Instructions to use Talha/URDU-ASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Talha/URDU-ASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Talha/URDU-ASR")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Talha/URDU-ASR") model = AutoModelForCTC.from_pretrained("Talha/URDU-ASR") - Notebooks
- Google Colab
- Kaggle
| { | |
| "bos_token": null, | |
| "do_lower_case": false, | |
| "eos_token": null, | |
| "name_or_path": "Talha/URDU-ASR", | |
| "pad_token": "[PAD]", | |
| "processor_class": "Wav2Vec2ProcessorWithLM", | |
| "replace_word_delimiter_char": " ", | |
| "special_tokens_map_file": "/home/talha/.cache/huggingface/transformers/4fe513b575fc984e7d81818eca5e05a17aaa2c7ad8887d98c7fe479944f81219.fea372b8528a479b7415f13ca4e27a2f5f3782cbb3f15b4d19bb3cbe734e8137", | |
| "tokenizer_class": "Wav2Vec2CTCTokenizer", | |
| "unk_token": "[UNK]", | |
| "word_delimiter_token": "|" | |
| } | |