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eelcor/
canary-1b-v2-mlx

Name: canary-1b-v2-mlx
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Canary 1B v2 MLX

This is a conversion of NVIDIA's Canary 1B v2 ASR model to Apple's MLX framework for fast inference on Apple Silicon.

Model Description

Canary 1B v2 is a state-of-the-art automatic speech recognition (ASR) model from NVIDIA, featuring:

Architecture: FastConformer Encoder (32 layers) + Transformer Decoder (8 layers)
Parameters: ~1B
Languages: 25 languages including Dutch, English, German, French, Spanish, and more
Training Data: NVIDIA Granary dataset

Performance

Benchmarked on Apple Silicon (M-series):

Metric	Value
Speed	~8.5x real-time
RTF (Real-Time Factor)	~0.12

Comparison with Whisper (10s Dutch audio):

Model	Speed	Dutch Quality
Canary 1B v2 MLX	8.5x RT	Excellent
Whisper large-v2 MLX	6.4x RT	Excellent

Canary is 33% faster than Whisper with comparable quality.

Usage

Installation

pip install canary-mlx

Python API

python

from canary_mlx import load_model

# Load the model
model = load_model("eelcor/canary-1b-v2-mlx")

# Transcribe audio (specify language for best results)
result = model.transcribe("audio.wav", language="nl")  # Dutch
print(result)

# For English
result = model.transcribe("audio.wav", language="en")

Supported Languages

The model supports 25 languages. Use the ISO 639-1 language codes:

nl - Dutch
en - English
de - German
fr - French
es - Spanish
it - Italian
pt - Portuguese
pl - Polish
ru - Russian
uk - Ukrainian
And more...

Conversion Details

This model was converted from the original NeMo format using:

PyTorch checkpoint extraction
Weight tensor transposition for MLX conv layers
SentencePiece tokenizer preservation

License

This model inherits the CC-BY-4.0 license from the original NVIDIA Canary model.

Citation

If you use this model, please cite the original NVIDIA work:

bibtex

@misc{nvidia_canary_2024,
  title={Canary: A Multilingual ASR Model},
  author={NVIDIA NeMo Team},
  year={2024},
  publisher={NVIDIA}
}

Acknowledgments

NVIDIA NeMo Team for the original Canary model
canary-mlx library for MLX inference support
Apple MLX framework