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:
- ebbe66082e571aad3f9f642e725e375814bc083046844c056889ea399465762e
- Size of remote file:
- 3.01 GB
- SHA256:
- 867cc98d783e4ba9704d5eb7fc047a98b136449a7adb4bc183ba202d4b79750d
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