Instructions to use switlydev/custom-stt-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use switlydev/custom-stt-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="switlydev/custom-stt-model")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("switlydev/custom-stt-model") model = AutoModelForSpeechSeq2Seq.from_pretrained("switlydev/custom-stt-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc4942857946a6e9126d54be9202e9c50975de85137b188956db79e7945e3ddb
- Size of remote file:
- 967 MB
- SHA256:
- efbe0677e71d88e5f7ed073cd4cce110e5d9b0cc80e5a3cce86e9571d6af28de
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