Discover and install AI models from our curated collection
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.
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Repository: localaiLicense: apache-2.0
Voxtral Mini 4B Realtime is a speech-to-text model from Mistral AI. It is a 4B parameter model optimized for fast, accurate audio transcription with low latency, making it ideal for real-time applications. The model uses the Voxtral architecture for efficient audio processing.
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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.
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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.
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Repository: localaiLicense: apache-2.0
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.
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Repository: localaiLicense: apache-2.0
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.
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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.
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Hybrid TDT+CTC FastConformer, 110M. F16 GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet), byte-identical to NeMo at WER 0. Faster than NeMo on CPU and GPU.
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Repository: localaiLicense: cc-by-4.0
Cache-aware streaming RNNT FastConformer with end-of-utterance (EOU) detection, 120M. Use with streaming transcription. F16 GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet), byte-identical to NeMo at WER 0. Faster than NeMo on CPU and GPU.
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CTC FastConformer, 0.6B. F16 GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet), byte-identical to NeMo at WER 0. Faster than NeMo on CPU and GPU.
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RNNT FastConformer, 0.6B. F16 GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet), byte-identical to NeMo at WER 0. Faster than NeMo on CPU and GPU.
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TDT FastConformer, 0.6B (v2). F16 GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet), byte-identical to NeMo at WER 0. Faster than NeMo on CPU and GPU.
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TDT FastConformer, 0.6B (v3, multilingual). F16 GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet), byte-identical to NeMo at WER 0. Faster than NeMo on CPU and GPU.
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Repository: localaiLicense: cc-by-4.0
Phonon 2.5, F16: a multilingual speech recognition model for 25 European languages, by Fermion Research. F16 file, full precision, about 1.4 GB. GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The weights are released under CC-BY-4.0: credit Fermion Research.
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Repository: localaiLicense: cc-by-4.0
Phonon 2.5, Q8_0: a multilingual speech recognition model for 25 European languages, by Fermion Research. Q8_0 quantized file, about 940 MB. GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The weights are released under CC-BY-4.0: credit Fermion Research.
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Repository: localaiLicense: cc-by-4.0
Phonon 2.5, Q4_K: a multilingual speech recognition model for 25 European languages, by Fermion Research. Q4_K quantized file, about 675 MB, the smallest of the regular builds. GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The weights are released under CC-BY-4.0: credit Fermion Research.
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Repository: localaiLicense: cc-by-4.0
Phonon 2.5, PACKED: a multilingual speech recognition model for 25 European languages, by Fermion Research. Packed weights, about 240 MB, the smallest file. It runs on the CPU only, because the GPU route for packed files is not in this build. For a GPU, use the f16, q8_0 or q4_k entry instead. GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The weights are released under CC-BY-4.0: credit Fermion Research.
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Repository: localaiLicense: cc-by-4.0
Phonon 2.5 English, F16: the English-only speech recognition model of the Phonon 2.5 family, by Fermion Research. F16 file, full precision, about 1.4 GB. GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The weights are released under CC-BY-4.0: credit Fermion Research.
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Repository: localaiLicense: cc-by-4.0
Phonon 2.5 English, Q8_0: the English-only speech recognition model of the Phonon 2.5 family, by Fermion Research. Q8_0 quantized file, about 940 MB. GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The weights are released under CC-BY-4.0: credit Fermion Research.
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Repository: localaiLicense: cc-by-4.0
Phonon 2.5 English, Q4_K: the English-only speech recognition model of the Phonon 2.5 family, by Fermion Research. Q4_K quantized file, about 675 MB, the smallest of the regular builds. GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The weights are released under CC-BY-4.0: credit Fermion Research.
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Repository: localaiLicense: cc-by-4.0
Phonon 2.5 English, PACKED: the English-only speech recognition model of the Phonon 2.5 family, by Fermion Research. Packed weights, about 240 MB, the smallest file. It runs on the CPU only, because the GPU route for packed files is not in this build. For a GPU, use the f16, q8_0 or q4_k entry instead. GGUF for the parakeet-cpp backend (C++/ggml port of NVIDIA NeMo Parakeet). The weights are released under CC-BY-4.0: credit Fermion Research.
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