Instructions to use shaeel12/moonshine-tiny-urdu2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shaeel12/moonshine-tiny-urdu2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="shaeel12/moonshine-tiny-urdu2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("shaeel12/moonshine-tiny-urdu2") model = AutoModelForSpeechSeq2Seq.from_pretrained("shaeel12/moonshine-tiny-urdu2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from shaeel12/moonshine-tiny-urdu2: direct link, hf CLI and curl.
- Browser
- Download file 312 Bytes
-
https://huggingface.co/shaeel12/moonshine-tiny-urdu2/resolve/main/processor_config.json
- Command line
-
hf download hf://shaeel12/moonshine-tiny-urdu2/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/shaeel12/moonshine-tiny-urdu2/resolve/main/processor_config.json
312 Bytes
| { | |
| "feature_extractor": { | |
| "do_normalize": false, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| }, | |
| "processor_class": "Wav2Vec2Processor" | |
| } | |