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)# pip install -U transformers accelerate # 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
Download preprocessor_config.json from abnerh/phonoq-2.0-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 212 Bytes
-
https://huggingface.co/abnerh/phonoq-2.0-multilingual/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://abnerh/phonoq-2.0-multilingual/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/abnerh/phonoq-2.0-multilingual/resolve/main/preprocessor_config.json
212 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |