Audio Classification
Transformers
Safetensors
English
wav2vec2multihead_3class
speech
speech classification
intent classification
custom_code
Instructions to use techsword/wav2vec2-base-fsc-ic-multiheadmultilabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use techsword/wav2vec2-base-fsc-ic-multiheadmultilabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="techsword/wav2vec2-base-fsc-ic-multiheadmultilabel", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("techsword/wav2vec2-base-fsc-ic-multiheadmultilabel", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from techsword/wav2vec2-base-fsc-ic-multiheadmultilabel: direct link, hf CLI and curl.
- Browser
- Download file 159 Bytes
-
https://huggingface.co/techsword/wav2vec2-base-fsc-ic-multiheadmultilabel/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://techsword/wav2vec2-base-fsc-ic-multiheadmultilabel/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/techsword/wav2vec2-base-fsc-ic-multiheadmultilabel/resolve/main/preprocessor_config.json
159 Bytes
| { | |
| "do_normalize": true, | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |