Instructions to use esc-benchmark/wav2vec2-aed-librispeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-aed-librispeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-aed-librispeech")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esc-benchmark/wav2vec2-aed-librispeech") model = AutoModelForSpeechSeq2Seq.from_pretrained("esc-benchmark/wav2vec2-aed-librispeech") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- defcbe0b75a8bafc4be63242f37f13a98f219ac64f63bb56d40406ca48fcd29a
- Size of remote file:
- 2.35 GB
- SHA256:
- 372221fdf608b28a94812360d641b0852fa54d1adc2d7c546610d3200abb28d8
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