Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
speech-encoder-decoder
Generated from Trainer
Instructions to use nacielo/hubert2BertMusicTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nacielo/hubert2BertMusicTest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nacielo/hubert2BertMusicTest")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("nacielo/hubert2BertMusicTest") model = AutoModelForSpeechSeq2Seq.from_pretrained("nacielo/hubert2BertMusicTest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from nacielo/hubert2BertMusicTest: direct link, hf CLI and curl.
- Browser
- Download file 213 Bytes
-
https://huggingface.co/nacielo/hubert2BertMusicTest/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://nacielo/hubert2BertMusicTest/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/nacielo/hubert2BertMusicTest/resolve/main/preprocessor_config.json
213 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |