Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ForCTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Wav2Vec2ForCTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-tiny-model-private/tiny-random-Wav2Vec2ForCTC")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForCTC") model = AutoModelForCTC.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForCTC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce23d8f6b796494615960fa565f93c0f327dae1e19a371a01b70ec85fad9e178
- Size of remote file:
- 118 kB
- SHA256:
- 1bac27aac79f63480218dfc7227fc0b6c4c8732ec0dd0d0b006b2a752852499b
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