README.md
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	## Usage

	RawNet3 is hosted via two repositories.
	Inference of any utterance with 16k 16bit mono format and Vox1-O benchmark is
	supported in this repository.

	Training recipe, on the other hand, will be supported in
	https://github.com/clovaai/voxceleb_trainer.

	Model weight parameters are served by huggingface at
	https://huggingface.co/jungjee/RawNet3, which is used as a submodule here

	To download the model, run:
	`git submodule update --init --recursive`

	### Single utterance inference
	Run: `python inference.py --inference_utterance --input {YOUR_INPUT_FILE}`

	Optionally, `--out_dir` can be set to direct where to save the extracted speaker embedding. (default: `./out.npy`)

	### Benchmark on the Vox1-O evaluation protocol
	Run: `python inference.py --vox1_o_benchmark --DB_dir`

	Note that `DB_dir` should direct the directory of VoxCeleb1 dataset.
	For example, if `DB_dir`="/home/abc/db/VoxCeleb1",
	VoxCeleb1 folder is expected to have 1,251 folders inside which corresponds to 1,251 speakers of the VoxCeleb1 dataset.

	If you successfully run the benchmark, you will get:
	`Vox1-O benchmark Finished. EER: 0.8932, minDCF:0.06690`.