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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
parakeet-cpp-nemotron-3-diarization
Nemotron-3-Diarization (Sortformer), Q8_0 GGUF for the parakeet-cpp backend
(C++/ggml port of NVIDIA NeMo). Speaker diarization only: served through
/v1/audio/diarization, returns per-segment start, end and speaker label
("0", "1", ...). It does not transcribe; pair it with an ASR model and set
asr_model to get speaker-attributed text from the same call. num_speakers,
min_speakers, max_speakers and clustering_threshold are not supported by
Sortformer and are ignored.
Repository: localaiLicense: openmdw-1.1
parakeet-cpp-nemotron-3-diarization-asr
Nemotron-3-Diarization (Sortformer) paired with the Parakeet TDT+CTC 110M
ASR model through the asr_model option, both Q8_0/F16 GGUF for the
parakeet-cpp backend (C++/ggml port of NVIDIA NeMo). Served through
/v1/audio/diarization with include_text: each speaker segment comes back
with its transcribed text in one call. Diarization model is
OpenMDW-1.1, ASR model is CC-BY-4.0.
Repository: localaiLicense: openmdw-1.1
parakeet-cpp-nemotron-3-diarization-speakers
Nemotron-3-Diarization (Sortformer) with WeSpeaker ResNet34 speaker
identification, for the parakeet-cpp backend. Speakers you register with
/v1/voice/register (using the voice-detect-wespeaker-resnet34 model) come back
by name in /v1/audio/diarization, next to the SPEAKER_NN label. Speakers that
are not registered keep only their SPEAKER_NN label. The diarization model is
OpenMDW-1.1, the speaker model is CC-BY-4.0. Naming was measured on one
two-voice fixture only; check the threshold on your own audio.
Repository: localaiLicense: openmdw-1.1
parakeet-cpp-nemotron-3-diarization-asr-speakers
Nemotron-3-Diarization (Sortformer) paired with the Parakeet TDT+CTC 110M
ASR model through the asr_model option, both Q8_0/F16 GGUF for the
parakeet-cpp backend (C++/ggml port of NVIDIA NeMo). Served through
/v1/audio/diarization with include_text: each speaker segment comes back
with its transcribed text in one call. Diarization model is
OpenMDW-1.1, ASR model is CC-BY-4.0. Also loads WeSpeaker ResNet34
(CC-BY-4.0) through the speaker_model option: speakers registered with
/v1/voice/register (voice-detect-wespeaker-resnet34 model) come back by
name, next to the SPEAKER_NN label.
Repository: localaiLicense: openmdw-1.1
parakeet-cpp-multilingual-diarization-speakers
Parakeet TDT 0.6B v3 (multilingual, 25 European languages) paired with
Nemotron-3-Diarization (Sortformer) through the diarization_model option
and WeSpeaker ResNet34 through the speaker_model option, all GGUF for the
parakeet-cpp backend (C++/ggml port of NVIDIA NeMo). One call to
/v1/audio/diarization with include_text and include_speaker_profiles
returns the speaker turns, the text of each turn in the spoken language,
and one voice-print embedding per speaker, so a client does not need a
separate diarization call and transcription call. Use it where the
English-only 110M ASR of parakeet-cpp-nemotron-3-diarization-asr-speakers
is not enough. Also serves /v1/audio/transcriptions. License per model:
transcription model CC-BY-4.0, diarization model OpenMDW-1.1, WeSpeaker
encoder CC-BY-4.0.
Repository: localaiLicense: cc-by-4.0
parakeet-cpp-bundle-small
Parakeet TDT+CTC 110M with diarization, sound events, speaker naming and VAD. One bundle GGUF file (about 338 MB) that holds five models for the parakeet-cpp backend
(C++/ggml port of NVIDIA NeMo Parakeet): Parakeet TDT+CTC 110M (English, Q8_0), Nemotron-3-Diarization (Q8_0), CED-Small sound
events (Q8_0), WeSpeaker ResNet34-LM speaker encoder (F32) and Silero VAD (F16). One install serves
transcription (/v1/audio/transcriptions), voice activity detection (/v1/vad), speaker diarization with
text and named speakers (/v1/audio/diarization) and sound events (/v1/audio/classification). The vad:true
option cuts long audio at pauses found by Silero before it transcribes. The bundle options
(diar_component, sound_component, speaker_component) load each model from the same file; see the
audio-to-text docs for the option list.
A bundle has no single license: each model keeps its own, listed in the file header and in the NOTICE file
next to the file. Parakeet TDT+CTC 110M by NVIDIA is CC-BY-4.0, Nemotron-3-Diarization by NVIDIA is OpenMDW-1.1, CED-Small is Apache-2.0
(as stated on the model card; the upstream code repository is GPL-3.0 and the original checkpoint records say
CC-BY-4.0, so the licence of the weights is not consistent upstream; the model here is converted, not trained),
WeSpeaker ResNet34-LM by the WeSpeaker project is CC-BY-4.0, Silero VAD by the Silero Team is MIT. The
weights were converted to GGUF and quantised where stated; nothing was retrained.
Repository: localaiLicense: other
parakeet-cpp-bundle-standard
Parakeet TDT 0.6B v3 (multilingual) with diarization, sound events, speaker naming and VAD. One bundle GGUF file (about 1101 MB) that holds five models for the parakeet-cpp backend
(C++/ggml port of NVIDIA NeMo Parakeet): Parakeet TDT 0.6B v3 (multilingual, Q8_0), Nemotron-3-Diarization (Q8_0), CED-Small sound
events (Q8_0), WeSpeaker ResNet34-LM speaker encoder (F32) and Silero VAD (F16). One install serves
transcription (/v1/audio/transcriptions), voice activity detection (/v1/vad), speaker diarization with
text and named speakers (/v1/audio/diarization) and sound events (/v1/audio/classification). The vad:true
option cuts long audio at pauses found by Silero before it transcribes. The bundle options
(diar_component, sound_component, speaker_component) load each model from the same file; see the
audio-to-text docs for the option list.
A bundle has no single license: each model keeps its own, listed in the file header and in the NOTICE file
next to the file. Parakeet TDT 0.6B v3 by NVIDIA is CC-BY-4.0, Nemotron-3-Diarization by NVIDIA is OpenMDW-1.1, CED-Small is Apache-2.0
(as stated on the model card; the upstream code repository is GPL-3.0 and the original checkpoint records say
CC-BY-4.0, so the licence of the weights is not consistent upstream; the model here is converted, not trained),
WeSpeaker ResNet34-LM by the WeSpeaker project is CC-BY-4.0, Silero VAD by the Silero Team is MIT. The
weights were converted to GGUF and quantised where stated; nothing was retrained.
Repository: localaiLicense: other
audio-cpp-sortformer-diarization
Sortformer Diarization 4-speaker v1 (audio.cpp, Q8_0) - speaker diarization
for up to four speakers, served by the audio-cpp backend through
/v1/audio/diarization. Returns per-segment start, end and speaker label; it
does not transcribe, so pair it with an ASR model for text.
Q8_0 because upstream records it as a clean Pass, the same as 16-bit, at
two thirds of the size.
Licensing: the base checkpoint nvidia/diar_sortformer_4spk-v1 is
CC BY-NC 4.0, so commercial use is not permitted.
Repository: localaiLicense: cc-by-nc-4.0
nemo-speech-cpp-sortformer-diarization-v2
Streaming Sortformer Diarization 4-speaker v2 (nemo-speech-cpp, Q8_0) -
speaker diarization for up to four speakers, served by the nemo-speech-cpp
backend through /v1/audio/diarization. Returns per-segment start, end and
speaker label; it does not transcribe, so pair it with an ASR model for
text. This is the streaming variant: use it when you cannot wait for the
full recording. For offline batch work the non-streaming v1 is faster and
more accurate.
Repository: localaiLicense: cc-by-4.0
nemo-speech-cpp-nemotron-3.5-asr-streaming-diarized
Nemotron 3.5 ASR Streaming 0.6B with streaming Sortformer Diarization
4-speaker v2 (nemo-speech-cpp, Q8_0). Multilingual streaming ASR with
per-word speaker tags: the ASR model transcribes and the attached
sortformer model labels each word with its speaker. Served through
/v1/audio/transcriptions; segments are cut at each speaker change.
Q8_0 for both models. The diarization model is the streaming v2 variant,
for use when you cannot wait for the full recording.
Repository: localaiLicense: openmdw-1.1
nemo-speech-cpp-parakeet-tdt-0.6b-v3-diarized
Parakeet TDT 0.6B v3 with streaming Sortformer Diarization 4-speaker v2
(nemo-speech-cpp, Q8_0). Multilingual streaming ASR with per-word speaker
tags: the ASR model transcribes and the attached sortformer model labels
each word with its speaker. Served through /v1/audio/transcriptions;
segments are cut at each speaker change.
Q8_0 for both models. The diarization model is the streaming v2 variant,
for use when you cannot wait for the full recording.
Repository: localaiLicense: cc-by-4.0