Instructions to use StephennFernandes/wav2vec2-XLS-R-300m-assamese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StephennFernandes/wav2vec2-XLS-R-300m-assamese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="StephennFernandes/wav2vec2-XLS-R-300m-assamese")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("StephennFernandes/wav2vec2-XLS-R-300m-assamese") model = AutoModelForMultimodalLM.from_pretrained("StephennFernandes/wav2vec2-XLS-R-300m-assamese") - Notebooks
- Google Colab
- Kaggle
| {"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "special_tokens_map_file": "XLS-R-assamese/special_tokens_map.json", "tokenizer_file": null, "name_or_path": "XLS-R-assamese-LM", "processor_class": "Wav2Vec2ProcessorWithLM", "tokenizer_class": "Wav2Vec2CTCTokenizer"} |