Audio Classification
Transformers
PyTorch
multilingual
wav2vec2
voice
classification
vocalization
speech
audio
Instructions to use padmalcom/wav2vec2-large-nonverbalvocalization-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use padmalcom/wav2vec2-large-nonverbalvocalization-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="padmalcom/wav2vec2-large-nonverbalvocalization-classification")# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("padmalcom/wav2vec2-large-nonverbalvocalization-classification") model = Wav2Vec2ForSpeechClassification.from_pretrained("padmalcom/wav2vec2-large-nonverbalvocalization-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from padmalcom/wav2vec2-large-nonverbalvocalization-classification: direct link, hf CLI and curl.
- Browser
- Download file 96 Bytes
-
https://huggingface.co/padmalcom/wav2vec2-large-nonverbalvocalization-classification/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://padmalcom/wav2vec2-large-nonverbalvocalization-classification/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/padmalcom/wav2vec2-large-nonverbalvocalization-classification/resolve/main/special_tokens_map.json
96 Bytes
| { | |
| "bos_token": "<s>", | |
| "eos_token": "</s>", | |
| "pad_token": "<pad>", | |
| "unk_token": "<unk>" | |
| } | |