Instructions to use esc-benchmark/wav2vec2-aed-gigaspeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-aed-gigaspeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-aed-gigaspeech")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esc-benchmark/wav2vec2-aed-gigaspeech") model = AutoModelForSpeechSeq2Seq.from_pretrained("esc-benchmark/wav2vec2-aed-gigaspeech") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf7b56eccd4f15d89787cb53c5cb7a1a928a9efe8a55b131be9d153466b06e2b
- Size of remote file:
- 2.35 GB
- SHA256:
- b3d62d641627747144e6a96bfe67702e648a5754f7c5989229ce1d829f9a0dea
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.