asr-streaming-conformer-librispeech / hyperparams.yaml

Remove original authors for the streaming hparams

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	# ############################################################################
	# Model: Streaming E2E Conformer-Transducer ASR
	# Encoder: Conformer
	# Decoder: LSTM + greedy search
	# Tokens: BPE with unigram
	# losses: Transducer + CTC (optional) + CE (optional)
	# Training: Librispeech 960h
	# Authors: Sylvain de Langen 2023, Titouan Parcollet 2023
	# ############################################################################

	save_folder: !ref librispeech-streaming-conformer-transducer

	# Feature parameters
	sample_rate: 16000
	n_fft: 512
	n_mels: 80
	win_length: 32

	# Streaming
	streaming: True # controls all Dynamic Chunk Training & chunk size & left context mechanisms

	# Model parameters
	# Transformer
	d_model: 512
	joint_dim: 640
	nhead: 8
	num_encoder_layers: 12
	num_decoder_layers: 0
	d_ffn: 2048
	transformer_dropout: 0.1
	activation: !name:torch.nn.GELU
	output_neurons: 1000
	dec_dim: 512
	dec_emb_dropout: 0.2
	dec_dropout: 0.1

	# Decoding parameters
	blank_index: 0
	bos_index: 0
	eos_index: 0
	pad_index: 0
	beam_size: 10
	nbest: 1
	# by default {state,expand}_beam = 2.3 as mention in paper
	# https://arxiv.org/abs/1904.02619
	state_beam: 2.3
	expand_beam: 2.3
	lm_weight: 0.50

	normalize: !new:speechbrain.processing.features.InputNormalization
	norm_type: global
	update_until_epoch: 4

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

	CNN: !new:speechbrain.lobes.models.convolution.ConvolutionFrontEnd
	input_shape: (8, 10, 80)
	num_blocks: 2
	num_layers_per_block: 1
	out_channels: (64, 32)
	kernel_sizes: (3, 3)
	strides: (2, 2)
	residuals: (False, False)

	Transformer: !new:speechbrain.lobes.models.transformer.TransformerASR.TransformerASR # yamllint disable-line rule:line-length
	input_size: 640
	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>
	encoder_module: conformer
	attention_type: RelPosMHAXL
	normalize_before: True
	causal: False

	# We must call an encoder wrapper so the decoder isn't run (we don't have any)
	enc: !new:speechbrain.lobes.models.transformer.TransformerASR.EncoderWrapper
	transformer: !ref <Transformer>

	# For MTL CTC over the encoder
	proj_ctc: !new:speechbrain.nnet.linear.Linear
	input_size: !ref <joint_dim>
	n_neurons: !ref <output_neurons>

	# Define some projection layers to make sure that enc and dec
	# output dim are the same before joining
	proj_enc: !new:speechbrain.nnet.linear.Linear
	input_size: !ref <d_model>
	n_neurons: !ref <joint_dim>
	bias: False

	proj_dec: !new:speechbrain.nnet.linear.Linear
	input_size: !ref <dec_dim>
	n_neurons: !ref <joint_dim>
	bias: False

	emb: !new:speechbrain.nnet.embedding.Embedding
	num_embeddings: !ref <output_neurons>
	consider_as_one_hot: True
	blank_id: !ref <blank_index>

	dec: !new:speechbrain.nnet.RNN.LSTM
	input_shape: [null, null, !ref <output_neurons> - 1]
	hidden_size: !ref <dec_dim>
	num_layers: 1
	re_init: True

	Tjoint: !new:speechbrain.nnet.transducer.transducer_joint.Transducer_joint
	joint: sum # joint [sum \| concat]
	nonlinearity: !ref <activation>

	transducer_lin: !new:speechbrain.nnet.linear.Linear
	input_size: !ref <joint_dim>
	n_neurons: !ref <output_neurons>
	bias: False

	modules:
	CNN: !ref <CNN>
	enc: !ref <enc>
	emb: !ref <emb>
	dec: !ref <dec>
	Tjoint: !ref <Tjoint>
	transducer_lin: !ref <transducer_lin>
	normalize: !ref <normalize>
	proj_ctc: !ref <proj_ctc>
	proj_dec: !ref <proj_dec>
	proj_enc: !ref <proj_enc>

	model: !new:torch.nn.ModuleList
	- [!ref <CNN>, !ref <enc>, !ref <emb>, !ref <dec>, !ref <proj_enc>, !ref <proj_dec>, !ref <proj_ctc>, !ref <transducer_lin>]

	# Tokenizer initialization
	tokenizer: !new:sentencepiece.SentencePieceProcessor

	Greedysearcher: !new:speechbrain.decoders.transducer.TransducerBeamSearcher
	decode_network_lst: [!ref <emb>, !ref <dec>, !ref <proj_dec>]
	tjoint: !ref <Tjoint>
	classifier_network: [!ref <transducer_lin>]
	blank_id: !ref <blank_index>
	beam_size: 1
	nbest: 1

	Beamsearcher: !new:speechbrain.decoders.transducer.TransducerBeamSearcher
	decode_network_lst: [!ref <emb>, !ref <dec>, !ref <proj_dec>]
	tjoint: !ref <Tjoint>
	classifier_network: [!ref <transducer_lin>]
	blank_id: !ref <blank_index>
	beam_size: !ref <beam_size>
	nbest: !ref <nbest>
	# lm_module: !ref <lm_model>
	# lm_weight: !ref <lm_weight>
	state_beam: !ref <state_beam>
	expand_beam: !ref <expand_beam>

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

	# inference stuff

	make_tokenizer_streaming_context: !name:speechbrain.tokenizers.SentencePiece.SentencePieceDecoderStreamingContext
	tokenizer_decode_streaming: !name:speechbrain.tokenizers.SentencePiece.spm_decode_preserve_leading_space

	make_decoder_streaming_context: !name:speechbrain.decoders.transducer.TransducerGreedySearcherStreamingContext # default constructor
	decoding_function: !name:speechbrain.decoders.transducer.TransducerBeamSearcher.transducer_greedy_decode_streaming
	- !ref <Greedysearcher> # self

	fea_streaming_extractor: !new:speechbrain.lobes.features.StreamingFeatureWrapper
	module: !new:speechbrain.nnet.containers.LengthsCapableSequential
	- !ref <compute_features>
	- !ref <normalize>
	- !ref <CNN>
	# don't consider normalization as part of the input filter chain.
	# normalization will operate at chunk level, which mismatches training
	# somewhat, but does not appear to result in noticeable degradation.
	properties: !apply:speechbrain.utils.filter_analysis.stack_filter_properties
	- [!ref <compute_features>, !ref <CNN>]