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ecapa-voxceleb-ft2-cryceleb / train_config.yaml

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	# Generated 2023-05-14 from:
	# /home/agorin/cryceleb2023/hparams/ecapa_voxceleb_basic.yaml
	# yamllint disable
	# ################################
	# Model: Speaker identification with ECAPA for CryCeleb
	# Authors: David Budaghyan
	# ################################

	ckpt_interval_minutes: 15 # save checkpoint every N min

	##### SEED
	seed: 3011
	__set_seed: !apply:crybrain_config_utils.set_seed [3011]

	# DataLoader
	bs: 32
	train_dataloader_options:
	batch_size: 32
	shuffle: true
	val_dataloader_options:
	batch_size: 2
	shuffle: false

	##### ESTIMATOR COMPONENTS
	# Fbank (feature extractor)
	n_mels: 80
	left_frames: 0
	right_frames: 0
	deltas: false
	compute_features: &id002 !new:speechbrain.lobes.features.Fbank
	n_mels: 80
	left_frames: 0
	right_frames: 0
	deltas: false

	# ECAPA
	emb_dim: 192
	embedding_model: &id001 !new:speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN
	input_size: 80
	channels: [1024, 1024, 1024, 1024, 3072]
	kernel_sizes: [5, 3, 3, 3, 1]
	dilations: [1, 2, 3, 4, 1]
	groups: [1, 1, 1, 1, 1]
	attention_channels: 128
	lin_neurons: 192

	# If you do not want to use the pretrained encoder you can simply delete pretrained_encoder field.
	pretrained_model_name: spkrec-ecapa-voxceleb
	pretrained_embedding_model_path: speechbrain/spkrec-ecapa-voxceleb/embedding_model.ckpt
	pretrained_embedding_model: !new:speechbrain.utils.parameter_transfer.Pretrainer
	collect_in: ./experiments/ecapa_voxceleb_ft_basic/ckpts
	loadables:
	model: *id001
	paths:
	model: speechbrain/spkrec-ecapa-voxceleb/embedding_model.ckpt

	# CLASSIFIER
	n_classes: 348
	# check-yaml disable


	classifier: &id003 !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
	input_size: 192
	out_neurons: 348

	##### EPOCH COUNTER
	n_epochs: 1000
	epoch_counter: &id005 !new:speechbrain.utils.epoch_loop.EpochCounter
	limit: 1000

	##### OPTIMIZER
	start_lr: 0.0001
	opt_class: !name:torch.optim.Adam
	lr: 0.0001
	weight_decay: 0.000002

	##### LEARNING RATE SCHEDULERS
	lrsched_name: cyclic
	# one of:
	# onplateau
	# cyclic
	lr_min: 0.0000000001
	lr_scheduler: &id006 !apply:crybrain_config_utils.choose_lrsched
	lrsched_name: cyclic
	#below are kwargs, only the ones relevant to the type of scheduler will be
	#used for initialization in `choose_lrsched`

	#onplateau (ReduceLROnPlateau)
	lr_min: 0.0000000001
	factor: 0.4
	patience: 10
	dont_halve_until_epoch: 35
	#cyclic (CyclicLRScheduler)
	base_lr: 0.00000001
	max_lr: 0.0001
	step_size: 100
	mode: triangular
	gamma: 1.0
	scale_fn:
	scale_mode: cycle

	sample_rate: 16000
	mean_var_norm: &id004 !new:speechbrain.processing.features.InputNormalization

	norm_type: sentence
	std_norm: false

	modules:
	compute_features: *id002
	embedding_model: *id001
	classifier: *id003
	mean_var_norm: *id004
	compute_cost: !new:speechbrain.nnet.losses.LogSoftmaxWrapper
	loss_fn: !new:speechbrain.nnet.losses.AdditiveAngularMargin
	margin: 0.2
	scale: 30

	classification_stats: !name:speechbrain.utils.metric_stats.ClassificationStats
	###################################################################
	### OUTPUT PATHS ###


	experiment_name: ecapa_voxceleb_ft_basic
	# must run from the directory which contains "experiments"


	experiment_dir: ./experiments/ecapa_voxceleb_ft_basic
	train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
	save_file: ./experiments/ecapa_voxceleb_ft_basic/train_log.txt

	checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
	checkpoints_dir: ./experiments/ecapa_voxceleb_ft_basic/ckpts
	recoverables:
	embedding_model: *id001
	classifier: *id003
	normalizer: *id004
	counter: *id005
	lr_scheduler: *id006