| --- |
| language: |
| - da |
| license: cc-by-4.0 |
| pipeline_tag: automatic-speech-recognition |
| library_name: mlx |
| base_model: KlangAI/pianissimo-da |
| tags: |
| - mlx |
| - speech |
| - audio |
| - fastconformer |
| - tdt |
| - danish |
| --- |
| |
| # Klang Pianissimo for Danish: MLX bf16 |
|
|
| A version of [Klang Pianissimo for Danish](https://huggingface.co/KlangAI/pianissimo-da), our Danish speech recognition model, for Macs with Apple silicon (M1 or later). It runs on the Mac's GPU using [MLX](https://github.com/ml-explore/mlx) and [parakeet-mlx](https://github.com/senstella/parakeet-mlx), without PyTorch or NeMo. |
|
|
| This repository contains the 16-bit (bfloat16) version, with almost the same accuracy as the full-size model at half the download size. |
|
|
| ## Versions |
|
|
| | Repository | Weights | Download | FLEURS test | Speed | Memory, 5 min | |
| |---|---|---:|---:|---:|---:| |
| | [pianissimo-da](https://huggingface.co/KlangAI/pianissimo-da) | Original fp32 | 2.51 GB | **7.43** | | | |
| | [pianissimo-da-mlx-8bit](https://huggingface.co/KlangAI/pianissimo-da-mlx-8bit) | 8-bit | 759 MB | 7.49 | 151× | **2.2 GB** | |
| | [pianissimo-da-mlx-4bit](https://huggingface.co/KlangAI/pianissimo-da-mlx-4bit) | 4-bit | **528 MB** | 7.80 | 126× | **2.2 GB** | |
| | [pianissimo-da-mlx](https://huggingface.co/KlangAI/pianissimo-da-mlx) | bf16 | 1.25 GB | 7.48 | 140× | 2.7 GB | |
| | [pianissimo-da-mlx-fp32](https://huggingface.co/KlangAI/pianissimo-da-mlx-fp32) | fp32 | 2.51 GB | **7.43** | **173×** | 3.8 GB | |
|
|
| The FLEURS column shows word error rate (WER, %) on all 930 Danish test clips, scored with our evaluation normaliser; lower is better. Speed is audio duration divided by processing time (RTFx), measured on a 30-second clip. |
| Speed and memory were measured on the Swedish model, which has the same architecture, on an Apple M5 Pro GPU (24 GB). Memory is peak GPU memory use while transcribing a 5-minute recording. |
|
|
| For other computers, use the [ONNX versions](https://huggingface.co/KlangAI/pianissimo-da-onnx). |
|
|
| ## Usage |
|
|
| ```bash |
| pip install parakeet-mlx==0.5.2 huggingface_hub sentencepiece |
| brew install ffmpeg # to read audio files |
| ``` |
|
|
| ```python |
| import sys |
| from huggingface_hub import snapshot_download |
| |
| path = snapshot_download("KlangAI/pianissimo-da-mlx") |
| sys.path.append(path) |
| import pianissimo_mlx |
| |
| model = pianissimo_mlx.load(path) |
| result = pianissimo_mlx.transcribe(model, "audio.wav") |
| print(result.text) |
| ``` |
|
|
| Recordings longer than 2 minutes are transcribed in 2-minute chunks with a 15-second overlap. Set `chunk_duration` to change the chunk length in seconds, or to `None` for a single pass. `transcribe` also accepts a 16 kHz mono NumPy array. Timestamps are available in `result.sentences` and `sentence.tokens`. |
|
|
| Use the `pianissimo_mlx.py` loader included in this repository. It supports the 8-bit and 4-bit weights and reproduces the audio preprocessing used during training. Calling parakeet-mlx directly does not support these quantized weights and uses slightly different preprocessing, adding about 0.2 percentage points of WER. |
|
|
| ### Vocabulary |
|
|
| Our implementation of NeMo's phrase boosting supports custom vocabulary and matches NeMo's results on the original model. List names and terms to help the model spell them correctly. |
|
|
| ```python |
| vocabulary = ["Klang AI", "Pianissimo", "Folketinget", "Aarhus Universitet"] |
| result = pianissimo_mlx.transcribe(model, "audio.wav", boost=vocabulary) |
| ``` |
|
|
| See the [main model card](https://huggingface.co/KlangAI/pianissimo-da#vocabulary) for vocabulary boosting with NeMo. |
|
|
| ## How they were made |
|
|
| All versions are converted from the released checkpoint and retain its local attention (256 frames on each side). The 8-bit and 4-bit versions use MLX's built-in quantization for the encoder's linear layers, with groups of 64 weights for 8-bit and 32 for 4-bit. The remaining weights in those two versions are stored in bfloat16. |
|
|
| FLEURS WER was measured with this code on Linux. In a separate Mac check, this version matched the normalised original fp32 reference on 195 of 200 FLEURS clips. See the [main model card](https://huggingface.co/KlangAI/pianissimo-da) for comparisons with other models. |
|
|
| ## License and citation |
|
|
| Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), like the original model. |
|
|
| ```bibtex |
| @misc{klang2026pianissimoda, |
| title = {Klang Pianissimo for Danish}, |
| author = {{Klang}}, |
| year = {2026}, |
| howpublished = {Hugging Face model repository}, |
| url = {https://huggingface.co/KlangAI/pianissimo-da} |
| } |
| ``` |
|
|
