asr-crdnn-transformerlm-librispeech / hyperparams.yaml

Update hyperparams.yaml

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	# ############################################################################
	# Model: E2E ASR with attention-based ASR
	# Encoder: CRDNN model
	# Decoder: GRU + beamsearch + Transformer
	# Tokens: BPE with unigram
	# losses: CTC+ NLL
	# Training: Librispeech 960h
	# Authors: Ju-Chieh Chou, Mirco Ravanelli, Abdel Heba, Peter Plantinga, Samuele Cornell
	# Sung-Lin Yeh, Titouan Parcollet 2020
	# ############################################################################

	# 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: 4
	rnn_neurons: 1024
	rnn_bidirectional: True
	dnn_blocks: 1
	dnn_neurons: 1024
	emb_size: 1024
	dec_neurons: 1024
	output_neurons: 5000 # Number of tokens (same as LM)
	blank_index: 0
	pad_index: -1
	bos_index: 1
	eos_index: 2
	unk_index: 0

	# Decoding parameters
	min_decode_ratio: 0.0
	max_decode_ratio: 1.0
	beam_size: 40
	eos_threshold: 1.5
	using_max_attn_shift: True
	max_attn_shift: 300
	lm_weight: 0.80
	ctc_weight_decode: 0.40
	ctc_window_size: 200
	coverage_penalty: 1.5
	temperature: 1.0
	temperature_lm: 1.0

	normalize: !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>

	enc: !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: True

	emb: !new:speechbrain.nnet.embedding.Embedding
	num_embeddings: !ref <output_neurons>
	embedding_dim: !ref <emb_size>

	dec: !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: 1
	scaling: 1.0
	channels: 10
	kernel_size: 100
	re_init: True
	dropout: !ref <dropout>

	ctc_lin: !new:speechbrain.nnet.linear.Linear
	input_size: !ref <dnn_neurons>
	n_neurons: !ref <output_neurons>

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

	log_softmax: !new:speechbrain.nnet.activations.Softmax
	apply_log: True

	# This is the Transformer LM that is used according to the Huggingface repository
	# Visit the HuggingFace model corresponding to the pretrained_lm_tokenizer_path
	# For more details about the model!
	# NB: It has to match the pre-trained TransformerLM!!
	lm: !new:speechbrain.lobes.models.transformer.TransformerLM.TransformerLM
	vocab: 5000
	d_model: 768
	nhead: 12
	num_encoder_layers: 12
	num_decoder_layers: 0
	d_ffn: 3072
	dropout: 0.0
	activation: !name:torch.nn.GELU
	normalize_before: False

	tokenizer: !new:sentencepiece.SentencePieceProcessor

	asr_model: !new:torch.nn.ModuleList
	- [!ref <enc>, !ref <emb>, !ref <dec>, !ref <ctc_lin>, !ref <seq_lin>]

	lm_model: !new:speechbrain.lobes.models.transformer.TransformerLM.TransformerLM
	vocab: 5000
	d_model: 768
	nhead: 12
	num_encoder_layers: 12
	num_decoder_layers: 0
	d_ffn: 3072
	dropout: 0.0
	activation: !name:torch.nn.GELU
	normalize_before: False

	beam_searcher: !new:speechbrain.decoders.S2SRNNBeamSearchTransformerLM
	embedding: !ref <emb>
	decoder: !ref <dec>
	linear: !ref <seq_lin>
	ctc_linear: !ref <ctc_lin>
	language_model: !ref <lm_model>
	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 <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>
	lm_weight: !ref <lm_weight>
	ctc_weight: !ref <ctc_weight_decode>
	ctc_window_size: !ref <ctc_window_size>
	temperature: !ref <temperature>
	temperature_lm: !ref <temperature_lm>

	modules:
	compute_features: !ref <compute_features>
	asr_enc: !ref <enc>
	asr_dec: !ref <dec>
	ctc_lin: !ref <ctc_lin>
	seq_lin: !ref <seq_lin>
	normalize: !ref <normalize>
	lm_model: !ref <lm_model>
	beam_searcher: !ref <beam_searcher>

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