Transformers
Safetensors
Slovenian
wav2vec2-bert
audio-frame-classification
prosody
segmentation
audio
speech
Instructions to use classla/wav2vecbert2-prosodicUnit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use classla/wav2vecbert2-prosodicUnit with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("classla/wav2vecbert2-prosodicUnit") model = AutoModelForAudioFrameClassification.from_pretrained("classla/wav2vecbert2-prosodicUnit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from classla/wav2vecbert2-prosodicUnit: direct link, hf CLI and curl.
- Browser
- Download file 275 Bytes
-
https://huggingface.co/classla/wav2vecbert2-prosodicUnit/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://classla/wav2vecbert2-prosodicUnit/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/classla/wav2vecbert2-prosodicUnit/resolve/main/preprocessor_config.json
275 Bytes
| { | |
| "feature_extractor_type": "SeamlessM4TFeatureExtractor", | |
| "feature_size": 80, | |
| "num_mel_bins": 80, | |
| "padding_side": "right", | |
| "padding_value": 1, | |
| "processor_class": "Wav2Vec2BertProcessor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "stride": 2 | |
| } | |