Instructions to use esc-bench/wav2vec2-aed-chime4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-bench/wav2vec2-aed-chime4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-bench/wav2vec2-aed-chime4")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esc-bench/wav2vec2-aed-chime4") model = AutoModelForSpeechSeq2Seq.from_pretrained("esc-bench/wav2vec2-aed-chime4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 280 Bytes
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"bos_token": "<s>",
"cls_token": "<s>",
"eos_token": "</s>",
"mask_token": {
"content": "<mask>",
"lstrip": true,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": "<pad>",
"sep_token": "</s>",
"unk_token": "<unk>"
}
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