Instructions to use esc-bench/wav2vec2-ctc-earnings22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-bench/wav2vec2-ctc-earnings22 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-bench/wav2vec2-ctc-earnings22")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esc-bench/wav2vec2-ctc-earnings22") model = AutoModelForCTC.from_pretrained("esc-bench/wav2vec2-ctc-earnings22", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 351 Bytes
0847fef | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"bos_token": "<s>",
"do_lower_case": false,
"eos_token": "</s>",
"name_or_path": "sanchit-gandhi/wav2vec2-ctc-earnings22-black-box-tokenizer",
"pad_token": "<pad>",
"replace_word_delimiter_char": " ",
"special_tokens_map_file": null,
"tokenizer_class": "Wav2Vec2CTCTokenizer",
"unk_token": "<unk>",
"word_delimiter_token": "|"
}
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