Audio Classification
Transformers
Safetensors
Spanish
wav2vec2-bert
emotion-recognition
speech-emotion-recognition
speech-processing
spanish
affective-computing
umuteam
Eval Results (legacy)
Instructions to use UMUTeam/w2v-bert-emotion-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMUTeam/w2v-bert-emotion-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="UMUTeam/w2v-bert-emotion-es")# Load model directly from transformers import AutoProcessor, CustomAudioClassification processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-emotion-es") model = CustomAudioClassification.from_pretrained("UMUTeam/w2v-bert-emotion-es") - Notebooks
- Google Colab
- Kaggle
File size: 275 Bytes
35ae1e3 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"feature_extractor_type": "SeamlessM4TFeatureExtractor",
"feature_size": 80,
"num_mel_bins": 80,
"padding_side": "right",
"padding_value": 1,
"processor_class": "Wav2Vec2BertProcessor",
"return_attention_mask": true,
"sampling_rate": 16000,
"stride": 2
}
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