Automatic Speech Recognition
Transformers
PyTorch
English
data2vec-audio
speech
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use facebook/data2vec-audio-base-960h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-audio-base-960h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/data2vec-audio-base-960h")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-audio-base-960h") model = AutoModelForCTC.from_pretrained("facebook/data2vec-audio-base-960h") - Notebooks
- Google Colab
- Kaggle
| {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "do_lower_case": false, "word_delimiter_token": "|", "replace_word_delimiter_char": " ", "return_attention_mask": false, "do_normalize": true, "special_tokens_map_file": "/home/patrick/.cache/huggingface/transformers/60230682499b8486f2a3109ba26ac7395fd4eba61426f05432329ccbfac7c190.9d6cd81ef646692fb1c169a880161ea1cb95f49694f220aced9b704b457e51dd", "name_or_path": "facebook/wav2vec2-large-lv60", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2Processor"} |