Audio Classification
Transformers
PyTorch
Safetensors
Russian
emotion-recognition
speech-emotion-recognition
audio
speech
russian
custom_code
Eval Results (legacy)
Instructions to use Aniemore/wav2vec2-bert-base-s-emotion-russian-resd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aniemore/wav2vec2-bert-base-s-emotion-russian-resd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Aniemore/wav2vec2-bert-base-s-emotion-russian-resd", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("Aniemore/wav2vec2-bert-base-s-emotion-russian-resd", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Aniemore/wav2vec2-bert-base-s-emotion-russian-resd: direct link, hf CLI and curl.
- Browser
- Download file 1.97 GB
-
https://huggingface.co/Aniemore/wav2vec2-bert-base-s-emotion-russian-resd/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Aniemore/wav2vec2-bert-base-s-emotion-russian-resd/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Aniemore/wav2vec2-bert-base-s-emotion-russian-resd/resolve/main/pytorch_model.bin
1.97 GB
- Xet hash:
- 176a724698f169c871394aca81801ebfafc26c11a2487d2bd59e7ab4e258f716
- Size of remote file:
- 1.97 GB
- SHA256:
- 9ac3d27af7862aebe37f4941d188f9cbdc5b419dca00ed1578ffdbc0a3f9f739
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