hyperparams.yaml

# ############################################################################
# model: Seq2Seq
# encoder: CRDNN model
# decoder: GRU + beamsearch
# tokens: BPE (unigram)
# losses: CTC+NLL
# training: Mozilla Common Voice 6.1, Spoken Wikipedia Corpus, M-AILABS Corpus
# authors: Ruhr-University Bochum 2021
#          adapted from 
#            Ju-Chieh Chou, 
#            Mirco Ravanelli,
#            Abdel Heba,
#            Peter Plantinga,
#            Samuele Cornell, 
#            Sung-Lin Yeh, 
#            Titouan Parcollet 2021
# ############################################################################

# set exp name
name: german-asr

# Feature parameters
sample_rate: 16000
n_fft: 400
n_mels: 40

# Model parameters
activation: !name:torch.nn.LeakyReLU
dropout: 0.15
cnn_blocks: 2
cnn_channels: (64, 128)
inter_layer_pooling_size: (2, 2)
cnn_kernelsize: (3, 3)
time_pooling_size: 4
rnn_class: !name:speechbrain.nnet.RNN.LSTM
rnn_layers: 4
rnn_neurons: 1024
rnn_bidirectional: True
dnn_blocks: 1
dnn_neurons: 1024
emb_size: 1024
dec_neurons: 1024
output_neurons: 5000  # Number of tokens (same as LM and tokenizer)


# Decoding parameters
blank_index: 0
pad_index: -1
bos_index: 1
eos_index: 2
unk_index: 0

min_decode_ratio: 0.0
max_decode_ratio: 1.0
beam_size: 30
eos_threshold: 1.5
using_max_attn_shift: True
max_attn_shift: 300
ctc_weight_decode: 0.3
ctc_window_size: 300
coverage_penalty: 1.5
temperature: 1.0


# Feature Extraction
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>

# Tokenizer
tokenizer: !new:sentencepiece.SentencePieceProcessor

# Encoder
enc: !new:speechbrain.lobes.models.CRDNN.CRDNN
   input_shape: [null, null, !ref <n_mels>]
   activation: !ref <activation>
   dropout: !ref <dropout>
   cnn_blocks: !ref <cnn_blocks>
   cnn_channels: !ref <cnn_channels>
   cnn_kernelsize: !ref <cnn_kernelsize>
   inter_layer_pooling_size: !ref <inter_layer_pooling_size>
   time_pooling: True
   using_2d_pooling: False
   time_pooling_size: !ref <time_pooling_size>
   rnn_class: !ref <rnn_class>
   rnn_layers: !ref <rnn_layers>
   rnn_neurons: !ref <rnn_neurons>
   rnn_bidirectional: !ref <rnn_bidirectional>
   rnn_re_init: True
   dnn_blocks: !ref <dnn_blocks>
   dnn_neurons: !ref <dnn_neurons>
   use_rnnp: True

# Decoder
emb: !new:speechbrain.nnet.embedding.Embedding
   num_embeddings: !ref <output_neurons>
   embedding_dim: !ref <emb_size>
   
dec: !new:speechbrain.nnet.RNN.AttentionalRNNDecoder
   enc_dim: !ref <dnn_neurons>
   input_size: !ref <emb_size>
   rnn_type: gru
   attn_type: location
   hidden_size: !ref <dec_neurons>
   attn_dim: 1024
   num_layers: 1
   scaling: 1.0
   channels: 10
   kernel_size: 100
   re_init: True
   dropout: !ref <dropout>


# Losses
log_softmax: !new:speechbrain.nnet.activations.Softmax
   apply_log: True
   
ctc_lin: !new:speechbrain.nnet.linear.Linear
   input_size: !ref <dnn_neurons>
   n_neurons: !ref <output_neurons>

seq_lin: !new:speechbrain.nnet.linear.Linear
   input_size: !ref <dec_neurons>
   n_neurons: !ref <output_neurons>


# Compile model
asr_model: !new:torch.nn.ModuleList
    - [!ref <enc>, !ref <emb>, !ref <dec>, !ref <ctc_lin>, !ref <seq_lin>]
    
# 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>
    model: !ref <enc>

# Beam searcher
decoder: !new:speechbrain.decoders.S2SRNNBeamSearcher
    embedding: !ref <emb>
    decoder: !ref <dec>
    linear: !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 <beam_size>
    eos_threshold: !ref <eos_threshold>
    using_max_attn_shift: !ref <using_max_attn_shift>
    max_attn_shift: !ref <max_attn_shift>
    temperature: !ref <temperature>

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

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