Instructions to use confit/wav2vec2-base-spkreg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use confit/wav2vec2-base-spkreg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="confit/wav2vec2-base-spkreg", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("confit/wav2vec2-base-spkreg", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 338 Bytes
726c4db add9456 726c4db e3a1b74 726c4db | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"auto_map": {
"AutoFeatureExtractor": "feature_extraction_wav2vec2_spkreg.Wav2Vec2SpkRegFeatureExtractor"
},
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2SpkRegFeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": false,
"sampling_rate": 16000
}
|