Instructions to use Lkhagvasurenam/STT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lkhagvasurenam/STT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Lkhagvasurenam/STT")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Lkhagvasurenam/STT") model = AutoModelForSpeechSeq2Seq.from_pretrained("Lkhagvasurenam/STT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a2e9d34aea1154a174968d1280c70c01a470ddc0e7e8c9c84f41d9061923d07
- Size of remote file:
- 967 MB
- SHA256:
- 0118bbc7e49416e0d6b43b31cdd8029e4125a93713619f083f47aa5d6516e0fa
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