Automatic Speech Recognition
Transformers
Safetensors
Northern Kurdish
wav2vec2-bert
kurdish
badini
kurmanji
low-resource
Instructions to use computeram/badini-w2v-bert-normalized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use computeram/badini-w2v-bert-normalized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="computeram/badini-w2v-bert-normalized")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("computeram/badini-w2v-bert-normalized") model = AutoModelForCTC.from_pretrained("computeram/badini-w2v-bert-normalized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,368 Bytes
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"added_tokens_decoder": {
"0": {
"content": "|",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"66": {
"content": "[UNK]",
"lstrip": true,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": false
},
"67": {
"content": "[PAD]",
"lstrip": true,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": false
},
"68": {
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"69": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"backend": "custom",
"bos_token": "<s>",
"do_lower_case": false,
"eos_token": "</s>",
"is_local": true,
"local_files_only": false,
"model_max_length": 1000000000000000019884624838656,
"model_specific_special_tokens": {
"word_delimiter_token": "|"
},
"pad_token": "[PAD]",
"processor_class": "Wav2Vec2BertProcessor",
"replace_word_delimiter_char": " ",
"target_lang": null,
"tokenizer_class": "Wav2Vec2CTCTokenizer",
"unk_token": "[UNK]",
"word_delimiter_token": "|"
}
|