Feature Extraction
Transformers
Safetensors
granite_speech_nar
speech
asr
non-autoregressive
ctc
speech_recognition
automatic_speech_recognition
custom_code
Instructions to use ibm-granite/granite-speech-4.1-2b-nar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibm-granite/granite-speech-4.1-2b-nar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ibm-granite/granite-speech-4.1-2b-nar", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ibm-granite/granite-speech-4.1-2b-nar", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 7df989dae917f6bf15359be52ac066e0392593fd587f0d490fdebef2033b220b
- Size of remote file:
- 152 kB
- SHA256:
- b82815f3bb2d0096278af69462261bc07e369ea27118ce103d12863d932432fa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.