# by HuggingFace # ############################################################################ # Model: E2E ASR with attention-based ASR # Encoder: CRDNN model # Decoder: GRU + beamsearch + RNNLM # Tokens: BPE with unigram # Authors: Ju-Chieh Chou, Mirco Ravanelli, Abdel Heba, Peter Plantinga 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: (128, 256) 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: 2 dnn_neurons: 512 emb_size: 128 dec_neurons: 1024 output_neurons: 1000 # index(blank/eos/bos) = 0 blank_index: 0 # Decoding parameters bos_index: 0 eos_index: 0 min_decode_ratio: 0.0 max_decode_ratio: 1.0 beam_size: 80 eos_threshold: 1.5 using_max_attn_shift: True max_attn_shift: 240 lm_weight: 0.50 coverage_penalty: 1.5 temperature: 1.25 temperature_lm: 1.25 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> 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 lm_model: !new:speechbrain.lobes.models.RNNLM.RNNLM output_neurons: !ref <output_neurons> embedding_dim: !ref <emb_size> activation: !name:torch.nn.LeakyReLU dropout: 0.0 rnn_layers: 2 rnn_neurons: 2048 dnn_blocks: 1 dnn_neurons: 512 return_hidden: True # For inference tokenizer: !new:sentencepiece.SentencePieceProcessor 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> decoder: !new:speechbrain.decoders.S2SRNNBeamSearchLM embedding: !ref <emb> decoder: !ref <dec> linear: !ref <seq_lin> language_model: !ref <lm_model> 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> coverage_penalty: !ref <coverage_penalty> lm_weight: !ref <lm_weight> temperature: !ref <temperature> temperature_lm: !ref <temperature_lm> modules: normalizer: !ref <normalizer> encoder: !ref <encoder> decoder: !ref <decoder> lm_model: !ref <lm_model> pretrained_path_local: /content/pretrained_models pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer loadables: normalizer: !ref <normalizer> asr: !ref <asr_model> lm: !ref <lm_model> tokenizer: !ref <tokenizer> paths: lm: !ref <pretrained_path_local>/lm.ckpt tokenizer: !ref <pretrained_path_local>/1000_unigram.model asr: !ref <pretrained_path_local>/model.ckpt normalizer: !ref <pretrained_path_local>/normalizer.ckpt