Instructions to use esc-benchmark/wav2vec2-ctc-voxpopuli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-ctc-voxpopuli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-ctc-voxpopuli")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esc-benchmark/wav2vec2-ctc-voxpopuli") model = AutoModelForCTC.from_pretrained("esc-benchmark/wav2vec2-ctc-voxpopuli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb229cfe0b050f14f938b6987741fd10b77ce0ff1177439ee6108e2b56725405
- Size of remote file:
- 1.26 GB
- SHA256:
- 1ffca1e1c2f8cbf19b33e85549fa8a64fba702365e266900d2e03e38e72ab305
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