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MOSS-Transcribe-Diarize

MOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, diarization, timestamps, and acoustic event awareness. It supports transcription and diarization across **50+** languages, single-pass inference on audio recordings up to **90 minutes** long, and custom hotword prompting for domain-specific terms. Given an audio or video file, the model generates a compact speaker-aware transcript in one pass, including timestamps and anonymous speaker labels such as `[S01]`, `[S02]`, and beyond.

Whisper-Medicalv1CNB1

Ad-hoc generation arguments can be passed to `model.generate`, including `num_beams` for beam-search, `return_timestamps` for segment-level timestamps, and `prompt_ids` for prompting. See the docstrings for more details. Unlike previous Distil-Whisper releases, distil-large-v3 is specifically designed to be compatible with OpenAI's sequential long-form transcription algorithm. This algorithm uses a sliding window for buffered inference of long audio files (> 30-seconds), and returns more accurate transcriptions compared to the chunked long-form algorithm. The sequential long-form algorithm should be used in either of the following scenarios: 1. Transcription accuracy is the most important factor, and latency is less of a consideration 2. You are transcribing **batches** of long audio files, in which case the latency of sequential is comparable to chunked, while being up to 0.5% WER more accurate

Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4

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. Governing Terms: Use of this model is governed by the NVIDIA Open Model Agreement

nb-whisper-large

Introducing the **_Norwegian NB-Whisper Large model_**, proudly developed by the National Library of Norway. NB-Whisper is a cutting-edge series of models designed for automatic speech recognition (ASR) and speech translation. These models are based on the work of OpenAI's Whisper. Each model in the series has been trained for 250,000 steps, utilizing a diverse dataset of 8 million samples. These samples consist of aligned audio clips, each 30 seconds long, culminating in a staggering 66,000 hours of speech. For an in-depth understanding of our training methodology and dataset composition, keep an eye out for our upcoming article. While the main models are suitable for most transcription task, we demonstrate how easy it is to change the output of the main model. The following models are trained 250 additional steps from the main models above, and might be suitable for more targetted use cases: - **Verbatim version**: This lower-cased variant is more literal and suitable for tasks requiring detailed transcription, such as linguistic analysis. You can try the models directly through the HuggingFace Inference API, accessible on the right side of this page. Be aware that initially, the model needs to load and will run on limited CPU capacity, which might be slow. To enhance your experience, we are temporarily hosting some models on TPUs for a few days, significantly boosting their performance. Explore these under the **Spaces** section on the Main Page.

whisper-small-gguf

GGUF conversions of openai/whisper-small for use with transcribe.cpp. Ported from upstream commit 973afd2, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper small — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). Encoder-decoder transformer; 30-second windows with chunked long-form decoding.

transcription-cleanup-llama3.2-1b

canary-qwen-2.5b

NVIDIA NeMo Canary-Qwen-2.5B is an English speech recognition model that achieves state-of-the art performance on multiple English speech benchmarks. With 2.5 billion parameters and running at 418 RTFx, Canary-Qwen-2.5B supports automatic speech-to-text recognition (ASR) in English with punctuation and capitalization (PnC). The model works in two modes: as a transcription tool (ASR mode) and as an LLM (LLM mode). In ASR mode, the model is only capable of transcribing the speech into text, but does not retain any LLM-specific skills such as reasoning. In LLM mode, the model retains all of the original LLM capabilities, which can be used to post-process the transcript, e.g. summarize it or answer questions about it. In LLM mode, the model does not "understand" the raw audio anymore - only its transcript. This model is ready for commercial use. Canary-Qwen-2.5B is released under the CC-BY-4.0 license. By using this model, you are agreeing to the terms and conditions of the license. For documentation, deployment guides, enterprise-ready APIs, and the latest open models—including Nemotron and other cutting-edge speech, translation, and generative AI—visit the NVIDIA Developer Portal at developer.nvidia.com. Join the community to access tools, support, and resources to accelerate your development with NVIDIA’s NeMo, Riva, NIM, and foundation models.

Voxtral-Mini-4B-Realtime-2602-gguf

GGUF conversions of mistralai/Voxtral-Mini-4B-Realtime-2602 for use with transcribe.cpp. Ported from upstream commit 2769294, pinned 2026-06-06. Validated against the Transformers reference at transcribe.cpp commit 483c122 on 2026-06-06. Streaming audio-LLM speech-to-text. A ~970M causal audio encoder (left-pad causal conv stem + 32-layer sliding-window RoPE transformer) feeds a 4-frame-group projector whose audio embeddings are added onto a ~3.4B Ministral decoder (26 layers, GQA 32/8, NEOX RoPE) with delay-token latency conditioning, emitting one text token per 80 ms audio slot (12.5 Hz). Takes a 16 kHz mono WAV and supports both incremental streaming (configurable latency/quality via --stream-chunk-ms and --stream-voxtral-delay) and offline transcription using an accuracy-first 2400 ms delay. Architecturally distinct from the offline Voxtral 2507 family — own arch, streaming frontend, causal encoder, additive audio fusion.

LightOnOCR-3-0.8B

**Smallest model.** LightOnOCR-3-0.8B is the lightest model of **LightOn's** LightOnOCR-3 family, with the same layout and visual understanding features. LightOnOCR-3 is a new family of highly performant lightweight OCR models. Compared to the previous generation, they bring significant improvements in speed and transcription quality and introduce new visual understanding features: the models now output bounding box coordinates with labels for all visual elements of a document, short descriptions of images, and the numerical data of figures and charts. With these capabilities, they offer a ready-to-use, easier-to-maintain alternative to complex document understanding pipelines. The models come in three sizes. The 1B keeps the LightOnOCR-2-1B architecture, while the 0.8B and 4B adopt the Qwen3.5 vision-language architecture, which simplifies integration with existing tools and brings a significant speed-up. All models are released under the Apache 2.0 license for research and commercial use.

granite-speech-4.1-2b-plus

We have updated the model weights and configuration. The updated model is more accurate, especially for keyword biasing and for word-level timestamps on long samples. It is a drop-in replacement for the previous version, but we have also updated our usage recommendations to get the best performance. In the tables below, GSP v2610 refers to the updated model and GSP v2604 to the original release. Granite-Speech-4.1-2B-Plus (GSP) has similar capabilities to the Granite-Speech-4.1-2B model. The plus model adds two new community-requested rich transcription features that can be activated with a simple prompt change: speaker-attributed ASR (speaker labels and word transcripts) and word-level timing information. Unlike the base model, the plus model doesn't provide punctuation and capitalization.