speechbrain_ja2en_st_63M_yt600h / hyperparams.yaml

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	# ############################################################################
	# Model: E2E ST JA->EN with Conformer
	# Encoder: Conformer Encoder
	# Decoder: Conformer Decoder + (CTC/ATT joint)
	# Tokens: BPE
	# losses: CTC
	# Training: Custom JA->EN youtube scrape, ~600h
	# Authors: Eric Engelhart, 2022
	# ############################################################################

	# Tokenier initialization
	tokenizer: !new:sentencepiece.SentencePieceProcessor

	# Features
	sample_rate: 16000
	n_fft: 400
	n_mels: 80

	# normalization
	normalizer: !new:speechbrain.processing.features.InputNormalization
	norm_type: global

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

	####################### Model parameters ###########################
	# Transformer
	d_model: 384
	nhead: 6
	num_encoder_layers: 12
	num_decoder_layers: 6
	d_ffn: 1536
	transformer_dropout: 0.1
	activation: !name:torch.nn.GELU
	output_neurons: 5000
	vocab_size: 5000
	attention_type: "regularMHA" # "RelPosMHAXL" or "regularMHA"
	kernel_size: 15
	encoder_module: conformer


	# Outputs
	blank_index: 0
	label_smoothing: 0.1
	pad_index: 0
	bos_index: 1
	eos_index: 2
	unk_index: 0

	# Decoding parameters
	min_decode_ratio: 0.0
	max_decode_ratio: 1.0
	valid_search_interval: 2
	valid_beam_size: 1
	test_beam_size: 25

	############################## models ################################
	CNN: !new:speechbrain.lobes.models.convolution.ConvolutionFrontEnd
	input_shape: (8, 10, 80)
	num_blocks: 2
	num_layers_per_block: 1
	out_channels: (256, 256)
	kernel_sizes: (3, 3)
	strides: (2, 2)
	residuals: (False, False)

	Transformer: !new:speechbrain.lobes.models.transformer.TransformerST.TransformerST # yamllint disable-line rule:line-length
	input_size: 5120
	tgt_vocab: !ref <output_neurons>
	d_model: !ref <d_model>
	nhead: !ref <nhead>
	num_encoder_layers: !ref <num_encoder_layers>
	num_decoder_layers: !ref <num_decoder_layers>
	d_ffn: !ref <d_ffn>
	dropout: !ref <transformer_dropout>
	activation: !ref <activation>
	ctc_weight: 0
	asr_weight: 0
	mt_weight: 0
	asr_tgt_vocab: !ref <output_neurons>
	mt_src_vocab: !ref <output_neurons>
	attention_type: !ref <attention_type>
	kernel_size: !ref <kernel_size>
	encoder_module: !ref <encoder_module>
	normalize_before: True
	causal: False
	max_length: 5000

	# only when multi-task setting is used
	ctc_lin: !new:speechbrain.nnet.linear.Linear
	input_size: !ref <d_model>
	n_neurons: !ref <output_neurons>

	seq_lin: !new:speechbrain.nnet.linear.Linear
	input_size: !ref <d_model>
	n_neurons: !ref <output_neurons>

	# when asr-weight > 0 and ctc-weight < 1
	asr_seq_lin: !new:speechbrain.nnet.linear.Linear
	input_size: !ref <d_model>
	n_neurons: !ref <vocab_size>


	st_model: !new:torch.nn.ModuleList
	- [!ref <CNN>, !ref <Transformer>, !ref <seq_lin>]


	Tencoder: !new:speechbrain.lobes.models.transformer.TransformerASR.EncoderWrapper
	transformer: !ref <Transformer>

	encoder: !new:speechbrain.nnet.containers.LengthsCapableSequential
	input_shape: [null, null, !ref <n_mels>]
	compute_features: !ref <compute_features>
	normalize: !ref <normalizer>
	cnn: !ref <CNN>
	transformer_encoder: !ref <Tencoder>



	decoder: !new:speechbrain.decoders.S2STransformerBeamSearch
	modules: [!ref <Transformer>, !ref <seq_lin>, null]
	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>
	using_eos_threshold: True
	length_normalization: True
	ctc_weight: 0
	lm_weight: 0


	modules:
	compute_features: !ref <compute_features>
	normalizer: !ref <normalizer>
	pre_transformer: !ref <CNN>
	Transformer: !ref <Transformer>
	asr_model: !ref <st_model>
	encoder: !ref <encoder>
	decoder: !ref <decoder>


	log_softmax: !new:torch.nn.LogSoftmax
	dim: -1



	# The pretrainer allows a mapping between pretrained files and instances that
	# are declared in the yaml.
	pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
	loadables:
	tokenizer: !ref <tokenizer>
	st: !ref <st_model>
	normalizer: !ref <normalizer>