Instructions to use esc-benchmark/wav2vec2-aed-voxpopuli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-aed-voxpopuli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-aed-voxpopuli")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esc-benchmark/wav2vec2-aed-voxpopuli") model = AutoModelForSpeechSeq2Seq.from_pretrained("esc-benchmark/wav2vec2-aed-voxpopuli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1197e62dc1ae523fd60e4c1dbdc878c8cf87166f5b922eb161f96f30bf2840a7
- Size of remote file:
- 2.35 GB
- SHA256:
- 329b37c05e36a573f3ebf65881d238a8dea772a044fb83f1ae2b37bd32d13e00
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