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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rnagabh/gemma4-audio-encoder") model = AutoModel.from_pretrained("rnagabh/gemma4-audio-encoder") - Notebooks
- Google Colab
- Kaggle
| { | |
| "feature_size": 128, | |
| "sampling_rate": 16000, | |
| "padding_value": 0.0, | |
| "padding_side": "right", | |
| "return_attention_mask": true, | |
| "feature_extractor_type": "Gemma4AudioFeatureExtractor", | |
| "fft_length": 512, | |
| "frame_length": 320, | |
| "hop_length": 160, | |
| "min_frequency": 0.0, | |
| "max_frequency": 8000.0, | |
| "preemphasis": 0.0, | |
| "preemphasis_htk_flavor": true, | |
| "fft_overdrive": false, | |
| "dither": 0.0, | |
| "input_scale_factor": 1.0, | |
| "mel_floor": 0.001, | |
| "per_bin_mean": null, | |
| "per_bin_stddev": null | |
| } |