Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
audio
speech
african-languages
multilingual
simba
low-resource
speech-recognition
asr
Instructions to use UBC-NLP/Simba-M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/Simba-M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UBC-NLP/Simba-M")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("UBC-NLP/Simba-M") model = AutoModelForCTC.from_pretrained("UBC-NLP/Simba-M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"bos_token": "<s>", "clean_up_tokenization_spaces": true, "do_lower_case": false, "eos_token": "</s>", "model_max_length": 1000000000000000019884624838656, "pad_token": "[PAD]", "replace_word_delimiter_char": " ", "target_lang": "multilingual_african", "tokenizer_class": "Wav2Vec2CTCTokenizer", "unk_token": "[UNK]", "word_delimiter_token": "|"} |