Feature Extraction
Transformers
Safetensors
PyTorch
liquid_codec
audio
audio-codec
bfloat16
custom_code
Instructions to use LiquidAI/Liquid-Codec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/Liquid-Codec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LiquidAI/Liquid-Codec", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LiquidAI/Liquid-Codec", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 946 Bytes
1150afc | 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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | {
"transformers_version": "5.19.0.dev0",
"architectures": [
"LiquidCodecModel"
],
"output_hidden_states": false,
"return_dict": true,
"dtype": "bfloat16",
"chunk_size_feed_forward": 0,
"is_encoder_decoder": false,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"problem_type": null,
"encoder_dim": 512,
"encoder_conv_dim": 256,
"stft_n_fft": 1280,
"stft_hop_length": 320,
"stft_win_length": 1280,
"stft_frame_stack": 6,
"quantizer_dim": 256,
"fsq_levels": [
8,
7,
6,
6
],
"n_q": 12,
"decoder_window": 30,
"sampling_rate": 24000,
"initializer_range": 0.02,
"model_type": "liquid_codec",
"output_attentions": false,
"sample_rate": 24000,
"frame_samples": 1920,
"auto_map": {
"AutoConfig": "configuration_liquid_codec.LiquidCodecConfig",
"AutoModel": "modeling_liquid_codec.LiquidCodecModel"
}
}
|