Automatic Speech Recognition
Transformers
Safetensors
Amharic
wav2vec2-bert
amharic
speech-recognition
ctc
leyu
Instructions to use caesar-abrham/Chaka-ASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use caesar-abrham/Chaka-ASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="caesar-abrham/Chaka-ASR")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("caesar-abrham/Chaka-ASR") model = AutoModelForCTC.from_pretrained("caesar-abrham/Chaka-ASR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download NOTICE.md from caesar-abrham/Chaka-ASR: direct link, hf CLI and curl.
- Browser
- Download file 460 Bytes
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https://huggingface.co/caesar-abrham/Chaka-ASR/resolve/main/NOTICE.md
- Command line
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hf download hf://caesar-abrham/Chaka-ASR/NOTICE.md
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curl -L -o NOTICE.md https://huggingface.co/caesar-abrham/Chaka-ASR/resolve/main/NOTICE.md
460 Bytes
Upstream credits
Chaka-ASR is fine-tuned from badrex/Ethio-ASR-amharic, which declares CC BY 4.0, and builds on facebook/w2v-bert-2.0.
Adaptation used Leyu Amharic speech datasets with WAXAL replay. Consult upstream model cards and dataset terms for attribution and applicable conditions.
Chaka-ASR is released under CC BY 4.0. See LICENSE for the license reference. This notice records upstream attribution.