changing model settings.

If the file path is rejected, convert the audio to a supported format and make sure the path is correct. If model loading fails, check the installed package version and its instructions for model access. An outdated or mismatched dependency is a common cause of errors, so confirm that the environment matches the documented requirements for your release.

After the basic run works, apply the same pipeline to a representative clip from your real use case. Start with the highest-quality recording available before testing difficult audio. If the result is unstable across similar files, keep the recording conditions consistent and make one change at a time instead of adjusting several settings together.