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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
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
voice-hi_IN-priyamvada-medium
A fast, local neural text to speech system that sounds great and is optimized for the Raspberry Pi 4. Piper is used in a variety of [projects](https://github.com/rhasspy/piper#people-using-piper).
Repository: localaiLicense: mit
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-moondream-ultra-f16
Moondream Ultra, F16: Moondream's post-trained derivative of NVIDIA parakeet-tdt-0.6b-v3 (TDT, 0.6B). Runs on CPU and GPU. Full-precision file, about 1.4 GB.
GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The
model has a voice-activity-detection head, and the vad:true option cuts long
audio at pauses before transcription. License CC-BY-4.0: credit Moondream and NVIDIA.
Repository: localaiLicense: cc-by-4.0
parakeet-cpp-moondream-ultra-q8_0
Moondream Ultra, Q8_0: the same model as the F16 file, quantized to 8 bits (about 0.9 GB). Runs on CPU and GPU.
GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The
model has a voice-activity-detection head, and the vad:true option cuts long
audio at pauses before transcription. License CC-BY-4.0: credit Moondream and NVIDIA.
Repository: localaiLicense: cc-by-4.0
parakeet-cpp-moondream-redux-packed
Moondream Redux, packed ternary weights (about 213 MB). Redux is Moondream's ternary-encoder derivative of NVIDIA parakeet-tdt-0.6b-v3 (TDT, 0.6B). CPU only and offline only: the library refuses to load it on a GPU backend and does not stream it. For a GPU or for streaming, use the redux-f16 or redux-q8_0 entry instead.
GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The
model has a voice-activity-detection head, and the vad:true option cuts long
audio at pauses before transcription. License CC-BY-4.0: credit Moondream and NVIDIA.
Repository: localaiLicense: cc-by-4.0
parakeet-cpp-moondream-redux-f16
Moondream Redux, dequantized to F16 (about 1.4 GB). Same model as the packed file, but it runs on any backend, including GPU, and can stream.
GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The
model has a voice-activity-detection head, and the vad:true option cuts long
audio at pauses before transcription. License CC-BY-4.0: credit Moondream and NVIDIA.
Repository: localaiLicense: cc-by-4.0
parakeet-cpp-moondream-redux-q8_0
Moondream Redux, dequantized and quantized to Q8_0 (about 0.9 GB). Runs on any backend, including GPU.
GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The
model has a voice-activity-detection head, and the vad:true option cuts long
audio at pauses before transcription. License CC-BY-4.0: credit Moondream and NVIDIA.
Repository: localaiLicense: cc-by-4.0
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
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