Instructions to use makamkkumar/mkk-Urdu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use makamkkumar/mkk-Urdu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="makamkkumar/mkk-Urdu")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("makamkkumar/mkk-Urdu") model = AutoModelForCTC.from_pretrained("makamkkumar/mkk-Urdu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from makamkkumar/mkk-Urdu: direct link, hf CLI and curl.
- Browser
- Download file 422 Bytes
-
https://huggingface.co/makamkkumar/mkk-Urdu/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://makamkkumar/mkk-Urdu/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/makamkkumar/mkk-Urdu/resolve/main/tokenizer_config.json
422 Bytes
| { | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": true, | |
| "do_lower_case": false, | |
| "eos_token": "</s>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "[PAD]", | |
| "processor_class": "Wav2Vec2Processor", | |
| "replace_word_delimiter_char": " ", | |
| "target_lang": null, | |
| "tokenizer_class": "Wav2Vec2CTCTokenizer", | |
| "tokenizer_file": null, | |
| "unk_token": "[UNK]", | |
| "word_delimiter_token": "|" | |
| } | |