Upload hyperparams.yaml

hyperparams.yaml

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+# ################################
+# Model: VGG2 + LSTM + time pooling
+# Augmentation: SpecAugment
+# Authors: Titouan Parcollet, Mirco Ravanelli, Peter Plantinga, Ju-Chieh Chou,
+# and Abdel HEBA 2020
+# ################################
+# Feature parameters (FBANKS etc)
+sample_rate: 16000
+n_fft: 400
+n_mels: 80
+
+# Model parameters
+activation: !name:torch.nn.LeakyReLU
+dropout: 0.15
+cnn_blocks: 3
+cnn_channels: (128, 200, 256)
+inter_layer_pooling_size: (2, 2, 2)
+cnn_kernelsize: (3, 3)
+time_pooling_size: 4
+rnn_class: !name:speechbrain.nnet.RNN.LSTM
+rnn_layers: 5
+rnn_neurons: 1024
+rnn_bidirectional: True
+dnn_blocks: 2
+dnn_neurons: 1024
+dec_neurons: 1024
+output_neurons: 1000  # index(blank/eos/bos) = 0
+joint_dim: 1024
+blank_index: 0
+
+# Outputs
+output_neurons: 1000  # BPE size, index(blank/eos/bos) = 0
+# Decoding parameters
+# Be sure that the bos and eos index match with the BPEs ones
+blank_index: 0
+bos_index: 0
+eos_index: 0
+
+min_decode_ratio: 0.0
+max_decode_ratio: 1.0
+beam_size: 4
+nbest: 1
+# by default {state,expand}_beam = 2.3 as mention in paper
+# https://arxiv.org/abs/1904.02619
+state_beam: 2.3
+expand_beam: 2.3
+
+
+
+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>
+
+enc_lin: !new:speechbrain.nnet.linear.Linear
+   input_size: !ref <dnn_neurons>
+   n_neurons: !ref <joint_dim>
+
+emb: !new:speechbrain.nnet.embedding.Embedding
+    num_embeddings: !ref <output_neurons>
+    consider_as_one_hot: True
+    blank_id: !ref <blank_index>
+
+dec: !new:speechbrain.nnet.RNN.GRU
+   input_shape: [null, null, !ref <output_neurons> - 1]
+   hidden_size: !ref <dec_neurons>
+   num_layers: 1
+   re_init: True
+
+# For MTL with LM over the decoder
+dec_lin: !new:speechbrain.nnet.linear.Linear
+   input_size: !ref <dec_neurons>
+   n_neurons: !ref <joint_dim>
+   bias: False
+
+Tjoint: !new:speechbrain.nnet.transducer.transducer_joint.Transducer_joint
+   joint: sum # joint [sum | concat]
+   nonlinearity: !ref <activation>
+
+transducer_lin: !new:speechbrain.nnet.linear.Linear
+   input_size: !ref <joint_dim>
+   n_neurons: !ref <output_neurons>
+   bias: False
+
+log_softmax: !new:speechbrain.nnet.activations.Softmax
+    apply_log: True
+
+asr_model: !new:torch.nn.ModuleList
+   - [!ref <enc>, !ref <emb>, !ref <dec>, !ref <transducer_lin>]
+
+
+
+tokenizer: !new:sentencepiece.SentencePieceProcessor
+# 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>
+
+decoder: !new:speechbrain.decoders.transducer.TransducerBeamSearcher
+   decode_network_lst: [!ref <emb>, !ref <dec>]
+   tjoint: !ref <Tjoint>
+   classifier_network: [!ref <transducer_lin>]
+   blank_id: !ref <blank_index>
+   beam_size: !ref <beam_size>
+   nbest: !ref <nbest>
+   state_beam: !ref <state_beam>
+   expand_beam: !ref <expand_beam>
+
+modules:
+    normalizer: !ref <normalizer>
+    encoder: !ref <encoder>
+    decoder: !ref <decoder>
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+    loadables:
+        normalizer: !ref <normalizer>
+        asr: !ref <asr_model>
+        tokenizer: !ref <tokenizer>