Audio Classification
Transformers
Safetensors
phonoq
feature-extraction
audio
speech
phonology
phonological-features
wav2vec2
multilingual
custom_code
Instructions to use abnerh/phonoq-2.0-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abnerh/phonoq-2.0-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="abnerh/phonoq-2.0-multilingual", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("abnerh/phonoq-2.0-multilingual", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "PhonoQForFeatureRecognition" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_phonoq.PhonoQConfig", | |
| "AutoModel": "modeling_phonoq.PhonoQForFeatureRecognition" | |
| }, | |
| "conv_kernel_size": 31, | |
| "dropout": 0.1, | |
| "feature_names": [ | |
| "silence", | |
| "stop", | |
| "nasal", | |
| "rhotic", | |
| "fricative", | |
| "affricate", | |
| "approximant", | |
| "lateral", | |
| "vowel", | |
| "high", | |
| "mid", | |
| "low", | |
| "front", | |
| "central", | |
| "back", | |
| "labial", | |
| "alveolar", | |
| "velar", | |
| "palatal", | |
| "postalveolar", | |
| "voiceless", | |
| "voiced" | |
| ], | |
| "ffn_expansion": 4, | |
| "hidden_dim": 512, | |
| "model_type": "phonoq", | |
| "num_heads": 4, | |
| "num_layers": 2, | |
| "relpos_max_distance": 128, | |
| "relpos_num_buckets": 32, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.51.3", | |
| "wav2vec_model_name": "facebook/wav2vec2-xlsr-53-espeak-cv-ft" | |
| } | |