Instructions to use 3funnn/wav2vec2-base-minilibrispeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 3funnn/wav2vec2-base-minilibrispeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="3funnn/wav2vec2-base-minilibrispeech")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("3funnn/wav2vec2-base-minilibrispeech") model = AutoModelForCTC.from_pretrained("3funnn/wav2vec2-base-minilibrispeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15815035d6cd9b6aaa8dfc802c4598103b29dcae79ee79e58f756a06495d6088
- Size of remote file:
- 4.92 kB
- SHA256:
- 7cc85f58d69b38270b4a23e113b56a808357860188221987b164a65b5a00dd88
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