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speaker-recognition-meetup

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speaker-recognition-meetup / hyperparams.yaml

Thomas.Chaigneau

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	pretrained_path: ChainYo/speaker-recognition-meetup

	# Model parameters
	n_mels: 23
	sample_rate: 16000
	n_classes: 2
	emb_dim: 512

	# Feature extraction
	compute_features: !new:speechbrain.lobes.features.Fbank
	n_mels: !ref <n_mels>

	# Mean and std normalization of the input features
	mean_var_norm: !new:speechbrain.processing.features.InputNormalization
	norm_type: sentence
	std_norm: False

	# To design a custom model, either just edit the simple CustomModel
	# class that's listed here, or replace this `!new` call with a line
	# pointing to a different file you've defined.
	embedding_model: !new:custom_model.Xvector
	in_channels: !ref <n_mels>
	activation: !name:torch.nn.LeakyReLU
	tdnn_blocks: 5
	tdnn_channels: [512, 512, 512, 512, 1500]
	tdnn_kernel_sizes: [5, 3, 3, 1, 1]
	tdnn_dilations: [1, 2, 3, 1, 1]
	lin_neurons: !ref <emb_dim>

	classifier: !new:custom_model.Classifier
	input_shape: [null, null, !ref <emb_dim>]
	activation: !name:torch.nn.LeakyReLU
	lin_blocks: 1
	lin_neurons: !ref <emb_dim>
	out_neurons: !ref <n_classes>

	label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder

	# Objects in "modules" dict will have their parameters moved to the correct
	# device, as well as having train()/eval() called on them by the Brain class.
	modules:
	compute_features: !ref <compute_features>
	embedding_model: !ref <embedding_model>
	classifier: !ref <classifier>
	mean_var_norm: !ref <mean_var_norm>

	pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
	loadables:
	embedding_model: !ref <embedding_model>
	classifier: !ref <classifier>
	label_encoder: !ref <label_encoder>
	paths:
	embedding_model: !ref <pretrained_path>/embedding_model.ckpt
	classifier: !ref <pretrained_path>/classifier.ckpt
	label_encoder: !ref <pretrained_path>/label_encoder.txt