From the model card
Fields below are copied from the tags and counters on the HuggingFace repository mlx-community/parakeet-tdt_ctc-110m at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- mlx-community
- Pipeline tag
- automatic-speech-recognition
- Library
- MLX
- Weight formats
- safetensors
- License tag
cc-by-4.0— read the license file in the repo before relying on it- Lineage
-
- base model nvidia/parakeet-tdt_ctc-110m
- fine-tune of nvidia/parakeet-tdt_ctc-110m
- Downloads (HF counter at last fetch)
- 351,300
- Likes (HF counter at last fetch)
- 1
- Model card
- https://huggingface.co/mlx-community/parakeet-tdt_ctc-110m
Use cases
- Low-latency English transcription on Apple Silicon Macs
- On-device ASR without cloud API dependency
- Real-time caption generation for meeting or lecture recording software
- Transcription in offline environments on MacBooks
- Comparing MLX runtime performance against whisper.cpp for English-only tasks
Pros
- MLX format provides native Metal GPU acceleration on Apple Silicon
- FastConformer + CTC decoding is faster than attention-decoder models at equivalent WER
- CC-BY-4.0 license; attribution only, commercial use allowed
- 110M parameters fit easily in Apple Silicon unified memory
Cons
- English only; no multilingual capability
- MLX runtime is Apple-only; not portable to Linux or Windows
- MLX community repackage may lag behind official NeMo Parakeet updates
- CTC decoding without language model produces more word boundary errors than beam search
Tags
mlxsafetensorsautomatic-speech-recognitionspeechaudioFastConformerConformerParakeetbase_model:nvidia/parakeet-tdt_ctc-110mbase_model:finetune:nvidia/parakeet-tdt_ctc-110mlicense:cc-by-4.0region:us