Audio Classification
Transformers
Safetensors
English
finvoc2vec
feature-extraction
audio
classification
Wav2Vec2
sentiment
earnings conference calls
custom_code
Instructions to use waiv/FinVoc2Vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waiv/FinVoc2Vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="waiv/FinVoc2Vec", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("waiv/FinVoc2Vec", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |