Instructions to use cromz22/wav2vec2-2-bart-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cromz22/wav2vec2-2-bart-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cromz22/wav2vec2-2-bart-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("cromz22/wav2vec2-2-bart-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("cromz22/wav2vec2-2-bart-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a82b95deb5f63d3314ae0015353deac66299f517a6e343fd2a1426d89e1ada31
- Size of remote file:
- 805 MB
- SHA256:
- f8c5e3f1f9ce8bdd2393f46a380784072a9fd50a1d61f21cf5876956a712a79d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.