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Update hyperparams.yaml
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streaming: True
# Decoding parameters
# Be sure that the bos and eos index match with the BPEs ones
# Decoding parameters
blank_index: 0
bos_index: 0
eos_index: 0
pad_index: 0
beam_size: 20
nbest: 1
# by default {state,expand}_beam = 2.3 as mention in paper
# https://arxiv.org/abs/1904.02619
state_beam: 2.5
expand_beam: 2.5
lm_weight: 0.1
sample_rate: 16000
n_fft: 512
n_mels: 80
win_length: 32
# Transformer
d_model: 256
joint_dim: 640
nhead: 4
num_encoder_layers: 12
num_decoder_layers: 0
d_ffn: 2048
activation: !name:torch.nn.GELU
output_neurons: 5000
dec_dim: 512
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>
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>
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
asr_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>]
Beamsearcher: !new:speechbrain.decoders.transducer.TransducerBeamSearcher
decode_network_lst: !new:torch.nn.ModuleList
- [!ref <emb>, !ref <dec>, !ref <proj_dec>]
tjoint: !ref <Tjoint>
classifier_network: !new:torch.nn.ModuleList
- [!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>
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
lm_model: !new:speechbrain.lobes.models.RNNLM.RNNLM
output_neurons: !ref <output_neurons>
embedding_dim: 256
activation: !name:torch.nn.LeakyReLU
dropout: 0.0
rnn_layers: 6
rnn_neurons: 512
dnn_blocks: 1
dnn_neurons: 256
return_hidden: True # For inference
modules:
CNN: !ref <CNN>
enc: !ref <enc>
emb: !ref <emb>
dec: !ref <dec>
Tjoint: !ref <Tjoint>
transducer_lin: !ref <transducer_lin>
normalize: !ref <normalizer>
proj_ctc: !ref <proj_ctc>
proj_dec: !ref <proj_dec>
proj_enc: !ref <proj_enc>
tokenizer: !new:sentencepiece.SentencePieceProcessor
# We compose the inference (encoder) pipeline.
encoder: !new:speechbrain.nnet.containers.LengthsCapableSequential
input_shape: [null, null]
compute_features: !ref <compute_features>
normalize: !ref <normalizer>
cnn: !ref <CNN>
transformer_encoder: !ref <enc>
proj_enc: !ref <proj_enc>
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
loadables:
asr: !ref <asr_model>
lm: !ref <lm_model>
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 <normalizer>
- !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>]