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The National Library of Sweden releases a new suite of Whisper models trained on over 50,000 hours of Swedish speech. In evaluations across FLEURS, CommonVoice and NST, our best performing model reduces the Word Error Rate (WER) by an average of 47% compared to OpenAI's `whisper-large-v3`. The performance of smaller Whisper model sizes on Swedish speech has also substantially improved, with `kb-whisper-small` outperforming `openai/whisper-large-v3` (a model six times its size). Table: **Word Error Rate (WER)** comparison between KBLab's Whisper models and the corresponding OpenAI versions. We provide checkpoints in different formats: `Hugging Face`, `whisper.cpp` (GGML), `onnx`, and `ctranslate2` (used in `faster-whisper` and `WhisperX`).

Speech recognition
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The National Library of Sweden/KBLab releases a robust, multi-label sentiment classifier finetuned on Megatron-BERT-large-165K. The model was trained on approximately 75K Swedish texts from multiple linguistic domains and datasets.

Text analysis
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This is a sentence-transformers model: It maps Swedish sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is a bilingual Swedish-English model trained according to instructions in the paper Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation and the documentation accompanying its companion python package. We have used the strongest available pretrained English Bi-Encoder (all-mpnet-base-v2) as a teacher model, and the pretrained Swedish KB-BERT as the student model. A more detailed description of the model can be found in an article we published on the KBLab blog here and for the updated model here. **Update**: We have released updated versions of the model since the initial release. The original model described in the blog post is **v1.0**. The current version is **v2.0**. The newer versions are trained on longer paragraphs, and have a longer max sequence length. **v2.0** is trained with a stronger teacher model and is the current default.

Embeddings & similarity
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Finetuned version of KBs VoxRex large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is **2.5%**. WER for Common Voice test set is **8.49%** directly and **7.37%** with a 4-gram language model. This model has been fine-tuned for 120000 updates on NST + CommonVoice and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed] .

Text generationOpen weights
Input Audio→ Output Text
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