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s1-mini-q4
S1-mini by Superwhisper is a 0.6B English text normalizer for raw speech
transcripts. It removes fillers and false starts, restores punctuation and
capitalization, and formats spoken numbers, dates, currency, and email
addresses as written text.
This default entry uses the publisher's 462 MB Q4_K_M GGUF and greedy
decoding. A higher-fidelity F16 model is available as a variant. Prefix the
transcript with the styling, structure, and context control line documented
on the model page.
Repository: localaiLicense: s1-mini-license
s1-mini-f16
S1-mini by Superwhisper in the publisher's 1.4 GB F16 GGUF format. This
variant preserves full model fidelity for hosts with enough memory.
Repository: localaiLicense: s1-mini-license
nemo-parakeet-tdt-0.6b
NVIDIA NeMo Parakeet TDT 0.6B v3 is an automatic speech recognition (ASR) model from NVIDIA's NeMo toolkit. Parakeet models are state-of-the-art ASR models trained on large-scale English audio data.
Repository: localaiLicense: cc-by-4.0
moonshine-tiny
Moonshine Tiny is a lightweight speech-to-text model optimized for fast transcription. It is designed for efficient on-device ASR with high accuracy relative to its size.
Repository: localaiLicense: apache-2.0
whisperx-tiny
WhisperX Tiny is a fast and accurate speech recognition model with speaker diarization capabilities. Built on OpenAI's Whisper with additional features for alignment and speaker segmentation.
Repository: localaiLicense: mit
omnilingual-0.3b-ctc-q8-sherpa
Omnilingual ASR CTC 300M (int8) is a multilingual automatic speech recognition model supporting 1,600+ languages. Based on Meta's omniASR_CTC_300M architecture (Wav2Vec2 with CTC head), quantized to int8 for efficient inference. Uses the sherpa-onnx backend with ONNX Runtime.
Repository: localaiLicense: apache-2.0
streaming-zipformer-en-sherpa
Streaming English ASR: sherpa-onnx zipformer transducer (int8, chunk-16 left-128). Low-latency real-time transcription with endpoint detection via sherpa-onnx's online recognizer. English-only; for multilingual offline ASR see omnilingual-0.3b-ctc-q8-sherpa.
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
vibevoice-cpp-asr
VibeVoice ASR 7B (C++ / GGML, Q4_K) - long-form speech-to-text with speaker
diarization. Returns per-speaker JSON segments with start/end timestamps.
English-only. ~10 GB download.
Repository: localaiLicense: mit
qwen3-asr-1.7b
Qwen3-ASR is an automatic speech recognition model supporting multiple languages and batch inference.
Repository: localaiLicense: apache-2.0
qwen3-asr-0.6b
Qwen3-ASR is an automatic speech recognition model supporting multiple languages and batch inference.
Repository: localaiLicense: apache-2.0
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
whisper-1
Port of OpenAI's Whisper model in C/C++
Repository: localaiLicense: mit
whisper-large-q5_0
Port of OpenAI's Whisper model in C/C++
Repository: localaiLicense: mit
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