!12247 Add shell scripts to Wavenet
From: @wanyiming Reviewed-by: Signed-off-by:
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@ -65,38 +65,43 @@ Dataset used: [The LJ Speech Dataset](<https://keithito.com/LJ-Speech-Dataset>)
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.
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├── audio
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└──wavenet
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├──datasets // Note the datasets folder should be downloaded from the above link
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├──egs // Note the egs folder should be downloaded from the above link
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├──utils // Note the utils folder should be downloaded from the above link
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├── audio.py // Audio utils. Note this script should be downloaded from a third party
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├── compute-meanvar-stats.py // Compute mean-variance normalization stats. Note this script should be downloaded from the above link
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├── evaluate.py // Evaluation
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├── export.py // Convert mindspore model to air model
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├── hparams.py // Hyper-parameter configuration. Note this script should be downloaded from the above link
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├── lrschedule.py // Learning rate scheduler. Note this script should be downloaded from the above link
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├── mksubset.py // Make subset of dataset. Note this script should be downloaded from the above link
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├── preprocess.py // Preprocess dataset. Note this script should be downloaded from the above link
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├── preprocess_normalize.py // Perform meanvar normalization to preprocessed features. Note this script should be downloaded from the above link
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├── README.md // Descriptions about WaveNet
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├── train.py // Training scripts
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├── train_pytorch.py // Note this script should be downloaded from the above link. The initial name of this script is train.py in the project from the link
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├── scripts
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│ ├──run_distribute_train_gpu.sh // launch distributed training with gpu platform(8p)
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│ ├──run_eval_cpu.sh // launch evaluation with cpu platform
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│ ├──run_eval_gpu.sh // launch evaluation with gpu platform
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│ ├──run_standalone_train_cpu.sh // launch standalone training with cpu platform
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│ └──run_standalone_train_gpu.sh // launch standalone training with gpu platform(1p)
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├──datasets // Note the datasets folder should be downloaded from the above link
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├──egs // Note the egs folder should be downloaded from the above link
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├──utils // Note the utils folder should be downloaded from the above link
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├── audio.py // Audio utils. Note this script should be downloaded from the above link
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├── compute-meanvar-stats.py // Compute mean-variance normalization stats. Note this script should be downloaded from the above link
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├── evaluate.py // Evaluation
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├── export.py // Convert mindspore model to air model
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├── hparams.py // Hyper-parameter configuration. Note this script should be downloaded from the above link
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├── mksubset.py // Make subset of dataset. Note this script should be downloaded from the above link
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├── preprocess.py // Preprocess dataset. Note this script should be downloaded from the above link
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├── preprocess_normalize.py // Perform meanvar normalization to preprocessed features. Note this script should be downloaded from the above link
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├── README.md // Descriptions about WaveNet
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├── train.py // Training scripts
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├── train_pytorch.py // Note this script should be downloaded from the above link. The initial name of this script is train.py in the project from the link
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├── src
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│ ├──__init__.py
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│ ├──dataset.py // Generate dataloader and data processing entry
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│ ├──callback.py // Callbacks to monitor the training
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│ ├──lr_generator.py // Learning rate generator
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│ └──loss.py // Loss function definition
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│ ├──dataset.py // Generate dataloader and data processing entry
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│ ├──callback.py // Callbacks to monitor the training
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│ ├──lr_generator.py // Learning rate generator
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│ └──loss.py // Loss function definition
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└── wavenet_vocoder
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├──__init__.py
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├──conv.py // Extended 1D convolution
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├──mixture.py // Loss function for training and sample function for testing
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├──modules.py // Modules for Wavenet construction
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├──upsample.py // Upsample layer definition
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├──util.py // Utils. Note this script should be downloaded from the above link
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├──wavenet.py // WaveNet networks
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└──tfcompat // Note this script should be downloaded from the above link
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├──conv.py // Extended 1D convolution
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├──mixture.py // Loss function for training and sample function for testing
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├──modules.py // Modules for Wavenet construction
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├──upsample.py // Upsample layer definition
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├──util.py // Utils. Note this script should be downloaded from the above link
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├──wavenet.py // WaveNet networks
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└──tfcompat // Note this script should be downloaded from the above link
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├──__init__.py
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└──hparam.py // Param management tools
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└──hparam.py // Param management tools
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```
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## [Script Parameters](#contents)
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@ -205,13 +210,13 @@ you can run the following command to train the network:
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```bash
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Standalone training
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GPU:
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python train.py --data_path=/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/ --preset=/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json --checkpoint_dir=path_to_save_ckpt
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sh ./scripts/run_standalone_train_gpu.sh [CUDA_DEVICE_ID] [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_save_ckpt]
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CPU:
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python train.py --data_path=/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/ --preset=/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json --checkpoint_dir=path_to_save_ckpt --platform=CPU
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sh ./scripts/run_standalone_train_cpu.sh [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_save_ckpt]
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Distributed training (on GPU only)
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CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' mpirun --allow-run-as-root -n 8 python train.py ----data_path= /path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/ --preset=/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json --checkpoint_dir=path_to_save_ckpt --is_distributed=True
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Distributed training(8p)
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sh ./scripts/run_distribute_train_gpu.sh [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_save_ckpt]
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```
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## [Evaluation Process](#contents)
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@ -221,11 +226,14 @@ this [link](https://bbs.huaweicloud.com/forum/thread-94852-1-1.html)
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```bash
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Evaluation
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GPU:
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python evaluate.py --data_path=/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/eval --preset=/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json --pretrain_ckpt=path_to_load_ckpt --is_numpy --output_path=path_to_save_audio
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GPU (using numpy):
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sh ./scripts/run_eval_gpu.sh [CUDA_DEVICE_ID] [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_load_ckpt] is_numpy [path_to_save_audio]
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GPU (using mindspore):
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sh ./scripts/run_eval_gpu.sh [CUDA_DEVICE_ID] [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_load_ckpt] [path_to_save_audio]
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CPU:
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python evaluate.py --data_path=/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/eval --preset=/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json --pretrain_ckpt=path_to_load_ckpt --is_numpy --output_path=path_to_save_audio --platform=CPU
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sh ./scripts/run_eval_cpu.sh [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_load_ckpt] [is_numpy] [path_to_save_audio]
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```
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## [Convert Process](#contents)
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@ -0,0 +1,20 @@
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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DATA_PATH=$1
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PRESET=$2
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CHECKPOINT_DIR=$3
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mpirun --allow-run-as-root -n 8 --output-filename log_output --merge-stderr-to-stdout \
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python ./train.py --data_path=$DATA_PATH --preset=$PRESET --checkpoint_dir=$CHECKPOINT_DIR --is_distributed > train.log 2>&1 &
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@ -0,0 +1,21 @@
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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if [ $# == 5 ]
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then
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python ./evaluate.py --data_path=$1 --preset=$2 --pretrain_ckpt=$3 --is_numpy --output_path=$5 > eval.log 2>&1 &
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else
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python ./evaluate.py --data_path=$1 --preset=$2 --pretrain_ckpt=$3 --output_path=$4 > eval.log 2>&1 &
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fi
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@ -0,0 +1,23 @@
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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if [ $# == 6 ]
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then
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CUDA_VISIBLE_DEVICES=$1 python ./evaluate.py --data_path=$2 --preset=$3 --pretrain_ckpt=$4 \
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--is_numpy --output_path=$6 > eval.log 2>&1 &
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else
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CUDA_VISIBLE_DEVICES=$1 python ./evaluate.py --data_path=$2 --preset=$3 --pretrain_ckpt=$4 \
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--output_path=$5 > eval.log 2>&1 &
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fi
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@ -0,0 +1,20 @@
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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DATA_PATH=$1
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PRESET=$2
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CHECKPOINT_DIR=$3
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python ./train.py --data_path=$DATA_PATH --preset=$PRESET --checkpoint_dir=$CHECKPOINT_DIR \
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--platform=CPU > train.log 2>&1 &
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@ -0,0 +1,21 @@
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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DEVICE_ID=$1
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DATA_PATH=$2
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PRESET=$3
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CHECKPOINT_DIR=$4
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CUDA_VISIBLE_DEVICES=$DEVICE_ID python ./train.py --data_path=$DATA_PATH --preset=$PRESET \
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--checkpoint_dir=$CHECKPOINT_DIR > train.log 2>&1 &
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