merge develop and fix train by step

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,42 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+For support and discussions, please use our [Discourse forums](https://github.com/PaddlePaddle/DeepSpeech/discussions).
+
+If you've found a bug then please create an issue with the following information:
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+** Environment (please complete the following information):**
+ - OS: [e.g. Ubuntu]
+ - GCC/G++ Version [e.g. 8.3]
+ - Python Version [e.g. 3.7]
+ - PaddlePaddle Version [e.g. 2.0.0]
+ - Model Version [e.g. 2.0.0]
+ - GPU/DRIVER Informationo [e.g. Tesla V100-SXM2-32GB/440.64.00]
+ - CUDA/CUDNN Version [e.g. cuda-10.2]
+ - MKL Version
+- TensorRT Version
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,24 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: "[Feature request]"
+labels: feature request
+assignees: ''
+
+---
+
+For support and discussions, please use our [Discourse forums](https://github.com/PaddlePaddle/DeepSpeech/discussions).
+
+If you've found a feature request then please create an issue with the following information:
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.travis/unittest.sh

@@ -11,6 +11,13 @@ abort(){
 
 unittest(){
     cd $1 > /dev/null
+    if [ -f "setup.sh" ]; then
+        bash setup.sh
+        export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
+    fi
+    if [ $? != 0 ]; then
+        exit 1
+    fi
     find . -path ./tools/venv -prune -false -o -name 'tests' -type d -print0 | \
         xargs -0 -I{} -n1 bash -c \
         'python3 -m unittest discover -v -s {}'
@@ -19,6 +26,15 @@ unittest(){
 
 coverage(){
     cd $1 > /dev/null
+
+    if [ -f "setup.sh" ]; then
+        bash setup.sh
+        export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
+    fi
+    if [ $? != 0 ]; then
+        exit 1
+    fi
+
     find . -path ./tools/venv -prune -false -o -name 'tests' -type d -print0 | \
         xargs -0 -I{} -n1 bash -c \
         'python3 -m coverage run --branch {}'

README.md

@@ -21,26 +21,13 @@
 * python>=3.7
 * paddlepaddle>=2.0.0
 
-- Run the setup script for the remaining dependencies
-
-```bash
-git clone https://github.com/PaddlePaddle/DeepSpeech.git
-cd DeepSpeech
-pushd tools; make; popd
-source tools/venv/bin/activate
-bash setup.sh
-```
-
-- Source venv before do experiment.
-
-```bash
-source tools/venv/bin/activate
-```
+Please see [install](docs/install.md).
 
 ## Getting Started
 
 Please see [Getting Started](docs/src/geting_started.md) and [tiny egs](examples/tiny/README.md).
 
+
 ## More Information  
 
 * [Install](docs/src/install.md)  
@@ -56,7 +43,7 @@ Please see [Getting Started](docs/src/geting_started.md) and [tiny egs](examples
 
 ## Questions and Help
 
-You are welcome to submit questions and bug reports in [Github Issues](https://github.com/PaddlePaddle/DeepSpeech/issues). You are also welcome to contribute to this project.
+You are welcome to submit questions in [Github Discussions](https://github.com/PaddlePaddle/DeepSpeech/discussions) and bug reports in [Github Issues](https://github.com/PaddlePaddle/DeepSpeech/issues). You are also welcome to contribute to this project.
 
 
 ## License

README_cn.md

@@ -18,24 +18,11 @@
 
 
 ## 安装
+
 * python>=3.7
 * paddlepaddle>=2.0.0
 
-- 安装依赖
-
-```bash
-git clone https://github.com/PaddlePaddle/DeepSpeech.git
-cd DeepSpeech
-pushd tools; make; popd
-source tools/venv/bin/activate
-bash setup.sh
-```
-
-- 开始实验前要source环境.
-
-```bash
-source tools/venv/bin/activate
-```
+参看 [安装](docs/install.md)。
 
 ## 开始
 
@@ -55,7 +42,7 @@ source tools/venv/bin/activate
 
 ## 问题和帮助
 
-欢迎您在[Github问题](https://github.com/PaddlePaddle/models/issues)中提交问题和bug。也欢迎您为这个项目做出贡献。
+欢迎您在[Github讨论](https://github.com/PaddlePaddle/DeepSpeech/discussions)提交问题，[Github问题](https://github.com/PaddlePaddle/models/issues)中反馈bug。也欢迎您为这个项目做出贡献。
 
 ## License
 

deepspeech/exps/deepspeech2/model.py

@@ -58,8 +58,6 @@ class DeepSpeech2Trainer(Trainer):
 
         losses_np = {
             'train_loss': float(loss),
-            'train_loss_div_batchsize':
-            float(loss) / self.config.data.batch_size
         }
         msg = "Train: Rank: {}, ".format(dist.get_rank())
         msg += "epoch: {}, ".format(self.epoch)
@@ -85,8 +83,6 @@ class DeepSpeech2Trainer(Trainer):
             loss = self.model(*batch)
 
             valid_losses['val_loss'].append(float(loss))
-            valid_losses['val_loss_div_batchsize'].append(
-                float(loss) / self.config.data.batch_size)
 
         # write visual log
         valid_losses = {k: np.mean(v) for k, v in valid_losses.items()}
@@ -265,7 +261,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         self.logger.info(msg)
 
     def run_test(self):
-        self.resume_or_load()
+        self.resume_or_scratch()
         try:
             self.test()
         except KeyboardInterrupt:

deepspeech/exps/u2/model.py

@@ -80,12 +80,15 @@ class U2Trainer(Trainer):
         self.model.train()
 
         start = time.time()
+        
         loss, attention_loss, ctc_loss = self.model(*batch_data)
         loss.backward()
         layer_tools.print_grads(self.model, print_func=None)
+        
         if self.iteration % train_conf.accum_grad == 0:
             self.optimizer.step()
             self.optimizer.clear_grad()
+            self.lr_scheduler.step()
 
         iteration_time = time.time() - start
 
@@ -102,11 +105,49 @@ class U2Trainer(Trainer):
         if self.iteration % train_conf.log_interval == 0:
             self.logger.info(msg)
 
+        # display
         if dist.get_rank() == 0 and self.visualizer:
             for k, v in losses_np.items():
                 self.visualizer.add_scalar("train/{}".format(k), v,
                                            self.iteration)
 
+    def train(self):
+        """The training process.
+        It includes forward/backward/update and periodical validation and 
+        saving.
+        """
+        # !!!IMPORTANT!!!
+        # Try to export the model by script, if fails, we should refine
+        # the code to satisfy the script export requirements
+        # script_model = paddle.jit.to_static(self.model)
+        # script_model_path = str(self.checkpoint_dir / 'init')
+        # paddle.jit.save(script_model, script_model_path)
+        
+        from_scratch = self.resume_or_scratch()
+        self.logger.info(
+            f"Train Total Examples: {len(self.train_loader.dataset)}")
+        
+        while self.epoch <= self.config.training.n_epoch:
+            try:
+                data_start_time = time.time()
+                for batch in self.train_loader:
+                    dataload_time = time.time() - data_start_time
+                    msg = "Train: Rank: {}, ".format(dist.get_rank())
+                    msg += "epoch: {}, ".format(self.epoch)
+                    msg += "step: {}, ".format(self.iteration)
+                    msg += "lr: {}, ".foramt(self.lr_scheduler())
+                    msg += "dataloader time: {:>.3f}s, ".format(dataload_time)
+                    self.iteration += 1
+                    self.train_batch(batch, msg)
+                    data_start_time = time.time()
+            except Exception as e:
+                self.logger.error(e)
+                raise e
+            
+            self.valid()
+            self.save()
+            self.new_epoch()
+            
     @mp_tools.rank_zero_only
     @paddle.no_grad()
     def valid(self):
@@ -365,7 +406,7 @@ class U2Tester(U2Trainer):
         self.logger.info(msg)
 
     def run_test(self):
-        self.resume_or_load()
+        self.resume_or_scratch()
         try:
             self.test()
         except KeyboardInterrupt:

deepspeech/models/deepspeech2.py

@@ -159,7 +159,8 @@ class DeepSpeech2Model(nn.Layer):
             enc_n_units=self.encoder.output_size,
             blank_id=dict_size,  # last token is <blank>
             dropout_rate=0.0,
-            reduction=True)
+            reduction=True,  # sum
+            batch_average=True)  # sum / batch_size
 
     def forward(self, audio, audio_len, text, text_len):
         """Compute Model loss

deepspeech/models/u2.py

@@ -834,7 +834,14 @@ class U2Model(U2BaseModel):
         decoder = TransformerDecoder(vocab_size,
                                      encoder.output_size(),
                                      **configs['decoder_conf'])
-        ctc = CTCDecoder(vocab_size, encoder.output_size())
+        ctc = CTCDecoder(
+            odim=vocab_size,
+            enc_n_units=encoder.output_size(),
+            blank_id=0,
+            dropout_rate=0.0,
+            reduction=True,  # sum
+            batch_average=True)  # sum / batch_size
+
         return vocab_size, encoder, decoder, ctc
 
     @classmethod

deepspeech/modules/ctc.py

@@ -37,14 +37,16 @@ class CTCDecoder(nn.Layer):
                  enc_n_units,
                  blank_id=0,
                  dropout_rate: float=0.0,
-                 reduction: bool=True):
+                 reduction: bool=True,
+                 batch_average: bool=True):
         """CTC decoder
 
         Args:
             odim ([int]): text vocabulary size
             enc_n_units ([int]): encoder output dimention
             dropout_rate (float): dropout rate (0.0 ~ 1.0)
-            reduction (bool): reduce the CTC loss into a scalar
+            reduction (bool): reduce the CTC loss into a scalar, True for 'sum' or 'none'
+            batch_average (bool): do batch dim wise average.
         """
         assert check_argument_types()
         super().__init__()
@@ -54,7 +56,10 @@ class CTCDecoder(nn.Layer):
         self.dropout_rate = dropout_rate
         self.ctc_lo = nn.Linear(enc_n_units, self.odim)
         reduction_type = "sum" if reduction else "none"
-        self.criterion = CTCLoss(blank=self.blank_id, reduction=reduction_type)
+        self.criterion = CTCLoss(
+            blank=self.blank_id,
+            reduction=reduction_type,
+            batch_average=batch_average)
 
         # CTCDecoder LM Score handle
         self._ext_scorer = None

deepspeech/modules/loss.py

@@ -24,32 +24,33 @@ __all__ = ['CTCLoss', "LabelSmoothingLoss"]
 
 
 class CTCLoss(nn.Layer):
-    def __init__(self, blank=0, reduction='sum'):
+    def __init__(self, blank=0, reduction='sum', batch_average=False):
         super().__init__()
         # last token id as blank id
         self.loss = nn.CTCLoss(blank=blank, reduction=reduction)
+        self.batch_average = batch_average
 
     def forward(self, logits, ys_pad, hlens, ys_lens):
         """Compute CTC loss.
 
         Args:
-            logits ([paddle.Tensor]): [description]
-            ys_pad ([paddle.Tensor]): [description]
-            hlens ([paddle.Tensor]): [description]
-            ys_lens ([paddle.Tensor]): [description]
+            logits ([paddle.Tensor]): [B, Tmax, D]
+            ys_pad ([paddle.Tensor]): [B, Tmax]
+            hlens ([paddle.Tensor]): [B]
+            ys_lens ([paddle.Tensor]): [B]
 
         Returns:
             [paddle.Tensor]: scalar. If reduction is 'none', then (N), where N = \text{batch size}.
         """
+        B = paddle.shape(logits)[0]
         # warp-ctc need logits, and do softmax on logits by itself
         # warp-ctc need activation with shape [T, B, V + 1]
         # logits: (B, L, D) -> (L, B, D)
         logits = logits.transpose([1, 0, 2])
         loss = self.loss(logits, ys_pad, hlens, ys_lens)
-
-        # wenet do batch-size average, deepspeech2 not do this
-        # Batch-size average
-        # loss = loss / paddle.shape(logits)[1]
+        if self.batch_average:
+            # Batch-size average
+            loss = loss / B
         return loss
 
 

deepspeech/training/scheduler.py

@@ -54,4 +54,4 @@ class WarmupLR(LRScheduler):
             step_num**-0.5, step_num * self.warmup_steps**-1.5)
 
     def set_step(self, step: int):
-        self.last_epoch = step
+        self.step(step)

deepspeech/training/trainer.py

@@ -139,7 +139,7 @@ class Trainer():
         checkpoint.save_parameters(self.checkpoint_dir, self.iteration,
                                    self.model, self.optimizer, infos)
 
-    def resume_or_load(self):
+    def resume_or_scratch(self):
         """Resume from latest checkpoint at checkpoints in the output 
         directory or load a specified checkpoint.
         
@@ -152,8 +152,20 @@ class Trainer():
             checkpoint_dir=self.checkpoint_dir,
             checkpoint_path=self.args.checkpoint_path)
         if infos:
+            # restore from ckpt
             self.iteration = infos["step"]
             self.epoch = infos["epoch"]
+            self.lr_scheduler.step(self.iteration)
+            if self.parallel:
+                self.train_loader.batch_sampler.set_epoch(self.epoch)
+            return False
+        else:
+            # from scratch, epoch and iteration init with zero
+            # save init model, i.e. 0 epoch
+            self.save()
+            # self.epoch start from 1.
+            self.new_epoch()
+            return True
 
     def new_epoch(self):
         """Reset the train loader and increment ``epoch``.
@@ -166,22 +178,22 @@ class Trainer():
     def train(self):
         """The training process.
         
-        It includes forward/backward/update and periodical validation and 
-        saving.
         """
+        from_scratch = self.resume_or_scratch()
+         
         self.logger.info(
             f"Train Total Examples: {len(self.train_loader.dataset)}")
-        self.new_epoch()
         while self.epoch <= self.config.training.n_epoch:
             try:
                 data_start_time = time.time()
                 for batch in self.train_loader:
                     dataload_time = time.time() - data_start_time
+                    # iteration start from 1.
+                    self.iteration += 1
                     msg = "Train: Rank: {}, ".format(dist.get_rank())
                     msg += "epoch: {}, ".format(self.epoch)
                     msg += "step: {}, ".format(self.iteration)
                     msg += "dataloader time: {:>.3f}s, ".format(dataload_time)
-                    self.iteration += 1
                     self.train_batch(batch, msg)
                     data_start_time = time.time()
             except Exception as e:
@@ -190,6 +202,7 @@ class Trainer():
 
             self.valid()
             self.save()
+            # lr control by epoch
             self.lr_scheduler.step()
             self.new_epoch()
 
@@ -197,7 +210,6 @@ class Trainer():
         """The routine of the experiment after setup. This method is intended
         to be used by the user.
         """
-        self.resume_or_load()
         try:
             self.train()
         except KeyboardInterrupt:
@@ -298,7 +310,7 @@ class Trainer():
 
         # global logger
         stdout = False
-        save_path = log_file
+        save_path = str(log_file)
         logging.basicConfig(
             level=logging.DEBUG if stdout else logging.INFO,
             format=format,

docs/src/install.md

@@ -45,7 +45,7 @@ source tools/venv/bin/activate
 
 ## Running in Docker Container (optional)
 
-Docker is an open source tool to build, ship, and run distributed applications in an isolated environment. A Docker image for this project has been provided in [hub.docker.com](https://hub.docker.com) with all the dependencies installed, including the pre-built PaddlePaddle, CTC decoders, and other necessary Python and third-party packages. This Docker image requires the support of NVIDIA GPU, so please make sure its availiability and the [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) has been installed.
+Docker is an open source tool to build, ship, and run distributed applications in an isolated environment. A Docker image for this project has been provided in [hub.docker.com](https://hub.docker.com) with all the dependencies installed. This Docker image requires the support of NVIDIA GPU, so please make sure its availiability and the [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) has been installed.
 
 Take several steps to launch the Docker image:
 
@@ -79,3 +79,7 @@ For example, for CUDA 10.1, CuDNN7.5 install paddle 2.0.0:
 ```bash
 python3 -m pip install paddlepaddle-gpu==2.0.0
 ```
+
+- Install Deepspeech
+
+Please see [Setup](#setup)  section.

docs/src/ngram_lm.md

@@ -1,6 +1,8 @@
 # Prepare Language Model
 
-A language model is required to improve the decoder's performance. We have prepared two language models (with lossy compression) for users to download and try. One is for English and the other is for Mandarin. Users can simply run this to download the preprared language models:
+A language model is required to improve the decoder's performance. We have prepared two language models (with lossy compression) for users to download and try. One is for English and the other is for Mandarin. The bash script to download LM is example's `local/download_lm_*.sh`.
+
+For example, users can simply run this to download the preprared mandarin language models:
 
 ```bash
 cd examples/aishell
@@ -8,7 +10,9 @@ source path.sh
 bash local/download_lm_ch.sh
 ```
 
-If you wish to train your own better language model, please refer to [KenLM](https://github.com/kpu/kenlm) for tutorials. Here we provide some tips to show how we preparing our English and Mandarin language models. You can take it as a reference when you train your own.
+If you wish to train your own better language model, please refer to [KenLM](https://github.com/kpu/kenlm) for tutorials.
+Here we provide some tips to show how we preparing our English and Mandarin language models.
+You can take it as a reference when you train your own.
 
 ## English LM
 

examples/aishell/.gitignore

@@ -2,3 +2,4 @@ data
 ckpt*
 demo_cache
 *.log
+log

examples/aishell/s0/conf/deepspeech2.yaml

@@ -29,8 +29,8 @@ model:
   use_gru: True 
   share_rnn_weights: False
 training:
-  n_epoch: 30
-  lr: 5e-4
+  n_epoch: 50
+  lr: 2e-3
   lr_decay: 0.83
   weight_decay: 1e-06
   global_grad_clip: 5.0
@@ -39,7 +39,7 @@ decoding:
   error_rate_type: cer 
   decoding_method: ctc_beam_search
   lang_model_path: data/lm/zh_giga.no_cna_cmn.prune01244.klm
-  alpha: 2.6
+  alpha: 1.9
   beta: 5.0
   beam_size: 300
   cutoff_prob: 0.99

examples/aishell/s0/local/infer.sh

@@ -1,6 +1,6 @@
 #! /usr/bin/env bash
 
-if [[ $# != 1 ]];
+if [[ $# != 1 ]]; then
     echo "usage: $0 ckpt-path"
     exit -1
 fi

examples/aishell/s0/local/train.sh

@@ -2,7 +2,7 @@
 
 # train model
 # if you wish to resume from an exists model, uncomment --init_from_pretrained_model
-export FLAGS_sync_nccl_allreduce=0
+#export FLAGS_sync_nccl_allreduce=0
 
 ngpu=$(echo ${CUDA_VISIBLE_DEVICES} | python -c 'import sys; a = sys.stdin.read(); print(len(a.split(",")));')
 echo "using $ngpu gpus..."

examples/aishell/s0/run.sh

@@ -7,7 +7,7 @@ source path.sh
 bash ./local/data.sh
 
 # train model
-CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./local/train.sh
+CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./local/train.sh baseline
 
 # test model
 CUDA_VISIBLE_DEVICES=0 bash ./local/test.sh
@@ -16,4 +16,4 @@ CUDA_VISIBLE_DEVICES=0 bash ./local/test.sh
 CUDA_VISIBLE_DEVICES=0 bash ./local/infer.sh ckpt/checkpoints/step-3284
 
 # export model
-bash ./local/export.sh ckpt/checkpoints/step-3284 jit.model
+bash ./local/export.sh ckpt/checkpoints/step-3284 jit.model

examples/librispeech/README.md

@@ -1 +1 @@
-* s0 for deepspeech2
+* s0 is for deepspeech

examples/librispeech/s0/conf/deepspeech2.yaml

@@ -29,8 +29,8 @@ model:
   use_gru: False 
   share_rnn_weights: True 
 training:
-  n_epoch: 20
-  lr: 5e-4
+  n_epoch: 50
+  lr: 1e-3
   lr_decay: 0.83
   weight_decay: 1e-06
   global_grad_clip: 5.0
@@ -39,7 +39,7 @@ decoding:
   error_rate_type: wer
   decoding_method: ctc_beam_search
   lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-  alpha: 2.5
+  alpha: 1.9
   beta: 0.3
   beam_size: 500
   cutoff_prob: 1.0

examples/librispeech/s0/local/infer.sh

@@ -1,6 +1,6 @@
 #! /usr/bin/env bash
 
-if [[ $# != 1 ]];
+if [[ $# != 1 ]];then
     echo "usage: $0 ckpt-path"
     exit -1
 fi

examples/librispeech/s0/local/train.sh

@@ -1,8 +1,9 @@
 #! /usr/bin/env bash
 
-export FLAGS_sync_nccl_allreduce=0
+#export FLAGS_sync_nccl_allreduce=0
+
 # https://github.com/PaddlePaddle/Paddle/pull/28484
-export NCCL_SHM_DISABLE=1
+#export NCCL_SHM_DISABLE=1
 
 ngpu=$(echo ${CUDA_VISIBLE_DEVICES} | python -c 'import sys; a = sys.stdin.read(); print(len(a.split(",")));')
 echo "using $ngpu gpus..."
@@ -11,7 +12,7 @@ python3 -u ${BIN_DIR}/train.py \
 --device 'gpu' \
 --nproc ${ngpu} \
 --config conf/deepspeech2.yaml \
---output ckpt
+--output ckpt-${1}
 
 if [ $? -ne 0 ]; then
     echo "Failed in training!"

examples/tiny/s0/local/infer.sh

@@ -1,6 +1,6 @@
 #! /usr/bin/env bash
 
-if [[ $# != 1 ]];
+if [[ $# != 1 ]];then
     echo "usage: $0 ckpt-path"
     exit -1
 fi

examples/tiny/s0/local/test.sh

@@ -6,7 +6,6 @@ if [ $? -ne 0 ]; then
     exit 1
 fi
 
-CUDA_VISIBLE_DEVICES=0 \
 python3 -u ${BIN_DIR}/test.py \
 --device 'gpu' \
 --nproc 1 \

examples/tiny/s0/local/train.sh

@@ -2,7 +2,6 @@
 
 export FLAGS_sync_nccl_allreduce=0
 
-CUDA_VISIBLE_DEVICES=0 \
 python3 -u ${BIN_DIR}/train.py \
 --device 'gpu' \
 --nproc 1 \

setup.sh

@@ -1,5 +1,8 @@
 #! /usr/bin/env  bash
 
+source utils/log.sh
+
+
 SUDO='sudo'
 if [ $(id -u) -eq 0 ]; then
   SUDO=''
@@ -8,6 +11,8 @@ fi
 if [ -e /etc/lsb-release ];then
     #${SUDO} apt-get update
     ${SUDO} apt-get install -y sox pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev
+    error_msg "Please using Ubuntu or install `pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev` by user."
+    exit -1
 fi
 
 # install python dependencies
@@ -15,17 +20,17 @@ if [ -f "requirements.txt" ]; then
     pip3 install -r requirements.txt
 fi
 if [ $? != 0 ]; then
-    echo "Install python dependencies failed !!!"
+    error_msg "Install python dependencies failed !!!"
     exit 1
 fi
 
 # install package libsndfile
 python3 -c "import soundfile"
 if [ $? != 0 ]; then
-    echo "Install package libsndfile into default system path."
+    info_msg "Install package libsndfile into default system path."
     wget "http://www.mega-nerd.com/libsndfile/files/libsndfile-1.0.28.tar.gz"
     if [ $? != 0 ]; then
-        echo "Download libsndfile-1.0.28.tar.gz failed !!!"
+        error_msg "Download libsndfile-1.0.28.tar.gz failed !!!"
         exit 1
     fi
     tar -zxvf libsndfile-1.0.28.tar.gz
@@ -43,6 +48,10 @@ if [ $? != 0 ]; then
     sh setup.sh
     cd - > /dev/null
 fi
+python3 -c "import pkg_resources; pkg_resources.require(\"swig_decoders==1.1\")"
+if [ $? != 0 ]; then
+   error_msg "Please check why decoder install error!"
+   exit -1
+fi
 
-
-echo "Install all dependencies successfully."
+info_msg "Install all dependencies successfully."

utils/log.sh

@@ -0,0 +1,11 @@
+_HDR_FMT="%.23s %s[%s]: "
+_ERR_MSG_FMT="ERROR: ${_HDR_FMT}%s\n"
+_INFO_MSG_FMT="INFO: ${_HDR_FMT}%s\n"
+
+error_msg() {
+  printf "$_ERR_MSG_FMT" $(date +%F.%T.%N) ${BASH_SOURCE[1]##*/} ${BASH_LINENO[0]} "${@}"
+}
+
+info_msg() {
+  printf "$_INFO_MSG_FMT" $(date +%F.%T.%N) ${BASH_SOURCE[1]##*/} ${BASH_LINENO[0]} "${@}"
+}