Instructions to use padmalcom/wav2vec2-large-emotion-detection-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use padmalcom/wav2vec2-large-emotion-detection-german with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="padmalcom/wav2vec2-large-emotion-detection-german")# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("padmalcom/wav2vec2-large-emotion-detection-german") model = Wav2Vec2ForSpeechClassification.from_pretrained("padmalcom/wav2vec2-large-emotion-detection-german", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6424dba48837a61be4d4fa3d0702c9d6142c27053badde68ca4d6658aa6f3977
- Size of remote file:
- 1.27 GB
- SHA256:
- cf74f54354de1de50fdc727d6b79cb56156f93ff455a1af6ef02284b367267bc
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