Repository: localaiLicense: nvidia-open-model-license
Cache-aware streaming RNNT FastConformer with end-of-utterance (EOU) detection, 120M, paired with Nemotron-3-Diarization and CED-Tiny through the diarization_model and sound_model options. F16/Q8_0 GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo). Use with streaming transcription: while a turn is live, closed speaker segments and sound events are surfaced alongside the ASR text (realtime conversation.item.input_audio_transcription.segment and conversation.item.sound_detection events). Live speaker/sound events only fire during speech turns under semantic_vad; sounds between turns are not seen by this path. License per model: transcription model NVIDIA Open Model License, diarization model OpenMDW-1.1, CED-Tiny Apache-2.0, WeSpeaker ResNet34 CC-BY-4.0. Also loads WeSpeaker ResNet34 through the speaker_model option, so live speaker segments carry the name of a voice registered with /v1/voice/register (voice-detect-wespeaker-resnet34 model) once the speaker is identified.
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Repository: localaiLicense: apache-2.0
CED-Tiny sound event tagger, Q8_0 GGUF for the parakeet-cpp backend (C++/ggml, loaded through third_party/ced.cpp). Served through /v1/audio/classification: 10 s windows are scored and averaged over the clip, then sorted by score with threshold and top_k applied. Smallest and fastest of the CED sizes; use ced-base for higher accuracy.
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Repository: localaiLicense: apache-2.0
CED-Base sound event tagger, Q8_0 GGUF for the parakeet-cpp backend (C++/ggml, loaded through third_party/ced.cpp). Served through /v1/audio/classification: 10 s windows are scored and averaged over the clip, then sorted by score with threshold and top_k applied. Larger and more accurate than ced-tiny, still CPU-friendly.
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Repository: localaiLicense: nvidia-open-model-license
Cache-aware streaming RNNT FastConformer with end-of-utterance (EOU) detection, 120M, paired with Nemotron-3-Diarization and CED-Tiny through the diarization_model and sound_model options. F16/Q8_0 GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo). Use with streaming transcription: while a turn is live, closed speaker segments and sound events are surfaced alongside the ASR text (realtime conversation.item.input_audio_transcription.segment and conversation.item.sound_detection events). Live speaker/sound events only fire during speech turns under semantic_vad; sounds between turns are not seen by this path. License per model: transcription model NVIDIA Open Model License, diarization model OpenMDW-1.1, CED-Tiny Apache-2.0.
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Repository: localaiLicense: cc-by-4.0
Parakeet TDT 0.6B v3 (multilingual, 25 European languages) paired with Nemotron-3-Diarization and CED-Tiny through the diarization_model and sound_model options: one parakeet-cpp backend transcribes, labels speakers and tags sound events. GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo). TDT is not a streaming model, so in a realtime pipeline use it with server_vad: set it as both transcription and sound_detection and turn on pipeline.diarization, and each committed turn gets speaker segments (conversation.item.input_audio_transcription.segment, with text) and sound tags (conversation.item.sound_detection). Also labels speakers on /v1/audio/transcriptions. Speaker labels are per turn. License per model: transcription model CC-BY-4.0, diarization model OpenMDW-1.1, CED-Tiny Apache-2.0.
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Repository: localaiLicense: nvidia-open-model-license
Cache-aware streaming RNNT FastConformer with end-of-utterance (EOU) detection, 120M, paired with Nemotron-3-Diarization and CED-Base (86M, the largest CED; more confident sound tags than CED-Tiny at a small extra cost: on CPU the live diarization + sound stream runs at 0.125 of real time against 0.103 with CED-Tiny) through the diarization_model and sound_model options. F16/Q8_0 GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo). Use with streaming transcription: while a turn is live, closed speaker segments and sound events are surfaced alongside the ASR text (realtime conversation.item.input_audio_transcription.segment and conversation.item.sound_detection events). Live speaker/sound events only fire during speech turns under semantic_vad; sounds between turns are not seen by this path. License per model: transcription model NVIDIA Open Model License, diarization model OpenMDW-1.1, CED-Base Apache-2.0.
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Repository: localaiLicense: cc-by-4.0
Parakeet TDT 0.6B v3 (multilingual, 25 European languages) paired with Nemotron-3-Diarization and CED-Base (86M, the largest CED; about 0.03 s of CPU per second of audio per committed turn, against 0.005 for CED-Tiny) through the diarization_model and sound_model options: one parakeet-cpp backend transcribes, labels speakers and tags sound events. GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo). TDT is not a streaming model, so in a realtime pipeline use it with server_vad: set it as both transcription and sound_detection and turn on pipeline.diarization, and each committed turn gets speaker segments (conversation.item.input_audio_transcription.segment, with text) and sound tags (conversation.item.sound_detection). Also labels speakers on /v1/audio/transcriptions. Speaker labels are per turn. License per model: transcription model CC-BY-4.0, diarization model OpenMDW-1.1, CED-Tiny Apache-2.0.
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