Instructions to use hts98/temp1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hts98/temp1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hts98/temp1")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hts98/temp1") model = AutoModelForSpeechSeq2Seq.from_pretrained("hts98/temp1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3cca0de96197a3a6c3e061c12197f3fcbcd13cf96bad7f2547cdee1c0712a216
- Size of remote file:
- 3.71 kB
- SHA256:
- f0706b90e2ffc19f95adf0c9c2e659f63698104d7bf2c16fa64fdc77c63ba3de
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