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
Download processor_config.json from caesar-abrham/Chaka-ASR: direct link, hf CLI and curl.
- Browser
- Download file 320 Bytes
-
https://huggingface.co/caesar-abrham/Chaka-ASR/resolve/main/processor_config.json
- Command line
-
hf download hf://caesar-abrham/Chaka-ASR/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/caesar-abrham/Chaka-ASR/resolve/main/processor_config.json
320 Bytes
| { | |
| "feature_extractor": { | |
| "feature_extractor_type": "SeamlessM4TFeatureExtractor", | |
| "feature_size": 80, | |
| "num_mel_bins": 80, | |
| "padding_side": "right", | |
| "padding_value": 1, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "stride": 2 | |
| }, | |
| "processor_class": "Wav2Vec2BertProcessor" | |
| } | |