[TTS] add opencpop HIFIGAN example (#3038)

* add opencpop voc, test=tts

* soft link

* add opencpop hifigan, test=tts

* update

examples/opencpop/README.md

+
+# Opencpop
+
+* svs1 - DiffSinger
+* voc1 - Parallel WaveGAN
+* voc5 - HiFiGAN

examples/opencpop/voc5/conf/default.yaml

+# This is the configuration file for CSMSC dataset.
+# This configuration is based on HiFiGAN V1, which is an official configuration. 
+# But I found that the optimizer setting does not work well with my implementation.
+# So I changed optimizer settings as follows:
+# - AdamW -> Adam
+# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
+# - Scheduler: ExponentialLR -> MultiStepLR
+# To match the shift size difference, the upsample scales is also modified from the original 256 shift setting.
+
+###########################################################
+#                FEATURE EXTRACTION SETTING               #
+###########################################################
+fs: 24000                # Sampling rate.
+n_fft: 512              # FFT size (samples).
+n_shift: 128             # Hop size (samples). 12.5ms
+win_length: 512         # Window length (samples). 50ms
+                         # If set to null, it will be the same as fft_size.
+window: "hann"           # Window function.
+n_mels: 80               # Number of mel basis.
+fmin: 80                 # Minimum freq in mel basis calculation. (Hz)
+fmax: 12000               # Maximum frequency in mel basis calculation. (Hz)
+
+###########################################################
+#         GENERATOR NETWORK ARCHITECTURE SETTING          #
+###########################################################
+generator_params:
+    in_channels: 80                       # Number of input channels.
+    out_channels: 1                       # Number of output channels.
+    channels: 512                         # Number of initial channels.
+    kernel_size: 7                        # Kernel size of initial and final conv layers.
+    upsample_scales: [8, 4, 2, 2]         # Upsampling scales.
+    upsample_kernel_sizes: [16, 8, 4, 4] # Kernel size for upsampling layers.
+    resblock_kernel_sizes: [3, 7, 11]     # Kernel size for residual blocks.
+    resblock_dilations:                   # Dilations for residual blocks.
+        - [1, 3, 5]
+        - [1, 3, 5]
+        - [1, 3, 5]
+    use_additional_convs: True            # Whether to use additional conv layer in residual blocks.
+    bias: True                            # Whether to use bias parameter in conv.
+    nonlinear_activation: "leakyrelu"     # Nonlinear activation type.
+    nonlinear_activation_params:          # Nonlinear activation paramters.
+        negative_slope: 0.1
+    use_weight_norm: True                 # Whether to apply weight normalization.
+
+
+###########################################################
+#       DISCRIMINATOR NETWORK ARCHITECTURE SETTING        #
+###########################################################
+discriminator_params:
+    scales: 3                              # Number of multi-scale discriminator.
+    scale_downsample_pooling: "AvgPool1D"  # Pooling operation for scale discriminator.
+    scale_downsample_pooling_params:
+        kernel_size: 4                     # Pooling kernel size.
+        stride: 2                          # Pooling stride.
+        padding: 2                         # Padding size.
+    scale_discriminator_params:
+        in_channels: 1                     # Number of input channels.
+        out_channels: 1                    # Number of output channels.
+        kernel_sizes: [15, 41, 5, 3]       # List of kernel sizes.
+        channels: 128                      # Initial number of channels.
+        max_downsample_channels: 1024      # Maximum number of channels in downsampling conv layers.
+        max_groups: 16                     # Maximum number of groups in downsampling conv layers.
+        bias: True
+        downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
+        nonlinear_activation: "leakyrelu"  # Nonlinear activation.
+        nonlinear_activation_params:
+            negative_slope: 0.1
+    follow_official_norm: True             # Whether to follow the official norm setting.
+    periods: [2, 3, 5, 7, 11]              # List of period for multi-period discriminator.
+    period_discriminator_params:
+        in_channels: 1                     # Number of input channels.
+        out_channels: 1                    # Number of output channels.
+        kernel_sizes: [5, 3]               # List of kernel sizes.
+        channels: 32                       # Initial number of channels.
+        downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
+        max_downsample_channels: 1024      # Maximum number of channels in downsampling conv layers.
+        bias: True                         # Whether to use bias parameter in conv layer."
+        nonlinear_activation: "leakyrelu"  # Nonlinear activation.
+        nonlinear_activation_params:       # Nonlinear activation paramters.
+            negative_slope: 0.1
+        use_weight_norm: True              # Whether to apply weight normalization.
+        use_spectral_norm: False           # Whether to apply spectral normalization.
+    
+
+###########################################################
+#                   STFT LOSS SETTING                     #
+###########################################################
+use_stft_loss: False                 # Whether to use multi-resolution STFT loss.
+use_mel_loss: True                   # Whether to use Mel-spectrogram loss.
+mel_loss_params:
+    fs: 24000
+    fft_size: 512
+    hop_size: 128
+    win_length: 512
+    window: "hann"
+    num_mels: 80
+    fmin: 30
+    fmax: 12000
+    log_base: null
+generator_adv_loss_params:
+    average_by_discriminators: False # Whether to average loss by #discriminators.
+discriminator_adv_loss_params:
+    average_by_discriminators: False # Whether to average loss by #discriminators.
+use_feat_match_loss: True
+feat_match_loss_params:
+    average_by_discriminators: False # Whether to average loss by #discriminators.
+    average_by_layers: False         # Whether to average loss by #layers in each discriminator.
+    include_final_outputs: False     # Whether to include final outputs in feat match loss calculation.
+
+###########################################################
+#               ADVERSARIAL LOSS SETTING                  #
+###########################################################
+lambda_aux: 45.0       # Loss balancing coefficient for STFT loss.
+lambda_adv: 1.0        # Loss balancing coefficient for adversarial loss.
+lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
+
+###########################################################
+#                  DATA LOADER SETTING                    #
+###########################################################
+batch_size: 16              # Batch size.
+batch_max_steps: 8400       # Length of each audio in batch. Make sure dividable by hop_size.
+num_workers: 1              # Number of workers in DataLoader.
+
+###########################################################
+#             OPTIMIZER & SCHEDULER SETTING               #
+###########################################################
+generator_optimizer_params:
+    beta1: 0.5
+    beta2: 0.9
+    weight_decay: 0.0                   # Generator's weight decay coefficient.
+generator_scheduler_params:
+    learning_rate: 2.0e-4               # Generator's learning rate.
+    gamma: 0.5                          # Generator's scheduler gamma.
+    milestones:                         # At each milestone, lr will be multiplied by gamma.
+        - 200000
+        - 400000
+        - 600000
+        - 800000
+generator_grad_norm: -1                 # Generator's gradient norm.
+discriminator_optimizer_params:
+    beta1: 0.5
+    beta2: 0.9
+    weight_decay: 0.0                   # Discriminator's weight decay coefficient.
+discriminator_scheduler_params:
+    learning_rate: 2.0e-4               # Discriminator's learning rate.
+    gamma: 0.5                          # Discriminator's scheduler gamma.
+    milestones:                         # At each milestone, lr will be multiplied by gamma.
+        - 200000
+        - 400000
+        - 600000
+        - 800000    
+discriminator_grad_norm: -1             # Discriminator's gradient norm.            
+
+###########################################################
+#                    INTERVAL SETTING                     #
+###########################################################
+generator_train_start_steps: 1     # Number of steps to start to train discriminator.
+discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
+train_max_steps: 2500000           # Number of training steps.
+save_interval_steps: 5000         # Interval steps to save checkpoint.
+eval_interval_steps: 1000          # Interval steps to evaluate the network.
+
+###########################################################
+#                     OTHER SETTING                       #
+###########################################################
+num_snapshots: 4                 # max number of snapshots to keep while training
+seed: 42                          # random seed for paddle, random, and np.random

examples/opencpop/voc5/conf/finetune.yaml

+# This is the configuration file for CSMSC dataset.
+# This configuration is based on HiFiGAN V1, which is an official configuration. 
+# But I found that the optimizer setting does not work well with my implementation.
+# So I changed optimizer settings as follows:
+# - AdamW -> Adam
+# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
+# - Scheduler: ExponentialLR -> MultiStepLR
+# To match the shift size difference, the upsample scales is also modified from the original 256 shift setting.
+
+###########################################################
+#                FEATURE EXTRACTION SETTING               #
+###########################################################
+fs: 24000                # Sampling rate.
+n_fft: 512              # FFT size (samples).
+n_shift: 128             # Hop size (samples). 12.5ms
+win_length: 512         # Window length (samples). 50ms
+                         # If set to null, it will be the same as fft_size.
+window: "hann"           # Window function.
+n_mels: 80               # Number of mel basis.
+fmin: 80                 # Minimum freq in mel basis calculation. (Hz)
+fmax: 12000               # Maximum frequency in mel basis calculation. (Hz)
+
+###########################################################
+#         GENERATOR NETWORK ARCHITECTURE SETTING          #
+###########################################################
+generator_params:
+    in_channels: 80                       # Number of input channels.
+    out_channels: 1                       # Number of output channels.
+    channels: 512                         # Number of initial channels.
+    kernel_size: 7                        # Kernel size of initial and final conv layers.
+    upsample_scales: [8, 4, 2, 2]         # Upsampling scales.
+    upsample_kernel_sizes: [16, 8, 4, 4] # Kernel size for upsampling layers.
+    resblock_kernel_sizes: [3, 7, 11]     # Kernel size for residual blocks.
+    resblock_dilations:                   # Dilations for residual blocks.
+        - [1, 3, 5]
+        - [1, 3, 5]
+        - [1, 3, 5]
+    use_additional_convs: True            # Whether to use additional conv layer in residual blocks.
+    bias: True                            # Whether to use bias parameter in conv.
+    nonlinear_activation: "leakyrelu"     # Nonlinear activation type.
+    nonlinear_activation_params:          # Nonlinear activation paramters.
+        negative_slope: 0.1
+    use_weight_norm: True                 # Whether to apply weight normalization.
+
+
+###########################################################
+#       DISCRIMINATOR NETWORK ARCHITECTURE SETTING        #
+###########################################################
+discriminator_params:
+    scales: 3                              # Number of multi-scale discriminator.
+    scale_downsample_pooling: "AvgPool1D"  # Pooling operation for scale discriminator.
+    scale_downsample_pooling_params:
+        kernel_size: 4                     # Pooling kernel size.
+        stride: 2                          # Pooling stride.
+        padding: 2                         # Padding size.
+    scale_discriminator_params:
+        in_channels: 1                     # Number of input channels.
+        out_channels: 1                    # Number of output channels.
+        kernel_sizes: [15, 41, 5, 3]       # List of kernel sizes.
+        channels: 128                      # Initial number of channels.
+        max_downsample_channels: 1024      # Maximum number of channels in downsampling conv layers.
+        max_groups: 16                     # Maximum number of groups in downsampling conv layers.
+        bias: True
+        downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
+        nonlinear_activation: "leakyrelu"  # Nonlinear activation.
+        nonlinear_activation_params:
+            negative_slope: 0.1
+    follow_official_norm: True             # Whether to follow the official norm setting.
+    periods: [2, 3, 5, 7, 11]              # List of period for multi-period discriminator.
+    period_discriminator_params:
+        in_channels: 1                     # Number of input channels.
+        out_channels: 1                    # Number of output channels.
+        kernel_sizes: [5, 3]               # List of kernel sizes.
+        channels: 32                       # Initial number of channels.
+        downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
+        max_downsample_channels: 1024      # Maximum number of channels in downsampling conv layers.
+        bias: True                         # Whether to use bias parameter in conv layer."
+        nonlinear_activation: "leakyrelu"  # Nonlinear activation.
+        nonlinear_activation_params:       # Nonlinear activation paramters.
+            negative_slope: 0.1
+        use_weight_norm: True              # Whether to apply weight normalization.
+        use_spectral_norm: False           # Whether to apply spectral normalization.
+    
+
+###########################################################
+#                   STFT LOSS SETTING                     #
+###########################################################
+use_stft_loss: False                 # Whether to use multi-resolution STFT loss.
+use_mel_loss: True                   # Whether to use Mel-spectrogram loss.
+mel_loss_params:
+    fs: 24000
+    fft_size: 512
+    hop_size: 128
+    win_length: 512
+    window: "hann"
+    num_mels: 80
+    fmin: 30
+    fmax: 12000
+    log_base: null
+generator_adv_loss_params:
+    average_by_discriminators: False # Whether to average loss by #discriminators.
+discriminator_adv_loss_params:
+    average_by_discriminators: False # Whether to average loss by #discriminators.
+use_feat_match_loss: True
+feat_match_loss_params:
+    average_by_discriminators: False # Whether to average loss by #discriminators.
+    average_by_layers: False         # Whether to average loss by #layers in each discriminator.
+    include_final_outputs: False     # Whether to include final outputs in feat match loss calculation.
+
+###########################################################
+#               ADVERSARIAL LOSS SETTING                  #
+###########################################################
+lambda_aux: 45.0       # Loss balancing coefficient for STFT loss.
+lambda_adv: 1.0        # Loss balancing coefficient for adversarial loss.
+lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
+
+###########################################################
+#                  DATA LOADER SETTING                    #
+###########################################################
+#batch_size: 16              # Batch size.
+batch_size: 1              # Batch size.
+batch_max_steps: 8400       # Length of each audio in batch. Make sure dividable by hop_size.
+num_workers: 1              # Number of workers in DataLoader.
+
+###########################################################
+#             OPTIMIZER & SCHEDULER SETTING               #
+###########################################################
+generator_optimizer_params:
+    beta1: 0.5
+    beta2: 0.9
+    weight_decay: 0.0                   # Generator's weight decay coefficient.
+generator_scheduler_params:
+    learning_rate: 2.0e-4               # Generator's learning rate.
+    gamma: 0.5                          # Generator's scheduler gamma.
+    milestones:                         # At each milestone, lr will be multiplied by gamma.
+        - 200000
+        - 400000
+        - 600000
+        - 800000
+generator_grad_norm: -1                 # Generator's gradient norm.
+discriminator_optimizer_params:
+    beta1: 0.5
+    beta2: 0.9
+    weight_decay: 0.0                   # Discriminator's weight decay coefficient.
+discriminator_scheduler_params:
+    learning_rate: 2.0e-4               # Discriminator's learning rate.
+    gamma: 0.5                          # Discriminator's scheduler gamma.
+    milestones:                         # At each milestone, lr will be multiplied by gamma.
+        - 200000
+        - 400000
+        - 600000
+        - 800000    
+discriminator_grad_norm: -1             # Discriminator's gradient norm.            
+
+###########################################################
+#                    INTERVAL SETTING                     #
+###########################################################
+generator_train_start_steps: 1     # Number of steps to start to train discriminator.
+discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
+train_max_steps: 2600000           # Number of training steps.
+save_interval_steps: 5000         # Interval steps to save checkpoint.
+eval_interval_steps: 1000          # Interval steps to evaluate the network.
+
+###########################################################
+#                     OTHER SETTING                       #
+###########################################################
+num_snapshots: 4                 # max number of snapshots to keep while training
+seed: 42                          # random seed for paddle, random, and np.random

examples/opencpop/voc5/finetune.sh

+#!/bin/bash
+
+source path.sh
+
+gpus=0
+stage=0
+stop_stage=100
+
+source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    python3 ${MAIN_ROOT}/paddlespeech/t2s/exps/diffsinger/gen_gta_mel.py \
+        --diffsinger-config=diffsinger_opencpop_ckpt_1.4.0/default.yaml \
+        --diffsinger-checkpoint=diffsinger_opencpop_ckpt_1.4.0/snapshot_iter_160000.pdz \
+        --diffsinger-stat=diffsinger_opencpop_ckpt_1.4.0/speech_stats.npy \
+        --diffsinger-stretch=diffsinger_opencpop_ckpt_1.4.0/speech_stretchs.npy \
+        --dur-file=~/datasets/Opencpop/segments/transcriptions.txt \
+        --output-dir=dump_finetune \
+        --phones-dict=diffsinger_opencpop_ckpt_1.4.0/phone_id_map.txt \
+        --dataset=opencpop \
+        --rootdir=~/datasets/Opencpop/segments/
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    python3 ${MAIN_ROOT}/utils/link_wav.py \
+        --old-dump-dir=dump \
+        --dump-dir=dump_finetune
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # get features' stats(mean and std)
+    echo "Get features' stats ..."
+    cp dump/train/feats_stats.npy dump_finetune/train/
+fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    # normalize, dev and test should use train's stats
+    echo "Normalize ..."
+   
+    python3 ${BIN_DIR}/../normalize.py \
+        --metadata=dump_finetune/train/raw/metadata.jsonl \
+        --dumpdir=dump_finetune/train/norm \
+        --stats=dump_finetune/train/feats_stats.npy
+    python3 ${BIN_DIR}/../normalize.py \
+        --metadata=dump_finetune/dev/raw/metadata.jsonl \
+        --dumpdir=dump_finetune/dev/norm \
+        --stats=dump_finetune/train/feats_stats.npy
+    
+    python3 ${BIN_DIR}/../normalize.py \
+        --metadata=dump_finetune/test/raw/metadata.jsonl \
+        --dumpdir=dump_finetune/test/norm \
+        --stats=dump_finetune/train/feats_stats.npy
+fi
+
+# create finetune env
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    echo "create finetune env"
+    python3 local/prepare_env.py \
+        --pretrained_model_dir=exp/default/checkpoints/ \
+        --output_dir=exp/finetune/
+fi 
+
+# finetune
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+    CUDA_VISIBLE_DEVICES=${gpus} \
+    FLAGS_cudnn_exhaustive_search=true \
+    FLAGS_conv_workspace_size_limit=4000 \
+    python ${BIN_DIR}/train.py \
+        --train-metadata=dump_finetune/train/norm/metadata.jsonl \
+        --dev-metadata=dump_finetune/dev/norm/metadata.jsonl \
+        --config=conf/finetune.yaml \
+        --output-dir=exp/finetune \
+        --ngpu=1
+fi 

examples/opencpop/voc5/local/PTQ_static.sh

+../../../csmsc/voc1/local/PTQ_static.sh
\ No newline at end of file

examples/opencpop/voc5/local/dygraph_to_static.sh

+#!/bin/bash
+
+config_path=$1
+train_output_path=$2
+ckpt_name=$3
+
+FLAGS_allocator_strategy=naive_best_fit \
+FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+python3 ${BIN_DIR}/../../dygraph_to_static.py \
+    --type=voc \
+    --voc=hifigan_opencpop \
+    --voc_config=${config_path} \
+    --voc_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+    --voc_stat=dump/train/feats_stats.npy \
+    --inference_dir=exp/default/inference/

examples/opencpop/voc5/local/prepare_env.py

+../../../other/tts_finetune/tts3/local/prepare_env.py
\ No newline at end of file

examples/opencpop/voc5/local/preprocess.sh

+../../voc1/local/preprocess.sh
\ No newline at end of file

examples/opencpop/voc5/local/synthesize.sh

+../../../csmsc/voc5/local/synthesize.sh
\ No newline at end of file

examples/opencpop/voc5/local/train.sh

+../../../csmsc/voc1/local/train.sh
\ No newline at end of file

examples/opencpop/voc5/path.sh

+../../csmsc/voc5/path.sh
\ No newline at end of file

examples/opencpop/voc5/run.sh

+#!/bin/bash
+
+set -e
+source path.sh
+
+gpus=0
+stage=0
+stop_stage=100
+
+conf_path=conf/default.yaml
+train_output_path=exp/default
+ckpt_name=snapshot_iter_2500000.pdz
+
+# with the following command, you can choose the stage range you want to run
+# such as `./run.sh --stage 0 --stop-stage 0`
+# this can not be mixed use with `$1`, `$2` ...
+source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # prepare data
+    ./local/preprocess.sh ${conf_path} || exit -1
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # train model, all `ckpt` under `train_output_path/checkpoints/` dir
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # synthesize
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
+fi
+
+# dygraph to static
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/dygraph_to_static.sh  ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
+fi
+
+# PTQ_static
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/PTQ_static.sh  ${train_output_path} hifigan_opencpop || exit -1
+fi

paddlespeech/t2s/exps/PTQ_static.py

@@ -43,6 +43,7 @@ def parse_args():
             'hifigan_ljspeech',
             'hifigan_vctk',
             'pwgan_opencpop',
+            'hifigan_opencpop',
         ],
         help='Choose model type of tts task.')
 

paddlespeech/t2s/exps/diffsinger/gen_gta_mel.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# generate mels using durations.txt
+# for mb melgan finetune
+import argparse
+import os
+from pathlib import Path
+
+import numpy as np
+import paddle
+import yaml
+from tqdm import tqdm
+from yacs.config import CfgNode
+
+from paddlespeech.t2s.datasets.preprocess_utils import get_sentences_svs
+from paddlespeech.t2s.models.diffsinger import DiffSinger
+from paddlespeech.t2s.models.diffsinger import DiffSingerInference
+from paddlespeech.t2s.modules.normalizer import ZScore
+from paddlespeech.t2s.utils import str2bool
+
+
+def evaluate(args, diffsinger_config):
+    rootdir = Path(args.rootdir).expanduser()
+    assert rootdir.is_dir()
+
+    # construct dataset for evaluation
+    with open(args.phones_dict, "r") as f:
+        phn_id = [line.strip().split() for line in f.readlines()]
+    vocab_size = len(phn_id)
+    print("vocab_size:", vocab_size)
+
+    phone_dict = {}
+    for phn, id in phn_id:
+        phone_dict[phn] = int(id)
+
+    if args.speaker_dict:
+        with open(args.speaker_dict, 'rt') as f:
+            spk_id_list = [line.strip().split() for line in f.readlines()]
+            spk_num = len(spk_id_list)
+    else:
+        spk_num = None
+
+    with open(args.diffsinger_stretch, "r") as f:
+        spec_min = np.load(args.diffsinger_stretch)[0]
+        spec_max = np.load(args.diffsinger_stretch)[1]
+        spec_min = paddle.to_tensor(spec_min)
+        spec_max = paddle.to_tensor(spec_max)
+    print("min and max spec done!")
+
+    odim = diffsinger_config.n_mels
+    diffsinger_config["model"]["fastspeech2_params"]["spk_num"] = spk_num
+    model = DiffSinger(
+        spec_min=spec_min,
+        spec_max=spec_max,
+        idim=vocab_size,
+        odim=odim,
+        **diffsinger_config["model"], )
+
+    model.set_state_dict(paddle.load(args.diffsinger_checkpoint)["main_params"])
+    model.eval()
+
+    stat = np.load(args.diffsinger_stat)
+    mu, std = stat
+    mu = paddle.to_tensor(mu)
+    std = paddle.to_tensor(std)
+    diffsinger_normalizer = ZScore(mu, std)
+
+    diffsinger_inference = DiffSingerInference(diffsinger_normalizer, model)
+    diffsinger_inference.eval()
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    sentences, speaker_set = get_sentences_svs(
+        args.dur_file,
+        dataset=args.dataset,
+        sample_rate=diffsinger_config.fs,
+        n_shift=diffsinger_config.n_shift, )
+
+    if args.dataset == "opencpop":
+        wavdir = rootdir / "wavs"
+        # split data into 3 sections
+        train_file = rootdir / "train.txt"
+        train_wav_files = []
+        with open(train_file, "r") as f_train:
+            for line in f_train.readlines():
+                utt = line.split("|")[0]
+                wav_name = utt + ".wav"
+                wav_path = wavdir / wav_name
+                train_wav_files.append(wav_path)
+
+        test_file = rootdir / "test.txt"
+        dev_wav_files = []
+        test_wav_files = []
+        num_dev = 106
+        count = 0
+        with open(test_file, "r") as f_test:
+            for line in f_test.readlines():
+                count += 1
+                utt = line.split("|")[0]
+                wav_name = utt + ".wav"
+                wav_path = wavdir / wav_name
+                if count > num_dev:
+                    test_wav_files.append(wav_path)
+                else:
+                    dev_wav_files.append(wav_path)
+    else:
+        print("dataset should in {opencpop} now!")
+
+    train_wav_files = [
+        os.path.basename(str(str_path)) for str_path in train_wav_files
+    ]
+    dev_wav_files = [
+        os.path.basename(str(str_path)) for str_path in dev_wav_files
+    ]
+    test_wav_files = [
+        os.path.basename(str(str_path)) for str_path in test_wav_files
+    ]
+
+    for i, utt_id in enumerate(tqdm(sentences)):
+        phones = sentences[utt_id][0]
+        durations = sentences[utt_id][1]
+        note = sentences[utt_id][2]
+        note_dur = sentences[utt_id][3]
+        is_slur = sentences[utt_id][4]
+        speaker = sentences[utt_id][-1]
+
+        phone_ids = [phone_dict[phn] for phn in phones]
+        phone_ids = paddle.to_tensor(np.array(phone_ids))
+
+        if args.speaker_dict:
+            speaker_id = int(
+                [item[1] for item in spk_id_list if speaker == item[0]][0])
+            speaker_id = paddle.to_tensor(speaker_id)
+        else:
+            speaker_id = None
+
+        durations = paddle.to_tensor(np.array(durations))
+        note = paddle.to_tensor(np.array(note))
+        note_dur = paddle.to_tensor(np.array(note_dur))
+        is_slur = paddle.to_tensor(np.array(is_slur))
+        # 生成的和真实的可能有 1, 2 帧的差距，但是 batch_fn 会修复
+        # split data into 3 sections
+
+        wav_path = utt_id + ".wav"
+
+        if wav_path in train_wav_files:
+            sub_output_dir = output_dir / ("train/raw")
+        elif wav_path in dev_wav_files:
+            sub_output_dir = output_dir / ("dev/raw")
+        elif wav_path in test_wav_files:
+            sub_output_dir = output_dir / ("test/raw")
+
+        sub_output_dir.mkdir(parents=True, exist_ok=True)
+
+        with paddle.no_grad():
+            mel = diffsinger_inference(
+                text=phone_ids,
+                note=note,
+                note_dur=note_dur,
+                is_slur=is_slur,
+                get_mel_fs2=False)
+        np.save(sub_output_dir / (utt_id + "_feats.npy"), mel)
+
+
+def main():
+    # parse args and config and redirect to train_sp
+    parser = argparse.ArgumentParser(
+        description="Generate mel with diffsinger.")
+    parser.add_argument(
+        "--dataset",
+        default="opencpop",
+        type=str,
+        help="name of dataset, should in {opencpop} now")
+    parser.add_argument(
+        "--rootdir", default=None, type=str, help="directory to dataset.")
+    parser.add_argument(
+        "--diffsinger-config", type=str, help="diffsinger config file.")
+    parser.add_argument(
+        "--diffsinger-checkpoint",
+        type=str,
+        help="diffsinger checkpoint to load.")
+    parser.add_argument(
+        "--diffsinger-stat",
+        type=str,
+        help="mean and standard deviation used to normalize spectrogram when training diffsinger."
+    )
+    parser.add_argument(
+        "--diffsinger-stretch",
+        type=str,
+        help="min and max mel used to stretch before training diffusion.")
+
+    parser.add_argument(
+        "--phones-dict",
+        type=str,
+        default="phone_id_map.txt",
+        help="phone vocabulary file.")
+
+    parser.add_argument(
+        "--speaker-dict", type=str, default=None, help="speaker id map file.")
+
+    parser.add_argument(
+        "--dur-file", default=None, type=str, help="path to durations.txt.")
+    parser.add_argument("--output-dir", type=str, help="output dir.")
+    parser.add_argument(
+        "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
+
+    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.diffsinger_config) as f:
+        diffsinger_config = CfgNode(yaml.safe_load(f))
+
+    print("========Args========")
+    print(yaml.safe_dump(vars(args)))
+    print("========Config========")
+    print(diffsinger_config)
+
+    evaluate(args, diffsinger_config)
+
+
+if __name__ == "__main__":
+    main()

paddlespeech/t2s/exps/dygraph_to_static.py

@@ -132,6 +132,7 @@ def parse_args():
             'pwgan_male',
             'hifigan_male',
             'pwgan_opencpop',
+            'hifigan_opencpop',
         ],
         help='Choose vocoder type of tts task.')
     parser.add_argument(