Instructions to use esc-benchmark/wav2vec2-ctc-gigaspeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-ctc-gigaspeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-ctc-gigaspeech")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esc-benchmark/wav2vec2-ctc-gigaspeech") model = AutoModelForCTC.from_pretrained("esc-benchmark/wav2vec2-ctc-gigaspeech") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1eee8c6a0c2d6cf331606261170567c9cfe40bad1c7cbedd11c3bffbc3f9e291
- Size of remote file:
- 1.26 GB
- SHA256:
- 8ca21f568585330f48442860620a806f2e4afc9e9e27b6927936c50aa66149ca
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