Audio Classification
Transformers
Safetensors
audioseg
feature-extraction
audio
topic-segmentation
whisper
custom_code
Instructions to use retkowski/audioseg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use retkowski/audioseg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="retkowski/audioseg", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("retkowski/audioseg", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 727 Bytes
2f71d81 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"architectures": [
"AudioSegModel"
],
"auto_map": {
"AutoConfig": "configuration_audioseg.AudioSegConfig",
"AutoModel": "modeling_audioseg.AudioSegModel"
},
"chunk_size_sec": 6.0,
"dtype": "float32",
"emb_dim": 384,
"encoder_chunk_size_sec": 30.0,
"encoder_dim": 1280,
"encoder_frames_per_chunk": 1500,
"max_frames_per_segment": 512,
"model_type": "audioseg",
"roformer_dim_feedforward": 2048,
"roformer_nhead": 8,
"roformer_num_layers": 12,
"sample_rate": 16000,
"segment_transformer_ff_mult": 4,
"segment_transformer_heads": 4,
"segment_transformer_num_layers": 3,
"threshold": 0.5,
"transformers_version": "4.57.0.dev0",
"whisper_model": "openai/whisper-large-v3"
}
|