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# use CNND
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###########################################################
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# FEATURE EXTRACTION SETTING #
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###########################################################
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fs: 24000 # sr
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n_fft: 2048 # FFT size (samples).
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n_shift: 300 # Hop size (samples). 12.5ms
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win_length: 1200 # Window length (samples). 50ms
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# If set to null, it will be the same as fft_size.
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window: "hann" # Window function.
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# Only used for feats_type != raw
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fmin: 80 # Minimum frequency of Mel basis.
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fmax: 7600 # Maximum frequency of Mel basis.
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n_mels: 80 # The number of mel basis.
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# Only used for the model using pitch features (e.g. FastSpeech2)
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f0min: 80 # Minimum f0 for pitch extraction.
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f0max: 400 # Maximum f0 for pitch extraction.
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###########################################################
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# DATA SETTING #
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###########################################################
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batch_size: 64
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num_workers: 4
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###########################################################
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# MODEL SETTING #
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###########################################################
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model:
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adim: 384 # attention dimension
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aheads: 2 # number of attention heads
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elayers: 4 # number of encoder layers
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eunits: 1536 # number of encoder ff units
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dlayers: 4 # number of decoder layers
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dunits: 1536 # number of decoder ff units
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positionwise_layer_type: conv1d # type of position-wise layer
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positionwise_conv_kernel_size: 3 # kernel size of position wise conv layer
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duration_predictor_layers: 2 # number of layers of duration predictor
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duration_predictor_chans: 256 # number of channels of duration predictor
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duration_predictor_kernel_size: 3 # filter size of duration predictor
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postnet_layers: 5 # number of layers of postnset
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postnet_filts: 5 # filter size of conv layers in postnet
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postnet_chans: 256 # number of channels of conv layers in postnet
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use_scaled_pos_enc: True # whether to use scaled positional encoding
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encoder_normalize_before: True # whether to perform layer normalization before the input
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decoder_normalize_before: True # whether to perform layer normalization before the input
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reduction_factor: 1 # reduction factor
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encoder_type: transformer # encoder type
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decoder_type: cnndecoder # decoder type
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init_type: xavier_uniform # initialization type
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init_enc_alpha: 1.0 # initial value of alpha of encoder scaled position encoding
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init_dec_alpha: 1.0 # initial value of alpha of decoder scaled position encoding
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transformer_enc_dropout_rate: 0.2 # dropout rate for transformer encoder layer
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transformer_enc_positional_dropout_rate: 0.2 # dropout rate for transformer encoder positional encoding
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transformer_enc_attn_dropout_rate: 0.2 # dropout rate for transformer encoder attention layer
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cnn_dec_dropout_rate: 0.2 # dropout rate for cnn decoder layer
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cnn_postnet_dropout_rate: 0.2
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cnn_postnet_resblock_kernel_sizes: [256, 256] # kernel sizes for residual block of cnn_postnet
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cnn_postnet_kernel_size: 5 # kernel size of cnn_postnet
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cnn_decoder_embedding_dim: 256
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pitch_predictor_layers: 5 # number of conv layers in pitch predictor
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pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
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pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
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pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
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pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
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pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
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stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
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energy_predictor_layers: 2 # number of conv layers in energy predictor
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energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
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energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
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energy_predictor_dropout: 0.5 # dropout rate in energy predictor
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energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
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energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy
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stop_gradient_from_energy_predictor: False # whether to stop the gradient from energy predictor to encoder
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###########################################################
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# UPDATER SETTING #
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###########################################################
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updater:
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use_masking: True # whether to apply masking for padded part in loss calculation
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###########################################################
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# OPTIMIZER SETTING #
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###########################################################
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optimizer:
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optim: adam # optimizer type
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learning_rate: 0.001 # learning rate
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###########################################################
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# TRAINING SETTING #
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###########################################################
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max_epoch: 1000
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num_snapshots: 5
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###########################################################
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# OTHER SETTING #
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###########################################################
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seed: 10086
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#!/bin/bash
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config_path=$1
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train_output_path=$2
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ckpt_name=$3
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stage=0
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stop_stage=0
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_streaming.py \
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--am=fastspeech2_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=pwgan_csmsc \
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--voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
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--voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
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--voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--inference_dir=${train_output_path}/inference
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fi
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# for more GAN Vocoders
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# multi band melgan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_streaming.py \
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--am=fastspeech2_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=mb_melgan_csmsc \
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--voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \
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--voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\
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--voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--inference_dir=${train_output_path}/inference
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fi
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# the pretrained models haven't release now
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# style melgan
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# style melgan's Dygraph to Static Graph is not ready now
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_streaming.py \
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--am=fastspeech2_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=style_melgan_csmsc \
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--voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \
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--voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \
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--voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt
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# --inference_dir=${train_output_path}/inference
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fi
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# hifigan
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "in hifigan syn_e2e"
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_streaming.py \
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--am=fastspeech2_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=hifigan_csmsc \
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--voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \
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--voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \
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--voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--inference_dir=${train_output_path}/inference
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fi
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#!/bin/bash
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set -e
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source path.sh
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gpus=0,1
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stage=0
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stop_stage=100
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conf_path=conf/cnndecoder.yaml
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train_output_path=exp/cnndecoder
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ckpt_name=snapshot_iter_153.pdz
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# with the following command, you can choose the stage range you want to run
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# such as `./run.sh --stage 0 --stop-stage 0`
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# this can not be mixed use with `$1`, `$2` ...
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source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# prepare data
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./local/preprocess.sh ${conf_path} || exit -1
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# train model, all `ckpt` under `train_output_path/checkpoints/` dir
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# synthesize, vocoder is pwgan
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# synthesize_e2e, vocoder is pwgan
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
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fi
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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# inference with static model
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CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path} || exit -1
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fi
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if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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# synthesize_e2e, vocoder is pwgan
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_streaming.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
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fi
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Loading…
Reference in new issue