Instructions to use dg845/univnet-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dg845/univnet-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dg845/univnet-dev")# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("dg845/univnet-dev") model = AutoModel.from_pretrained("dg845/univnet-dev") - Notebooks
- Google Colab
- Kaggle
| { | |
| "center": false, | |
| "compression_clip_val": 1e-05, | |
| "compression_factor": 1.0, | |
| "do_normalize": false, | |
| "feature_extractor_type": "UnivNetFeatureExtractor", | |
| "feature_size": 1, | |
| "filter_length": 1024, | |
| "fmax": 12000.0, | |
| "fmin": 0.0, | |
| "hop_length": 256, | |
| "max_length_s": 10, | |
| "mel_floor": 1e-09, | |
| "model_in_channels": 64, | |
| "normalize_max": 2.3143386840820312, | |
| "normalize_min": -11.512925148010254, | |
| "num_mel_bins": 100, | |
| "pad_end_length": 10, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 24000, | |
| "win_function": "hann_window", | |
| "win_length": 1024 | |
| } | |