Instructions to use sanchit-gandhi/flax-wav2vec2-2-bart-large-cv9-prelim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sanchit-gandhi/flax-wav2vec2-2-bart-large-cv9-prelim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sanchit-gandhi/flax-wav2vec2-2-bart-large-cv9-prelim")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("sanchit-gandhi/flax-wav2vec2-2-bart-large-cv9-prelim") model = AutoModelForMultimodalLM.from_pretrained("sanchit-gandhi/flax-wav2vec2-2-bart-large-cv9-prelim") - Notebooks
- Google Colab
- Kaggle
| {"errors": "replace", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>", "add_prefix_space": false, "trim_offsets": true, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "./", "tokenizer_class": "BartTokenizer"} |