TianYuan
3 years ago
parent
dc7daa2a61
commit
a6ac497f8e
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# This is the hyperparameter configuration file for MelGAN.
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# Please make sure this is adjusted for the CSMSC dataset. If you want to
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# apply to the other dataset, you might need to carefully change some parameters.
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# This configuration requires ~ 8GB memory and will finish within 7 days on Titan V.
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# This configuration is based on full-band MelGAN but the hop size and sampling
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# rate is different from the paper (16kHz vs 24kHz). The number of iteraions
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# is not shown in the paper so currently we train 1M iterations (not sure enough
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# to converge). The optimizer setting is based on @dathudeptrai advice.
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# https://github.com/kan-bayashi/ParallelWaveGAN/issues/143#issuecomment-632539906
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###########################################################
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# FEATURE EXTRACTION SETTING #
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###########################################################
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fs: 24000 # Sampling rate.
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n_fft: 2048 # FFT size. (in samples)
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n_shift: 300 # Hop size. (in samples)
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win_length: 1200 # Window length. (in samples)
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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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n_mels: 80 # Number of mel basis.
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fmin: 80 # Minimum freq in mel basis calculation. (Hz)
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fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
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###########################################################
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# GENERATOR NETWORK ARCHITECTURE SETTING #
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###########################################################
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generator_params:
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in_channels: 80 # Number of input channels.
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out_channels: 4 # Number of output channels.
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kernel_size: 7 # Kernel size of initial and final conv layers.
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channels: 384 # Initial number of channels for conv layers.
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upsample_scales: [5, 5, 3] # List of Upsampling scales.
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stack_kernel_size: 3 # Kernel size of dilated conv layers in residual stack.
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stacks: 4 # Number of stacks in a single residual stack module.
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use_weight_norm: True # Whether to use weight normalization.
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use_causal_conv: False # Whether to use causal convolution.
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use_final_nonlinear_activation: True
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###########################################################
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# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
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###########################################################
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discriminator_params:
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in_channels: 1 # Number of input channels.
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out_channels: 1 # Number of output channels.
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scales: 3 # Number of multi-scales.
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downsample_pooling: "AvgPool1D" # Pooling type for the input downsampling.
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downsample_pooling_params: # Parameters of the above pooling function.
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kernel_size: 4
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stride: 2
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padding: 1
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exclusive: True
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kernel_sizes: [5, 3] # List of kernel size.
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channels: 16 # Number of channels of the initial conv layer.
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max_downsample_channels: 512 # Maximum number of channels of downsampling layers.
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downsample_scales: [4, 4, 4] # List of downsampling scales.
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nonlinear_activation: "LeakyReLU" # Nonlinear activation function.
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nonlinear_activation_params: # Parameters of nonlinear activation function.
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negative_slope: 0.2
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use_weight_norm: True # Whether to use weight norm.
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###########################################################
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# STFT LOSS SETTING #
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###########################################################
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use_stft_loss: true
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stft_loss_params:
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fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss.
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hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss
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win_lengths: [600, 1200, 240] # List of window length for STFT-based loss.
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window: "hann" # Window function for STFT-based loss
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use_subband_stft_loss: true
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subband_stft_loss_params:
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fft_sizes: [384, 683, 171] # List of FFT size for STFT-based loss.
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hop_sizes: [30, 60, 10] # List of hop size for STFT-based loss
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win_lengths: [150, 300, 60] # List of window length for STFT-based loss.
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window: "hann" # Window function for STFT-based loss
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###########################################################
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# ADVERSARIAL LOSS SETTING #
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###########################################################
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use_feat_match_loss: false # Whether to use feature matching loss.
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lambda_adv: 2.5 # Loss balancing coefficient for adversarial loss.
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###########################################################
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# DATA LOADER SETTING #
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###########################################################
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batch_size: 64 # Batch size.
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batch_max_steps: 16200 # Length of each audio in batch. Make sure dividable by hop_size.
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num_workers: 2 # Number of workers in DataLoader.
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###########################################################
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# OPTIMIZER & SCHEDULER SETTING #
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###########################################################
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generator_optimizer_params:
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epsilon: 1.0e-7 # Generator's epsilon.
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weight_decay: 0.0 # Generator's weight decay coefficient.
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generator_grad_norm: -1 # Generator's gradient norm.
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generator_scheduler_params:
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learning_rate: 1.0e-3 # Generator's learning rate.
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gamma: 0.5 # Generator's scheduler gamma.
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milestones: # At each milestone, lr will be multiplied by gamma.
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- 100000
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- 200000
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- 300000
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- 400000
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- 500000
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- 600000
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discriminator_optimizer_params:
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epsilon: 1.0e-7 # Discriminator's epsilon.
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weight_decay: 0.0 # Discriminator's weight decay coefficient.
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discriminator_grad_norm: -1 # Discriminator's gradient norm.
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discriminator_scheduler_params:
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learning_rate: 1.0e-3 # Discriminator's learning rate.
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gamma: 0.5 # Discriminator's scheduler gamma.
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milestones: # At each milestone, lr will be multiplied by gamma.
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- 100000
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- 200000
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- 300000
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- 400000
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- 500000
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- 600000
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###########################################################
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# INTERVAL SETTING #
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###########################################################
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discriminator_train_start_steps: 200000 # Number of steps to start to train discriminator.
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train_max_steps: 1200000 # Number of training steps.
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save_interval_steps: 1000 # Interval steps to save checkpoint.
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eval_interval_steps: 1000 # Interval steps to evaluate the network.
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###########################################################
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# OTHER SETTING #
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###########################################################
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num_snapshots: 10 # max number of snapshots to keep while training
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seed: 42 # random seed for paddle, random, and np.random
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#!/bin/bash
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source path.sh
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gpus=0
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stage=0
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stop_stage=100
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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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python3 ${MAIN_ROOT}/paddlespeech/t2s/exps/fastspeech2/gen_gt_duration_mel.py \
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--fastspeech2-config=fastspeech2_nosil_baker_ckpt_0.4/default.yaml \
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--fastspeech2-checkpoint=fastspeech2_nosil_baker_ckpt_0.4/snapshot_iter_76000.pdz \
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--fastspeech2-stat=fastspeech2_nosil_baker_ckpt_0.4/speech_stats.npy \
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--dur-file=durations.txt \
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--output-dir=dump_finetune \
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--phones-dict=fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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python3 local/link_wav.py \
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--old-dump-dir=dump \
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--dump-dir=dump_finetune
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# get features' stats(mean and std)
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echo "Get features' stats ..."
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python3 ${MAIN_ROOT}/utils/compute_statistics.py \
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--metadata=dump_finetune/train/raw/metadata.jsonl \
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--field-name="feats"
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# normalize, dev and test should use train's stats
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echo "Normalize ..."
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python3 ${BIN_DIR}/../normalize.py \
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--metadata=dump_finetune/train/raw/metadata.jsonl \
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--dumpdir=dump_finetune/train/norm \
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--stats=dump_finetune/train/feats_stats.npy
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python3 ${BIN_DIR}/../normalize.py \
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--metadata=dump_finetune/dev/raw/metadata.jsonl \
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--dumpdir=dump_finetune/dev/norm \
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--stats=dump_finetune/train/feats_stats.npy
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python3 ${BIN_DIR}/../normalize.py \
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--metadata=dump_finetune/test/raw/metadata.jsonl \
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--dumpdir=dump_finetune/test/norm \
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--stats=dump_finetune/train/feats_stats.npy
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fi
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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CUDA_VISIBLE_DEVICES=${gpus} \
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FLAGS_cudnn_exhaustive_search=true \
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FLAGS_conv_workspace_size_limit=4000 \
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python ${BIN_DIR}/train.py \
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--train-metadata=dump_finetune/train/norm/metadata.jsonl \
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--dev-metadata=dump_finetune/dev/norm/metadata.jsonl \
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--config=conf/finetune.yaml \
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--output-dir=exp/finetune \
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--ngpu=1
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fi
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@ -0,0 +1,85 @@
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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 os
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from operator import itemgetter
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from pathlib import Path
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import jsonlines
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import numpy as np
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def main():
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# parse config and args
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parser = argparse.ArgumentParser(
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description="Preprocess audio and then extract features .")
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parser.add_argument(
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"--old-dump-dir",
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default=None,
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type=str,
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help="directory to dump feature files.")
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parser.add_argument(
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"--dump-dir",
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type=str,
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required=True,
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help="directory to finetune dump feature files.")
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args = parser.parse_args()
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old_dump_dir = Path(args.old_dump_dir).expanduser()
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old_dump_dir = old_dump_dir.resolve()
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dump_dir = Path(args.dump_dir).expanduser()
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# use absolute path
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dump_dir = dump_dir.resolve()
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dump_dir.mkdir(parents=True, exist_ok=True)
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assert old_dump_dir.is_dir()
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assert dump_dir.is_dir()
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for sub in ["train", "dev", "test"]:
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# 把 old_dump_dir 里面的 *-wave.npy 软连接到 dump_dir 的对应位置
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output_dir = dump_dir / sub
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output_dir.mkdir(parents=True, exist_ok=True)
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results = []
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for name in os.listdir(output_dir / "raw"):
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# 003918_feats.npy
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utt_id = name.split("_")[0]
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mel_path = output_dir / ("raw/" + name)
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gen_mel = np.load(mel_path)
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wave_name = utt_id + "_wave.npy"
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wav = np.load(old_dump_dir / sub / ("raw/" + wave_name))
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os.symlink(old_dump_dir / sub / ("raw/" + wave_name),
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output_dir / ("raw/" + wave_name))
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num_sample = wav.shape[0]
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num_frames = gen_mel.shape[0]
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wav_path = output_dir / ("raw/" + wave_name)
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record = {
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"utt_id": utt_id,
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"num_samples": num_sample,
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"num_frames": num_frames,
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"feats": str(mel_path),
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"wave": str(wav_path),
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}
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results.append(record)
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results.sort(key=itemgetter("utt_id"))
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with jsonlines.open(output_dir / "raw/metadata.jsonl", 'w') as writer:
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for item in results:
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writer.write(item)
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if __name__ == "__main__":
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main()
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@ -0,0 +1,167 @@
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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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# generate mels using durations.txt
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# for mb melgan finetune
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# 长度和原本的 mel 不一致怎么办？
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import argparse
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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 yaml
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from yacs.config import CfgNode
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from paddlespeech.t2s.datasets.preprocess_utils import get_phn_dur
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from paddlespeech.t2s.datasets.preprocess_utils import merge_silence
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
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from paddlespeech.t2s.models.fastspeech2 import StyleFastSpeech2Inference
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from paddlespeech.t2s.modules.normalizer import ZScore
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def evaluate(args, fastspeech2_config):
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# construct dataset for evaluation
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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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phone_dict = {}
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for phn, id in phn_id:
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phone_dict[phn] = int(id)
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odim = fastspeech2_config.n_mels
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model = FastSpeech2(
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idim=vocab_size, odim=odim, **fastspeech2_config["model"])
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model.set_state_dict(
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paddle.load(args.fastspeech2_checkpoint)["main_params"])
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model.eval()
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stat = np.load(args.fastspeech2_stat)
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mu, std = stat
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mu = paddle.to_tensor(mu)
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std = paddle.to_tensor(std)
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fastspeech2_normalizer = ZScore(mu, std)
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fastspeech2_inference = StyleFastSpeech2Inference(fastspeech2_normalizer,
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model)
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fastspeech2_inference.eval()
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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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sentences, speaker_set = get_phn_dur(args.dur_file)
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merge_silence(sentences)
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for i, utt_id in enumerate(sentences):
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phones = sentences[utt_id][0]
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durations = sentences[utt_id][1]
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speaker = sentences[utt_id][2]
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# 裁剪掉开头和结尾的 sil
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if args.cut_sil:
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if phones[0] == "sil" and len(durations) > 1:
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durations = durations[1:]
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phones = phones[1:]
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if phones[-1] == 'sil' and len(durations) > 1:
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durations = durations[:-1]
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phones = phones[:-1]
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# sentences[utt_id][0] = phones
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# sentences[utt_id][1] = durations
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phone_ids = [phone_dict[phn] for phn in phones]
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phone_ids = paddle.to_tensor(np.array(phone_ids))
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durations = paddle.to_tensor(np.array(durations))
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# 生成的和真实的可能有 1, 2 帧的差距，但是 batch_fn 会修复
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# split data into 3 sections
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if args.dataset == "baker":
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num_train = 9800
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num_dev = 100
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if i in range(0, num_train):
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sub_output_dir = output_dir / ("train/raw")
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elif i in range(num_train, num_train + num_dev):
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sub_output_dir = output_dir / ("dev/raw")
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else:
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sub_output_dir = output_dir / ("test/raw")
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sub_output_dir.mkdir(parents=True, exist_ok=True)
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with paddle.no_grad():
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mel = fastspeech2_inference(phone_ids, durations=durations)
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np.save(sub_output_dir / (utt_id + "_feats.npy"), mel)
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def main():
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# parse args and config and redirect to train_sp
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parser = argparse.ArgumentParser(
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description="Synthesize with fastspeech2 & parallel wavegan.")
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parser.add_argument(
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"--dataset",
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default="baker",
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type=str,
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help="name of dataset, should in {baker, ljspeech, vctk} now")
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parser.add_argument(
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"--fastspeech2-config", type=str, help="fastspeech2 config file.")
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parser.add_argument(
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"--fastspeech2-checkpoint",
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type=str,
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help="fastspeech2 checkpoint to load.")
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parser.add_argument(
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"--fastspeech2-stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training fastspeech2."
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)
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parser.add_argument(
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"--phones-dict",
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type=str,
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default="phone_id_map.txt",
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help="phone vocabulary file.")
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parser.add_argument(
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"--dur-file", default=None, type=str, help="path to durations.txt.")
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parser.add_argument("--output-dir", type=str, help="output dir.")
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parser.add_argument(
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"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
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parser.add_argument("--verbose", type=int, default=1, help="verbose.")
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def str2bool(str):
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return True if str.lower() == 'true' else False
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parser.add_argument(
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"--cut-sil",
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type=str2bool,
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default=True,
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help="whether cut sil in the edge of audio")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.ngpu == 0:
|
||||
paddle.set_device("cpu")
|
||||
elif args.ngpu > 0:
|
||||
paddle.set_device("gpu")
|
||||
else:
|
||||
print("ngpu should >= 0 !")
|
||||
|
||||
with open(args.fastspeech2_config) as f:
|
||||
fastspeech2_config = CfgNode(yaml.safe_load(f))
|
||||
|
||||
print("========Args========")
|
||||
print(yaml.safe_dump(vars(args)))
|
||||
print("========Config========")
|
||||
print(fastspeech2_config)
|
||||
|
||||
evaluate(args, fastspeech2_config)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in new issue