LocalAI/docs/content/features/voice-recognition.md

+++ disableToc = false title = "Voice Recognition" weight = 36 url = "/features/voice-recognition/" +++

LocalAI supports voice (speaker) recognition: speaker verification (1:1), speaker identification (1:N) against a built-in vector store, speaker embedding, and demographic analysis (age / gender / emotion from voice).

The audio analog to Face Recognition, served over the same /v1/voice/* HTTP API by two backends:

• voice-detect (recommended, default). A standalone C++/ggml engine (voice-detect.cpp): no Python, no onnxruntime, no torch runtime. Each gallery entry is a single self-describing GGUF. This is the recommended option for new deployments.
• speaker-recognition (Python). The original SpeechBrain / ONNX backend. Still supported; see the Python backend below.

Both backends expose the identical wire format, so the API examples on this page work with either - only the gallery entry name (the model field) changes.

voice-detect (ggml) backend

The voice-detect backend reads the embedding (or analysis) architecture (voicedetect.arch) directly from the GGUF metadata, so installing a gallery entry is all that is needed to select an engine. It drives the VoiceEmbed / VoiceVerify / VoiceAnalyze gRPC rpcs behind the /v1/voice/{embed,verify,analyze,register,identify,forget} endpoints.

Gallery entries

Gallery entry	Model	Embedding dim	License
voice-detect-ecapa-tdnn	SpeechBrain ECAPA-TDNN (VoxCeleb)	192	Apache 2.0 - commercial-safe
voice-detect-wespeaker-resnet34	WeSpeaker ResNet34 (VoxCeleb)	256	CC-BY-4.0
voice-detect-eres2net	3D-Speaker ERes2Net (VoxCeleb)	192	Apache 2.0 - commercial-safe
voice-detect-campplus	3D-Speaker CAM++ (VoxCeleb)	192	Apache 2.0 - commercial-safe
voice-detect-emotion-wav2vec2	audEERING wav2vec2 (age / gender / emotion)	analyze head	CC-BY-NC-SA-4.0 - non-commercial

The four speaker-recognition entries drive verify / embed / identify. voice-detect-emotion-wav2vec2 is the analysis head behind /v1/voice/analyze (continuous age estimate plus gender and emotion class scores) and is non-commercial / research use only.

Quickstart

Install the default entry (recommended for copy-paste):

local-ai models install voice-detect-ecapa-tdnn

Verify that two audio clips were spoken by the same person:

curl -sX POST http://localhost:8080/v1/voice/verify \
  -H "Content-Type: application/json" \
  -d '{
    "model": "voice-detect-ecapa-tdnn",
    "audio1": "https://example.com/alice_1.wav",
    "audio2": "https://example.com/alice_2.wav"
  }'

Analyze age / gender / emotion (install the analyze entry first):

local-ai models install voice-detect-emotion-wav2vec2

curl -sX POST http://localhost:8080/v1/voice/analyze \
  -H "Content-Type: application/json" \
  -d '{"model": "voice-detect-emotion-wav2vec2", "audio": "https://example.com/alice.wav"}'

The 1:N register / identify / forget workflow and the rest of the API are identical to the API reference below - just pass a voice-detect-* model name. The default verify threshold is ~0.25 for the ECAPA-TDNN / ERes2Net / CAM++ recognizers and ~0.30 for WeSpeaker ResNet34.

speaker-recognition (Python) backend

The speaker-recognition backend follows the same two-engine pattern under one image.

Engines

Gallery entry	Model	Size	License
speechbrain-ecapa-tdnn	ECAPA-TDNN on VoxCeleb (SpeechBrain)	~17 MB	Apache 2.0 - commercial-safe
wespeaker-resnet34	WeSpeaker ResNet34 ONNX	~26 MB	Apache 2.0 - commercial-safe

Both entries are commercial-safe Apache-2.0. SpeechBrain is the default - it's a lightweight pure-PyTorch checkpoint that auto- downloads on first use. The wespeaker-resnet34 entry wires the direct-ONNX path for CPU-only deployments that don't want the torch runtime.

Quickstart

Install the default backend and model:

local-ai models install speechbrain-ecapa-tdnn

Verify that two audio clips were spoken by the same person:

curl -sX POST http://localhost:8080/v1/voice/verify \
  -H "Content-Type: application/json" \
  -d '{
    "model": "speechbrain-ecapa-tdnn",
    "audio1": "https://example.com/alice_1.wav",
    "audio2": "https://example.com/alice_2.wav"
  }'

Response:

{
  "verified": true,
  "distance": 0.18,
  "threshold": 0.25,
  "confidence": 28.0,
  "model": "speechbrain-ecapa-tdnn",
  "processing_time_ms": 340.0
}

1:N identification workflow (register → identify → forget)

Same flow as face recognition, same in-memory vector store under the hood.

1. Register known speakers:

   curl -sX POST http://localhost:8080/v1/voice/register \
     -H "Content-Type: application/json" \
     -d '{
       "model": "speechbrain-ecapa-tdnn",
       "name": "Alice",
       "audio": "https://example.com/alice.wav"
     }'
   # → {"id": "b2f...", "name": "Alice", "registered_at": "2026-04-22T..."}
   

2. Identify an unknown probe:

   curl -sX POST http://localhost:8080/v1/voice/identify \
     -H "Content-Type: application/json" \
     -d '{
       "model": "speechbrain-ecapa-tdnn",
       "audio": "https://example.com/unknown.wav",
       "top_k": 5
     }'
   # → {"matches": [{"id":"b2f...","name":"Alice","distance":0.19,"match":true,...}]}
   

3. Remove a speaker by ID:

   curl -sX POST http://localhost:8080/v1/voice/forget \
     -d '{"id": "b2f..."}'
   # → 204 No Content
   

{{% notice warning %}} Storage caveat. The default vector store is in-memory. All registered speakers are lost when LocalAI restarts. Persistent storage (pgvector) is a tracked future enhancement shared with face recognition - the voice-recognition HTTP API is designed to swap the backing store without changing the wire format. {{% /notice %}}

Naming speakers in diarization and live transcription

The parakeet-cpp backend can put the names of registered voices on diarization results and on live transcription speaker segments. Without this, speakers only carry labels such as SPEAKER_00.

1. Register each voice with the WeSpeaker encoder. Install the model with local-ai models install voice-detect-wespeaker-resnet34, then call /v1/voice/register with "model": "voice-detect-wespeaker-resnet34" (see the 1:N workflow).
2. Install one of the gallery models that loads the same encoder: parakeet-cpp-nemotron-3-diarization-speakers (diarization), parakeet-cpp-nemotron-3-diarization-asr-speakers (diarization with include_text) or parakeet-cpp-realtime-scene-speakers (live transcription). Each one adds speaker_model:voice-detect-wespeaker-resnet34.gguf to a parakeet-cpp model config.
3. Call /v1/audio/diarization with that model. Matched segments gain a name and a name_score, and the matching entry in speakers gains a name. speaker stays SPEAKER_NN, and RTTM output is unchanged. See [Speaker Diarization]({{% relref "audio-diarization" %}}) for the response.

Which voices are used

LocalAI sends the backend only the registered voices made by the same encoder as the model's speaker_model: file. Each registered voice is tagged with the name of the voice-detect model that made it, which by default is the GGUF file name (voice-detect-wespeaker-resnet34.gguf for the gallery entry). The tag must equal the base name of the speaker_model: file. Voices made with another encoder are ignored, and LocalAI logs a warning when that leaves no usable voice. Voices registered before the tag existed have no tag: they are used when their embedding size matches the tagged ones (or all of them, when no voice carries a matching tag). The backend skips a voice whose embedding size does not match the speaker model's, with a warning in the LocalAI log. Naming then falls back to the remaining voices, or to no names.

{{% notice warning %}} Do not set a model_name: option on the voice-detect model config. It replaces the default name, the voices are then tagged with it, and they no longer match the speaker_model: file. Keep the default name. {{% /notice %}}

Options

These go in the options: list of the parakeet-cpp model config (see [Audio to Text]({{% relref "audio-to-text" %}}) for the other parakeet-cpp options).

Option	Default	Meaning
speaker_model:<path>	none	speaker encoder GGUF; needs a diarization model (the primary one, or diarization_model:)
speaker_threshold:<float>	0.5	largest distance (1 minus cosine similarity, the unit /v1/voice/identify reports) at which a speaker is named; must be in (0, 2)
speaker_margin:<float>	0.05	the best match must beat the runner-up by this much, otherwise the speaker stays unnamed; must be in [0, 1)

parakeet.cpp's measured starting values for speaker_threshold are 0.5 for WeSpeaker ResNet34 and CAM++, and 0.3 for ECAPA. A lower value names fewer speakers and makes fewer mistakes.

Limits

• The voice registry is in memory and global. Registered names disappear when LocalAI restarts, and every user of the instance shares them.
• Anyone who is allowed to call a model with speaker_model: can learn which registered names match their audio, and their audio is matched against voices registered by any user, because the voice registry is global. Restrict such models with the per-user model allowlist.
• With include_text=true the names use the default threshold and margin: speaker_threshold and speaker_margin only apply to diarization without text.
• In live transcription, a speaker segment that closes before its speaker is identified has no name. Later segments of that speaker do.
• Overlapping speech is not resolved.
• Accuracy was measured on one fixture (two read-speech voices). Check the threshold on your own audio.
• The backend needs a libparakeet with C-API v10. With an older library a model config that sets speaker_model: fails to load.

API reference

POST /v1/voice/verify (1:1)

field	type	description
model	string	gallery entry name (e.g. speechbrain-ecapa-tdnn)
audio1, audio2	string	URL, base64, or data-URI of an audio file
threshold	float, optional	cosine-distance cutoff; default 0.25 for ECAPA-TDNN
anti_spoofing	bool, optional	reserved - unused in the current release

Returns verified, distance, threshold, confidence, model, and processing_time_ms.

POST /v1/voice/analyze

Returns demographic attributes (age, gender, emotion) inferred from speech:

field	type	description
model	string	gallery entry
audio	string	URL / base64 / data-URI
actions	string[]	subset of ["age","gender","emotion"]; empty = all supported

Emotion is inferred from the SUPERB emotion-recognition checkpoint (superb/wav2vec2-base-superb-er, Apache 2.0) - 4-way categorical neutral / happy / angry / sad. The model auto-downloads on the first analyze call.

Age and gender are opt-in: no standard-transformers checkpoint with a clean classifier head is shipped as the default. The high-accuracy Audeering age/gender model uses a custom multi-task head that AutoModelForAudioClassification doesn't load safely (the age weights are silently dropped and the classifier is re-initialised with random values). To enable age/gender, set age_gender_model:<repo> in the model YAML's options: pointing at a checkpoint with a vanilla Wav2Vec2ForSequenceClassification head. Override the emotion default similarly via emotion_model:. Set either to an empty string to disable that head.

If a head fails to load (offline, disk full, transformers missing), the engine degrades gracefully: it still returns the attributes it could compute. When nothing can be computed the backend returns 501 Unimplemented.

Analyze is supported by both speechbrain-ecapa-tdnn and wespeaker-resnet34 - the speaker recognizer and the analysis head are independent.

POST /v1/voice/register (1:N enrollment)

field	type	description
model	string	voice recognition model
audio	string	speaker audio to enroll
name	string	human-readable label
labels	map[string]string, optional	arbitrary metadata
store	string, optional	vector store model; defaults to local-store

Returns {id, name, registered_at}. The id is an opaque UUID used by /v1/voice/identify and /v1/voice/forget.

POST /v1/voice/identify (1:N recognition)

field	type	description
model	string	voice recognition model
audio	string	probe audio
top_k	int, optional	max matches to return; default 5
threshold	float, optional	cosine-distance cutoff; default 0.25
store	string, optional	vector store model

Returns a list of matches sorted by ascending distance, each with id, name, labels, distance, confidence, and match (distance ≤ threshold).

POST /v1/voice/forget

field	type	description
id	string	ID returned by /v1/voice/register

Returns 204 No Content on success, 404 Not Found if the ID is unknown.

POST /v1/voice/embed

Returns the L2-normalized speaker embedding vector.

field	type	description
model	string	voice model
audio	string	URL / base64 / data-URI

Returns {embedding: float[], dim: int, model: string}. Dimension depends on the recognizer: 192 for ECAPA-TDNN, 256 for WeSpeaker ResNet34.

Note: the OpenAI-compatible /v1/embeddings endpoint is intentionally text-only - it does nothing useful with audio input. Use /v1/voice/embed for audio.

Audio input

Audio is materialised by the HTTP layer to a temporary WAV file before the gRPC call. All audio fields accept:

• http:// / https:// URLs (downloaded server-side, subject to ValidateExternalURL safety checks).
• Raw base64 (no prefix).
• Data URIs (data:audio/wav;base64,...).

The backend itself always receives a filesystem path - the same convention the Whisper / Voxtral transcription backends use.

Threshold reference

Recognizer	Cosine-distance threshold
ECAPA-TDNN (SpeechBrain, VoxCeleb)	~0.25
WeSpeaker ResNet34	~0.30
3D-Speaker ERes2Net	~0.28

Pass threshold explicitly when switching recognizers - the per-model default only applies when omitted.

Related features

• Face Recognition - the image analog; the two share a registry design.
• Audio to Text - transcription (Whisper, Voxtral, faster-whisper). Runs in addition to, not instead of, voice recognition.
• Stores - the generic vector store powering both the face and voice 1:N recognition pipelines.
• Embeddings - text-only OpenAI-compatible embedding endpoint; for audio embeddings use /v1/voice/embed.