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speechbrain
/
asr-transformer-aishell

Automatic Speech Recognition
speechbrain
PyTorch
English
CTC
Attention
Transformers
Model card Files Community
1
asr-transformer-aishell
File size: 3,984 Bytes 
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# ############################################################################
# Model: E2E ASR with Transformer
# Encoder: Transformer Encoder
# Decoder: Transformer Decoder + (CTC/ATT joint) beamsearch
# Tokens: BPE with unigram
# losses: CTC + KLdiv (Label Smoothing loss)
# Training: AISHELL-1
# Authors:  Jianyuan Zhong, Titouan Parcollet
# ############################################################################

# 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: 0.1
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 # 1.0
valid_search_interval: 10
valid_beam_size: 10
test_beam_size: 10
ctc_weight_decode: 0.40

############################## models ################################

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

normalizer: !new:speechbrain.processing.features.InputNormalization
    norm_type: global


CNN: !new:speechbrain.lobes.models.convolution.ConvolutionFrontEnd
    input_shape: (8, 10, 80)
    num_blocks: 2
    num_layers_per_block: 1
    out_channels: (256, 256)
    kernel_sizes: (3, 3)
    strides: (2, 2)
    residuals: (False, False)
    norm: !name:speechbrain.nnet.normalization.BatchNorm2d

Transformer: !new:speechbrain.lobes.models.transformer.TransformerASR.TransformerASR # yamllint disable-line rule:line-length
    input_size: 5120
    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: True

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>

tokenizer: !new:sentencepiece.SentencePieceProcessor

asr_model: !new:torch.nn.ModuleList
    - [!ref <CNN>, !ref <Transformer>, !ref <seq_lin>, !ref <ctc_lin>]

# Here, we extract the encoder from the Transformer model
Tencoder: !new:speechbrain.lobes.models.transformer.TransformerASR.EncoderWrapper
    transformer: !ref <Transformer>

# We compose the inference (encoder) pipeline.
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>

ctc_scorer: !new:speechbrain.decoders.scorer.CTCScorer
    eos_index: !ref <eos_index>
    blank_index: !ref <blank_index>
    ctc_fc: !ref <ctc_lin>
    
scorer: !new:speechbrain.decoders.scorer.ScorerBuilder
    full_scorers: [!ref <ctc_scorer>]
    weights:
        ctc: !ref <ctc_weight_decode>
        
decoder: !new:speechbrain.decoders.S2STransformerBeamSearcher
    modules: [!ref <Transformer>, !ref <seq_lin>]
    bos_index: !ref <bos_index>
    eos_index: !ref <eos_index>
    min_decode_ratio: !ref <min_decode_ratio>
    max_decode_ratio: !ref <max_decode_ratio>
    beam_size: !ref <test_beam_size>
    using_eos_threshold: False
    length_normalization: True
    scorer: !ref <scorer>

modules:
    normalizer: !ref <normalizer>
    encoder: !ref <encoder>
    decoder: !ref <decoder>

log_softmax: !new:torch.nn.LogSoftmax
    dim: -1

pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
    loadables:
        normalizer: !ref <normalizer>
        asr: !ref <asr_model>
        tokenizer: !ref <tokenizer>