en_tr_titanet_large / conf /titanet-finetune.yaml

Peng Wei

setup the fine tune job

bc69a87 9 months ago

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4.66 kB

	name: &name "TitaNet-Finetune"
	sample_rate: &sample_rate 16000

	init_from_pretrained_model:
	speaker_tasks:
	name: 'titanet_large'
	include: ["preprocessor","encoder"]
	exclude: ["decoder.final"] # Add specific layer names here to exlude or just ["decoder"] if to exclude all of decoder pretrained weights

	model:
	train_ds:
	manifest_filepath: ???
	sample_rate: 16000
	labels: null
	batch_size: 64
	shuffle: True
	is_tarred: False
	tarred_audio_filepaths: null
	tarred_shard_strategy: "scatter"
	augmentor:
	speed:
	prob: 0.3
	sr: *sample_rate
	resample_type: 'kaiser_fast'
	min_speed_rate: 0.95
	max_speed_rate: 1.05

	validation_ds:
	manifest_filepath: ???
	sample_rate: 16000
	labels: null
	batch_size: 128
	shuffle: False

	test_ds:
	manifest_filepath: ???
	sample_rate: 16000
	labels: null
	batch_size: 1
	shuffle: False
	embedding_dir: './embeddings'

	model_defaults:
	filters: 1024
	repeat: 3
	dropout: 0.1
	separable: true
	se: true
	se_context_size: -1
	kernel_size_factor: 1.0

	preprocessor:
	_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
	normalize: "per_feature"
	window_size: 0.025
	sample_rate: *sample_rate
	window_stride: 0.01
	window: "hann"
	features: &n_mels 80
	n_fft: 512
	frame_splicing: 1
	dither: 0.00001

	encoder:
	_target_: nemo.collections.asr.modules.ConvASREncoder
	feat_in: *n_mels
	activation: relu
	conv_mask: true

	jasper:
	- filters: ${model.model_defaults.filters}
	repeat: 1
	kernel: [3]
	stride: [1]
	dilation: [1]
	dropout: 0.0
	residual: false
	separable: ${model.model_defaults.separable}
	se: ${model.model_defaults.se}
	se_context_size: ${model.model_defaults.se_context_size}

	- filters: ${model.model_defaults.filters}
	repeat: ${model.model_defaults.repeat}
	kernel: [7]
	stride: [1]
	dilation: [1]
	dropout: ${model.model_defaults.dropout}
	residual: true
	separable: ${model.model_defaults.separable}
	se: ${model.model_defaults.se}
	se_context_size: ${model.model_defaults.se_context_size}

	- filters: ${model.model_defaults.filters}
	repeat: ${model.model_defaults.repeat}
	kernel: [11]
	stride: [1]
	dilation: [1]
	dropout: ${model.model_defaults.dropout}
	residual: true
	separable: ${model.model_defaults.separable}
	se: ${model.model_defaults.se}
	se_context_size: ${model.model_defaults.se_context_size}

	- filters: ${model.model_defaults.filters}
	repeat: ${model.model_defaults.repeat}
	kernel: [15]
	stride: [1]
	dilation: [1]
	dropout: ${model.model_defaults.dropout}
	residual: true
	separable: ${model.model_defaults.separable}
	se: ${model.model_defaults.se}
	se_context_size: ${model.model_defaults.se_context_size}

	- filters: &enc_feat_out 3072
	repeat: 1
	kernel: [1]
	stride: [1]
	dilation: [1]
	dropout: 0.0
	residual: false
	separable: ${model.model_defaults.separable}
	se: ${model.model_defaults.se}
	se_context_size: ${model.model_defaults.se_context_size}

	decoder:
	_target_: nemo.collections.asr.modules.SpeakerDecoder
	feat_in: *enc_feat_out
	num_classes: ???
	pool_mode: 'attention'
	emb_sizes: 192

	loss:
	_target_: nemo.collections.asr.losses.angularloss.AngularSoftmaxLoss # you could also use cross-entrophy loss
	scale: 30
	margin: 0.2

	optim_param_groups:
	encoder:
	lr: .001

	optim:
	name: adamw
	lr: .0001 #(original titanet-large was trained with 0.08 lr)
	weight_decay: 0.0002

	# scheduler setup
	sched:
	name: CosineAnnealing
	warmup_ratio: 0.1
	min_lr: 0.0

	trainer:
	devices: 1 # number of gpus (original titanet-large was trained on 4 nodes with 8 gpus each)
	max_epochs: 10
	max_steps: -1 # computed at runtime if not set
	num_nodes: 1
	accelerator: gpu
	strategy: ddp
	deterministic: True
	enable_checkpointing: False
	logger: False
	log_every_n_steps: 1 # Interval of logging.
	val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
	gradient_clip_val: 1.0

	exp_manager:
	exp_dir: null
	name: *name
	create_tensorboard_logger: True
	create_checkpoint_callback: True