# ############################################################################
# Model: E2E ASR with Transformer
# Encoder: Transformer Encoder
# Decoder: Transformer Decoder + (CTC/ATT joint) beamsearch + TransformerLM
# Tokens: unigram
# losses: CTC + KLdiv (Label Smoothing loss)
# Training: Librispeech 960h
# Authors: Jianyuan Zhong, Titouan Parcollet 2021
# ############################################################################
# Feature parameters
sample_rate: 16000
n_fft: 400
n_mels: 80
####################### Model parameters ###########################
# Transformer
d_model: 768
nhead: 8
num_encoder_layers: 12
num_decoder_layers: 6
d_ffn: 3072
transformer_dropout: 0.0
activation: !name:torch.nn.GELU
output_neurons: 5000
vocab_size: 5000
# 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: 10
valid_beam_size: 10
test_beam_size: 66
lm_weight: 0.60
ctc_weight_decode: 0.52
############################## models ################################
CNN: !new:speechbrain.lobes.models.convolution.ConvolutionFrontEnd
input_shape: (8, 10, 80)
num_blocks: 3
num_layers_per_block: 1
out_channels: (128, 256, 512)
kernel_sizes: (3, 3, 1)
strides: (2, 2, 1)
residuals: (False, False, False)
Transformer: !new:speechbrain.lobes.models.transformer.TransformerASR.TransformerASR
input_size: 10240
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>
normalize_before: False
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: 1.15
temperature_lm: 1.15
using_eos_threshold: False
length_normalization: True
log_softmax: !new:torch.nn.LogSoftmax
dim: -1
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>
# 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_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
tokenizer: !new:sentencepiece.SentencePieceProcessor
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>
# Models
asr_model: !new:torch.nn.ModuleList
- [!ref <CNN>, !ref <Transformer>, !ref <seq_lin>, !ref <ctc_lin>]
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>