Feature Extraction
Transformers
Safetensors
English
multilingual
gemma4_audio
audio
speech
conformer
gemma4
usm
google
Eval Results (legacy)
Instructions to use rnagabh/gemma4-audio-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rnagabh/gemma4-audio-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rnagabh/gemma4-audio-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rnagabh/gemma4-audio-encoder") model = AutoModel.from_pretrained("rnagabh/gemma4-audio-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download gemma4_tsne_speakers.png from rnagabh/gemma4-audio-encoder: direct link, hf CLI and curl.
- Browser
- Download file 193 kB
-
https://huggingface.co/rnagabh/gemma4-audio-encoder/resolve/main/gemma4_tsne_speakers.png
- Command line
-
hf download hf://rnagabh/gemma4-audio-encoder/gemma4_tsne_speakers.png
-
curl -L -o gemma4_tsne_speakers.png https://huggingface.co/rnagabh/gemma4-audio-encoder/resolve/main/gemma4_tsne_speakers.png
193 kB

- Xet hash:
- de22f2a0b5a31fbefc52e963cdf6bec0f831a0dc51077ba63c0738c66ac7b4bd
- Size of remote file:
- 193 kB
- SHA256:
- 95f60ef8f3570e9a0a27039c0a5d8851393ca053b1748f8cfe8ee6f86340a5d6
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