Instructions to use esc-benchmark/wav2vec2-aed-spgispeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-aed-spgispeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-aed-spgispeech")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esc-benchmark/wav2vec2-aed-spgispeech") model = AutoModelForSpeechSeq2Seq.from_pretrained("esc-benchmark/wav2vec2-aed-spgispeech") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6830face2c73bdc701ba1459d946e7e2f5f2dbd32f30e1bd4b70af61ec3a5570
- Size of remote file:
- 2.35 GB
- SHA256:
- 7af9662fb94f987aa7df7c25c8cbc68679a0c709e18f799cd6f85e3c3db6fd22
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