TianYuan
2 years ago
committed by
GitHub
commit
5bff096715
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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_streaming \
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--phones_dict=dump/phone_id_map.txt \
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--am_streaming=True
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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_streaming \
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--phones_dict=dump/phone_id_map.txt \
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--am_streaming=True
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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_streaming \
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--phones_dict=dump/phone_id_map.txt \
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--am_streaming=True
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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_streaming \
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--phones_dict=dump/phone_id_map.txt \
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--am_streaming=True
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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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@ -0,0 +1,274 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import math
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from pathlib import Path
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import numpy as np
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import paddle
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import soundfile as sf
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import yaml
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from timer import timer
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from yacs.config import CfgNode
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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from paddlespeech.t2s.exps.syn_utils import get_frontend
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from paddlespeech.t2s.exps.syn_utils import get_sentences
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from paddlespeech.t2s.exps.syn_utils import get_voc_inference
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from paddlespeech.t2s.exps.syn_utils import model_alias
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from paddlespeech.t2s.utils import str2bool
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def denorm(data, mean, std):
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return data * std + mean
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def get_chunks(data, chunk_size, pad_size):
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data_len = data.shape[1]
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chunks = []
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n = math.ceil(data_len / chunk_size)
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for i in range(n):
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start = max(0, i * chunk_size - pad_size)
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end = min((i + 1) * chunk_size + pad_size, data_len)
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chunks.append(data[:, start:end, :])
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return chunks
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def evaluate(args):
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# Init body.
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with open(args.am_config) as f:
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am_config = CfgNode(yaml.safe_load(f))
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with open(args.voc_config) as f:
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voc_config = CfgNode(yaml.safe_load(f))
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print("========Args========")
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print(yaml.safe_dump(vars(args)))
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print("========Config========")
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print(am_config)
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print(voc_config)
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sentences = get_sentences(args)
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# frontend
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frontend = get_frontend(args)
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with open(args.phones_dict, "r") as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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print("vocab_size:", vocab_size)
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# acoustic model, only support fastspeech2 here now!
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# am_inference, am_name, am_dataset = get_am_inference(args, am_config)
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# model: {model_name}_{dataset}
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am_name = args.am[:args.am.rindex('_')]
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am_dataset = args.am[args.am.rindex('_') + 1:]
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odim = am_config.n_mels
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am_class = dynamic_import(am_name, model_alias)
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am = am_class(idim=vocab_size, odim=odim, **am_config["model"])
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am.set_state_dict(paddle.load(args.am_ckpt)["main_params"])
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am.eval()
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am_mu, am_std = np.load(args.am_stat)
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am_mu = paddle.to_tensor(am_mu)
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am_std = paddle.to_tensor(am_std)
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# vocoder
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voc_inference = get_voc_inference(args, voc_config)
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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merge_sentences = True
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N = 0
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T = 0
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chunk_size = args.chunk_size
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pad_size = args.pad_size
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for utt_id, sentence in sentences:
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with timer() as t:
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get_tone_ids = False
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if args.lang == 'zh':
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input_ids = frontend.get_input_ids(
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sentence,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids)
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phone_ids = input_ids["phone_ids"]
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else:
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print("lang should in be 'zh' here!")
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# merge_sentences=True here, so we only use the first item of phone_ids
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phone_ids = phone_ids[0]
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with paddle.no_grad():
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# acoustic model
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orig_hs, h_masks = am.encoder_infer(phone_ids)
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if args.am_streaming:
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hss = get_chunks(orig_hs, chunk_size, pad_size)
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chunk_num = len(hss)
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mel_list = []
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for i, hs in enumerate(hss):
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before_outs, _ = am.decoder(hs)
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after_outs = before_outs + am.postnet(
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before_outs.transpose((0, 2, 1))).transpose(
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(0, 2, 1))
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normalized_mel = after_outs[0]
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sub_mel = denorm(normalized_mel, am_mu, am_std)
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# clip output part of pad
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if i == 0:
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sub_mel = sub_mel[:-pad_size]
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elif i == chunk_num - 1:
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# 最后一块的右侧一定没有 pad 够
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sub_mel = sub_mel[pad_size:]
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else:
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# 倒数几块的右侧也可能没有 pad 够
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sub_mel = sub_mel[pad_size:(chunk_size + pad_size) -
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sub_mel.shape[0]]
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mel_list.append(sub_mel)
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mel = paddle.concat(mel_list, axis=0)
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else:
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before_outs, _ = am.decoder(orig_hs)
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after_outs = before_outs + am.postnet(
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before_outs.transpose((0, 2, 1))).transpose((0, 2, 1))
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normalized_mel = after_outs[0]
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mel = denorm(normalized_mel, am_mu, am_std)
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# vocoder
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wav = voc_inference(mel)
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wav = wav.numpy()
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N += wav.size
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T += t.elapse
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speed = wav.size / t.elapse
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rtf = am_config.fs / speed
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print(
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f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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sf.write(
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str(output_dir / (utt_id + ".wav")), wav, samplerate=am_config.fs)
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print(f"{utt_id} done!")
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print(f"generation speed: {N / T}Hz, RTF: {am_config.fs / (N / T) }")
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def parse_args():
|
||||
# parse args and config and redirect to train_sp
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Synthesize with acoustic model & vocoder")
|
||||
# acoustic model
|
||||
parser.add_argument(
|
||||
'--am',
|
||||
type=str,
|
||||
default='fastspeech2_csmsc',
|
||||
choices=['fastspeech2_csmsc'],
|
||||
help='Choose acoustic model type of tts task.')
|
||||
parser.add_argument(
|
||||
'--am_config',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Config of acoustic model. Use deault config when it is None.')
|
||||
parser.add_argument(
|
||||
'--am_ckpt',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Checkpoint file of acoustic model.')
|
||||
parser.add_argument(
|
||||
"--am_stat",
|
||||
type=str,
|
||||
default=None,
|
||||
help="mean and standard deviation used to normalize spectrogram when training acoustic model."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--phones_dict", type=str, default=None, help="phone vocabulary file.")
|
||||
parser.add_argument(
|
||||
"--tones_dict", type=str, default=None, help="tone vocabulary file.")
|
||||
|
||||
# vocoder
|
||||
parser.add_argument(
|
||||
'--voc',
|
||||
type=str,
|
||||
default='pwgan_csmsc',
|
||||
choices=[
|
||||
'pwgan_csmsc',
|
||||
'pwgan_ljspeech',
|
||||
'pwgan_aishell3',
|
||||
'pwgan_vctk',
|
||||
'mb_melgan_csmsc',
|
||||
'style_melgan_csmsc',
|
||||
'hifigan_csmsc',
|
||||
'hifigan_ljspeech',
|
||||
'hifigan_aishell3',
|
||||
'hifigan_vctk',
|
||||
'wavernn_csmsc',
|
||||
],
|
||||
help='Choose vocoder type of tts task.')
|
||||
parser.add_argument(
|
||||
'--voc_config',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Config of voc. Use deault config when it is None.')
|
||||
parser.add_argument(
|
||||
'--voc_ckpt', type=str, default=None, help='Checkpoint file of voc.')
|
||||
parser.add_argument(
|
||||
"--voc_stat",
|
||||
type=str,
|
||||
default=None,
|
||||
help="mean and standard deviation used to normalize spectrogram when training voc."
|
||||
)
|
||||
# other
|
||||
parser.add_argument(
|
||||
'--lang',
|
||||
type=str,
|
||||
default='zh',
|
||||
help='Choose model language. zh or en')
|
||||
|
||||
parser.add_argument(
|
||||
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
|
||||
parser.add_argument(
|
||||
"--text",
|
||||
type=str,
|
||||
help="text to synthesize, a 'utt_id sentence' pair per line.")
|
||||
|
||||
parser.add_argument(
|
||||
"--am_streaming",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="whether use streaming acoustic model")
|
||||
parser.add_argument(
|
||||
"--chunk_size", type=int, default=42, help="chunk size of am streaming")
|
||||
parser.add_argument(
|
||||
"--pad_size", type=int, default=12, help="pad size of am streaming")
|
||||
|
||||
parser.add_argument("--output_dir", type=str, help="output dir.")
|
||||
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
|
||||
if args.ngpu == 0:
|
||||
paddle.set_device("cpu")
|
||||
elif args.ngpu > 0:
|
||||
paddle.set_device("gpu")
|
||||
else:
|
||||
print("ngpu should >= 0 !")
|
||||
|
||||
evaluate(args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in new issue