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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
lfm3-audio-2b
LiquidAI LFM3-Audio-2B in GGUF (Q4_K_M weights with the Q8_0 audio projector). An any-to-any model: one llama.cpp session takes speech or text
and answers with speech, text or both, and the same model also transcribes speech (ASR, with a language
hint) and synthesises speech with the named voices female or male.
No VAD model is set: the client commits each turn with input_audio_buffer.commit. Use the -vad entry
for automatic turn detection.
Speech-to-speech over /v1/realtime is experimental and needs a LocalAI build whose llama.cpp pin includes the
LFM3 audio and realtime support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-q8
LiquidAI LFM3-Audio-2B in GGUF (Q8_0 weights with the Q8_0 audio projector). An any-to-any model: one llama.cpp session takes speech or text
and answers with speech, text or both, and the same model also transcribes speech (ASR, with a language
hint) and synthesises speech with the named voices female or male.
No VAD model is set: the client commits each turn with input_audio_buffer.commit. Use the -vad entry
for automatic turn detection.
Speech-to-speech over /v1/realtime is experimental and needs a LocalAI build whose llama.cpp pin includes the
LFM3 audio and realtime support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-vad
LiquidAI LFM3-Audio-2B in GGUF (Q4_K_M weights with the Q8_0 audio projector). An any-to-any model: one llama.cpp session takes speech or text
and answers with speech, text or both, and the same model also transcribes speech (ASR, with a language
hint) and synthesises speech with the named voices female or male.
Server-side turn detection is on: the entry sets pipeline.vad to silero-vad-ggml, so install the
silero-vad-ggml model first (the gallery does not install it for you). The VAD only decides when a turn
ends; the model itself answers the speech.
Speech-to-speech over /v1/realtime is experimental and needs a LocalAI build whose llama.cpp pin includes the
LFM3 audio and realtime support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-q8-vad
LiquidAI LFM3-Audio-2B in GGUF (Q8_0 weights with the Q8_0 audio projector). An any-to-any model: one llama.cpp session takes speech or text
and answers with speech, text or both, and the same model also transcribes speech (ASR, with a language
hint) and synthesises speech with the named voices female or male.
Server-side turn detection is on: the entry sets pipeline.vad to silero-vad-ggml, so install the
silero-vad-ggml model first (the gallery does not install it for you). The VAD only decides when a turn
ends; the model itself answers the speech.
Speech-to-speech over /v1/realtime is experimental and needs a LocalAI build whose llama.cpp pin includes the
LFM3 audio and realtime support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-pipeline
LiquidAI LFM3-Audio-2B in GGUF (Q4_K_M weights with the Q8_0 audio projector), used as a chained realtime pipeline: transcription,
chat and speech synthesis (voice female) all run on this one llama.cpp model, one stage after the other,
instead of as one speech-to-speech session. Pick it when you want per-stage text, the regular chat
path (tools, templates, streaming) or the transcript before the answer.
No VAD model is set: the client commits each turn with input_audio_buffer.commit. Use the
-pipeline-vad entry for automatic turn detection.
The pipeline slots name this entry, so keep the installed model name as it is.
Needs a LocalAI build whose llama.cpp pin includes the LFM3 audio support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-q8-pipeline
LiquidAI LFM3-Audio-2B in GGUF (Q8_0 weights with the Q8_0 audio projector), used as a chained realtime pipeline: transcription,
chat and speech synthesis (voice female) all run on this one llama.cpp model, one stage after the other,
instead of as one speech-to-speech session. Pick it when you want per-stage text, the regular chat
path (tools, templates, streaming) or the transcript before the answer.
No VAD model is set: the client commits each turn with input_audio_buffer.commit. Use the
-pipeline-vad entry for automatic turn detection.
The pipeline slots name this entry, so keep the installed model name as it is.
Needs a LocalAI build whose llama.cpp pin includes the LFM3 audio support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-pipeline-vad
LiquidAI LFM3-Audio-2B in GGUF (Q4_K_M weights with the Q8_0 audio projector), used as a chained realtime pipeline: transcription,
chat and speech synthesis (voice female) all run on this one llama.cpp model, one stage after the other,
instead of as one speech-to-speech session. Pick it when you want per-stage text, the regular chat
path (tools, templates, streaming) or the transcript before the answer.
Server-side turn detection is on: the entry sets pipeline.vad to silero-vad-ggml, so install the
silero-vad-ggml model first (the gallery does not install it for you).
The pipeline slots name this entry, so keep the installed model name as it is.
Needs a LocalAI build whose llama.cpp pin includes the LFM3 audio support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
lfm3-audio-2b-q8-pipeline-vad
LiquidAI LFM3-Audio-2B in GGUF (Q8_0 weights with the Q8_0 audio projector), used as a chained realtime pipeline: transcription,
chat and speech synthesis (voice female) all run on this one llama.cpp model, one stage after the other,
instead of as one speech-to-speech session. Pick it when you want per-stage text, the regular chat
path (tools, templates, streaming) or the transcript before the answer.
Server-side turn detection is on: the entry sets pipeline.vad to silero-vad-ggml, so install the
silero-vad-ggml model first (the gallery does not install it for you).
The pipeline slots name this entry, so keep the installed model name as it is.
Needs a LocalAI build whose llama.cpp pin includes the LFM3 audio support.
Licensed under the LFM Open License v1.0 (https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF/blob/main/LICENSE).
Repository: localaiLicense: lfm1.0
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