Instructions to use hf-internal-testing/tiny-random-Wav2Vec2ForCTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Wav2Vec2ForCTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/tiny-random-Wav2Vec2ForCTC")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ForCTC") model = AutoModelForCTC.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ForCTC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b09472469deaa6deb8a64e4bd1e00286fe268c0131051d55bfbf7352586b724
- Size of remote file:
- 135 kB
- SHA256:
- b10d2694df93ecf3631ad261279ab13aadbd22f6d907e41641356ad2f0bc5665
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.