hyperparams.yaml

# ############################################################################
# Model: E2E ASR with attention-based ASR
# Encoder: CRDNN model
# Decoder: GRU + beamsearch + Transformer
# Tokens: BPE with unigram
# losses: CTC+ NLL
# Training: Librispeech 960h
# Authors:  Ju-Chieh Chou, Mirco Ravanelli, Abdel Heba, Peter Plantinga, Samuele Cornell
#           Sung-Lin Yeh, Titouan Parcollet 2020
# ############################################################################

# 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)
blank_index: 0
pad_index: -1
bos_index: 1
eos_index: 2
unk_index: 0

# Decoding parameters
min_decode_ratio: 0.0
max_decode_ratio: 1.0
beam_size: 66
eos_threshold: 1.5
using_max_attn_shift: True
max_attn_shift: 300
lm_weight: 0.80
ctc_weight_decode: 0.40
ctc_window_size: 200
coverage_penalty: 1.5
temperature: 1.0
temperature_lm: 1.0

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>

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

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>

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>

log_softmax: !new:speechbrain.nnet.activations.Softmax
   apply_log: True

# 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: !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

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

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

# 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>

# Scorer
ctc_scorer: !new:speechbrain.decoders.scorer.CTCScorer
    eos_index: !ref <eos_index>
    blank_index: !ref <blank_index>
    ctc_fc: !ref <ctc_lin>
    ctc_window_size: !ref <ctc_window_size>

transformerlm_scorer: !new:speechbrain.decoders.scorer.TransformerLMScorer
   language_model: !ref <lm_model>
   temperature: !ref <temperature_lm>

coverage_scorer: !new:speechbrain.decoders.scorer.CoverageScorer
   vocab_size: !ref <output_neurons>
   
scorer: !new:speechbrain.decoders.scorer.ScorerBuilder
   full_scorers: [!ref <transformerlm_scorer>,
                  !ref <coverage_scorer>,
                  !ref <ctc_scorer>]
   weights:
      transformerlm: !ref <lm_weight>
      coverage: !ref <coverage_penalty>
      ctc: !ref <ctc_weight_decode>
      
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>
   scorer: !ref <scorer>

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

# 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>