Instructions to use esc-benchmark/wav2vec2-aed-tedlium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-aed-tedlium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-aed-tedlium")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esc-benchmark/wav2vec2-aed-tedlium") model = AutoModelForSpeechSeq2Seq.from_pretrained("esc-benchmark/wav2vec2-aed-tedlium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from esc-benchmark/wav2vec2-aed-tedlium: direct link, hf CLI and curl.
- Browser
- Download file 2.35 GB
-
https://huggingface.co/esc-benchmark/wav2vec2-aed-tedlium/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://esc-benchmark/wav2vec2-aed-tedlium/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/esc-benchmark/wav2vec2-aed-tedlium/resolve/main/flax_model.msgpack
2.35 GB
- Xet hash:
- d2f93539706e7b94ca98a351b7b2266408597b42d17c28761422f844c8c881ca
- Size of remote file:
- 2.35 GB
- SHA256:
- 4dff7590ad263bc52a238d3c7b2d76cb572915f2f12deb3ee64046fdd196b438
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