pushing model

hyperparams.yaml

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+# ############################################################################
+# 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: 40
+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
+
+normalize: !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>]
+
+beam_searcher: !new:speechbrain.decoders.S2SRNNBeamSearchTransformerLM
+   embedding: !ref <emb>
+   decoder: !ref <dec>
+   linear: !ref <seq_lin>
+   ctc_linear: !ref <ctc_lin>
+   language_model: !ref <lm_model>
+   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 <beam_size>
+   eos_threshold: !ref <eos_threshold>
+   using_max_attn_shift: !ref <using_max_attn_shift>
+   max_attn_shift: !ref <max_attn_shift>
+   coverage_penalty: !ref <coverage_penalty>
+   lm_weight: !ref <lm_weight>
+   ctc_weight: !ref <ctc_weight_decode>
+   ctc_window_size: !ref <ctc_window_size>
+   temperature: !ref <temperature>
+   temperature_lm: !ref <temperature_lm>
+
+modules:
+   compute_features: !ref <compute_features>
+   asr_enc: !ref <enc>
+   asr_dec: !ref <dec>
+   ctc_lin: !ref <ctc_lin>
+   seq_lin: !ref <seq_lin>
+   normalize: !ref <normalize>
+   lm_model: !ref <lm_model>
+   beam_searcher: !ref <beam_searcher>
+
+# The pretrainer allows a mapping between pretrained files and instances that
+# are declared in the yaml.
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+   loadables:
+      asr: !ref <asr_model>
+      lm: !ref <lm_model>
+      tokenizer: !ref <tokenizer>