Automatic Speech Recognition
Transformers
Safetensors
Afrikaans
vits
text-to-audio
audio
speech
african-languages
multilingual
simba
low-resource
speech-recognition
asr
Instructions to use UBC-NLP/Simba-TTS-afr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/Simba-TTS-afr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UBC-NLP/Simba-TTS-afr")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/Simba-TTS-afr") model = AutoModelForTextToWaveform.from_pretrained("UBC-NLP/Simba-TTS-afr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_blank": true, | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "g", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "41": { | |
| "content": "<unk>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "clean_up_tokenization_spaces": true, | |
| "is_uroman": false, | |
| "language": "afr", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "normalize": true, | |
| "pad_token": "g", | |
| "phonemize": false, | |
| "tokenizer_class": "VitsTokenizer", | |
| "unk_token": "<unk>", | |
| "verbose": false | |
| } | |