Instructions to use ibm-granite/granite-4.0-1b-speech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibm-granite/granite-4.0-1b-speech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ibm-granite/granite-4.0-1b-speech")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ibm-granite/granite-4.0-1b-speech") model = AutoModelForMultimodalLM.from_pretrained("ibm-granite/granite-4.0-1b-speech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| - dataset: | |
| id: hf-audio/open-asr-leaderboard | |
| task_id: mean_wer | |
| value: 5.52 | |
| date: '2026-02-27' | |
| source: | |
| url: https://huggingface.co/hf-audio | |
| name: open-asr-leaderboard | |
| user: hf-audio | |
| - dataset: | |
| id: hf-audio/open-asr-leaderboard | |
| task_id: rtfx | |
| value: 280.02 | |
| date: '2026-02-27' | |
| source: | |
| url: https://huggingface.co/hf-audio | |
| name: open-asr-leaderboard | |
| user: hf-audio | |
| - dataset: | |
| id: hf-audio/open-asr-leaderboard | |
| task_id: ami_wer | |
| value: 8.44 | |
| date: '2026-02-27' | |
| source: | |
| url: https://huggingface.co/hf-audio | |
| name: open-asr-leaderboard | |
| user: hf-audio | |
| - dataset: | |
| id: hf-audio/open-asr-leaderboard | |
| task_id: earnings22_wer | |
| value: 8.48 | |
| date: '2026-02-27' | |
| source: | |
| url: https://huggingface.co/hf-audio | |
| name: open-asr-leaderboard | |
| user: hf-audio | |
| - dataset: | |
| id: hf-audio/open-asr-leaderboard | |
| task_id: gigaspeech_wer | |
| value: 10.14 | |
| date: '2026-02-27' | |
| source: | |
| url: https://huggingface.co/hf-audio | |
| name: open-asr-leaderboard | |
| user: hf-audio | |
| - dataset: | |
| id: hf-audio/open-asr-leaderboard | |
| task_id: librispeech_clean_wer | |
| value: 1.42 | |
| date: '2026-02-27' | |
| source: | |
| url: https://huggingface.co/hf-audio | |
| name: open-asr-leaderboard | |
| user: hf-audio | |
| - dataset: | |
| id: hf-audio/open-asr-leaderboard | |
| task_id: librispeech_other_wer | |
| value: 2.85 | |
| date: '2026-02-27' | |
| source: | |
| url: https://huggingface.co/hf-audio | |
| name: open-asr-leaderboard | |
| user: hf-audio | |
| - dataset: | |
| id: hf-audio/open-asr-leaderboard | |
| task_id: spgispeech_wer | |
| value: 3.89 | |
| date: '2026-02-27' | |
| source: | |
| url: https://huggingface.co/hf-audio | |
| name: open-asr-leaderboard | |
| user: hf-audio | |
| - dataset: | |
| id: hf-audio/open-asr-leaderboard | |
| task_id: tedlium_wer | |
| value: 3.1 | |
| date: '2026-02-27' | |
| source: | |
| url: https://huggingface.co/hf-audio | |
| name: open-asr-leaderboard | |
| user: hf-audio | |
| - dataset: | |
| id: hf-audio/open-asr-leaderboard | |
| task_id: voxpopuli_wer | |
| value: 5.84 | |
| date: '2026-02-27' | |
| source: | |
| url: https://huggingface.co/hf-audio | |
| name: open-asr-leaderboard | |
| user: hf-audio | |