Instructions to use esc-benchmark/wav2vec2-ctc-earnings22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-ctc-earnings22 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-ctc-earnings22")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esc-benchmark/wav2vec2-ctc-earnings22") model = AutoModelForCTC.from_pretrained("esc-benchmark/wav2vec2-ctc-earnings22", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e9cb457533c2762e7d297dabbdd35bc055b912e1ecf20cac106071eafe34bea
- Size of remote file:
- 1.26 GB
- SHA256:
- 1c9d752d9dd01de9dd0913f0b68aee02c0bff99ca90b1b36cd8abd049f6aae23
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