Merge pull request #968 from yt605155624/merge_paddlespeech
[TTS] change nprocs to ngpupull/971/head
Hui Zhang
3 years ago
committed by
GitHub
commit
56d06f2aaf
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# This is the hyperparameter configuration file for Parallel WaveGAN.
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# Please make sure this is adjusted for the VCTK corpus. 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 12 GB GPU memory and takes ~3 days on RTX TITAN.
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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: 1 # Number of input channels.
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out_channels: 1 # Number of output channels.
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kernel_size: 3 # Kernel size of dilated convolution.
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layers: 30 # Number of residual block layers.
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stacks: 3 # Number of stacks i.e., dilation cycles.
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residual_channels: 64 # Number of channels in residual conv.
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gate_channels: 128 # Number of channels in gated conv.
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skip_channels: 64 # Number of channels in skip conv.
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aux_channels: 80 # Number of channels for auxiliary feature conv.
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# Must be the same as num_mels.
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aux_context_window: 2 # Context window size for auxiliary feature.
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# If set to 2, previous 2 and future 2 frames will be considered.
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dropout: 0.0 # Dropout rate. 0.0 means no dropout applied.
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use_weight_norm: true # Whether to use weight norm.
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# If set to true, it will be applied to all of the conv layers.
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upsample_scales: [4, 5, 3, 5] # Upsampling scales. Prodcut of these must be the same as hop size.
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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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kernel_size: 3 # Number of output channels.
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layers: 10 # Number of conv layers.
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conv_channels: 64 # Number of chnn layers.
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bias: true # Whether to use bias parameter in conv.
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use_weight_norm: true # Whether to use weight norm.
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# If set to true, it will be applied to all of the conv layers.
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nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv.
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nonlinear_activation_params: # Nonlinear function parameters
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negative_slope: 0.2 # Alpha in LeakyReLU.
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###########################################################
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# STFT LOSS SETTING #
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###########################################################
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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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###########################################################
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# ADVERSARIAL LOSS SETTING #
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###########################################################
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lambda_adv: 4.0 # Loss balancing coefficient.
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###########################################################
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# DATA LOADER SETTING #
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###########################################################
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batch_size: 8 # Batch size.
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batch_max_steps: 24000 # Length of each audio in batch. Make sure dividable by hop_size.
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pin_memory: true # Whether to pin memory in Pytorch DataLoader.
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num_workers: 4 # Number of workers in Pytorch DataLoader.
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remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps.
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allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory.
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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-6 # Generator's epsilon.
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weight_decay: 0.0 # Generator's weight decay coefficient.
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generator_scheduler_params:
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learning_rate: 0.0001 # Generator's learning rate.
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step_size: 200000 # Generator's scheduler step size.
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gamma: 0.5 # Generator's scheduler gamma.
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# At each step size, lr will be multiplied by this parameter.
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generator_grad_norm: 10 # Generator's gradient norm.
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discriminator_optimizer_params:
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epsilon: 1.0e-6 # Discriminator's epsilon.
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weight_decay: 0.0 # Discriminator's weight decay coefficient.
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discriminator_scheduler_params:
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learning_rate: 0.00005 # Discriminator's learning rate.
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step_size: 200000 # Discriminator's scheduler step size.
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gamma: 0.5 # Discriminator's scheduler gamma.
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# At each step size, lr will be multiplied by this parameter.
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discriminator_grad_norm: 1 # Discriminator's gradient norm.
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###########################################################
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# INTERVAL SETTING #
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###########################################################
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discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator.
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train_max_steps: 1000000 # Number of training steps.
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save_interval_steps: 5000 # 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_save_intermediate_results: 4 # Number of results to be saved as intermediate results.
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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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stage=0
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stop_stage=100
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config_path=$1
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# get durations from MFA's result
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echo "Generate durations.txt from MFA results ..."
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python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
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--inputdir=./aishell3_alignment_tone \
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--output=durations.txt \
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--config=${config_path}
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# extract features
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echo "Extract features ..."
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python3 ${BIN_DIR}/../preprocess.py \
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--rootdir=~/datasets/data_aishell3/ \
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--dataset=aishell3 \
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--dumpdir=dump \
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--dur-file=durations.txt \
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--config=${config_path} \
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--cut-sil=True \
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--num-cpu=20
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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/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/train/raw/metadata.jsonl \
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--dumpdir=dump/train/norm \
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--stats=dump/train/feats_stats.npy
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python3 ${BIN_DIR}/../normalize.py \
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--metadata=dump/dev/raw/metadata.jsonl \
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--dumpdir=dump/dev/norm \
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--stats=dump/train/feats_stats.npy
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python3 ${BIN_DIR}/../normalize.py \
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--metadata=dump/test/raw/metadata.jsonl \
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--dumpdir=dump/test/norm \
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--stats=dump/train/feats_stats.npy
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fi
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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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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/synthesize.py \
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--config=${config_path} \
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--checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
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--test-metadata=dump/test/norm/metadata.jsonl \
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--output-dir=${train_output_path}/test
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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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FLAGS_cudnn_exhaustive_search=true \
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FLAGS_conv_workspace_size_limit=4000 \
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python ${BIN_DIR}/train.py \
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--train-metadata=dump/train/norm/metadata.jsonl \
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--dev-metadata=dump/dev/norm/metadata.jsonl \
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--config=${config_path} \
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--output-dir=${train_output_path} \
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--ngpu=1
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#!/bin/bash
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export MAIN_ROOT=`realpath ${PWD}/../../../`
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export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
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export LC_ALL=C
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export PYTHONDONTWRITEBYTECODE=1
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# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
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export PYTHONIOENCODING=UTF-8
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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=parallelwave_gan
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}
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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
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stage=0
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stop_stage=100
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conf_path=conf/default.yaml
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train_output_path=exp/default
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ckpt_name=snapshot_iter_5000.pdz
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# with the following command, you can choice 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 ] && [ ${stop_stage} -ge 2 ]; then
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# synthesize
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
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fi
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# G2P
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For g2p, we use BZNSYP's phone label as the ground truth and we delete silence tokens in labels and predicted phones.
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You should Download BZNSYP from it's [Official Website](https://test.data-baker.com/data/index/source) and extract it. Assume the path to the dataset is `~/datasets/BZNSYP`.
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We use `WER` as evaluation criterion.
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# Start
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Run the command below to get the results of test.
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```bash
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./run.sh
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```
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The `avg WER` of g2p is: 0.027495061517943988
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```text
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,--------------------------------------------------------------------.
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| | # Snt # Wrd | Corr Sub Del Ins Err S.Err |
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|--------+-----------------+-----------------------------------------|
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| Sum/Avg| 9996 299181 | 97.3 2.7 0.0 0.0 2.7 52.5 |
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`--------------------------------------------------------------------'
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```
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#!/bin/bash
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source path.sh
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USE_SCLITE=true
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# test g2p
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echo "Start get g2p test data ..."
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python3 get_g2p_data.py --root-dir=~/datasets/BZNSYP --output-dir=data/g2p
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echo "Start test g2p ..."
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python3 test_g2p.py --input-dir=data/g2p --output-dir=exp/g2p
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# whether use sclite to get more detail information of WER
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if [ "$USE_SCLITE" = true ];then
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echo "Start sclite g2p ..."
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${MAIN_ROOT}/tools/sctk/bin/sclite -i wsj -r ./exp/g2p/text.ref.clean trn -h ./exp/g2p/text.g2p trn -e utf-8 -o all
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fi
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@ -1,38 +0,0 @@
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# Chinese Text Frontend Example
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Here's an example for Chinese text frontend, including g2p and text normalization.
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## G2P
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For g2p, we use BZNSYP's phone label as the ground truth and we delete silence tokens in labels and predicted phones.
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You should Download BZNSYP from it's [Official Website](https://test.data-baker.com/data/index/source) and extract it. Assume the path to the dataset is `~/datasets/BZNSYP`.
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We use `WER` as evaluation criterion.
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## Text Normalization
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For text normalization, the test data is `data/textnorm_test_cases.txt`, we use `|` as the separator of raw_data and normed_data.
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We use `CER` as evaluation criterion.
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## Start
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If you want to use sclite to get more detail information of WER, you should run the command below to make sclite first.
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```bash
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./make_sclite.sh
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```
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Run the command below to get the results of test.
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```bash
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./run.sh
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```
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The `avg WER` of g2p is: 0.027495061517943988
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```text
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,--------------------------------------------------------------------.
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| | # Snt # Wrd | Corr Sub Del Ins Err S.Err |
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|--------+-----------------+-----------------------------------------|
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| Sum/Avg| 9996 299181 | 97.3 2.7 0.0 0.0 2.7 52.5 |
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`--------------------------------------------------------------------'
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```
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The `avg CER` of text normalization is: 0.006388318503308237
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```text
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,-----------------------------------------------------------------.
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| | # Snt # Wrd | Corr Sub Del Ins Err S.Err |
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|--------+--------------+-----------------------------------------|
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| Sum/Avg| 125 2254 | 99.4 0.1 0.5 0.1 0.7 3.2 |
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`-----------------------------------------------------------------'
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```
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@ -1,13 +0,0 @@
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#!/bin/bash
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if [ ! -d "./SCTK" ];then
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echo "Clone SCTK ..."
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git clone https://github.com/usnistgov/SCTK
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echo "Clone SCTK done!"
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fi
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if [ ! -d "./SCTK/bin" ];then
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echo "Start make SCTK ..."
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pushd SCTK && make config && make all && make check && make install && make doc && popd
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echo "SCTK make done!"
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fi
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@ -1,25 +0,0 @@
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#!/bin/bash
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USE_SCLITE=true
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# test g2p
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echo "Start get g2p test data ..."
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python3 get_g2p_data.py --root-dir=~/datasets/BZNSYP --output-dir=data/g2p
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echo "Start test g2p ..."
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python3 test_g2p.py --input-dir=data/g2p --output-dir=exp/g2p
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# test text normalization
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echo "Start get text normalization test data ..."
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python3 get_textnorm_data.py --test-file=data/textnorm_test_cases.txt --output-dir=data/textnorm
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echo "Start test text normalization ..."
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python3 test_textnorm.py --input-dir=data/textnorm --output-dir=exp/textnorm
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# whether use sclite to get more detail information of WER
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if [ "$USE_SCLITE" = true ];then
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echo "Start sclite g2p ..."
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./SCTK/bin/sclite -i wsj -r ./exp/g2p/text.ref.clean trn -h ./exp/g2p/text.g2p trn -e utf-8 -o all
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echo
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echo "Start sclite textnorm ..."
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./SCTK/bin/sclite -i wsj -r ./exp/textnorm/text.ref.clean trn -h ./exp/textnorm/text.tn trn -e utf-8 -o all
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fi
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# Text Normalization
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For text normalization, the test data is `data/textnorm_test_cases.txt`, we use `|` as the separator of raw_data and normed_data.
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We use `CER` as evaluation criterion.
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## Start
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Run the command below to get the results of test.
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```bash
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./run.sh
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```
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The `avg CER` of text normalization is: 0.006388318503308237
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```text
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,-----------------------------------------------------------------.
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| | # Snt # Wrd | Corr Sub Del Ins Err S.Err |
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|--------+--------------+-----------------------------------------|
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| Sum/Avg| 125 2254 | 99.4 0.1 0.5 0.1 0.7 3.2 |
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`-----------------------------------------------------------------'
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```
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#!/bin/bash
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source path.sh
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USE_SCLITE=true
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# test text normalization
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echo "Start get text normalization test data ..."
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python3 get_textnorm_data.py --test-file=data/textnorm_test_cases.txt --output-dir=data/textnorm
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echo "Start test text normalization ..."
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python3 test_textnorm.py --input-dir=data/textnorm --output-dir=exp/textnorm
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# whether use sclite to get more detail information of WER
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if [ "$USE_SCLITE" = true ];then
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echo "Start sclite textnorm ..."
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${MAIN_ROOT}/tools/sctk/bin/sclite -i wsj -r ./exp/textnorm/text.ref.clean trn -h ./exp/textnorm/text.tn trn -e utf-8 -o all
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fi
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