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NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF
NVIDIA Nemotron 3 Nano Omni is a multimodal large language model that unifies video, audio, image, and text understanding to support enterprise-grade Q&A, summarization, transcription, and document intelligence workflows. It extends the Nemotron Nano family with integrated video+speech comprehension, Graphical User Interface (GUI), Optical Character Recognition (OCR), and speech transcription capabilities, enabling end-to-end processing of rich enterprise content such as meeting recordings, M&E assets, training videos, and complex business documents. NVIDIA Nemotron 3 Nano Omni was developed by NVIDIA as part of the Nemotron model family. This model was improved using Qwen3-VL-30B-A3B-Instruct, Qwen3.5-122B-A10B, Qwen3.5-397B-A17B, Qwen2.5-VL-72B-Instruct, and gpt-oss-120b. For more information, please see the Training Dataset section below. This model is designed for enterprise customers requiring multimodal understanding capabilities. Expected users include: - Customer service applications (e.g., Doordash video of drop-off at a given address via OCR, drive-thru order verification) - Media and Entertainment (M&E) — video and speech analysis, dense captions, video search and summarization - Document intelligence for AI assistants (contracts, SOW/MSA, scientific discovery, financial documents) - GUI automation for AI agentic applications (incident management, agentic search, browser agents, email agents)
Breeze-ASR-26
**BreezeASR-Taigi** is a Taiwanese Hokkien (Taigi / 台語) automatic speech recognition (ASR) model developed as part of the **Breeze Taigi** framework — a comprehensive framework centered on standardized benchmarks and evaluation methodologies for Taiwanese Hokkien speech technologies. Taiwanese Hokkien, also known as Taigi, is a critically important language with deep historical and cultural significance in Taiwan. While significant progress has been made in speech recognition for languages such as English and Mandarin Chinese, developing effective speech technologies for Taigi presents both opportunities and methodological challenges due to its complex phonological system, significant geographical accents, and diverse forms of Chinese character writing. BreezeASR-Taigi is built by fine-tuning the **Whisper** multilingual speech recognition framework on approximately **10,000 hours** of large-scale synthetic Taiwanese Hokkien speech data. The model transcribes spoken Taigi audio and outputs **Mandarin Chinese character** transcriptions, leveraging the substantial lexical overlap between Taigi and Mandarin for a pragmatic and reproducible evaluation framework.
LightOnOCR-3-4B-GGUF
> **LightOnOCR-3-4B**, developed by lightonai, is the largest and most accurate model in the LightOnOCR-3 family of end-to-end OCR vision-language models, built on the Qwen3.5-4B architecture and released under the Apache 2.0 license. Recommended for most OCR tasks, it serves as a powerful, single-model alternative to complex document understanding pipelines by combining high-quality text transcription (triggered by an empty prompt) with advanced visual understanding features. When used with the `grounding` prompt, the model outputs labeled bounding boxes for all document elements, generates short descriptions for images, and extracts numerical data from charts into structured HTML tables. Highly versatile and optimized for seamless integration with Transformers and vLLM, it excels at processing complex layouts—including tables, receipts, forms, multi-column documents, and math notation—and performs best when documents are preprocessed at 400 DPI with a target longest dimension of 2048px.
mimo-asr-GGUF
GGUF conversion of `XiaomiMiMo/MiMo-V2.5-ASR` for **CrispStrobe/CrispASR**. Pure C++ inference — no Python, no Transformers, runs on Apple Silicon (Metal), CPU, and CUDA. The runtime is functional end-to-end: greedy decode through the 36-layer Qwen2 LM, full prefill + step-decode KV-cached graphs, prompt construction matching the upstream `MimoAudio.asr_sft` reference exactly. JFK transcription test passes verbatim. The default `mimo-asr-q4_k.gguf` (re-uploaded May 2026, PLAN #60d) ships with per-LM-layer Q/K/V projections fused into a single `model.layers.{i}.attn.qkv.{weight,bias}` tensor pair, yielding ~1.7× faster per-step decode on M1 vs the prior unfused layout (3058 ms/step → 1806 ms/step on a contended-disk run; ~1.1-1.2× pure-compute on a quiet box). The CrispASR runtime auto-detects either layout: the F16 file above keeps working unchanged via the separate-Q/K/V fallback path. Re-upload of a fused F16 is queued behind disk-headroom availability.
tara
**Tara is a frontier automatic speech-recognition model for Hindi and mixed-code (Hinglish) transcription.** On the AI4Bharat Vistaar Hindi benchmark suite it achieves state-of-the-art aggregate accuracy, outperforming leading commercial Hindi ASR systems on the 7-benchmark Vistaar mean, while natively handling Hindi–English code-switched speech through a dedicated **mixed-code mode** that renders English words in Latin script and Hindi in Devanagari, the way real Hinglish is written. The mixed-code mode (the ` ` prefix above) conditions generation only: on pure-Hindi audio it neither degrades accuracy nor forces transliteration; on mixed-code audio it renders English words in Latin script. Evaluation code, the exact text normalizer, and Tara's per-utterance predictions for every benchmark below are published at TrelisResearch/tara, so all numbers can be reproduced or re-scored under alternative normalizers.
typhoon-whisper-turbo
**Typhoon Whisper Turbo** is a state-of-the-art Thai Automatic Speech Recognition (ASR) model fine-tuned on the OpenAI Whisper Large v3 Turbo architecture. It is engineered to deliver a balance of high accuracy and low latency for offline transcription, significantly outperforming standard Whisper models in throughput while maintaining robust performance on Thai speech. The model was presented in the paper Typhoon ASR Real-time: FastConformer-Transducer for Thai Automatic Speech Recognition. By using this model, you agree to the OpenTyphoon Terms and Conditions and acknowledge the Privacy Notice: https://opentyphoon.ai/tac · https://opentyphoon.ai/privacy
silero-vad-v6.2-gguf
Ported from upstream commit 5cd7945, pinned 2026-10-09. Validated against the silero-vad 6.2.3 (TorchScript, CPU, F32) reference at transcribe.cpp commit 2674b95d on 2026-10-10. Voice activity detection by the Silero Team. Not a transcription model: the VAD role produces per-frame speech probabilities (offline or streaming) and offline speech segments. CPU is the default backend for reference fidelity. transcribe.cpp also loads whisper.cpp's `ggml-silero-v5.1.2.bin` / `v6.2.0.bin`. From the C API, use the VAD role (`include/transcribe/vad.h`): offline segments with `transcribe_vad_run`, live per-frame probabilities with `transcribe_vad_stream_feed`.
Voxtral-Mini-4B-Realtime-2602
Voxtral Mini 4B Realtime 2602 is a **multilingual, realtime speech-transcription model** and among the first open-source solutions to achieve accuracy comparable to offline systems with a delay of ** = 3600 / 0.8 = 45000`. In theory, you should be able to record with no limit; in practice, pre-allocations of RoPE parameters among other things limits `--max-model-len`. For the best user experience, we recommend to simply instantiate vLLM with the default parameters which will automatically set a maximum model length of 131072 (~ca. 3h). - We strongly recommend using websockets to set up audio streaming sessions. For more info on how to do so, check Usage. - We recommend using a delay of 480ms as we found it to be the sweet spot of performance and low latency. If, however, you want to adapt the delay, you can change the `"transcription_delay_ms": 480` parameter in the tekken.json file to any multiple of 80ms between 80 and 1200, as well as 2400 as a standalone value. We compare Voxtral Mini 4B Realtime to similar models - both offline models and realtime. Voxtral Mini 4B Realtime is competitive to leading offline models and shows significant gains over existing open-source realtime solutions. > [!Warning] > Due to its novel architecture, Voxtral Realtime is currently only support in vLLM. We very much welcome community contributions > to add the architecture to Transformers and Llama.cpp.
whisperkit-coreml
WhisperKit is part of Argmax OSS, an On-device Speech AI SDK for Apple Silicon: https://github.com/argmaxinc/argmax-oss-swift For real-time transcription with speakers and custom vocabulary, check out Argmax Pro SDK: https://www.argmaxinc.com/blog/argmax-sdk-2
gemma-4-E2B-it-RKLLM
Text generation, image questions, and short audio transcription on Rockchip RK3576 and RK3588, converted from google/gemma-4-E2B-it. **RK3576 preview:** image and audio requests work in the functional tests, but the vision encoder misses the numerical accuracy target. RK3576 W8A8 also fails one exact-recall check. See the measured results below before choosing this variant. RK3588 W8A8 passes the functional and encoder checks performed here. The new matched multimodal set is under **`v1.3.1/`**. Use the exact SoC for your board. Each variant needs its language file and vision encoder; add the audio encoder to enable speech input. The two RK3576 variants share the same encoders.