Instructions to use Respair/Whisper_Large_v2_Encoder_Block with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Respair/Whisper_Large_v2_Encoder_Block with Transformers:
# Load model directly from transformers import WhisperEncoderOnly model = WhisperEncoderOnly.from_pretrained("Respair/Whisper_Large_v2_Encoder_Block", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "openai/whisper-large-v2", | |
| "activation_dropout": 0.0, | |
| "activation_function": "gelu", | |
| "apply_spec_augment": false, | |
| "architectures": [ | |
| "WhisperEncoderOnly" | |
| ], | |
| "attention_dropout": 0.0, | |
| "classifier_proj_size": 256, | |
| "d_model": 1280, | |
| "dropout": 0.0, | |
| "encoder_attention_heads": 20, | |
| "encoder_ffn_dim": 5120, | |
| "encoder_layerdrop": 0.0, | |
| "encoder_layers": 32, | |
| "init_std": 0.02, | |
| "mask_feature_length": 10, | |
| "mask_feature_min_masks": 0, | |
| "mask_feature_prob": 0.0, | |
| "mask_time_length": 10, | |
| "mask_time_min_masks": 2, | |
| "mask_time_prob": 0.05, | |
| "max_length": 448, | |
| "max_source_positions": 1500, | |
| "median_filter_width": 7, | |
| "model_type": "whisper_encoder", | |
| "num_hidden_layers": 32, | |
| "num_mel_bins": 80, | |
| "scale_embedding": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.44.0", | |
| "use_weighted_layer_sum": false, | |
| "vocab_size": 51865 | |
| } |