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 embed_audio.safetensors from rnagabh/gemma4-audio-encoder: direct link, hf CLI and curl.
- Browser
- Download file 4.72 MB
-
https://huggingface.co/rnagabh/gemma4-audio-encoder/resolve/main/embed_audio.safetensors
- Command line
-
hf download hf://rnagabh/gemma4-audio-encoder/embed_audio.safetensors
-
curl -L -o embed_audio.safetensors https://huggingface.co/rnagabh/gemma4-audio-encoder/resolve/main/embed_audio.safetensors
4.72 MB
- Xet hash:
- 153edea67bae02711a48d8548bf6b67d6f22f747f64b59a1e346f0145abb6c4f
- Size of remote file:
- 4.72 MB
- SHA256:
- 966aa3b0b2a3513c4d4cfcdb0396fb802d291dfe68edc07f7efaa5487ee8f099
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