Automatic Speech Recognition
Transformers
Safetensors
hubert
audio-classification
audio
speech
african-languages
multilingual
simba
low-resource
speech-recognition
asr
spoken-language-identification
language-identification
Instructions to use UBC-NLP/Simba-SLID-49 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/Simba-SLID-49 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UBC-NLP/Simba-SLID-49")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("UBC-NLP/Simba-SLID-49") model = AutoModelForAudioClassification.from_pretrained("UBC-NLP/Simba-SLID-49", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 213 Bytes
4701d9d | 1 2 3 4 5 6 7 8 9 10 | {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0,
"return_attention_mask": false,
"sampling_rate": 16000
}
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