Instructions to use KevinKibe/omniASR-CTC-300M-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KevinKibe/omniASR-CTC-300M-v2-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KevinKibe/omniASR-CTC-300M-v2-GGUF", device_map="auto")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KevinKibe/omniASR-CTC-300M-v2-GGUF", dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
OmniASR-CTC-300M-v2 GGUF
GGUF quantizations of OmniASR-CTC-300M-v2, a multilingual CTC-based Automatic Speech Recognition (ASR) model supporting over 1,600 languages.
This repository provides pre-quantized GGUF variants for efficient CPU and edge deployment with CrispASR and other GGUF-compatible runtimes.
Original Model
For Hugging Face Transformers format and usage, see the original model repository:
Available Quantizations
| Quantization | Description |
|---|---|
q2_k |
2-bit K-quant |
q3_k |
3-bit K-quant |
q4_0 |
4-bit quantization |
q4_1 |
4-bit quantization |
q4_k |
4-bit K-quant |
q5_0 |
5-bit quantization |
q5_1 |
5-bit quantization |
q5_k |
5-bit K-quant |
q6_k |
6-bit K-quant |
q8_0 |
8-bit quantization |
Usage with CrispASR
Installation
Follow the CrispASR installation guide:
https://github.com/CrispStrobe/CrispASR#install--build
Example
./build/bin/crispasr \
--backend omniasr \
--vad \
-m omniasr-ctc-300m-v2-q8_0.gguf \
-f audio.wav
Quantization Documentation
For details on GGUF quantization formats and instructions for generating additional quantized variants, see:
https://github.com/CrispStrobe/CrispASR/blob/main/docs/quantize.md
License
Apache 2.0
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Hardware compatibility
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