sepformer-libri4mix / hyperparams.yaml

Update hyperparams.yaml

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	# ################################
	# 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: 1234
	__set_seed: !apply:torch.manual_seed [!ref <seed>]

	# Data params

	# e.g. '/yourpath/Libri3Mix/train-clean-360/'
	# the data folder is needed even if dynamic mixing is applied
	data_folder: /mnt3/Libri4Mix_48k_own/Libri4Mix/

	# This is needed only if dynamic mixing is applied
	base_folder_dm: /yourpath/LibriSpeech/train-clean-360/

	experiment_name: sepformer-libri4mix-48k
	output_folder: !ref results/<experiment_name>/<seed>
	train_log: !ref <output_folder>/train_log.txt
	save_folder: !ref <output_folder>/save
	train_data: !ref <save_folder>/libri4mix_train-360.csv
	valid_data: !ref <save_folder>/libri4mix_test.csv
	test_data: !ref <save_folder>/libri4mix_test.csv
	skip_prep: False

	ckpt_interval_minutes: 60

	# Experiment params
	auto_mix_prec: True # Set it to True for mixed precision
	num_spks: 4
	noprogressbar: False
	save_audio: False # Save estimated sources on disk
	sample_rate: 48000

	# Training parameters
	N_epochs: 143
	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: True
	# this is the length of sequences if we choose to limit
	# the signal length of training sequences
	training_signal_len: 100000

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

	# Parameters for data augmentation
	use_wavedrop: False
	use_speedperturb: True
	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: !ref <sample_rate>
	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: !ref <sample_rate>

	# 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
	d_ffn: 1024

	# Dataloader options
	dataloader_opts:
	batch_size: !ref <batch_size>
	num_workers: 3


	# Specifying the network
	Encoder: !new:speechbrain.lobes.models.dual_path.Encoder
	kernel_size: !ref <kernel_size>
	out_channels: !ref <N_encoder_out>


	SBtfintra: !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
	num_layers: 8
	d_model: !ref <out_channels>
	nhead: 8
	d_ffn: !ref <d_ffn>
	dropout: 0
	use_positional_encoding: True
	norm_before: True

	SBtfinter: !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
	num_layers: 8
	d_model: !ref <out_channels>
	nhead: 8
	d_ffn: !ref <d_ffn>
	dropout: 0
	use_positional_encoding: True
	norm_before: True

	MaskNet: !new:speechbrain.lobes.models.dual_path.Dual_Path_Model
	num_spks: !ref <num_spks>
	in_channels: !ref <N_encoder_out>
	out_channels: !ref <out_channels>
	num_layers: 2
	K: 250
	intra_model: !ref <SBtfintra>
	inter_model: !ref <SBtfinter>
	norm: ln
	linear_layer_after_inter_intra: False
	skip_around_intra: True

	Decoder: !new:speechbrain.lobes.models.dual_path.Decoder
	in_channels: !ref <N_encoder_out>
	out_channels: 1
	kernel_size: !ref <kernel_size>
	stride: !ref <kernel_stride>
	bias: False

	optimizer: !name:torch.optim.Adam
	lr: !ref <lr>
	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: !new:speechbrain.utils.epoch_loop.EpochCounter
	limit: !ref <N_epochs>

	modules:
	encoder: !ref <Encoder>
	decoder: !ref <Decoder>
	masknet: !ref <MaskNet>

	checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
	checkpoints_dir: !ref <save_folder>
	recoverables:
	encoder: !ref <Encoder>
	decoder: !ref <Decoder>
	masknet: !ref <MaskNet>
	counter: !ref <epoch_counter>
	# lr_scheduler: !ref <lr_scheduler>

	train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
	save_file: !ref <train_log>

	# If you do not want to use the pretrained separator you can simply delete pretrained_separator field.
	pretrained_separator: !new:speechbrain.utils.parameter_transfer.Pretrainer
	collect_in: !ref <save_folder>
	loadables:
	encoder: !ref <Encoder>
	decoder: !ref <Decoder>
	masknet: !ref <MaskNet>
	paths:
	encoder: speechbrain/sepformer-wsj03mix/encoder.ckpt
	decoder: speechbrain/sepformer-wsj03mix/decoder.ckpt
	masknet: speechbrain/sepformer-wsj03mix/masknet.ckpt

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