File size: 4,563 Bytes
92233a4 48158be 92233a4 f94e4bc 92233a4 f94e4bc 92233a4 f94e4bc 92233a4 f94e4bc 92233a4 f94e4bc 92233a4 f94e4bc 92233a4 f94e4bc 92233a4 f94e4bc 92233a4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
# ############################################################################
# Model: E2E ASR with Transformer
# Encoder: Conformer Encoder
# Decoder: Transformer Decoder + (CTC/ATT joint) beamsearch + TransformerLM
# Tokens: unigram
# losses: CTC + KLdiv (Label Smoothing loss)
# Training: KsponSpeech 965.2h
# Based on the works of: Jianyuan Zhong, Titouan Parcollet 2021
# Authors: Dongwon Kim, Dongwoo Kim 2021
# ############################################################################
# Seed needs to be set at top of yaml, before objects with parameters are made
# Feature parameters
sample_rate: 16000
n_fft: 400
n_mels: 80
####################### Model parameters ###########################
# Transformer
d_model: 256
nhead: 4
num_encoder_layers: 12
num_decoder_layers: 6
d_ffn: 2048
transformer_dropout: 0.0
activation: !name:torch.nn.GELU
output_neurons: 5000
vocab_size: 5000
# Outputs
blank_index: 0
pad_index: 0
bos_index: 1
eos_index: 2
unk_index: 0
# Decoding parameters
min_decode_ratio: 0.0
max_decode_ratio: 1.0
test_beam_size: 10
lm_weight: 0.0
ctc_weight_decode: 0.40
############################## models ################################
normalizer: !new:speechbrain.processing.features.InputNormalization
norm_type: global
CNN: !new:speechbrain.lobes.models.convolution.ConvolutionFrontEnd
input_shape: (8, 10, 80)
num_blocks: 3
num_layers_per_block: 1
out_channels: (64, 64, 64)
kernel_sizes: (5, 5, 1)
strides: (2, 2, 1)
residuals: (False, False, True)
Transformer: !new:speechbrain.lobes.models.transformer.TransformerASR.TransformerASR # yamllint disable-line rule:line-length
input_size: 1280
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
# NB: It has to match the pre-trained TransformerLM!!
lm_model: !new:speechbrain.lobes.models.transformer.TransformerLM.TransformerLM # yamllint disable-line rule:line-length
vocab: !ref <output_neurons>
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
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>
decoder: !new:speechbrain.decoders.S2STransformerBeamSearch
modules: [!ref <Transformer>, !ref <seq_lin>, !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>
ctc_weight: !ref <ctc_weight_decode>
lm_weight: !ref <lm_weight>
lm_modules: !ref <lm_model>
temperature: 1.30
temperature_lm: 1.30
using_eos_threshold: False
length_normalization: True
log_softmax: !new:torch.nn.LogSoftmax
dim: -1
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>
asr_model: !new:torch.nn.ModuleList
- [!ref <CNN>, !ref <Transformer>, !ref <seq_lin>, !ref <ctc_lin>]
compute_features: !new:speechbrain.lobes.features.Fbank
sample_rate: !ref <sample_rate>
n_fft: !ref <n_fft>
n_mels: !ref <n_mels>
modules:
compute_features: !ref <compute_features>
normalizer: !ref <normalizer>
pre_transformer: !ref <CNN>
transformer: !ref <Transformer>
asr_model: !ref <asr_model>
lm_model: !ref <lm_model>
encoder: !ref <encoder>
decoder: !ref <decoder>
# The pretrainer allows a mapping between pretrained files and instances that
# are declared in the yaml.
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
loadables:
normalizer: !ref <normalizer>
asr: !ref <asr_model>
lm: !ref <lm_model>
tokenizer: !ref <tokenizer>
|