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accent-id-commonaccent_ecapa

Audio Classification

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accent-id-commonaccent_ecapa / hyperparams.yaml

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	# ############################################################################
	# Model: ECAPA-TDNN for Accent Identification
	# ############################################################################

	# Pretrain folder (HuggingFace)
	pretrained_path: Jzuluaga/accent-id-commonaccent_ecapa

	# Feature parameters
	n_mels: 80

	# Output parameters
	n_languages: 16 # Possible languages in the dataset
	emb_dim: 192 # dimensionality of the embeddings

	# Model params
	compute_features: !new:speechbrain.lobes.features.Fbank
	n_mels: !ref <n_mels>

	mean_var_norm: !new:speechbrain.processing.features.InputNormalization
	norm_type: sentence
	std_norm: False

	# Embedding Model
	embedding_model: !new:speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN
	input_size: !ref <n_mels>
	activation: !name:torch.nn.LeakyReLU
	channels: [1024, 1024, 1024, 1024, 3072]
	kernel_sizes: [5, 3, 3, 3, 1]
	dilations: [1, 2, 3, 4, 1]
	attention_channels: 128
	lin_neurons: !ref <emb_dim>

	# Classifier based on cosine distance
	classifier: !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
	input_size: !ref <emb_dim>
	out_neurons: !ref <n_languages>

	modules:
	compute_features: !ref <compute_features>
	mean_var_norm: !ref <mean_var_norm>
	embedding_model: !ref <embedding_model>
	classifier: !ref <classifier>

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

	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>/accent_encoder.txt