Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
speech-encoder-decoder
Generated from Trainer
Instructions to use speech-seq2seq/wav2vec2-2-bert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use speech-seq2seq/wav2vec2-2-bert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="speech-seq2seq/wav2vec2-2-bert-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("speech-seq2seq/wav2vec2-2-bert-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("speech-seq2seq/wav2vec2-2-bert-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f019453341f77b42c524e7fa619332b61f064e4b573cdbf1a4dd177c821e763
- Size of remote file:
- 3.08 GB
- SHA256:
- 393b8a8206a48764356e4f9f8f7b4729afb6f4cea722fc4051338f334a38df96
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