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voxtral-mini-4b-realtime
Voxtral Mini 4B Realtime is a speech-to-text model from Mistral AI. It is a 4B parameter model optimized for fast, accurate audio transcription with low latency, making it ideal for real-time applications. The model uses the Voxtral architecture for efficient audio processing.
Repository: localaiLicense: apache-2.0
ced-base-f16
CED (Consistent Ensemble Distillation, Xiaomi) is a sound-event classifier that tags everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark, ...) into the 527-class AudioSet ontology. This is the f16 GGUF for the ced backend (a standalone C++/ggml port). Recommended default: fastest on CPU and near-lossless. Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-base-q8
CED (Consistent Ensemble Distillation, Xiaomi) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). This is the q8_0 GGUF for the ced backend: smallest footprint (~88 MB, ~6.5x less memory than the PyTorch reference) and near-lossless (identical top-5 tags). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-tiny-f16
CED-tiny (5.5M params, Pi-class / edge) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). f16 GGUF for the ced backend (recommended (fastest on CPU)). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-tiny-q8
CED-tiny (5.5M params, Pi-class / edge) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). q8_0 GGUF for the ced backend (smallest footprint, near-lossless). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-mini-f16
CED-mini (9.6M params, low-power) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). f16 GGUF for the ced backend (recommended (fastest on CPU)). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-mini-q8
CED-mini (9.6M params, low-power) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). q8_0 GGUF for the ced backend (smallest footprint, near-lossless). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-small-f16
CED-small (22M params, balanced size/accuracy) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). f16 GGUF for the ced backend (recommended (fastest on CPU)). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-small-q8
CED-small (22M params, balanced size/accuracy) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). q8_0 GGUF for the ced backend (smallest footprint, near-lossless). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
vibevoice-cpp
VibeVoice Realtime 0.5B (C++ / GGML, Q8_0) - native C++ port of Microsoft VibeVoice
via the vibevoice-cpp backend. 24kHz mono TTS with a selectable precomputed
voice prompt. Default voice prompt: en-Carter_man. This realtime variant does
not accept raw Voice Library reference WAVs.
Repository: localaiLicense: mit
vits-streaming-assistant
English voice assistant preset: Silero VAD, Parakeet TDT 0.6B v3, Qwen3
0.6B chat, and VITS-LJS English single-speaker TTS through sherpa-onnx.
Buffers streamed LLM output into clauses and emits audio progressively.
Each clause starts a separate synthesis; this is not native incremental
text input. This installs only the pipeline configuration. First install
silero-vad-ggml, parakeet-cpp-tdt-0.6b-v3, qwen3-0.6b, and vits-ljs-sherpa.
WebRTC also needs the opus backend. No browser or hardware performance
is established by this preset.
Repository: localaiLicense: mit
piper-streaming-assistant
English voice assistant preset: Silero VAD, Parakeet TDT 0.6B v3, Qwen3
0.6B chat, and Piper Amy US English TTS through piper.
Buffers streamed LLM output into clauses and emits audio progressively.
Each clause starts a separate synthesis; this is not native incremental
text input. This installs only the pipeline configuration. First install
silero-vad-ggml, parakeet-cpp-tdt-0.6b-v3, qwen3-0.6b, and voice-en-us-amy-low.
WebRTC also needs the opus backend. No browser or hardware performance
is established by this preset.
Repository: localaiLicense: mit
qwen3-streaming-assistant
English voice assistant preset: Silero VAD, Parakeet TDT 0.6B v3, Qwen3
0.6B chat, and Qwen3-TTS 0.6B CustomVoice with Ryan's voice. Streams LLM
text into one persistent TTS session while audio is emitted.
This installs only the pipeline configuration. First install the separate
gallery models silero-vad-ggml, parakeet-cpp-tdt-0.6b-v3, qwen3-0.6b, and
qwen3-tts-cpp-customvoice. Pin its variant to qwen3-tts-cpp-customvoice
for Q8. WebRTC also needs the opus backend.
Requires a LocalAI build with incremental Qwen3-TTS input support.
CPU synthesis may be slower than playback; GPU and browser performance
are not established by this preset.
Repository: localaiLicense: mit
lfm2.5-audio-1.5b-realtime
LFM2.5-Audio-1.5B is LiquidAI's any-to-any audio foundation model. The
1.2B LFM2.5 backbone plus a FastConformer audio encoder and an LFM2-based
audio detokenizer give real-time speech-to-speech with text + audio output
interleaved at 12.5 Hz / 24 kHz. This entry runs in S2S (speech-to-speech)
mode and is the model the LocalAI realtime API any-to-any path consumes.
Switch to ASR, TTS, or chat by picking the sibling gallery entries.
Repository: localaiLicense: LFM-Open-License-v1.0
localvqe-v1.1-1.3m
LocalVQE v1.1 (1.3 M parameters, F32) — joint acoustic echo cancellation,
noise suppression, and dereverberation for 16 kHz mono speech.
DeepVQE-style architecture with an S4D bottleneck and an in-graph
DCT-II filterbank. ~9.6× realtime on a desktop CPU; 16 ms algorithmic
latency. ~5 MB on disk. v1.1 ships the v16 echoaware checkpoint with
improved double-talk and near-end single-talk AECMOS scores.
Repository: localaiLicense: apache-2.0
localvqe-v1.2-1.3m
LocalVQE v1.2 (1.3 M parameters, F32) — compact joint acoustic echo
cancellation, noise suppression, and dereverberation for 16 kHz mono
speech. Shares the same DeepVQE-style architecture (arch_version 3) as
v1.3 but with narrower encoder/decoder widths, so it runs at ~9.7×
realtime (~1.6 ms per 16 ms frame on a 4-thread Zen4 CPU) — about ¼ the
per-hop cost of v1.3. Widens the echo-search window to 1024 ms (v1.1 used
512 ms). ~5 MB on disk. The budget-friendly choice for low-core or
power-constrained devices.
Repository: localaiLicense: apache-2.0
localvqe-v1.3-4.8m
LocalVQE v1.3 (4.8 M parameters, F32) — current default release. Joint
acoustic echo cancellation, noise suppression, and dereverberation for
16 kHz mono speech, with a wider encoder/decoder trained from scratch
under a noise-floor-aware loss recipe. ~4.7× realtime (~3.3 ms per 16 ms
frame on a 4-thread Zen4 CPU); ~19 MB on disk. Improves doubletalk speech
quality (+0.25 deg MOS) and far-end echo cancellation (ERLE +5.2–9.3 dB)
over v1.2; on far-end-only scenes some users may still prefer v1.2's
gentler trade-off. Same 16 ms algorithmic latency as the compact models.
Repository: localaiLicense: apache-2.0
parakeet-cpp-realtime_eou_120m-v1
Cache-aware streaming RNNT FastConformer with end-of-utterance (EOU) detection, 120M. Use with streaming transcription. F16 GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet),
byte-identical to NeMo at WER 0. Faster than NeMo on CPU and GPU.
Repository: localaiLicense: cc-by-4.0
parakeet-cpp-realtime-scene-speakers
Cache-aware streaming RNNT FastConformer with end-of-utterance (EOU)
detection, 120M, paired with Nemotron-3-Diarization and CED-Tiny through
the diarization_model and sound_model options. F16/Q8_0 GGUF for the
parakeet-cpp backend (C++/ggml port of NVIDIA NeMo). Use with streaming
transcription: while a turn is live, closed speaker segments and sound
events are surfaced alongside the ASR text (realtime
conversation.item.input_audio_transcription.segment and
conversation.item.sound_detection events). Live speaker/sound events only
fire during speech turns under semantic_vad; sounds between turns are not
seen by this path. License per model: transcription model NVIDIA Open
Model License, diarization model OpenMDW-1.1, CED-Tiny Apache-2.0,
WeSpeaker ResNet34 CC-BY-4.0. Also loads WeSpeaker ResNet34 through the
speaker_model option, so live speaker segments carry the name of a voice
registered with /v1/voice/register (voice-detect-wespeaker-resnet34 model)
once the speaker is identified.
Repository: localaiLicense: nvidia-open-model-license
parakeet-cpp-realtime-scene
Cache-aware streaming RNNT FastConformer with end-of-utterance (EOU)
detection, 120M, paired with Nemotron-3-Diarization and CED-Tiny through
the diarization_model and sound_model options. F16/Q8_0 GGUF for the
parakeet-cpp backend (C++/ggml port of NVIDIA NeMo). Use with streaming
transcription: while a turn is live, closed speaker segments and sound
events are surfaced alongside the ASR text (realtime
conversation.item.input_audio_transcription.segment and
conversation.item.sound_detection events). Live speaker/sound events only
fire during speech turns under semantic_vad; sounds between turns are not
seen by this path. License per model: transcription model NVIDIA Open
Model License, diarization model OpenMDW-1.1, CED-Tiny Apache-2.0.
Repository: localaiLicense: nvidia-open-model-license
parakeet-cpp-realtime-scene-tdt
Parakeet TDT 0.6B v3 (multilingual, 25 European languages) paired with
Nemotron-3-Diarization and CED-Tiny through the diarization_model and
sound_model options: one parakeet-cpp backend transcribes, labels speakers
and tags sound events. GGUF for the parakeet-cpp backend (C++/ggml port of
NVIDIA NeMo). TDT is not a streaming model, so in a realtime pipeline use
it with server_vad: set it as both transcription and sound_detection and
turn on pipeline.diarization, and each committed turn gets speaker segments
(conversation.item.input_audio_transcription.segment, with text) and
sound tags (conversation.item.sound_detection). Also labels speakers on
/v1/audio/transcriptions. Speaker labels are per turn. License per model:
transcription model CC-BY-4.0, diarization model OpenMDW-1.1, CED-Tiny
Apache-2.0.
Repository: localaiLicense: cc-by-4.0
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