lp-initial-aed-short / hyperparams.yaml

Aku Rouhe

Hyperparams

f6bcbf4 over 2 years ago

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	num_units: 1750
	tokenizer: !new:sentencepiece.SentencePieceProcessor

	# Audio input normalization:
	# Yes, the input should be 22050 and then features computed with the
	# wrong sample rate...
	audio_normalizer: !new:speechbrain.dataio.preprocess.AudioNormalizer
	sample_rate: 22050

	# Feature parameters
	sample_rate: 16000
	n_fft: 400
	n_mels: 40

	# Model parameters
	activation: !name:torch.nn.LeakyReLU
	dropout: 0.15
	cnn_blocks: 2
	cnn_channels: (64, 128)
	inter_layer_pooling_size: (2, 2)
	cnn_kernelsize: (3, 3)
	time_pooling_size: 4
	rnn_class: !name:speechbrain.nnet.RNN.LSTM
	rnn_layers: 3
	rnn_neurons: 512
	rnn_bidirectional: True
	dnn_blocks: 1
	dnn_neurons: 512
	emb_size: 128
	dec_neurons: 512
	dec_layers: 1
	output_neurons: !ref <num_units> #Number of tokens (same as LM)
	blank_index: 0
	bos_index: 0
	eos_index: 0
	unk_index: 0

	min_decode_ratio: 0.0
	max_decode_ratio: 1.0
	valid_beam_size: 4
	test_beam_size: 12
	eos_threshold: 1.2
	using_max_attn_shift: False
	max_attn_shift: 240
	ctc_weight_decode: 0.0
	coverage_penalty: 3.0
	temperature: 2.0

	# Feature extraction
	compute_features: !new:speechbrain.lobes.features.Fbank
	sample_rate: !ref <sample_rate>
	n_fft: !ref <n_fft>
	n_mels: !ref <n_mels>

	# Feature normalization (mean and std)
	normalize: !new:speechbrain.processing.features.InputNormalization
	norm_type: global

	# The CRDNN model is an encoder that combines CNNs, RNNs, and DNNs.
	encoder: !new:speechbrain.lobes.models.CRDNN.CRDNN
	input_shape: [null, null, !ref <n_mels>]
	activation: !ref <activation>
	dropout: !ref <dropout>
	cnn_blocks: !ref <cnn_blocks>
	cnn_channels: !ref <cnn_channels>
	cnn_kernelsize: !ref <cnn_kernelsize>
	inter_layer_pooling_size: !ref <inter_layer_pooling_size>
	time_pooling: True
	using_2d_pooling: False
	time_pooling_size: !ref <time_pooling_size>
	rnn_class: !ref <rnn_class>
	rnn_layers: !ref <rnn_layers>
	rnn_neurons: !ref <rnn_neurons>
	rnn_bidirectional: !ref <rnn_bidirectional>
	rnn_re_init: True
	dnn_blocks: !ref <dnn_blocks>
	dnn_neurons: !ref <dnn_neurons>
	use_rnnp: False

	# Embedding (from indexes to an embedding space of dimension emb_size).
	embedding: !new:speechbrain.nnet.embedding.Embedding
	num_embeddings: !ref <output_neurons>
	embedding_dim: !ref <emb_size>

	# Attention-based RNN decoder.
	decoder: !new:speechbrain.nnet.RNN.AttentionalRNNDecoder
	enc_dim: !ref <dnn_neurons>
	input_size: !ref <emb_size>
	rnn_type: gru
	attn_type: location
	hidden_size: !ref <dec_neurons>
	attn_dim: 1024
	num_layers: !ref <dec_layers>
	scaling: 1.0
	channels: 10
	kernel_size: 100
	re_init: True
	dropout: !ref <dropout>

	# Linear transformation on the top of the decoder.
	seq_lin: !new:speechbrain.nnet.linear.Linear
	input_size: !ref <dec_neurons>
	n_neurons: !ref <output_neurons>

	# Linear transformation on the top of the encoder.
	ctc_lin: !new:speechbrain.nnet.linear.Linear
	input_size: !ref <dnn_neurons>
	n_neurons: !ref <output_neurons>

	# Final softmax (for log posteriors computation).
	log_softmax: !new:speechbrain.nnet.activations.Softmax
	apply_log: True


	# Gathering all the submodels in a single model object.
	model: !new:torch.nn.ModuleList
	- - !ref <encoder>
	- !ref <embedding>
	- !ref <decoder>
	- !ref <ctc_lin>
	- !ref <seq_lin>

	full_encode_step: !new:speechbrain.nnet.containers.LengthsCapableSequential
	input_shape: [null, null, !ref <n_mels>]
	compute_features: !ref <compute_features>
	normalize: !ref <normalize>
	model: !ref <encoder>


	test_search: !new:speechbrain.decoders.S2SRNNBeamSearcher
	embedding: !ref <embedding>
	decoder: !ref <decoder>
	linear: !ref <seq_lin>
	ctc_linear: !ref <ctc_lin>
	bos_index: !ref <bos_index>
	eos_index: !ref <eos_index>
	blank_index: !ref <blank_index>
	min_decode_ratio: !ref <min_decode_ratio>
	max_decode_ratio: !ref <max_decode_ratio>
	beam_size: !ref <test_beam_size>
	eos_threshold: !ref <eos_threshold>
	using_max_attn_shift: !ref <using_max_attn_shift>
	max_attn_shift: !ref <max_attn_shift>
	coverage_penalty: !ref <coverage_penalty>
	ctc_weight: !ref <ctc_weight_decode>
	temperature: !ref <temperature>

	# Objects in "modules" dict will have their parameters moved to the correct
	# device, as well as having train()/eval() called on them by the Brain class
	modules:
	encoder: !ref <full_encode_step>
	decoder: !ref <test_search>

	pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
	loadables:
	model: !ref <model>
	normalizer: !ref <normalize>
	tokenizer: !ref <tokenizer>