Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
speech-encoder-decoder
Generated from Trainer
Instructions to use speech-seq2seq/wav2vec2-2-bert-large-no-adapter 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-no-adapter 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-no-adapter")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("speech-seq2seq/wav2vec2-2-bert-large-no-adapter") model = AutoModelForSpeechSeq2Seq.from_pretrained("speech-seq2seq/wav2vec2-2-bert-large-no-adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a41867c8512ebdc750104393b5d87a682d56a859c14ecdf25e833f31e72e2d1
- Size of remote file:
- 3.12 kB
- SHA256:
- 4bd8893938e9da9621d458ed93fa1bf00ff6f5ad3c78ad43e6e10dd7cc9c37ba
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