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silero-vad-sherpa
Silero VAD served through the sherpa-onnx backend. Uses the same ONNX weights as the dedicated silero-vad backend, loaded through sherpa-onnx's C VAD API. Pairs with the sherpa-onnx ASR entries for round-trip audio pipelines.
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-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-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
silero-vad
Silero VAD - pre-trained enterprise-grade Voice Activity Detector.
Repository: localai
silero-vad-ggml
Silero VAD - pre-trained enterprise-grade Voice Activity Detector.
Repository: localai
parakeet-cpp-silero-vad-f16
Silero VAD v6.2.3 as a GGUF (F16, about 1.3 MB) for the parakeet-cpp backend. It only detects speech: use it for the VAD endpoint, or as the vad_model: option of a parakeet-cpp ASR model that has no VAD head.
Converted from the official Silero VAD model, MIT licensed, copyright Silero Team: https://github.com/snakers4/silero-vad
Repository: localaiLicense: mit
parakeet-cpp-tdt-0.6b-v3-silero-vad
TDT FastConformer, 0.6B (v3, multilingual) with Silero VAD. The vad_model option makes the backend cut long audio at pauses found by Silero before it transcribes, so recordings of any length work. The model has no VAD head of its own, so Silero does the cutting.
Both files are GGUF for the parakeet-cpp backend. The Silero model is MIT licensed, copyright Silero Team.
Repository: localaiLicense: cc-by-4.0
audio-cpp-silero-vad
Silero VAD (audio.cpp) - voice activity detection over /v1/vad. The model
ships inside the audio-cpp backend package, so installing this entry
downloads nothing.
Repository: localaiLicense: mit