Automatic Speech Recognition
NeMo
English
speech
audio
NeMo
parakeet
TDT
FastConformer
quantization
ternary
bitnet
quantization-aware-training
knowledge-distillation
Eval Results (legacy)
Instructions to use rajb3/kea-t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use rajb3/kea-t with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("rajb3/kea-t") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Download tokenizer/tokenizer.model from rajb3/kea-t: direct link, hf CLI and curl.
- Browser
- Download file 251 kB
-
https://huggingface.co/rajb3/kea-t/resolve/main/tokenizer/tokenizer.model
- Command line
-
hf download hf://rajb3/kea-t/tokenizer/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/rajb3/kea-t/resolve/main/tokenizer/tokenizer.model
251 kB
- Xet hash:
- 0650c52fa6416e417175ae4e34ade57486f08e2f9a45d41a418b9c3cf1ace82b
- Size of remote file:
- 251 kB
- SHA256:
- 5a3a82c48998709f9bc9f5db2924d99a899d20bf82550ca52272da7c7557b0a0
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