You can deploy this model and start earning money today!

haoxiangsnr/
claritycec2spatial_fsb_fa_codec_enroll

Name: clarity__cec2__spatial_fsb_fa_codec_enroll
Availability: InStock

tensorboard

mit

Views

No views yet

claritycec2spatial_fsb_fa_codec_enroll

This project was initially created for personal use and is now archived. While the code and documentation may be incomplete, I'm happy to share it in case others find it useful.

This model was uploaded from AudioZen at 2025-01-03 17:34:20.

Overview

Training folder: /home/xhao/proj/audiozen/recipes/clarity/cec2/spatial_fsb_fa_codec_enroll
Git hash: 04870401fa510446f1f1eb934a0907c0906a0b08

Usage

Refer to https://huggingface.co/docs/hub/en/models-downloading for instructions on how to download this model. For example, you can use the following code to download the model:

bash

# Install git lfs if you haven't already
git lfs install

# Go into the directory where you want to download the model
cd /home/xhao/proj/audiozen/recipes/clarity/cec2

# Clone the model checkpoint
git clone https://huggingface.co/haoxiangsnr/clarity__cec2__spatial_fsb_fa_codec_enroll
# or
git clone git@hf.co:/haoxiangsnr/clarity__cec2__spatial_fsb_fa_codec_enroll

Model Architecture

Model architecture:
Hyperparameters: Check the config.yaml file in the training folder for hyperparameters.

Environment Info

Platform: Linux-6.1.0-27-amd64-x86_64-with-glibc2.36
Python version: 3.10.16
Numpy version: 2.0.2
PyTorch version (GPU?): 2.5.1 (True)
System RAM: 503.54 GB
GPU Available: True
GPU IDs: 8
GPU type: NVIDIA GeForce RTX 3090

Note: This information was generated automatically when the model was uploaded instead of when the model was trained.

Upload Arguments

python

{
    "create_model_card": true,
    "model_folder_path": "/home/xhao/proj/audiozen/recipes/clarity/cec2/spatial_fsb_fa_codec_enroll",
    "only_best_checkpoint": true,
    "only_exp_folder": false,
    "private": false
}