Hui Zhang
5 years ago
parent
9626e99ce4
commit
2fa6bbbed5
@ -1,21 +0,0 @@
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#! /usr/bin/env bash
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. ${MAIN_ROOT}/utils/utility.sh
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DIR=data/pretrain
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mkdir -p ${DIR}
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URL='https://deepspeech.bj.bcebos.com/eng_models/librispeech_model_fluid.tar.gz'
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MD5=fafb11fe57c3ecd107147056453f5348
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TARGET=${DIR}/librispeech_model_fluid.tar.gz
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echo "Download LibriSpeech model ..."
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download $URL $MD5 $TARGET
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if [ $? -ne 0 ]; then
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echo "Fail to download LibriSpeech model!"
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exit 1
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fi
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tar -zxvf $TARGET -C ${DIR}
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exit 0
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@ -0,0 +1,90 @@
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# network architecture
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# encoder related
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encoder: conformer
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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linear_units: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
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normalize_before: true
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cnn_module_kernel: 15
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use_cnn_module: True
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activation_type: 'swish'
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pos_enc_layer_type: 'rel_pos'
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selfattention_layer_type: 'rel_selfattn'
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causal: true
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use_dynamic_chunk: true
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cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
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use_dynamic_left_chunk: false
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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# use raw_wav or kaldi feature
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raw_wav: true
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# feature extraction
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collate_conf:
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# waveform level config
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wav_distortion_conf:
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wav_dither: 1.0
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wav_distortion_rate: 0.0
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distortion_methods: []
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speed_perturb: true
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feature_extraction_conf:
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feature_type: 'fbank'
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mel_bins: 80
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frame_shift: 10
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frame_length: 25
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using_pitch: false
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# spec level config
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# spec_swap: false
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feature_dither: 0.0 # add dither [-feature_dither,feature_dither] on fbank feature
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spec_aug: true
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spec_aug_conf:
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warp_for_time: False
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num_t_mask: 2
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num_f_mask: 2
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max_t: 50
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max_f: 10
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max_w: 80
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# dataset related
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dataset_conf:
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max_length: 40960
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min_length: 0
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batch_type: 'static' # static or dynamic
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# the size of batch_size should be set according to your gpu memory size, here we used 2080ti gpu whose memory size is 11GB
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batch_size: 16
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sort: true
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grad_clip: 5
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accum_grad: 1
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max_epoch: 180
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log_interval: 100
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optim: adam
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optim_conf:
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lr: 0.001
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scheduler: warmuplr # pytorch v1.1.0+ required
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scheduler_conf:
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warmup_steps: 25000
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@ -0,0 +1,83 @@
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# network architecture
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# encoder related
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encoder: transformer
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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linear_units: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: conv2d # encoder architecture type
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normalize_before: true
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use_dynamic_chunk: true
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use_dynamic_left_chunk: false
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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# use raw_wav or kaldi feature
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raw_wav: true
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# feature extraction
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collate_conf:
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# waveform level config
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wav_distortion_conf:
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wav_dither: 0.0
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wav_distortion_rate: 0.0
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distortion_methods: []
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speed_perturb: false
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feature_extraction_conf:
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feature_type: 'fbank'
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mel_bins: 80
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frame_shift: 10
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frame_length: 25
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using_pitch: false
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# spec level config
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# spec_swap: false
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feature_dither: 0.0 # add dither [-feature_dither,feature_dither] on fbank feature
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spec_aug: true
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spec_aug_conf:
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warp_for_time: False
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num_t_mask: 2
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num_f_mask: 2
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max_t: 50
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max_f: 10
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max_w: 80
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# dataset related
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dataset_conf:
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max_length: 40960
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min_length: 0
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batch_type: 'static' # static or dynamic
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# the size of batch_size should be set according to your gpu memory size, here we used 2080ti gpu whose memory size is 11GB
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batch_size: 16
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sort: true
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grad_clip: 5
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accum_grad: 1
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max_epoch: 180
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log_interval: 100
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optim: adam
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optim_conf:
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lr: 0.002
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scheduler: warmuplr # pytorch v1.1.0+ required
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scheduler_conf:
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warmup_steps: 25000
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@ -0,0 +1,86 @@
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# network architecture
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# encoder related
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encoder: conformer
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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linear_units: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
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normalize_before: true
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cnn_module_kernel: 15
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use_cnn_module: True
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activation_type: 'swish'
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pos_enc_layer_type: 'rel_pos'
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selfattention_layer_type: 'rel_selfattn'
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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# use raw_wav or kaldi feature
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raw_wav: true
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# feature extraction
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collate_conf:
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# waveform level config
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wav_distortion_conf:
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wav_dither: 0.1
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wav_distortion_rate: 0.0
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distortion_methods: []
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speed_perturb: true
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feature_extraction_conf:
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feature_type: 'fbank'
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mel_bins: 80
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frame_shift: 10
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frame_length: 25
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using_pitch: false
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# spec level config
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# spec_swap: false
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feature_dither: 0.0 # add dither [-feature_dither,feature_dither] on fbank feature
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spec_aug: true
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spec_aug_conf:
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warp_for_time: False
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num_t_mask: 2
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num_f_mask: 2
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max_t: 50
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max_f: 10
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max_w: 80
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# dataset related
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dataset_conf:
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max_length: 40960
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min_length: 0
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batch_type: 'static' # static or dynamic
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# the size of batch_size should be set according to your gpu memory size, here we used 2080ti gpu whose memory size is 11GB
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batch_size: 16
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sort: true
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grad_clip: 5
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accum_grad: 4
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max_epoch: 240
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log_interval: 100
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optim: adam
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optim_conf:
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lr: 0.002
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scheduler: warmuplr # pytorch v1.1.0+ required
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scheduler_conf:
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warmup_steps: 25000
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@ -0,0 +1,80 @@
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# network architecture
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# encoder related
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encoder: transformer
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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linear_units: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: conv2d # encoder architecture type
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normalize_before: true
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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# use raw_wav or kaldi feature
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raw_wav: true
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# feature extraction
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collate_conf:
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# waveform level config
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wav_distortion_conf:
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wav_dither: 0.1
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wav_distortion_rate: 0.0
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distortion_methods: []
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speed_perturb: true
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feature_extraction_conf:
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feature_type: 'fbank'
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mel_bins: 80
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frame_shift: 10
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frame_length: 25
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using_pitch: false
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# spec level config
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feature_dither: 0.0 # add dither [-feature_dither,feature_dither] on fbank feature
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spec_aug: true
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spec_aug_conf:
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warp_for_time: False
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num_t_mask: 2
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num_f_mask: 2
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max_t: 50
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max_f: 10
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max_w: 80
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# dataset related
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dataset_conf:
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max_length: 40960
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min_length: 0
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batch_type: 'static' # static or dynamic
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# the size of batch_size should be set according to your gpu memory size, here we used 2080ti gpu whose memory size is 11GB
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batch_size: 26
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sort: true
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grad_clip: 5
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accum_grad: 1
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max_epoch: 240
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log_interval: 100
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optim: adam
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optim_conf:
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lr: 0.002
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scheduler: warmuplr # pytorch v1.1.0+ required
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scheduler_conf:
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warmup_steps: 25000
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@ -0,0 +1 @@
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../../s0/local/data.sh
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@ -0,0 +1 @@
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../../s0/local/download_lm_en.sh
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@ -0,0 +1,14 @@
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export MAIN_ROOT=${PWD}/../../../
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export PATH=${MAIN_ROOT}:${PWD}/tools:${PATH}
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export LC_ALL=C
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# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
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export PYTHONIOENCODING=UTF-8
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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
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MODEL=u2
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export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin
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@ -0,0 +1,16 @@
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#!/bin/bash
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set -e
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source path.sh
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# prepare data
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bash ./local/data.sh
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# train model
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bash ./local/train.sh
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# test model
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bash ./local/test.sh
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# infer model
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bash ./local/infer.sh
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