Instructions to use ibrainf/result with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibrainf/result with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="ibrainf/result")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("ibrainf/result") model = AutoModelForTextToSpectrogram.from_pretrained("ibrainf/result", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c3dc69f56dbf33588e2599524994e5ab13852f3081d26470da676b5979924e7
- Size of remote file:
- 5.5 kB
- SHA256:
- 7a82cec9d67eaea345deb9575b6d868e3152a41199cdd1f51e9e2ff88f43c71c
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