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")# pip install -U transformers accelerate # 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
Download preprocessor_config.json from speech-seq2seq/wav2vec2-2-bert-large-no-adapter: direct link, hf CLI and curl.
- Browser
- Download file 214 Bytes
-
https://huggingface.co/speech-seq2seq/wav2vec2-2-bert-large-no-adapter/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://speech-seq2seq/wav2vec2-2-bert-large-no-adapter/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/speech-seq2seq/wav2vec2-2-bert-large-no-adapter/resolve/main/preprocessor_config.json
214 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |