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AI Models 535

• nb-whisper-large↗

  NbAiLab

  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↗

  handy-computer

  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.

• moss-transcribe-diarize-gguf↗

  handy-computer

  Ported from upstream commit d7231bb, pinned 2026-07-12. Validated against the MOSS author repo (OpenMOSS/MOSS-Transcribe-Diarize) reference at transcribe.cpp commit 3f5e15c on 2026-07-12. Offline English/Chinese speech-to-text with speaker diarization. A 0.9B audio-LLM: a Whisper-Medium encoder (24 layers, d_model=1024) feeds a 4x temporal merge + VQAdaptor bridge into a Qwen3-0.6B decoder (28 layers) via audio-token injection. The model emits `[start][Sxx]text[end]`; the runtime parses those generated markers into clean text and segment rows. Speaker attribution is opt-in (`--diarize`) and returns structured speaker ids/turns. Not a streaming model. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.

• whisper-1↗

  OpenAI

  Whisper can transcribe speech to text and translate multiple languages into English.

• whisper-large-v3-turbo-8bit↗

  mlx-community

• typhoon-whisper-turbo-timestamped↗

  supersuphot

• ultravox-v0_5-llama-3_1-8b↗

  fixie-ai

  Ultravox is a multimodal Speech LLM built around a pretrained Llama3.1-8B-Instruct and whisper-large-v3-turbo backbone. Ultravox is a multimodal model that can consume both speech and text as input (e.g., a text system prompt and voice user message). The input to the model is given as a text prompt with a special ` ` pseudo-token, and the model processor will replace this magic token with embeddings derived from the input audio. Using the merged embeddings as input, the model will then generate output text as usual. In a future revision of Ultravox, we plan to expand the token vocabulary to support generation of semantic and acoustic audio tokens, which can then be fed to a vocoder to produce voice output. No preference tuning has been applied to this revision of the model.

• emotion2vec_plus_large↗

  emotion2vec

  emotion2vec+ is a series of foundational models for speech emotion recognition (SER). We aim to train a "whisper" in the field of speech emotion recognition, overcoming the effects of language and recording environments through data-driven methods to achieve universal, robust emotion recognition capabilities. The performance of emotion2vec+ significantly exceeds other highly downloaded open-source models on Hugging Face. This version (emotion2vec_plus_large) uses a large-scale pseudo-labeled data for finetuning to obtain a large size model (~300M), and currently supports the following categories: 0: angry 1: disgusted 2: fearful 3: happy 4: neutral 5: other 6: sad 7: surprised 8: unknown GitHub Repo: emotion2vec |Model|⭐Model Scope|🤗Hugging Face|Fine-tuning Data (Hours)| |:---:|:-------------:|:-----------:|:-------------:| |emotion2vec|Link|Link|/| emotion2vec+ seed|Link|Link|201| emotion2vec+ base|Link|Link|4788| emotion2vec+ large|Link|Link|42526|

• roest-v3-whisper-1.5b↗

  CoRal-project

• whisper-large-v3-turbo-gguf↗

  xkeyC

  Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation. You can find more details about it in this GitHub discussion. **Disclaimer**: Content for this model card has partly been written by the 🤗 Hugging Face team, and partly copied and pasted from the original model card.