Instructions to use esc-benchmark/wav2vec2-aed-earnings22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-aed-earnings22 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-aed-earnings22")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esc-benchmark/wav2vec2-aed-earnings22") model = AutoModelForSpeechSeq2Seq.from_pretrained("esc-benchmark/wav2vec2-aed-earnings22") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c59c22aa90f09f56069cdc93f9704dcb728355ff174ab9135f3d25560bf6b002
- Size of remote file:
- 2.35 GB
- SHA256:
- 080843f28d5770e6add69fbe6e3ade1db01b2110831b4b2c9526c6f6f973f6c1
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