Instructions to use DBD-research-group/AudioProtoPNet-1-BirdSet-XCL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DBD-research-group/AudioProtoPNet-1-BirdSet-XCL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="DBD-research-group/AudioProtoPNet-1-BirdSet-XCL", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("DBD-research-group/AudioProtoPNet-1-BirdSet-XCL", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from DBD-research-group/AudioProtoPNet-1-BirdSet-XCL: direct link, hf CLI and curl.
- Browser
- Download file 547 Bytes
-
https://huggingface.co/DBD-research-group/AudioProtoPNet-1-BirdSet-XCL/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://DBD-research-group/AudioProtoPNet-1-BirdSet-XCL/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/DBD-research-group/AudioProtoPNet-1-BirdSet-XCL/resolve/main/preprocessor_config.json
547 Bytes
| { | |
| "auto_map": { | |
| "AutoFeatureExtractor": "processing_protonet.AudioProtoNetFeatureExtractor" | |
| }, | |
| "db_scale": null, | |
| "feature_extractor_type": "AudioProtoNetFeatureExtractor", | |
| "feature_size": 1, | |
| "hop_length": 256, | |
| "mean": -13.369, | |
| "mel_scale": null, | |
| "n_fft": 2048, | |
| "n_mels": 256, | |
| "n_stft": 1025, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "power": 2.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 32000, | |
| "spec_transform": null, | |
| "std": 13.162, | |
| "stype": "power", | |
| "top_db": 80 | |
| } | |