Feature Extraction
Transformers
Safetensors
English
meralion_bestrq
speech
best-rq
meralion
custom_code
Instructions to use MERaLiON/MERaLiON-SpeechEncoder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MERaLiON/MERaLiON-SpeechEncoder-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MERaLiON/MERaLiON-SpeechEncoder-v1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MERaLiON/MERaLiON-SpeechEncoder-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "activation_dropout": 0.0, | |
| "architectures": [ | |
| "MeralionBestRqModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_bestrq_conformer.MeralionBestRqConformerEncoderConfig", | |
| "AutoModel": "modeling_bestrq_conformer.MeralionBestRqModel", | |
| "AutoModelForCTC": "modeling_bestrq_conformer.MeralionBestRqModelForCTC" | |
| }, | |
| "conformer_conv_dropout": 0.0, | |
| "conv_depthwise_kernel_size": 5, | |
| "ctc_loss_reduction": "sum", | |
| "ctc_zero_infinity": false, | |
| "feat_proj_dropout": 0.0, | |
| "ffn_dim": 4096, | |
| "final_dropout": 0.0, | |
| "hidden_act": "swish", | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 1024, | |
| "input_channels": 1, | |
| "input_dim": 80, | |
| "layerdrop": 0.0, | |
| "max_source_positions": 3000, | |
| "model_type": "meralion_bestrq", | |
| "no_scale_embedding": false, | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 24, | |
| "position_embeddings_type": "relative", | |
| "rotary_embedding_base": 10000, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.45.2", | |
| "vocab_size": 1023 | |
| } | |