Automatic Speech Recognition
Transformers
Safetensors
joint_aed_ctc_speech-encoder-decoder
custom_code
Instructions to use BUT-FIT/ED-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/ED-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BUT-FIT/ED-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("BUT-FIT/ED-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,156 Bytes
4d69b96 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | {
"added_tokens_decoder": {
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"content": "([bos])",
"lstrip": false,
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"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "([eos])",
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},
"2": {
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},
"3": {
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},
"4": {
"content": "([mask])",
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"rstrip": false,
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"special": true
}
},
"bos_token": "([bos])",
"clean_up_tokenization_spaces": true,
"eos_token": "([eos])",
"mask_token": "([mask])",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "([pad])",
"tokenizer_class": "PreTrainedTokenizerFast",
"unk_token": "([unk])"
}
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