Instructions to use yangwang825/mert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yangwang825/mert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="yangwang825/mert-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yangwang825/mert-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from yangwang825/mert-base: direct link, hf CLI and curl.
- Browser
- Download file 1.91 kB
-
https://huggingface.co/yangwang825/mert-base/resolve/main/config.json
- Command line
-
hf download hf://yangwang825/mert-base/config.json
-
curl -L -o config.json https://huggingface.co/yangwang825/mert-base/resolve/main/config.json
1.91 kB
| { | |
| "_name_or_path": "yangwang825/mert-base", | |
| "activation_dropout": 0.0, | |
| "apply_spec_augment": true, | |
| "architectures": [ | |
| "MERTModel" | |
| ], | |
| "attention_dropout": 0.1, | |
| "attention_relax": -1.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_mert.MERTConfig", | |
| "AutoModel": "modeling_mert.MERTModel", | |
| "AutoModelForAudioClassification": "modeling_mert.MERTForSequenceClassification" | |
| }, | |
| "bos_token_id": 1, | |
| "classifier_proj_size": 256, | |
| "conv_bias": false, | |
| "conv_dim": [ | |
| 512, | |
| 512, | |
| 512, | |
| 512, | |
| 512, | |
| 512, | |
| 512 | |
| ], | |
| "conv_kernel": [ | |
| 10, | |
| 3, | |
| 3, | |
| 3, | |
| 3, | |
| 2, | |
| 2 | |
| ], | |
| "conv_stride": [ | |
| 5, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "ctc_loss_reduction": "sum", | |
| "ctc_zero_infinity": false, | |
| "deepnorm": false, | |
| "do_stable_layer_norm": false, | |
| "eos_token_id": 2, | |
| "feat_extract_activation": "gelu", | |
| "feat_extract_dropout": 0.0, | |
| "feat_extract_norm": "group", | |
| "feat_proj_dropout": 0.1, | |
| "feat_proj_layer_norm": true, | |
| "feature_extractor_cqt": false, | |
| "feature_extractor_cqt_bins": 336, | |
| "final_dropout": 0.1, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout": 0.1, | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-05, | |
| "layerdrop": 0.05, | |
| "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, | |
| "model_type": "mert_model", | |
| "num_attention_heads": 12, | |
| "num_conv_pos_embedding_groups": 16, | |
| "num_conv_pos_embeddings": 128, | |
| "num_feat_extract_layers": 7, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "sample_rate": 24000, | |
| "tokenizer_class": "Wav2Vec2CTCTokenizer", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.25.1", | |
| "use_weighted_layer_sum": false, | |
| "vocab_size": 32 | |
| } | |