Instructions to use h9LtLSb/whisper-small-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use h9LtLSb/whisper-small-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="h9LtLSb/whisper-small-es")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("h9LtLSb/whisper-small-es") model = AutoModelForSpeechSeq2Seq.from_pretrained("h9LtLSb/whisper-small-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b03e8dac5c503d42743a87d7896161a3f6ad2888c5797a0f55b81dbe291cf88
- Size of remote file:
- 3.63 kB
- SHA256:
- 58305bd29e59e9a762faf739298493f29e6229ec44fd681dc471275e9684d170
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