LocalAI/docs/content/features/audio-diarization.md

+++ disableToc = false title = "Speaker Diarization" weight = 33 url = "/features/audio-diarization/" +++

Speaker diarization answers the question "who spoke when?" - given an audio clip with multiple speakers, it returns time-stamped segments labelled with a stable speaker ID (SPEAKER_00, SPEAKER_01, …).

LocalAI exposes this through the /v1/audio/diarization endpoint, modelled after /v1/audio/transcriptions. Five backends are supported today:

• sherpa-onnx - pyannote-3.0 segmentation + a speaker-embedding extractor (3D-Speaker, NeMo, WeSpeaker) + fast clustering. Pure diarization - no transcription cost. Recommended when you only need speaker turns.
• vibevoice.cpp - produces speaker-labelled segments as a by-product of its long-form ASR pass, so you can optionally get a transcript per segment for free.
• NeMo-Speech.cpp - NVIDIA Sortformer, served standalone by the [NeMo-Speech.cpp backend]({{%relref "features/nemo-speech-cpp" %}}). It is end to end, so the speaker capacity is fixed by the checkpoint and the count hints are ignored. The same backend can instead put speaker tags on a transcript, by attaching a Sortformer model to an ASR one.
• audio.cpp - the sortformer_diar family, served by the multi-modality [audio.cpp backend]({{%relref "features/audio-cpp" %}}).
• parakeet.cpp - NVIDIA Nemotron-3-Diarization (Sortformer, up to 8 speakers), served standalone or paired with a Parakeet ASR model for per-segment text. See the [Audio to Text]({{% relref "audio-to-text" %}}) page for the parakeet-cpp option reference.

Because diarization is exposed as a regular OpenAI-compatible endpoint, any HTTP client works. There is no Python dependency on pyannote or NeMo on the consumer side.

Endpoint

POST /v1/audio/diarization
Content-Type: multipart/form-data

Field	Type	Description
file	file (required)	audio file in any format ffmpeg accepts
model	string (required)	name of the diarization-capable model
num_speakers	int	exact speaker count when known (>0 forces; 0 = auto)
min_speakers	int	hint when auto-detecting
max_speakers	int	hint when auto-detecting
clustering_threshold	float	cosine distance threshold used when num_speakers is unknown
min_duration_on	float	discard segments shorter than this many seconds
min_duration_off	float	merge gaps shorter than this many seconds
language	string	only meaningful for backends that bundle ASR (e.g. vibevoice)
include_text	bool	when the backend can emit per-segment transcript for free, populate it
response_format	string	json (default), verbose_json, or rttm

Response - json (default)

Compact payload, no transcription, no per-speaker summary:

{
  "task": "diarize",
  "duration": 12.34,
  "num_speakers": 2,
  "segments": [
    {"id": 0, "speaker": "SPEAKER_00", "label": "0", "start": 0.00, "end": 2.34},
    {"id": 1, "speaker": "SPEAKER_01", "label": "1", "start": 2.34, "end": 4.10}
  ]
}

speaker is the normalized, zero-padded label clients should display. label preserves the raw backend-emitted ID for clients that maintain their own speaker dictionary.

Response - verbose_json

Adds per-speaker totals and (when the backend supports it and include_text=true) the per-segment transcript:

{
  "task": "diarize",
  "duration": 12.34,
  "language": "en",
  "num_speakers": 2,
  "segments": [
    {"id": 0, "speaker": "SPEAKER_00", "label": "0", "start": 0.00, "end": 2.34, "text": "Hello, world."},
    {"id": 1, "speaker": "SPEAKER_01", "label": "1", "start": 2.34, "end": 4.10, "text": "How are you?"}
  ],
  "speakers": [
    {"id": "SPEAKER_00", "label": "0", "total_speech_duration": 5.6, "segment_count": 3},
    {"id": "SPEAKER_01", "label": "1", "total_speech_duration": 1.76, "segment_count": 1}
  ]
}

Speaker names

With a parakeet-cpp model that has a speaker_model: and voices registered through /v1/voice/register, segments whose speaker matches a registered voice gain name and name_score (the cosine similarity of the match), and the matching speakers entry gains name. Both fields are omitted for a speaker that was not identified, so an unnamed response looks exactly as before. speaker stays SPEAKER_NN, and RTTM output still uses SPEAKER_NN. See [Voice Recognition]({{% relref "voice-recognition" %}}#naming-speakers-in-diarization-and-live-transcription) for the setup and the limits.

{
  "task": "diarize",
  "duration": 12.34,
  "num_speakers": 2,
  "segments": [
    {"id": 0, "speaker": "SPEAKER_00", "label": "0", "start": 0.00, "end": 2.34, "text": "Hello, world.", "name": "Alice", "name_score": 0.82},
    {"id": 1, "speaker": "SPEAKER_01", "label": "1", "start": 2.34, "end": 4.10, "text": "How are you?"}
  ],
  "speakers": [
    {"id": "SPEAKER_00", "label": "0", "name": "Alice", "total_speech_duration": 5.6, "segment_count": 3},
    {"id": "SPEAKER_01", "label": "1", "total_speech_duration": 1.76, "segment_count": 1}
  ]
}

Response - rttm

NIST RTTM, the standard interchange format used by pyannote.metrics / dscore:

SPEAKER audio 1 0.000 2.340 <NA> <NA> SPEAKER_00 <NA> <NA>
SPEAKER audio 1 2.340 1.760 <NA> <NA> SPEAKER_01 <NA> <NA>

Returned as Content-Type: text/plain; charset=utf-8.

Quick start

First install a diarization-capable model from the gallery. The example below uses vibevoice-cpp-asr, which serves the vibevoice.cpp backend and returns speaker-labelled segments (and, optionally, a transcript):

local-ai run vibevoice-cpp-asr

curl http://localhost:8080/v1/audio/diarization \
  -H "Content-Type: multipart/form-data" \
  -F file="@meeting.wav" \
  -F model="vibevoice-cpp-asr" \
  -F num_speakers=3

The sections below show how to configure the two supported backends by hand when you want full control over the segmentation and embedding models.

Backend setup - sherpa-onnx (pure diarization)

Sherpa-onnx needs two ONNX models: pyannote segmentation and a speaker-embedding extractor. Place them under your LocalAI models directory and reference them from the YAML:

name: pyannote-diarization
backend: sherpa-onnx
type: diarization
parameters:
  model: sherpa-onnx-pyannote-segmentation-3-0/model.onnx
options:
  - diarize.embedding_model=3dspeaker_speech_campplus_sv_zh-cn_16k-common.onnx
  # Optional clustering knobs (per-call DiarizeRequest fields override these):
  - diarize.threshold=0.5
  - diarize.min_duration_on=0.3
  - diarize.min_duration_off=0.5
known_usecases:
  - FLAG_DIARIZATION

Both model: and diarize.embedding_model= are resolved relative to the LocalAI models directory.

Backend setup - vibevoice.cpp (diarization + ASR)

vibevoice.cpp's ASR mode emits [{Start, End, Speaker, Content}] natively, so a single pass gives both diarization and transcription:

name: vibevoice-diarize
backend: vibevoice-cpp
parameters:
  model: vibevoice-asr.gguf
options:
  - type=asr
  - tokenizer=vibevoice-tokenizer.gguf
known_usecases:
  - FLAG_DIARIZATION
  - FLAG_TRANSCRIPT

Pass include_text=true on the request to populate the text field on each diarization segment.

curl http://localhost:8080/v1/audio/diarization \
  -H "Content-Type: multipart/form-data" \
  -F file="@interview.wav" \
  -F model="vibevoice-diarize" \
  -F include_text=true \
  -F response_format=verbose_json

Backend setup - parakeet-cpp (Nemotron-3-Diarization)

Nemotron-3-Diarization is Sortformer, served standalone or paired with a Parakeet ASR model. Install parakeet-cpp-nemotron-3-diarization from the gallery for diarization only, or parakeet-cpp-nemotron-3-diarization-asr for the same model paired with parakeet-cpp-tdt_ctc-110m through the asr_model option:

name: parakeet-diarize
backend: parakeet-cpp
parameters:
  model: nemotron-3-diarization-q8_0.gguf
options:
  - asr_model:tdt_ctc-110m-f16.gguf
known_usecases:
  - diarization

Getting text on each segment needs both: an asr_model companion loaded on the model, and include_text=true on the request. With only one of the two, segments carry no text and no error is raised. Sortformer has a fixed speaker capacity and no clustering stage, so num_speakers, min_speakers, max_speakers and clustering_threshold are ignored (logged at debug); min_duration_on and min_duration_off are honored. Speaker labels are the decimal index the model assigned ("0", "1", …), or "unknown" when a segment has no diarized speaker.

curl http://localhost:8080/v1/audio/diarization \
  -H "Content-Type: multipart/form-data" \
  -F file="@meeting.wav" \
  -F model="parakeet-diarize" \
  -F include_text=true \
  -F response_format=verbose_json

Sortformer clusters on voice-like characteristics, not on "is this a human". A loud non-speech sound with voice-like pitch and rhythm (a rooster crow, in one test clip) can come back as its own speaker segment alongside the real speakers. This is model behavior, not a bug in the LocalAI integration: treat an unexpected extra speaker as a hint the clip may contain a non-speech sound, and use [Sound Classification]({{% relref "audio-classification" %}}) to confirm what it is.

Notes

• Speaker identity across files: speaker IDs (SPEAKER_00, SPEAKER_01, …) are local to each request. To track the same person across multiple recordings, combine /v1/audio/diarization with /v1/voice/embed (speaker embedding) and maintain your own embedding store.
• Hints vs. forces: num_speakers overrides clustering when set; min_speakers / max_speakers are advisory and only honored by backends that expose a range hint. vibevoice.cpp and parakeet-cpp (Sortformer) ignore them - the model picks the count itself.
• Sample rate: input is automatically converted to 16 kHz mono via ffmpeg before the backend sees it; sherpa-onnx pyannote-3.0 requires 16 kHz.

See also

• [Sound Classification]({{% relref "audio-classification" %}}) - tag non-speech sound events (alarms, glass breaking, baby cry) in a clip.