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")# pip install -U transformers accelerate # 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
Download training_args.bin from padmalcom/wav2vec2-large-emotion-detection-german: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/padmalcom/wav2vec2-large-emotion-detection-german/resolve/main/training_args.bin
- Command line
-
hf download hf://padmalcom/wav2vec2-large-emotion-detection-german/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/padmalcom/wav2vec2-large-emotion-detection-german/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- c6f8e6abbb6461bc6bb403bad85b65cc9e7cfea2423f41ae9764f2c5c6996aa7
- Size of remote file:
- 3.44 kB
- SHA256:
- f467a7ade5e8b40aca9dfb19ae33f607c6d26604d6545450f1b4dc1d32cc7afa
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