sepformer-libri3mix / hyperparams.yaml

updated config.yaml and hyperparams.yaml

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	# Generated 2021-06-27 from:
	# /home/mila/s/subakany/speechbrain_new/recipes/LibriMix/separation/hparams/sepformer-libri3mix.yaml
	# yamllint disable
	# ################################
	# Model: SepFormer for source separation
	# https://arxiv.org/abs/2010.13154
	# Dataset : Libri3Mix
	# ################################
	#
	# Basic parameters
	# Seed needs to be set at top of yaml, before objects with parameters are made
	#
	seed: 111
	__set_seed: !apply:torch.manual_seed [111]

	# Data params

	# e.g. '/yourpath/Libri3Mix/train-clean-360/'
	# the data folder is needed even if dynamic mixing is applied
	data_folder: /miniscratch/subakany/LibriMixData/Libri3Mix/

	# This is needed only if dynamic mixing is applied
	base_folder_dm: /miniscratch/subakany/LibriMixData/LibriSpeech/train-clean-360_processed/

	experiment_name: sepformer-libri3mix
	output_folder: results/sepformer-libri3mix/111
	train_log: results/sepformer-libri3mix/111/train_log.txt
	save_folder: results/sepformer-libri3mix/111/save
	train_data: results/sepformer-libri3mix/111/save/libri3mix_train-360.csv
	valid_data: results/sepformer-libri3mix/111/save/libri3mix_dev.csv
	test_data: results/sepformer-libri3mix/111/save/libri3mix_test.csv
	skip_prep: false

	ckpt_interval_minutes: 60

	# Experiment params
	auto_mix_prec: true # Set it to True for mixed precision
	test_only: true
	num_spks: 3
	progressbar: true
	save_audio: false # Save estimated sources on disk
	sample_rate: 8000

	# Training parameters
	N_epochs: 200
	batch_size: 1
	lr: 0.00015
	clip_grad_norm: 5
	loss_upper_lim: 999999 # this is the upper limit for an acceptable loss
	# if True, the training sequences are cut to a specified length
	limit_training_signal_len: false
	# this is the length of sequences if we choose to limit
	# the signal length of training sequences
	training_signal_len: 32000000

	# Set it to True to dynamically create mixtures at training time
	dynamic_mixing: true
	use_wham_noise: false

	# Parameters for data augmentation
	use_wavedrop: false
	use_speedperturb: true
	use_speedperturb_sameforeachsource: false
	use_rand_shift: false
	min_shift: -8000
	max_shift: 8000

	speedperturb: !new:speechbrain.lobes.augment.TimeDomainSpecAugment
	perturb_prob: 1.0
	drop_freq_prob: 0.0
	drop_chunk_prob: 0.0
	sample_rate: 8000
	speeds: [95, 100, 105]

	wavedrop: !new:speechbrain.lobes.augment.TimeDomainSpecAugment
	perturb_prob: 0.0
	drop_freq_prob: 1.0
	drop_chunk_prob: 1.0
	sample_rate: 8000

	# loss thresholding -- this thresholds the training loss
	threshold_byloss: true
	threshold: -30

	# Encoder parameters
	N_encoder_out: 256
	out_channels: 256
	kernel_size: 16
	kernel_stride: 8

	# Dataloader options
	dataloader_opts:
	batch_size: 1
	num_workers: 3


	# Specifying the network
	Encoder: &id003 !new:speechbrain.lobes.models.dual_path.Encoder
	kernel_size: 16
	out_channels: 256


	SBtfintra: &id001 !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
	num_layers: 8
	d_model: 256
	nhead: 8
	d_ffn: 1024
	dropout: 0
	use_positional_encoding: true
	norm_before: true

	SBtfinter: &id002 !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
	num_layers: 8
	d_model: 256
	nhead: 8
	d_ffn: 1024
	dropout: 0
	use_positional_encoding: true
	norm_before: true

	MaskNet: &id005 !new:speechbrain.lobes.models.dual_path.Dual_Path_Model

	num_spks: 3
	in_channels: 256
	out_channels: 256
	num_layers: 2
	K: 250
	intra_model: *id001
	inter_model: *id002
	norm: ln
	linear_layer_after_inter_intra: false
	skip_around_intra: true

	Decoder: &id004 !new:speechbrain.lobes.models.dual_path.Decoder
	in_channels: 256
	out_channels: 1
	kernel_size: 16
	stride: 8
	bias: false

	optimizer: !name:torch.optim.Adam
	lr: 0.00015
	weight_decay: 0

	loss: !name:speechbrain.nnet.losses.get_si_snr_with_pitwrapper

	lr_scheduler: !new:speechbrain.nnet.schedulers.ReduceLROnPlateau
	factor: 0.5
	patience: 2
	dont_halve_until_epoch: 5

	epoch_counter: &id006 !new:speechbrain.utils.epoch_loop.EpochCounter
	# lr_scheduler: !ref <lr_scheduler>

	limit: 200

	modules:
	encoder: *id003
	decoder: *id004
	masknet: *id005
	checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
	checkpoints_dir: results/sepformer-libri3mix/111/save
	recoverables:
	encoder: *id003
	decoder: *id004
	masknet: *id005
	counter: *id006
	train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
	save_file: results/sepformer-libri3mix/111/train_log.txt


	pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
	loadables:
	encoder: !ref <Encoder>
	masknet: !ref <MaskNet>
	decoder: !ref <Decoder>