Instructions to use esc-benchmark/wav2vec2-ctc-spgispeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-ctc-spgispeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-ctc-spgispeech")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esc-benchmark/wav2vec2-ctc-spgispeech") model = AutoModelForCTC.from_pretrained("esc-benchmark/wav2vec2-ctc-spgispeech") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dcd7204b1262ec7e8e17534a49f54caebae8a157e2dfe320878c8ce54f8e4258
- Size of remote file:
- 1.26 GB
- SHA256:
- f4cf21d7c33e7324cf75d5d7e400917deb822b7fe40cae5075ca9ae6dfbbf320
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