[CLI][Demo][Text]Refactor punctuation_restoration. (#1013)

* Refactor punctuation_restoration.

* Add text cli and punc demo.

demos/punctuation_restoration/README.md

@@ -0,0 +1,73 @@
+# Punctuation Restoration
+
+## Introduction
+Punctuation restoration is a common post-processing problem for Automatic Speech Recognition (ASR) systems. It is important to improve the readability of the transcribed text for the human reader and facilitate NLP tasks. 
+
+This demo is an implementation to restore punctuation from a raw text. It can be done by a single command or a few lines in python using `PaddleSpeech`. 
+
+## Usage
+### 1. Installation
+```bash
+pip install paddlespeech
+```
+
+### 2. Prepare Input
+Input of this demo should be a text of the specific language that can be passed via argument.
+
+
+### 3. Usage
+- Command Line(Recommended)
+  ```bash
+  paddlespeech text --input 今天的天气真不错啊你下午有空吗我想约你一起去吃饭
+  ```
+  Usage:
+  ```bash
+  paddlespeech text --help
+  ```
+  Arguments:
+  - `input`(required): Input raw text.
+  - `task`: Choose subtask. Default: `punc`.
+  - `model`: Model type of text task. Default: `ernie_linear_wudao`.
+  - `lang`: Choose model language.. Default: `zh`.
+  - `config`: Config of text task. Use pretrained model when it is None. Default: `None`.
+  - `ckpt_path`: Model checkpoint. Use pretrained model when it is None. Default: `None`.
+  - `punc_vocab`: Vocabulary file of punctuation restoration task. Default: `None`.
+  - `device`: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.
+
+  Output:
+  ```bash
+  [2021-12-14 19:50:22,200] [    INFO] [log.py] [L57] - Text Result:
+  今天的天气真不错啊！你下午有空吗？我想约你一起去吃饭。
+  ```
+
+- Python API
+  ```python
+  import paddle
+  from paddlespeech.cli import TextExecutor
+
+  text_executor = TextExecutor()
+  result = text_executor(
+      text='今天的天气真不错啊你下午有空吗我想约你一起去吃饭',
+      task='punc',
+      model='ernie_linear_wudao',
+      lang='zh',
+      config=None,
+      ckpt_path=None,
+      punc_vocab=None,
+      device=paddle.get_device())
+  print('Text Result: \n{}'.format(result))
+  ```
+  Output:
+  ```bash
+  Text Result:
+  今天的天气真不错啊！你下午有空吗？我想约你一起去吃饭。
+  ```
+
+
+### 4.Pretrained Models
+
+Here is a list of pretrained models released by PaddleSpeech that can be used by command and python api:
+
+| Model | Task | Language
+| :--- | :---: | :---:
+| ernie_linear_wudao| punc(Punctuation Restoration) | zh

demos/punctuation_restoration/run.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+paddlespeech text --input 今天的天气真好啊你下午有空吗我想约你一起去吃饭

examples/iwslt2012/punc0/conf/ernie_linear.yaml

@@ -0,0 +1,36 @@
+data:
+  dataset_type: Ernie
+  train_path: data/iwslt2012_zh/train.txt
+  dev_path: data/iwslt2012_zh/dev.txt
+  test_path: data/iwslt2012_zh/test.txt
+  data_params: 
+    pretrained_token: ernie-1.0
+    punc_path: data/iwslt2012_zh/punc_vocab
+    seq_len: 100
+  batch_size: 64
+  sortagrad: True 
+  shuffle_method: batch_shuffle
+  num_workers: 0
+
+checkpoint:
+  kbest_n: 5
+  latest_n: 10
+  metric_type: F1
+
+model_type: ErnieLinear
+
+model_params:
+  pretrained_token: ernie-1.0
+  num_classes: 4
+
+training:
+  n_epoch: 100
+  lr: !!float 1e-5
+  lr_decay: 1.0 
+  weight_decay: !!float 1e-06
+  global_grad_clip: 5.0
+  log_interval: 10
+  log_path: log/train_ernie_linear.log
+
+testing:
+  log_path: log/test_ernie_linear.log

examples/iwslt2012/punc0/local/data.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+
+if [ ! -d data ]; then
+    wget -c https://paddlespeech.bj.bcebos.com/datasets/iwslt2012.tar.gz
+    tar -xzf iwslt2012.tar.gz
+fi
+
+echo "Finish data preparation."
+exit 0

examples/iwslt2012/punc0/local/train.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
-if [ $# != 2 ];then
-    echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name"
+if [ $# != 3 ];then
+    echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name log_dir"
     exit -1
 fi
 
@@ -10,13 +10,15 @@ echo "using $ngpu gpus..."
 
 config_path=$1
 ckpt_name=$2
+log_dir=$3
 
 mkdir -p exp
 
 python3 -u ${BIN_DIR}/train.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
---output exp/${ckpt_name}
+--output_dir exp/${ckpt_name} \
+--log_dir ${log_dir}
 
 if [ $? -ne 0 ]; then
     echo "Failed in training!"

examples/iwslt2012/punc0/path.sh

@@ -1,3 +1,4 @@
+#!/bin/bash
 export MAIN_ROOT=${PWD}/../../../
 
 export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
@@ -9,5 +10,5 @@ export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
 
 export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
 
-
-export BIN_DIR=${MAIN_ROOT}/speechtask/punctuation_restoration/bin
+MODEL=$1
+export BIN_DIR=${MAIN_ROOT}/paddlespeech/text/exps/${MODEL}

examples/iwslt2012/punc0/run.sh

@@ -1,12 +1,9 @@
 #!/bin/bash
 set -e
-source path.sh
 
-
-##  stage, gpu, data_pre_config, train_config, avg_num
-if [ $# -lt 4 ]; then
-    echo "usage: bash ./run.sh  stage gpu train_config avg_num data_config" 
-    echo "eg: bash ./run.sh  0 0 train_config 1 data_config " 
+if [ $# -ne 4 ]; then
+    echo "usage: bash ./run.sh stage gpu train_config avg_num" 
+    echo "eg: bash ./run.sh  1 0 train_config 1" 
     exit -1
 fi
 
@@ -17,31 +14,27 @@ conf_path=$3
 avg_num=$4
 avg_ckpt=avg_${avg_num}
 ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
-echo "checkpoint name ${ckpt}"
-
-if [ $stage -le 0 ]; then 
-    if [ $# -eq 5 ]; then
-        data_pre_conf=$5
-        # prepare data
-        bash ./local/data.sh ${data_pre_conf} || exit -1
-    else
-        echo "data_pre_conf is not exist!"
-        exit -1
-    fi
-fi
+log_dir=log
+
+source path.sh ${ckpt}
 
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    # train model, all `ckpt` under `exp` dir
-    CUDA_VISIBLE_DEVICES=${gpus} bash ./local/train.sh ${conf_path} ${ckpt}
+    # prepare data
+    bash ./local/data.sh
 fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # train model, all `ckpt` under `exp` dir
+    CUDA_VISIBLE_DEVICES=${gpus} bash ./local/train.sh ${conf_path} ${ckpt} ${log_dir}
+fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # avg n best model
     bash ./local/avg.sh exp/${ckpt}/checkpoints ${avg_num}
 fi
 
-if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
-    # test ckpt avg_n
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+   # test ckpt avg_n
    CUDA_VISIBLE_DEVICES=${gpus} bash ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi

paddlespeech/cli/__init__.py

@@ -18,6 +18,7 @@ from .base_commands import BaseCommand
 from .base_commands import HelpCommand
 from .cls import CLSExecutor
 from .st import STExecutor
+from .text import TextExecutor
 from .tts import TTSExecutor
 
 _locale._getdefaultlocale = (lambda *args: ['en_US', 'utf8'])

paddlespeech/cli/text/__init__.py

@@ -11,20 +11,4 @@
 # 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.
-from functools import wraps
-
-from paddle import distributed as dist
-
-__all__ = ["rank_zero_only"]
-
-
-def rank_zero_only(func):
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        rank = dist.get_rank()
-        if rank != 0:
-            return
-        result = func(*args, **kwargs)
-        return result
-
-    return wrapper
+from .infer import TextExecutor

paddlespeech/cli/text/infer.py

@@ -0,0 +1,281 @@
+# 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.
+import argparse
+import os
+import re
+from typing import List
+from typing import Optional
+from typing import Union
+
+import paddle
+
+from ...s2t.utils.dynamic_import import dynamic_import
+from ..executor import BaseExecutor
+from ..log import logger
+from ..utils import cli_register
+from ..utils import download_and_decompress
+from ..utils import MODEL_HOME
+
+__all__ = ['TextExecutor']
+
+pretrained_models = {
+    # The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
+    # e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
+    # Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
+    # "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
+    "ernie_linear_wudao-punc-zh": {
+        'url':
+        'https://paddlespeech.bj.bcebos.com/text/ernie_linear_wudao-punc-zh.tar.gz',
+        'md5':
+        '12283e2ddde1797c5d1e57036b512746',
+        'cfg_path':
+        'ckpt/model_config.json',
+        'ckpt_path':
+        'ckpt/model_state.pdparams',
+        'vocab_file':
+        'punc_vocab.txt',
+    },
+}
+
+model_alias = {
+    "ernie_linear": "paddlespeech.text.models:ErnieLinear",
+}
+
+tokenizer_alias = {
+    "ernie_linear": "paddlenlp.transformers:ErnieTokenizer",
+}
+
+
+@cli_register(name='paddlespeech.text', description='Text infer command.')
+class TextExecutor(BaseExecutor):
+    def __init__(self):
+        super(TextExecutor, self).__init__()
+
+        self.parser = argparse.ArgumentParser(
+            prog='paddlespeech.text', add_help=True)
+        self.parser.add_argument(
+            '--input', type=str, required=True, help='Input text.')
+        self.parser.add_argument(
+            '--task',
+            type=str,
+            default='punc',
+            choices=['punc'],
+            help='Choose text task.')
+        self.parser.add_argument(
+            '--model',
+            type=str,
+            default='ernie_linear_wudao',
+            choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
+            help='Choose model type of text task.')
+        self.parser.add_argument(
+            '--lang',
+            type=str,
+            default='zh',
+            choices=['zh', 'en'],
+            help='Choose model language.')
+        self.parser.add_argument(
+            '--config',
+            type=str,
+            default=None,
+            help='Config of cls task. Use deault config when it is None.')
+        self.parser.add_argument(
+            '--ckpt_path',
+            type=str,
+            default=None,
+            help='Checkpoint file of model.')
+        self.parser.add_argument(
+            '--punc_vocab',
+            type=str,
+            default=None,
+            help='Vocabulary file of punctuation restoration task.')
+        self.parser.add_argument(
+            '--device',
+            type=str,
+            default=paddle.get_device(),
+            help='Choose device to execute model inference.')
+
+    def _get_pretrained_path(self, tag: str) -> os.PathLike:
+        """
+            Download and returns pretrained resources path of current task.
+        """
+        assert tag in pretrained_models, 'Can not find pretrained resources of {}.'.format(
+            tag)
+
+        res_path = os.path.join(MODEL_HOME, tag)
+        decompressed_path = download_and_decompress(pretrained_models[tag],
+                                                    res_path)
+        decompressed_path = os.path.abspath(decompressed_path)
+        logger.info(
+            'Use pretrained model stored in: {}'.format(decompressed_path))
+
+        return decompressed_path
+
+    def _init_from_path(self,
+                        task: str='punc',
+                        model_type: str='ernie_linear_wudao',
+                        lang: str='zh',
+                        cfg_path: Optional[os.PathLike]=None,
+                        ckpt_path: Optional[os.PathLike]=None,
+                        vocab_file: Optional[os.PathLike]=None):
+        """
+            Init model and other resources from a specific path.
+        """
+        if hasattr(self, 'model'):
+            logger.info('Model had been initialized.')
+            return
+
+        self.task = task
+
+        if cfg_path is None or ckpt_path is None or vocab_file is None:
+            tag = '-'.join([model_type, task, lang])
+            self.res_path = self._get_pretrained_path(tag)
+            self.cfg_path = os.path.join(self.res_path,
+                                         pretrained_models[tag]['cfg_path'])
+            self.ckpt_path = os.path.join(self.res_path,
+                                          pretrained_models[tag]['ckpt_path'])
+            self.vocab_file = os.path.join(self.res_path,
+                                           pretrained_models[tag]['vocab_file'])
+        else:
+            self.cfg_path = os.path.abspath(cfg_path)
+            self.ckpt_path = os.path.abspath(ckpt_path)
+            self.vocab_file = os.path.abspath(vocab_file)
+
+        model_name = model_type[:model_type.rindex('_')]
+        if self.task == 'punc':
+            # punc list
+            self._punc_list = []
+            with open(self.vocab_file, 'r') as f:
+                for line in f:
+                    self._punc_list.append(line.strip())
+
+            # model
+            model_class = dynamic_import(model_name, model_alias)
+            tokenizer_class = dynamic_import(model_name, tokenizer_alias)
+            self.model = model_class(
+                cfg_path=self.cfg_path, ckpt_path=self.ckpt_path)
+            self.tokenizer = tokenizer_class.from_pretrained('ernie-1.0')
+        else:
+            raise NotImplementedError
+
+        self.model.eval()
+
+    def _clean_text(self, text):
+        text = text.lower()
+        text = re.sub('[^A-Za-z0-9\u4e00-\u9fa5]', '', text)
+        text = re.sub(f'[{"".join([p for p in self._punc_list][1:])}]', '',
+                      text)
+        return text
+
+    def preprocess(self, text: Union[str, os.PathLike]):
+        """
+            Input preprocess and return paddle.Tensor stored in self.input.
+            Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
+        """
+        logger.info("Preprocessing input text: " + text)
+        if self.task == 'punc':
+            clean_text = self._clean_text(text)
+            assert len(clean_text) > 0, f'Invalid input string: {text}'
+
+            tokenized_input = self.tokenizer(
+                list(clean_text), return_length=True, is_split_into_words=True)
+
+            self._inputs['input_ids'] = tokenized_input['input_ids']
+            self._inputs['seg_ids'] = tokenized_input['token_type_ids']
+            self._inputs['seq_len'] = tokenized_input['seq_len']
+        else:
+            raise NotImplementedError
+
+    @paddle.no_grad()
+    def infer(self):
+        """
+            Model inference and result stored in self.output.
+        """
+        if self.task == 'punc':
+            input_ids = paddle.to_tensor(self._inputs['input_ids']).unsqueeze(0)
+            seg_ids = paddle.to_tensor(self._inputs['seg_ids']).unsqueeze(0)
+            logits, _ = self.model(input_ids, seg_ids)
+            preds = paddle.argmax(logits, axis=-1).squeeze(0)
+
+            self._outputs['preds'] = preds
+        else:
+            raise NotImplementedError
+
+    def postprocess(self) -> Union[str, os.PathLike]:
+        """
+            Output postprocess and return human-readable results such as texts and audio files.
+        """
+        if self.task == 'punc':
+            input_ids = self._inputs['input_ids']
+            seq_len = self._inputs['seq_len']
+            preds = self._outputs['preds']
+
+            tokens = self.tokenizer.convert_ids_to_tokens(
+                input_ids[1:seq_len - 1])
+            labels = preds[1:seq_len - 1].tolist()
+            assert len(tokens) == len(labels)
+
+            text = ''
+            for t, l in zip(tokens, labels):
+                text += t
+                if l != 0:  # Non punc.
+                    text += self._punc_list[l]
+
+            return text
+        else:
+            raise NotImplementedError
+
+    def execute(self, argv: List[str]) -> bool:
+        """
+            Command line entry.
+        """
+        parser_args = self.parser.parse_args(argv)
+
+        text = parser_args.input
+        task = parser_args.task
+        model_type = parser_args.model
+        lang = parser_args.lang
+        cfg_path = parser_args.config
+        ckpt_path = parser_args.ckpt_path
+        punc_vocab = parser_args.punc_vocab
+        device = parser_args.device
+
+        try:
+            res = self(text, task, model_type, lang, cfg_path, ckpt_path,
+                       punc_vocab, device)
+            logger.info('Text Result:\n{}'.format(res))
+            return True
+        except Exception as e:
+            logger.exception(e)
+            return False
+
+    def __call__(
+            self,
+            text: str,
+            task: str='punc',
+            model: str='ernie_linear_wudao',
+            lang: str='zh',
+            config: os.PathLike=None,
+            ckpt_path: os.PathLike=None,
+            punc_vocab: os.PathLike=None,
+            device: str=paddle.get_device(), ):
+        """
+            Python API to call an executor.
+        """
+        paddle.set_device(device)
+        self._init_from_path(task, model, lang, config, ckpt_path, punc_vocab)
+        self.preprocess(text)
+        self.infer()
+        res = self.postprocess()  # Retrieve result of text task.
+
+        return res

paddlespeech/text/.gitignore

@@ -1,7 +0,0 @@
-data
-glove
-.pyc
-checkpoints
-epoch
-__pycache__
-glove.840B.300d.zip

paddlespeech/text/README.md

@@ -1,25 +0,0 @@
-# PaddleSpeechTask
-A speech library to deal with a series of related front-end and back-end tasks  
-
-## 环境
-- python==3.6.13
-- paddle==2.1.1
-
-## 中/英文文本加标点任务 punctuation restoration：
-
-### 数据集: data
-- 中文数据来源：data/chinese  
-1.iwlst2012zh  
-2.平凡的世界
-
--  英文数据来源: data/english  
-1.iwlst2012en
-
-- iwlst2012数据获取过程见data/README.md
-
-### 模型：speechtask/punctuation_restoration/model
-1.BLSTM模型
-
-2.BertLinear模型
-
-3.BertBLSTM模型

paddlespeech/text/examples/punctuation_restoration/chinese/conf/blstm.yaml

@@ -1,34 +0,0 @@
-data:
-  language: chinese
-  raw_path: /data4/mahaoxin/PaddleSpeechTask/data/chinese/PFDSJ  #path to raw dataset
-  raw_train_file: train
-  raw_dev_file: dev
-  raw_test_file: test
-  vocab_file: vocab
-  punc_file: punc_vocab 
-  save_path: data/PFDSJ  #path to save dataset
-  seq_len: 100
-  batch_size: 10
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 0
-
-model_type: blstm
-model_params:
-  vocab_size: 3751
-  embedding_size: 200
-  hidden_size: 100
-  num_layers: 3
-  num_class: 5
-  init_scale: 0.1
-
-training:
-  n_epoch: 32
-  lr: !!float 1e-4
-  lr_decay: 1.0 
-  weight_decay: !!float 1e-06
-  global_grad_clip: 5.0
-  log_interval: 10
-
-
-

paddlespeech/text/examples/punctuation_restoration/chinese/conf/data_conf/chinese.yaml

@@ -1,7 +0,0 @@
-type: chinese
-raw_path: /data4/mahaoxin/PaddleSpeechTask/data/chinese/iwslt2012_zh  #path to raw dataset
-raw_train_file: iwslt2012_train_zh
-raw_dev_file: iwslt2010_dev_zh
-raw_test_file: biaobei_asr
-punc_file: punc_vocab 
-save_path: data/iwslt2012_zh  #path to save dataset

paddlespeech/text/examples/punctuation_restoration/chinese/conf/train_conf/bertBLSTM_zh.yaml

@@ -1,49 +0,0 @@
-data:
-  dataset_type: Bert
-  train_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/data/iwslt2012_zh/train
-  dev_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/data/iwslt2012_zh/dev
-  test_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/data/iwslt2012_zh/test2012_revise
-  data_params: 
-    pretrained_token: bert-base-chinese
-    punc_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/data/iwslt2012_zh/punc_vocab
-    seq_len: 100
-  batch_size: 64
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 0
-
-checkpoint:
-  kbest_n: 5
-  latest_n: 10
-  metric_type: F1
-
-
-model_type: BertBLSTM
-model_params:
-  pretrained_token: bert-base-chinese
-  output_size: 4
-  dropout: 0.0
-  bert_size: 768
-  blstm_size: 128
-  num_blstm_layers: 2
-  init_scale: 0.1
-
-# model_type: BertChLinear
-# model_params: bert-base-chinese
-#   pretrained_token:
-#   output_size: 4
-#   dropout: 0.0
-#   bert_size: 768
-
-training:
-  n_epoch: 100
-  lr: !!float 1e-5
-  lr_decay: 1.0 
-  weight_decay: !!float 1e-06
-  global_grad_clip: 5.0
-  log_interval: 10
-  log_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/log/bertBLSTM_zh0812.log
-
-testing:
-  log_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/log/test_bertBLSTM_zh0812.log
-

paddlespeech/text/examples/punctuation_restoration/chinese/conf/train_conf/bertLinear_zh.yaml

@@ -1,42 +0,0 @@
-data:
-  dataset_type: Bert
-  train_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/data/iwslt2012_zh/train
-  dev_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/data/iwslt2012_zh/dev
-  test_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/data/iwslt2012_zh/test2012
-  data_params: 
-    pretrained_token: bert-base-chinese
-    punc_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/data/iwslt2012_zh/punc_vocab
-    seq_len: 100
-  batch_size: 32
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 0
-
-checkpoint:
-  kbest_n: 10
-  latest_n: 10
-  metric_type: F1
-
-
-model_type: BertLinear
-model_params:
-  pretrained_token: bert-base-uncased
-  output_size: 4
-  dropout: 0.2
-  bert_size: 768
-  hiddensize: 1568
-
-
-training:
-  n_epoch: 50
-  lr: !!float 1e-5
-  lr_decay: 1.0 
-  weight_decay: !!float 1e-06
-  global_grad_clip: 5.0
-  log_interval: 10
-  log_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/log/train_linear0812.log
-
-testing:
-  log_interval: 10
-  log_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/chinese/log/test_linear0812.log
-

paddlespeech/text/examples/punctuation_restoration/chinese/local/data.sh

@@ -1,19 +0,0 @@
-#!/bin/bash
-
-if [ $# != 1 ];then
-    echo "usage: ${0} data_pre_conf"
-    echo $1
-    exit -1
-fi
-
-data_pre_conf=$1
-
-python3 -u ${BIN_DIR}/pre_data.py \
---config ${data_pre_conf}
-
-if [ $? -ne 0 ]; then
-    echo "Failed in training!"
-    exit 1
-fi
-
-exit 0

paddlespeech/text/examples/punctuation_restoration/chinese/local/train.sh

@@ -1,26 +0,0 @@
-#!/bin/bash
-
-if [ $# != 2 ];then
-    echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name"
-    exit -1
-fi
-
-ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
-echo "using $ngpu gpus..."
-
-config_path=$1
-ckpt_name=$2
-
-mkdir -p exp
-
-python3 -u ${BIN_DIR}/train.py \
---ngpu ${ngpu} \
---config ${config_path} \
---output exp/${ckpt_name}
-
-if [ $? -ne 0 ]; then
-    echo "Failed in training!"
-    exit 1
-fi
-
-exit 0

paddlespeech/text/examples/punctuation_restoration/english/README.md

@@ -1,23 +0,0 @@
-# 英文实验例程
-## 测试数据：
-- IWLST2012英文：test2011
-
-## 运行代码
-- 运行 `run.sh 0 0 conf/train_conf/bertBLSTM_base_en.yaml 1 conf/data_conf/english.yaml `
-
-
-## 相关论文实验结果：
-> * Nagy, Attila, Bence Bial, and Judit Ács. "Automatic punctuation restoration with BERT models." arXiv preprint arXiv:2101.07343 (2021)*  
->
-
-
-## 实验结果：
-- BertBLSTM
-  - 实验配置：conf/train_conf/bertLinear_en.yaml
-  - 测试结果：exp/bertLinear_enRe/checkpoints/3.pdparams
-
-    |           | COMMA     | PERIOD    | QUESTION  | OVERALL  |  
-    |-----------|-----------|-----------|-----------|--------- |  
-    |Precision  |0.667910   |0.715778   |0.822222   |0.735304  |
-    |Recall     |0.755274   |0.868188   |0.804348   |0.809270  |
-    |F1         |0.708911   |0.784651   |0.813187   |0.768916  |  

paddlespeech/text/examples/punctuation_restoration/english/conf/data_conf/english.yaml

@@ -1,7 +0,0 @@
-type: english
-raw_path: /data4/mahaoxin/PaddleSpeechTask/data/english/iwslt2012_en  #path to raw dataset
-raw_train_file: iwslt2012_train_en
-raw_dev_file: iwslt2010_dev_en
-raw_test_file: iwslt2011_test_en
-punc_file: punc_vocab 
-save_path: data/iwslt2012_en  #path to save dataset

paddlespeech/text/examples/punctuation_restoration/english/conf/train_conf/bertBLSTM_base_en.yaml

@@ -1,47 +0,0 @@
-data:
-  dataset_type: Bert
-  train_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/english/data/iwslt2012_en/train
-  dev_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/english/data/iwslt2012_en/dev
-  test_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/english/data/iwslt2012_en/test2011
-  data_params: 
-    pretrained_token: bert-base-uncased  #english
-    punc_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/english/data/iwslt2012_en/punc_vocab
-    seq_len: 50
-  batch_size: 32
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 0
-
-checkpoint:
-  kbest_n: 10
-  latest_n: 10
-
-model_type: BertBLSTM
-model_params:
-  pretrained_token: bert-base-uncased
-  output_size: 4
-  dropout: 0.0
-  bert_size: 768
-  blstm_size: 128
-  num_blstm_layers: 2
-  init_scale: 0.2
-# model_type: BertChLinear
-# model_params:
-#   pretrained_token: bert-large-uncased
-#   output_size: 4
-#   dropout: 0.0
-#   bert_size: 768
-
-training:
-  n_epoch: 100
-  lr: !!float 1e-5
-  lr_decay: 1.0 
-  weight_decay: !!float 1e-06
-  global_grad_clip: 5.0
-  log_interval: 10
-  log_path: log/bertBLSTM_base0812.log
-
-testing:
-  log_path: log/testbertBLSTM_base0812.log
-
-

paddlespeech/text/examples/punctuation_restoration/english/conf/train_conf/bertLinear_en.yaml

@@ -1,39 +0,0 @@
-data:
-  dataset_type: Bert
-  train_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/english/data/iwslt2012_en/train
-  dev_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/english/data/iwslt2012_en/dev
-  test_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/english/data/iwslt2012_en/test2011
-  data_params: 
-    pretrained_token: bert-base-uncased  #english
-    punc_path: /data4/mahaoxin/PaddleSpeechTask/examples/punctuation_restoration/english/data/iwslt2012_en/punc_vocab
-    seq_len: 100
-  batch_size: 32
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 0
-
-checkpoint:
-  kbest_n: 10
-  latest_n: 10
-
-model_type: BertLinear
-model_params: 
-  pretrained_token: bert-base-uncased
-  output_size: 4
-  dropout: 0.2
-  bert_size: 768
-  hiddensize: 1568
-
-training:
-  n_epoch: 20
-  lr: !!float 1e-5
-  lr_decay: 1.0 
-  weight_decay: !!float 1e-06
-  global_grad_clip: 3.0
-  log_interval: 10
-  log_path: log/train_linear0820.log
-
-testing:
-  log_path: log/test2011_linear0820.log
-
-

paddlespeech/text/examples/punctuation_restoration/english/local/avg.sh

@@ -1,23 +0,0 @@
-#! /usr/bin/env bash
-
-if [ $# != 2 ]; then
-    echo "usage: ${0} ckpt_dir avg_num"
-    exit -1
-fi
-
-ckpt_dir=${1}
-average_num=${2}
-decode_checkpoint=${ckpt_dir}/avg_${average_num}.pdparams
-
-python3 -u ${BIN_DIR}/avg_model.py \
---dst_model ${decode_checkpoint} \
---ckpt_dir ${ckpt_dir}  \
---num ${average_num} \
---val_best
-
-if [ $? -ne 0 ]; then
-    echo "Failed in avg ckpt!"
-    exit 1
-fi
-
-exit 0

paddlespeech/text/examples/punctuation_restoration/english/local/data.sh

@@ -1,18 +0,0 @@
-#!/bin/bash
-
-if [ $# != 1 ];then
-    echo "usage: ${0} config_path"
-    exit -1
-fi
-
-config_path=$1
-
-python3 -u ${BIN_DIR}/pre_data.py \
---config ${config_path}
-
-if [ $? -ne 0 ]; then
-    echo "Failed in training!"
-    exit 1
-fi
-
-exit 0

paddlespeech/text/examples/punctuation_restoration/english/local/test.sh

@@ -1,27 +0,0 @@
-
-#!/bin/bash
-
-if [ $# != 2 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix"
-    exit -1
-fi
-
-ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
-echo "using $ngpu gpus..."
-
-config_path=$1
-ckpt_prefix=$2
-
-
-python3 -u ${BIN_DIR}/test.py \
---ngpu 1 \
---config ${config_path} \
---result_file ${ckpt_prefix}.rsl \
---checkpoint_path ${ckpt_prefix}
-
-if [ $? -ne 0 ]; then
-    echo "Failed in evaluation!"
-    exit 1
-fi
-
-exit 0

paddlespeech/text/examples/punctuation_restoration/english/path.sh

@@ -1,13 +0,0 @@
-export MAIN_ROOT=${PWD}/../../../
-
-export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
-export LC_ALL=C
-
-# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
-export PYTHONIOENCODING=UTF-8
-export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
-
-export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
-
-
-export BIN_DIR=${MAIN_ROOT}/speechtask/punctuation_restoration/bin

paddlespeech/text/examples/punctuation_restoration/english/run.sh

@@ -1,47 +0,0 @@
-#!/bin/bash
-set -e
-source path.sh
-
-
-##  stage, gpu, data_pre_config, train_config, avg_num
-if [ $# -lt 4 ]; then
-    echo "usage: bash ./run.sh  stage gpu train_config avg_num data_config" 
-    echo "eg: bash ./run.sh  0 0 train_config 1 data_config " 
-    exit -1
-fi
-
-stage=$1
-stop_stage=100
-gpus=$2
-conf_path=$3
-avg_num=$4
-avg_ckpt=avg_${avg_num}
-ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
-echo "checkpoint name ${ckpt}"
-
-if [ $stage -le 0 ]; then 
-    if [ $# -eq 5 ]; then
-        data_pre_conf=$5
-        # prepare data
-        bash ./local/data.sh ${data_pre_conf} || exit -1
-    else
-        echo "data_pre_conf is not exist!"
-        exit -1
-    fi
-fi
-
-
-if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    # train model, all `ckpt` under `exp` dir
-    CUDA_VISIBLE_DEVICES=${gpus} bash ./local/train.sh ${conf_path} ${ckpt}
-fi
-
-if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
-    # avg n best model
-    bash ./local/avg.sh exp/${ckpt}/checkpoints ${avg_num}
-fi
-
-if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
-    # test ckpt avg_n
-   CUDA_VISIBLE_DEVICES=${gpus} bash ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
-fi

paddlespeech/text/exps/ernie_linear/avg_model.py

@@ -31,14 +31,14 @@ def main(args):
         jsons = glob.glob(f'{args.ckpt_dir}/[!train]*.json')
         for y in jsons:
             with open(y, 'r') as f:
-                dic_json = json.load(f)
-            loss = dic_json['F1']
-            epoch = dic_json['epoch']
+                dict_json = json.load(f)
+            loss = dict_json['F1']
+            epoch = dict_json['epoch']
             if epoch >= args.min_epoch and epoch <= args.max_epoch:
                 val_scores.append((epoch, loss))
 
         val_scores = np.array(val_scores)
-        sort_idx = np.argsort(val_scores[:, 1])
+        sort_idx = np.argsort(-val_scores[:, 1])
         sorted_val_scores = val_scores[sort_idx]
         path_list = [
             args.ckpt_dir + '/{}.pdparams'.format(int(epoch))

paddlespeech/text/exps/ernie_linear/test.py

@@ -13,9 +13,10 @@
 # limitations under the License.
 """Evaluation for model."""
 import yaml
-from speechtask.punctuation_restoration.training.trainer import Tester
-from speechtask.punctuation_restoration.utils.default_parser import default_argument_parser
-from speechtask.punctuation_restoration.utils.utility import print_arguments
+
+from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.text.training.trainer import Tester
+from paddlespeech.text.utils.default_parser import default_argument_parser
 
 
 def main_sp(config, args):

paddlespeech/text/exps/ernie_linear/train.py

@@ -14,9 +14,10 @@
 """Trainer for punctuation_restoration task."""
 import yaml
 from paddle import distributed as dist
-from speechtask.punctuation_restoration.training.trainer import Trainer
-from speechtask.punctuation_restoration.utils.default_parser import default_argument_parser
-from speechtask.punctuation_restoration.utils.utility import print_arguments
+
+from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.text.training.trainer import Trainer
+from paddlespeech.text.utils.default_parser import default_argument_parser
 
 
 def main_sp(config, args):

paddlespeech/text/models/__init__.py

@@ -0,0 +1,15 @@
+# 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.
+from .ernie_crf import ErnieCrf
+from .ernie_linear import ErnieLinear

paddlespeech/text/models/ernie_crf/__init__.py

@@ -11,3 +11,4 @@
 # 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.
+from .model import ErnieCrf

paddlespeech/text/models/ernie_crf/model.py

@@ -0,0 +1,65 @@
+# 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.
+import paddle
+import paddle.nn as nn
+from paddlenlp.layers.crf import LinearChainCrf
+from paddlenlp.layers.crf import LinearChainCrfLoss
+from paddlenlp.layers.crf import ViterbiDecoder
+from paddlenlp.transformers import ErnieForTokenClassification
+
+
+class ErnieCrf(nn.Layer):
+    def __init__(self,
+                 num_classes,
+                 pretrained_token='ernie-1.0',
+                 crf_lr=100,
+                 **kwargs):
+        super().__init__()
+        self.ernie = ErnieForTokenClassification.from_pretrained(
+            pretrained_token, num_classes=num_classes, **kwargs)
+        self.num_classes = num_classes
+        self.crf = LinearChainCrf(
+            self.num_classes, crf_lr=crf_lr, with_start_stop_tag=False)
+        self.crf_loss = LinearChainCrfLoss(self.crf)
+        self.viterbi_decoder = ViterbiDecoder(
+            self.crf.transitions, with_start_stop_tag=False)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                position_ids=None,
+                attention_mask=None,
+                lengths=None,
+                labels=None):
+        logits = self.ernie(
+            input_ids,
+            token_type_ids=token_type_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids)
+
+        if lengths is None:
+            lengths = paddle.ones(
+                shape=[input_ids.shape[0]],
+                dtype=paddle.int64) * input_ids.shape[1]
+
+        _, prediction = self.viterbi_decoder(logits, lengths)
+        prediction = prediction.reshape([-1])
+
+        if labels is not None:
+            labels = labels.reshape([input_ids.shape[0], -1])
+            loss = self.crf_loss(logits, lengths, labels)
+            avg_loss = paddle.mean(loss)
+            return avg_loss, prediction
+        else:
+            return prediction

paddlespeech/text/models/ernie_linear/__init__.py

@@ -0,0 +1,14 @@
+# 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.
+from .model import ErnieLinear

paddlespeech/text/models/ernie_linear/dataset.py

@@ -0,0 +1,155 @@
+# 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.
+import numpy as np
+import paddle
+from paddle.io import Dataset
+from paddlenlp.transformers import ErnieTokenizer
+
+__all__ = ["PuncDataset", "PuncDatasetFromErnieTokenizer"]
+
+
+class PuncDataset(Dataset):
+    def __init__(self, train_path, vocab_path, punc_path, seq_len=100):
+        self.seq_len = seq_len
+
+        self.word2id = self.load_vocab(
+            vocab_path, extra_word_list=['<UNK>', '<END>'])
+        self.id2word = {v: k for k, v in self.word2id.items()}
+        self.punc2id = self.load_vocab(punc_path, extra_word_list=[" "])
+        self.id2punc = {k: v for (v, k) in self.punc2id.items()}
+
+        tmp_seqs = open(train_path, encoding='utf-8').readlines()
+        self.txt_seqs = [i for seq in tmp_seqs for i in seq.split()]
+        self.preprocess(self.txt_seqs)
+
+    def __len__(self):
+        """return the sentence nums in .txt
+        """
+        return self.in_len
+
+    def __getitem__(self, index):
+        return self.input_data[index], self.label[index]
+
+    def load_vocab(self, vocab_path, extra_word_list=[], encoding='utf-8'):
+        n = len(extra_word_list)
+        with open(vocab_path, encoding='utf-8') as vf:
+            vocab = {word.strip(): i + n for i, word in enumerate(vf)}
+        for i, word in enumerate(extra_word_list):
+            vocab[word] = i
+        return vocab
+
+    def preprocess(self, txt_seqs: list):
+        input_data = []
+        label = []
+        input_r = []
+        label_r = []
+
+        count = 0
+        length = len(txt_seqs)
+        for token in txt_seqs:
+            count += 1
+            if count == length:
+                break
+            if token in self.punc2id:
+                continue
+            punc = txt_seqs[count]
+            if punc not in self.punc2id:
+                label.append(self.punc2id[" "])
+                input_data.append(
+                    self.word2id.get(token, self.word2id["<UNK>"]))
+                input_r.append(token)
+                label_r.append(' ')
+            else:
+                label.append(self.punc2id[punc])
+                input_data.append(
+                    self.word2id.get(token, self.word2id["<UNK>"]))
+                input_r.append(token)
+                label_r.append(punc)
+        if len(input_data) != len(label):
+            assert 'error: length input_data != label'
+
+        self.in_len = len(input_data) // self.seq_len
+        len_tmp = self.in_len * self.seq_len
+        input_data = input_data[:len_tmp]
+        label = label[:len_tmp]
+
+        self.input_data = paddle.to_tensor(
+            np.array(input_data, dtype='int64').reshape(-1, self.seq_len))
+        self.label = paddle.to_tensor(
+            np.array(label, dtype='int64').reshape(-1, self.seq_len))
+
+
+class PuncDatasetFromErnieTokenizer(Dataset):
+    def __init__(self,
+                 train_path,
+                 punc_path,
+                 pretrained_token='ernie-1.0',
+                 seq_len=100):
+        self.tokenizer = ErnieTokenizer.from_pretrained(pretrained_token)
+        self.paddingID = self.tokenizer.pad_token_id
+        self.seq_len = seq_len
+
+        self.punc2id = self.load_vocab(punc_path, extra_word_list=[" "])
+        self.id2punc = {k: v for (v, k) in self.punc2id.items()}
+
+        tmp_seqs = open(train_path, encoding='utf-8').readlines()
+        self.txt_seqs = [i for seq in tmp_seqs for i in seq.split()]
+        self.preprocess(self.txt_seqs)
+
+    def __len__(self):
+        return self.in_len
+
+    def __getitem__(self, index):
+        return self.input_data[index], self.label[index]
+
+    def load_vocab(self, vocab_path, extra_word_list=[], encoding='utf-8'):
+        n = len(extra_word_list)
+        with open(vocab_path, encoding='utf-8') as vf:
+            vocab = {word.strip(): i + n for i, word in enumerate(vf)}
+        for i, word in enumerate(extra_word_list):
+            vocab[word] = i
+        return vocab
+
+    def preprocess(self, txt_seqs: list):
+        input_data = []
+        label = []
+        count = 0
+        for i in range(len(txt_seqs) - 1):
+            word = txt_seqs[i]
+            punc = txt_seqs[i + 1]
+            if word in self.punc2id:
+                continue
+
+            token = self.tokenizer(word)
+            x = token["input_ids"][1:-1]
+            input_data.extend(x)
+
+            for i in range(len(x) - 1):
+                label.append(self.punc2id[" "])
+
+            if punc not in self.punc2id:
+                label.append(self.punc2id[" "])
+            else:
+                label.append(self.punc2id[punc])
+
+        if len(input_data) != len(label):
+            assert 'error: length input_data != label'
+
+        self.in_len = len(input_data) // self.seq_len
+        len_tmp = self.in_len * self.seq_len
+        input_data = input_data[:len_tmp]
+        label = label[:len_tmp]
+        self.input_data = np.array(
+            input_data, dtype='int64').reshape(-1, self.seq_len)
+        self.label = np.array(label, dtype='int64').reshape(-1, self.seq_len)

paddlespeech/text/models/ernie_linear/model.py

@@ -0,0 +1,65 @@
+# 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.
+import os
+
+import paddle
+import paddle.nn as nn
+from paddlenlp.transformers import ErnieForTokenClassification
+
+
+class ErnieLinear(nn.Layer):
+    def __init__(self,
+                 num_classes=None,
+                 pretrained_token='ernie-1.0',
+                 cfg_path=None,
+                 ckpt_path=None,
+                 **kwargs):
+        super(ErnieLinear, self).__init__()
+
+        if cfg_path is not None and ckpt_path is not None:
+            cfg_path = os.path.abspath(os.path.expanduser(cfg_path))
+            ckpt_path = os.path.abspath(os.path.expanduser(ckpt_path))
+
+            assert os.path.isfile(
+                cfg_path), 'Config file is not valid: {}'.format(cfg_path)
+            assert os.path.isfile(
+                ckpt_path), 'Checkpoint file is not valid: {}'.format(ckpt_path)
+
+            self.ernie = ErnieForTokenClassification.from_pretrained(
+                os.path.dirname(cfg_path))
+        else:
+            assert isinstance(
+                num_classes, int
+            ) and num_classes > 0, 'Argument `num_classes` must be an integer.'
+            self.ernie = ErnieForTokenClassification.from_pretrained(
+                pretrained_token, num_classes=num_classes, **kwargs)
+
+        self.num_classes = self.ernie.num_classes
+        self.softmax = nn.Softmax()
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                position_ids=None,
+                attention_mask=None):
+        y = self.ernie(
+            input_ids,
+            token_type_ids=token_type_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids)
+
+        y = paddle.reshape(y, shape=[-1, self.num_classes])
+        logits = self.softmax(y)
+
+        return y, logits

paddlespeech/text/requirements.txt

@@ -1,6 +0,0 @@
-numpy
-pyyaml
-tensorboardX
-tqdm
-ujson
-yacs

paddlespeech/text/speechtask/punctuation_restoration/bin/pre_data.py

@@ -1,48 +0,0 @@
-# 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.
-"""Data preparation for punctuation_restoration task."""
-import yaml
-from speechtask.punctuation_restoration.utils.default_parser import default_argument_parser
-from speechtask.punctuation_restoration.utils.punct_pre import process_chinese_pure_senetence
-from speechtask.punctuation_restoration.utils.punct_pre import process_english_pure_senetence
-from speechtask.punctuation_restoration.utils.utility import print_arguments
-
-
-# create dataset from raw data files
-def main(config, args):
-    print("Start preparing data from raw data.")
-    if (config['type'] == 'chinese'):
-        process_chinese_pure_senetence(config)
-    elif (config['type'] == 'english'):
-        print('english!!!!')
-        process_english_pure_senetence(config)
-    else:
-        print('Error: Type should be chinese or english!!!!')
-        raise ValueError('Type should be chinese or english')
-
-    print("Finish preparing data.")
-
-
-if __name__ == "__main__":
-    parser = default_argument_parser()
-    args = parser.parse_args()
-    print_arguments(args, globals())
-
-    # https://yaml.org/type/float.html
-    with open(args.config, "r") as f:
-        config = yaml.load(f, Loader=yaml.FullLoader)
-
-    # config.freeze()
-    print(config)
-    main(config, args)

paddlespeech/text/speechtask/punctuation_restoration/io/collator.py

@@ -1,64 +0,0 @@
-# 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.
-import numpy as np
-
-__all__ = ["TextCollator"]
-
-
-class TextCollator():
-    def __init__(self, padding_value):
-        self.padding_value = padding_value
-
-    def __call__(self, batch):
-        """batch examples
-        Args:
-            batch ([List]): batch is (text, punctuation)
-                text (List[int] ) shape (batch, L)
-                punctuation (List[int] or str): shape (batch, L)
-        Returns:
-            tuple(text, punctuation): batched data.
-                text : (B, Lmax)
-                punctuation : (B, Lmax)
-        """
-        texts = []
-        punctuations = []
-        for text, punctuation in batch:
-
-            texts.append(text)
-            punctuations.append(punctuation)
-
-        #[B, T, D]
-        x_pad = self.pad_sequence(texts).astype(np.int64)
-        # print(x_pad.shape)
-        # pad_list(audios, 0.0).astype(np.float32)
-        # ilens = np.array(audio_lens).astype(np.int64)
-        y_pad = self.pad_sequence(punctuations).astype(np.int64)
-        # print(y_pad.shape)
-        # olens = np.array(text_lens).astype(np.int64)
-        return x_pad, y_pad
-
-    def pad_sequence(self, sequences):
-        # assuming trailing dimensions and type of all the Tensors
-        # in sequences are same and fetching those from sequences[0]
-        max_len = max([len(s) for s in sequences])
-        out_dims = (len(sequences), max_len)
-
-        out_tensor = np.full(out_dims,
-                             self.padding_value)  #, dtype=sequences[0].dtype)
-        for i, tensor in enumerate(sequences):
-            length = len(tensor)
-            # use index notation to prevent duplicate references to the tensor
-            out_tensor[i, :length] = tensor
-
-        return out_tensor

paddlespeech/text/speechtask/punctuation_restoration/io/common.py

@@ -1,55 +0,0 @@
-# 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.
-import codecs
-import re
-import unicodedata
-
-import ujson
-
-PAD = "<PAD>"
-UNK = "<UNK>"
-NUM = "<NUM>"
-END = "</S>"
-SPACE = "_SPACE"
-
-
-def write_json(filename, dataset):
-    with codecs.open(filename, mode="w", encoding="utf-8") as f:
-        ujson.dump(dataset, f)
-
-
-def word_convert(word, keep_number=True, lowercase=True):
-    if not keep_number:
-        if is_digit(word):
-            word = NUM
-    if lowercase:
-        word = word.lower()
-    return word
-
-
-def is_digit(word):
-    try:
-        float(word)
-        return True
-    except ValueError:
-        pass
-    try:
-        unicodedata.numeric(word)
-        return True
-    except (TypeError, ValueError):
-        pass
-    result = re.compile(r'^[-+]?[0-9]+,[0-9]+$').match(word)
-    if result:
-        return True
-    return False

paddlespeech/text/speechtask/punctuation_restoration/io/dataset.py

@@ -1,310 +0,0 @@
-# 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.
-import os
-import random
-
-import numpy as np
-import paddle
-from paddle.io import Dataset
-from paddlenlp.transformers import BertTokenizer
-# from speechtask.punctuation_restoration.utils.punct_prepro import load_dataset
-
-__all__ = ["PuncDataset", "PuncDatasetFromBertTokenizer"]
-
-
-class PuncDataset(Dataset):
-    """Representing a Dataset
-    superclass
-    ----------
-    data.Dataset :
-        Dataset is a abstract class, representing the real data.
-    """
-
-    def __init__(self, train_path, vocab_path, punc_path, seq_len=100):
-        # 检查文件是否存在
-        print(train_path)
-        print(vocab_path)
-        assert os.path.exists(train_path), "train文件不存在"
-        assert os.path.exists(vocab_path), "词典文件不存在"
-        assert os.path.exists(punc_path), "标点文件不存在"
-        self.seq_len = seq_len
-
-        self.word2id = self.load_vocab(
-            vocab_path, extra_word_list=['<UNK>', '<END>'])
-        self.id2word = {v: k for k, v in self.word2id.items()}
-        self.punc2id = self.load_vocab(punc_path, extra_word_list=[" "])
-        self.id2punc = {k: v for (v, k) in self.punc2id.items()}
-
-        tmp_seqs = open(train_path, encoding='utf-8').readlines()
-        self.txt_seqs = [i for seq in tmp_seqs for i in seq.split()]
-        # print(self.txt_seqs[:10])
-        # with open('./txt_seq', 'w', encoding='utf-8') as w:
-        #     print(self.txt_seqs, file=w)
-        self.preprocess(self.txt_seqs)
-        print('---punc-')
-        print(self.punc2id)
-
-    def __len__(self):
-        """return the sentence nums in .txt
-        """
-        return self.in_len
-
-    def __getitem__(self, index):
-        """返回指定索引的张量对 (输入文本id的序列 , 其对应的标点id序列)
-        Parameters
-        ----------
-        index : int 索引
-        """
-        return self.input_data[index], self.label[index]
-
-    def load_vocab(self, vocab_path, extra_word_list=[], encoding='utf-8'):
-        n = len(extra_word_list)
-        with open(vocab_path, encoding='utf-8') as vf:
-            vocab = {word.strip(): i + n for i, word in enumerate(vf)}
-        for i, word in enumerate(extra_word_list):
-            vocab[word] = i
-        return vocab
-
-    def preprocess(self, txt_seqs: list):
-        """将文本转为单词和应预测标点的id pair
-        Parameters
-        ----------
-        txt : 文本
-            文本每个单词跟随一个空格，符号也跟一个空格
-        """
-        input_data = []
-        label = []
-        input_r = []
-        label_r = []
-        # txt_seqs is a list like: ['char', 'char', 'char', '*，*', 'char', ......]
-        count = 0
-        length = len(txt_seqs)
-        for token in txt_seqs:
-            count += 1
-            if count == length:
-                break
-            if token in self.punc2id:
-                continue
-            punc = txt_seqs[count]
-            if punc not in self.punc2id:
-                # print('标点{}：'.format(count), self.punc2id[" "])
-                label.append(self.punc2id[" "])
-                input_data.append(
-                    self.word2id.get(token, self.word2id["<UNK>"]))
-                input_r.append(token)
-                label_r.append(' ')
-            else:
-                # print('标点{}：'.format(count), self.punc2id[punc])
-                label.append(self.punc2id[punc])
-                input_data.append(
-                    self.word2id.get(token, self.word2id["<UNK>"]))
-                input_r.append(token)
-                label_r.append(punc)
-        if len(input_data) != len(label):
-            assert 'error: length input_data != label'
-        # code below is for using 100 as a hidden size
-        print(len(input_data))
-        self.in_len = len(input_data) // self.seq_len
-        len_tmp = self.in_len * self.seq_len
-        input_data = input_data[:len_tmp]
-        label = label[:len_tmp]
-
-        self.input_data = paddle.to_tensor(
-            np.array(input_data, dtype='int64').reshape(-1, self.seq_len))
-        self.label = paddle.to_tensor(
-            np.array(label, dtype='int64').reshape(-1, self.seq_len))
-
-
-# unk_token='[UNK]'
-# sep_token='[SEP]'
-# pad_token='[PAD]'
-# cls_token='[CLS]'
-# mask_token='[MASK]'
-
-
-class PuncDatasetFromBertTokenizer(Dataset):
-    """Representing a Dataset
-    superclass
-    ----------
-    data.Dataset :
-        Dataset is a abstract class, representing the real data.
-    """
-
-    def __init__(self,
-                 train_path,
-                 is_eval,
-                 pretrained_token,
-                 punc_path,
-                 seq_len=100):
-        # 检查文件是否存在
-        print(train_path)
-        self.tokenizer = BertTokenizer.from_pretrained(
-            pretrained_token, do_lower_case=True)
-        self.paddingID = self.tokenizer.pad_token_id
-        assert os.path.exists(train_path), "train文件不存在"
-        assert os.path.exists(punc_path), "标点文件不存在"
-        self.seq_len = seq_len
-
-        self.punc2id = self.load_vocab(punc_path, extra_word_list=[" "])
-        self.id2punc = {k: v for (v, k) in self.punc2id.items()}
-
-        tmp_seqs = open(train_path, encoding='utf-8').readlines()
-        self.txt_seqs = [i for seq in tmp_seqs for i in seq.split()]
-        # print(self.txt_seqs[:10])
-        # with open('./txt_seq', 'w', encoding='utf-8') as w:
-        #     print(self.txt_seqs, file=w)
-        if (is_eval):
-            self.preprocess(self.txt_seqs)
-        else:
-            self.preprocess_shift(self.txt_seqs)
-        print("data len: %d" % (len(self.input_data)))
-        print('---punc-')
-        print(self.punc2id)
-
-    def __len__(self):
-        """return the sentence nums in .txt
-        """
-        return self.in_len
-
-    def __getitem__(self, index):
-        """返回指定索引的张量对 (输入文本id的序列 , 其对应的标点id序列)
-        Parameters
-        ----------
-        index : int 索引
-        """
-        return self.input_data[index], self.label[index]
-
-    def load_vocab(self, vocab_path, extra_word_list=[], encoding='utf-8'):
-        n = len(extra_word_list)
-        with open(vocab_path, encoding='utf-8') as vf:
-            vocab = {word.strip(): i + n for i, word in enumerate(vf)}
-        for i, word in enumerate(extra_word_list):
-            vocab[word] = i
-        return vocab
-
-    def preprocess(self, txt_seqs: list):
-        """将文本转为单词和应预测标点的id pair
-        Parameters
-        ----------
-        txt : 文本
-            文本每个单词跟随一个空格，符号也跟一个空格
-        """
-        input_data = []
-        label = []
-        # txt_seqs is a list like: ['char', 'char', 'char', '*，*', 'char', ......]
-        count = 0
-        for i in range(len(txt_seqs) - 1):
-            word = txt_seqs[i]
-            punc = txt_seqs[i + 1]
-            if word in self.punc2id:
-                continue
-
-            token = self.tokenizer(word)
-            x = token["input_ids"][1:-1]
-            input_data.extend(x)
-
-            for i in range(len(x) - 1):
-                label.append(self.punc2id[" "])
-
-            if punc not in self.punc2id:
-                # print('标点{}：'.format(count), self.punc2id[" "])
-                label.append(self.punc2id[" "])
-            else:
-                label.append(self.punc2id[punc])
-
-        if len(input_data) != len(label):
-            assert 'error: length input_data != label'
-        # code below is for using 100 as a hidden size
-
-        # print(len(input_data[0]))
-        # print(len(label))
-        self.in_len = len(input_data) // self.seq_len
-        len_tmp = self.in_len * self.seq_len
-        input_data = input_data[:len_tmp]
-        label = label[:len_tmp]
-        # # print(input_data)
-        # print(type(input_data))
-        # tmp=np.array(input_data)
-        # print('--~~~~~~~~~~~~~')
-        # print(type(tmp))
-        # print(tmp.shape)
-        self.input_data = paddle.to_tensor(
-            np.array(input_data, dtype='int64').reshape(
-                -1, self.seq_len))  #, dtype='int64'
-        self.label = paddle.to_tensor(
-            np.array(label, dtype='int64').reshape(
-                -1, self.seq_len))  #, dtype='int64'
-
-    def preprocess_shift(self, txt_seqs: list):
-        """将文本转为单词和应预测标点的id pair
-        Parameters
-        ----------
-        txt : 文本
-            文本每个单词跟随一个空格，符号也跟一个空格
-        """
-        input_data = []
-        label = []
-        # txt_seqs is a list like: ['char', 'char', 'char', '*，*', 'char', ......]
-        count = 0
-        for i in range(len(txt_seqs) - 1):
-            word = txt_seqs[i]
-            punc = txt_seqs[i + 1]
-            if word in self.punc2id:
-                continue
-
-            token = self.tokenizer(word)
-            x = token["input_ids"][1:-1]
-            input_data.extend(x)
-
-            for i in range(len(x) - 1):
-                label.append(self.punc2id[" "])
-
-            if punc not in self.punc2id:
-                # print('标点{}：'.format(count), self.punc2id[" "])
-                label.append(self.punc2id[" "])
-            else:
-                label.append(self.punc2id[punc])
-
-        if len(input_data) != len(label):
-            assert 'error: length input_data != label'
-
-        # print(len(input_data[0]))
-        # print(len(label))
-        start = 0
-        processed_data = []
-        processed_label = []
-        while (start < len(input_data) - self.seq_len):
-            # end=start+self.seq_len
-            end = random.randint(start + self.seq_len // 2,
-                                 start + self.seq_len)
-            processed_data.append(input_data[start:end])
-            processed_label.append(label[start:end])
-
-            start = start + random.randint(1, self.seq_len // 2)
-
-        self.in_len = len(processed_data)
-        # # print(input_data)
-        # print(type(input_data))
-        # tmp=np.array(input_data)
-        # print('--~~~~~~~~~~~~~')
-        # print(type(tmp))
-        # print(tmp.shape)
-        self.input_data = processed_data
-        #paddle.to_tensor(np.array(processed_data, dtype='int64'))  #, dtype='int64'
-        self.label = processed_label
-        #paddle.to_tensor(np.array(processed_label, dtype='int64')) #, dtype='int64'
-
-
-if __name__ == '__main__':
-    dataset = PuncDataset()

paddlespeech/text/speechtask/punctuation_restoration/model/BertBLSTM.py

@@ -1,74 +0,0 @@
-# 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.
-import paddle
-import paddle.nn as nn
-import paddle.nn.initializer as I
-from paddlenlp.transformers import BertForTokenClassification
-
-
-class BertBLSTMPunc(nn.Layer):
-    def __init__(self,
-                 pretrained_token="bert-large-uncased",
-                 output_size=4,
-                 dropout=0.0,
-                 bert_size=768,
-                 blstm_size=128,
-                 num_blstm_layers=2,
-                 init_scale=0.1):
-        super(BertBLSTMPunc, self).__init__()
-        self.output_size = output_size
-        self.bert = BertForTokenClassification.from_pretrained(
-            pretrained_token, num_classes=bert_size)
-        # self.bert_vocab_size = vocab_size
-        # self.bn = nn.BatchNorm1d(segment_size*self.bert_vocab_size)
-        # self.fc = nn.Linear(segment_size*self.bert_vocab_size, output_size)
-
-        self.lstm = nn.LSTM(
-            input_size=bert_size,
-            hidden_size=blstm_size,
-            num_layers=num_blstm_layers,
-            direction="bidirect",
-            weight_ih_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)),
-            weight_hh_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)))
-
-        # NOTE dense*2 使用bert中间层 dense hidden_state self.bert_size
-        self.dropout = nn.Dropout(dropout)
-        self.fc = nn.Linear(blstm_size * 2, output_size)
-        self.softmax = nn.Softmax()
-
-    def forward(self, x):
-        # print('input :', x.shape)
-        x = self.bert(x)  #[0]
-        # print('after bert :', x.shape)
-
-        y, (_, _) = self.lstm(x)
-        # print('after lstm :', y.shape)
-        y = self.fc(self.dropout(y))
-        y = paddle.reshape(y, shape=[-1, self.output_size])
-        # print('after fc :', y.shape)
-
-        logit = self.softmax(y)
-        # print('after softmax :', logit.shape)
-
-        return y, logit
-
-
-if __name__ == '__main__':
-    print('start model')
-    model = BertBLSTMPunc()
-    x = paddle.randint(low=0, high=40, shape=[2, 5])
-    print(x)
-    y, logit = model(x)

paddlespeech/text/speechtask/punctuation_restoration/model/BertLinear.py

@@ -1,63 +0,0 @@
-# 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.
-import paddle
-import paddle.nn as nn
-from paddlenlp.transformers import BertForTokenClassification
-
-
-class BertLinearPunc(nn.Layer):
-    def __init__(self,
-                 pretrained_token="bert-base-uncased",
-                 output_size=4,
-                 dropout=0.2,
-                 bert_size=768,
-                 hiddensize=1568):
-        super(BertLinearPunc, self).__init__()
-        self.output_size = output_size
-        self.bert = BertForTokenClassification.from_pretrained(
-            pretrained_token, num_classes=bert_size)
-        # self.bert_vocab_size = vocab_size
-        # self.bn = nn.BatchNorm1d(segment_size*self.bert_vocab_size)
-        # self.fc = nn.Linear(segment_size*self.bert_vocab_size, output_size)
-
-        # NOTE dense*2 使用bert中间层 dense hidden_state self.bert_size
-        self.dropout1 = nn.Dropout(dropout)
-        self.fc1 = nn.Linear(bert_size, hiddensize)
-        self.dropout2 = nn.Dropout(dropout)
-        self.relu = nn.ReLU()
-        self.fc2 = nn.Linear(hiddensize, output_size)
-        self.softmax = nn.Softmax()
-
-    def forward(self, x):
-        # print('input :', x.shape)
-        x = self.bert(x)  #[0]
-        # print('after bert :', x.shape)
-
-        x = self.fc1(self.dropout1(x))
-        x = self.fc2(self.relu(self.dropout2(x)))
-        x = paddle.reshape(x, shape=[-1, self.output_size])
-        # print('after fc :', x.shape)
-
-        logit = self.softmax(x)
-        # print('after softmax :', logit.shape)
-
-        return x, logit
-
-
-if __name__ == '__main__':
-    print('start model')
-    model = BertLinearPunc()
-    x = paddle.randint(low=0, high=40, shape=[2, 5])
-    print(x)
-    y, logit = model(x)

paddlespeech/text/speechtask/punctuation_restoration/model/blstm.py

@@ -1,89 +0,0 @@
-# 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.
-import paddle
-import paddle.nn as nn
-import paddle.nn.initializer as I
-
-
-class BiLSTM(nn.Layer):
-    """LSTM for Punctuation Restoration
-    """
-
-    def __init__(self,
-                 vocab_size,
-                 embedding_size,
-                 hidden_size,
-                 num_layers,
-                 num_class,
-                 init_scale=0.1):
-        super(BiLSTM, self).__init__()
-        # hyper parameters
-        self.vocab_size = vocab_size
-        self.embedding_size = embedding_size
-        self.hidden_size = hidden_size
-        self.num_layers = num_layers
-        self.num_class = num_class
-
-        # 网络中的层
-        self.embedding = nn.Embedding(
-            vocab_size,
-            embedding_size,
-            weight_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)))
-        # print(hidden_size)
-        # print(embedding_size)
-        self.lstm = nn.LSTM(
-            input_size=embedding_size,
-            hidden_size=hidden_size,
-            num_layers=num_layers,
-            direction="bidirect",
-            weight_ih_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)),
-            weight_hh_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)))
-        # Here is a one direction LSTM. If bidirection LSTM, (hidden_size*2(,))
-        self.fc = nn.Linear(
-            in_features=hidden_size * 2,
-            out_features=num_class,
-            weight_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)),
-            bias_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)))
-        # self.fc = nn.Linear(hidden_size, num_class)
-
-        self.softmax = nn.Softmax()
-
-    def forward(self, input):
-        """The forward process of Net
-        Parameters
-        ----------
-        inputs : tensor
-            Training data, batch first
-        """
-        # Inherit the knowledge of context
-
-        # hidden = self.init_hidden(inputs.size(0))
-        # print('input_size',inputs.size())
-        embedding = self.embedding(input)
-        # print('embedding_size', embedding.size())
-        # packed = pack_sequence(embedding, inputs_lengths, batch_first=True)
-        # embedding本身是同样长度的，用这个函数主要是为了用pack
-        # *****************************************************************************
-        y, (_, _) = self.lstm(embedding)
-
-        # print(y.size())
-        y = self.fc(y)
-        y = paddle.reshape(y, shape=[-1, self.num_class])
-        logit = self.softmax(y)
-        return y, logit

paddlespeech/text/speechtask/punctuation_restoration/model/lstm.py

@@ -1,85 +0,0 @@
-# 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.
-import paddle
-import paddle.nn as nn
-import paddle.nn.initializer as I
-
-
-class RnnLm(nn.Layer):
-    def __init__(self,
-                 vocab_size,
-                 punc_size,
-                 hidden_size,
-                 num_layers=1,
-                 init_scale=0.1,
-                 dropout=0.0):
-        super(RnnLm, self).__init__()
-        self.hidden_size = hidden_size
-        self.num_layers = num_layers
-        self.init_scale = init_scale
-        self.punc_size = punc_size
-
-        self.embedder = nn.Embedding(
-            vocab_size,
-            hidden_size,
-            weight_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)))
-
-        self.lstm = nn.LSTM(
-            input_size=hidden_size,
-            hidden_size=hidden_size,
-            num_layers=num_layers,
-            dropout=dropout,
-            weight_ih_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)),
-            weight_hh_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)))
-
-        self.fc = nn.Linear(
-            hidden_size,
-            punc_size,
-            weight_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)),
-            bias_attr=paddle.ParamAttr(initializer=I.Uniform(
-                low=-init_scale, high=init_scale)))
-
-        self.dropout = nn.Dropout(p=dropout)
-        self.softmax = nn.Softmax()
-
-    def forward(self, inputs):
-        x = inputs
-        x_emb = self.embedder(x)
-        x_emb = self.dropout(x_emb)
-
-        y, (_, _) = self.lstm(x_emb)
-
-        y = self.dropout(y)
-        y = self.fc(y)
-        y = paddle.reshape(y, shape=[-1, self.punc_size])
-        logit = self.softmax(y)
-        return y, logit
-
-
-class CrossEntropyLossForLm(nn.Layer):
-    def __init__(self):
-        super(CrossEntropyLossForLm, self).__init__()
-
-    def forward(self, y, label):
-        label = paddle.unsqueeze(label, axis=2)
-        loss = paddle.nn.functional.cross_entropy(
-            input=y, label=label, reduction='none')
-        loss = paddle.squeeze(loss, axis=[2])
-        loss = paddle.mean(loss, axis=[0])
-        loss = paddle.sum(loss)
-        return loss

paddlespeech/text/speechtask/punctuation_restoration/modules/activation.py

@@ -1,141 +0,0 @@
-# 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.
-from collections import OrderedDict
-
-import paddle
-from paddle import nn
-
-__all__ = ["get_activation", "brelu", "LinearGLUBlock", "ConvGLUBlock"]
-
-
-def brelu(x, t_min=0.0, t_max=24.0, name=None):
-    # paddle.to_tensor is dygraph_only can not work under JIT
-    t_min = paddle.full(shape=[1], fill_value=t_min, dtype='float32')
-    t_max = paddle.full(shape=[1], fill_value=t_max, dtype='float32')
-    return x.maximum(t_min).minimum(t_max)
-
-
-class LinearGLUBlock(nn.Layer):
-    """A linear Gated Linear Units (GLU) block."""
-
-    def __init__(self, idim: int):
-        """ GLU.
-        Args:
-            idim (int): input and output dimension
-        """
-        super().__init__()
-        self.fc = nn.Linear(idim, idim * 2)
-
-    def forward(self, xs):
-        return glu(self.fc(xs), dim=-1)
-
-
-class ConvGLUBlock(nn.Layer):
-    def __init__(self, kernel_size, in_ch, out_ch, bottlececk_dim=0,
-                 dropout=0.):
-        """A convolutional Gated Linear Units (GLU) block.
-
-        Args:
-            kernel_size (int): kernel size
-            in_ch (int): number of input channels
-            out_ch (int): number of output channels
-            bottlececk_dim (int): dimension of the bottleneck layers for computational efficiency. Defaults to 0.
-            dropout (float): dropout probability. Defaults to 0..
-        """
-
-        super().__init__()
-
-        self.conv_residual = None
-        if in_ch != out_ch:
-            self.conv_residual = nn.utils.weight_norm(
-                nn.Conv2D(
-                    in_channels=in_ch, out_channels=out_ch, kernel_size=(1, 1)),
-                name='weight',
-                dim=0)
-            self.dropout_residual = nn.Dropout(p=dropout)
-
-        self.pad_left = ConstantPad2d((0, 0, kernel_size - 1, 0), 0)
-
-        layers = OrderedDict()
-        if bottlececk_dim == 0:
-            layers['conv'] = nn.utils.weight_norm(
-                nn.Conv2D(
-                    in_channels=in_ch,
-                    out_channels=out_ch * 2,
-                    kernel_size=(kernel_size, 1)),
-                name='weight',
-                dim=0)
-            # TODO(hirofumi0810): padding?
-            layers['dropout'] = nn.Dropout(p=dropout)
-            layers['glu'] = GLU()
-
-        elif bottlececk_dim > 0:
-            layers['conv_in'] = nn.utils.weight_norm(
-                nn.Conv2D(
-                    in_channels=in_ch,
-                    out_channels=bottlececk_dim,
-                    kernel_size=(1, 1)),
-                name='weight',
-                dim=0)
-            layers['dropout_in'] = nn.Dropout(p=dropout)
-            layers['conv_bottleneck'] = nn.utils.weight_norm(
-                nn.Conv2D(
-                    in_channels=bottlececk_dim,
-                    out_channels=bottlececk_dim,
-                    kernel_size=(kernel_size, 1)),
-                name='weight',
-                dim=0)
-            layers['dropout'] = nn.Dropout(p=dropout)
-            layers['glu'] = GLU()
-            layers['conv_out'] = nn.utils.weight_norm(
-                nn.Conv2D(
-                    in_channels=bottlececk_dim,
-                    out_channels=out_ch * 2,
-                    kernel_size=(1, 1)),
-                name='weight',
-                dim=0)
-            layers['dropout_out'] = nn.Dropout(p=dropout)
-
-        self.layers = nn.Sequential(layers)
-
-    def forward(self, xs):
-        """Forward pass.
-        Args:
-            xs (FloatTensor): `[B, in_ch, T, feat_dim]`
-        Returns:
-            out (FloatTensor): `[B, out_ch, T, feat_dim]`
-        """
-        residual = xs
-        if self.conv_residual is not None:
-            residual = self.dropout_residual(self.conv_residual(residual))
-        xs = self.pad_left(xs)  # `[B, embed_dim, T+kernel-1, 1]`
-        xs = self.layers(xs)  # `[B, out_ch * 2, T ,1]`
-        xs = xs + residual
-        return xs
-
-
-def get_activation(act):
-    """Return activation function."""
-    # Lazy load to avoid unused import
-    activation_funcs = {
-        "hardtanh": paddle.nn.Hardtanh,
-        "tanh": paddle.nn.Tanh,
-        "relu": paddle.nn.ReLU,
-        "selu": paddle.nn.SELU,
-        "swish": paddle.nn.Swish,
-        "gelu": paddle.nn.GELU,
-        "brelu": brelu,
-    }
-
-    return activation_funcs[act]()

paddlespeech/text/speechtask/punctuation_restoration/modules/attention.py

@@ -1,229 +0,0 @@
-# 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.
-"""Multi-Head Attention layer definition."""
-import math
-from typing import Optional
-from typing import Tuple
-
-import paddle
-from paddle import nn
-from paddle.nn import initializer as I
-
-__all__ = ["MultiHeadedAttention", "RelPositionMultiHeadedAttention"]
-
-# Relative Positional Encodings
-# https://www.jianshu.com/p/c0608efcc26f
-# https://zhuanlan.zhihu.com/p/344604604
-
-
-class MultiHeadedAttention(nn.Layer):
-    """Multi-Head Attention layer."""
-
-    def __init__(self, n_head: int, n_feat: int, dropout_rate: float):
-        """Construct an MultiHeadedAttention object.
-        Args:
-            n_head (int): The number of heads.
-            n_feat (int): The number of features.
-            dropout_rate (float): Dropout rate.
-        """
-        super().__init__()
-        assert n_feat % n_head == 0
-        # We assume d_v always equals d_k
-        self.d_k = n_feat // n_head
-        self.h = n_head
-        self.linear_q = nn.Linear(n_feat, n_feat)
-        self.linear_k = nn.Linear(n_feat, n_feat)
-        self.linear_v = nn.Linear(n_feat, n_feat)
-        self.linear_out = nn.Linear(n_feat, n_feat)
-        self.dropout = nn.Dropout(p=dropout_rate)
-
-    def forward_qkv(self,
-                    query: paddle.Tensor,
-                    key: paddle.Tensor,
-                    value: paddle.Tensor
-                    ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
-        """Transform query, key and value.
-        Args:
-            query (paddle.Tensor): Query tensor (#batch, time1, size).
-            key (paddle.Tensor): Key tensor (#batch, time2, size).
-            value (paddle.Tensor): Value tensor (#batch, time2, size).
-        Returns:
-            paddle.Tensor: Transformed query tensor, size
-                (#batch, n_head, time1, d_k).
-            paddle.Tensor: Transformed key tensor, size
-                (#batch, n_head, time2, d_k).
-            paddle.Tensor: Transformed value tensor, size
-                (#batch, n_head, time2, d_k).
-        """
-        n_batch = query.size(0)
-        q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
-        k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
-        v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
-        q = q.transpose([0, 2, 1, 3])  # (batch, head, time1, d_k)
-        k = k.transpose([0, 2, 1, 3])  # (batch, head, time2, d_k)
-        v = v.transpose([0, 2, 1, 3])  # (batch, head, time2, d_k)
-
-        return q, k, v
-
-    def forward_attention(self,
-                          value: paddle.Tensor,
-                          scores: paddle.Tensor,
-                          mask: Optional[paddle.Tensor]) -> paddle.Tensor:
-        """Compute attention context vector.
-        Args:
-            value (paddle.Tensor): Transformed value, size
-                (#batch, n_head, time2, d_k).
-            scores (paddle.Tensor): Attention score, size
-                (#batch, n_head, time1, time2).
-            mask (paddle.Tensor): Mask, size (#batch, 1, time2) or
-                (#batch, time1, time2).
-        Returns:
-            paddle.Tensor: Transformed value weighted 
-                by the attention score, (#batch, time1, d_model).
-        """
-        n_batch = value.size(0)
-        if mask is not None:
-            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
-            scores = scores.masked_fill(mask, -float('inf'))
-            attn = paddle.softmax(
-                scores, axis=-1).masked_fill(mask,
-                                             0.0)  # (batch, head, time1, time2)
-        else:
-            attn = paddle.softmax(
-                scores, axis=-1)  # (batch, head, time1, time2)
-
-        p_attn = self.dropout(attn)
-        x = paddle.matmul(p_attn, value)  # (batch, head, time1, d_k)
-        x = x.transpose([0, 2, 1, 3]).contiguous().view(
-            n_batch, -1, self.h * self.d_k)  # (batch, time1, d_model)
-
-        return self.linear_out(x)  # (batch, time1, d_model)
-
-    def forward(self,
-                query: paddle.Tensor,
-                key: paddle.Tensor,
-                value: paddle.Tensor,
-                mask: Optional[paddle.Tensor]) -> paddle.Tensor:
-        """Compute scaled dot product attention.
-        Args:
-            query (torch.Tensor): Query tensor (#batch, time1, size).
-            key (torch.Tensor): Key tensor (#batch, time2, size).
-            value (torch.Tensor): Value tensor (#batch, time2, size).
-            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
-                (#batch, time1, time2).
-        Returns:
-            torch.Tensor: Output tensor (#batch, time1, d_model).
-        """
-        q, k, v = self.forward_qkv(query, key, value)
-        scores = paddle.matmul(q,
-                               k.transpose([0, 1, 3, 2])) / math.sqrt(self.d_k)
-        return self.forward_attention(v, scores, mask)
-
-
-class RelPositionMultiHeadedAttention(MultiHeadedAttention):
-    """Multi-Head Attention layer with relative position encoding."""
-
-    def __init__(self, n_head, n_feat, dropout_rate):
-        """Construct an RelPositionMultiHeadedAttention object.
-        Paper: https://arxiv.org/abs/1901.02860
-        Args:
-            n_head (int): The number of heads.
-            n_feat (int): The number of features.
-            dropout_rate (float): Dropout rate.
-        """
-        super().__init__(n_head, n_feat, dropout_rate)
-        # linear transformation for positional encoding
-        self.linear_pos = nn.Linear(n_feat, n_feat, bias_attr=False)
-        # these two learnable bias are used in matrix c and matrix d
-        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
-        #self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
-        #self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
-        #torch.nn.init.xavier_uniform_(self.pos_bias_u)
-        #torch.nn.init.xavier_uniform_(self.pos_bias_v)
-        pos_bias_u = self.create_parameter(
-            [self.h, self.d_k], default_initializer=I.XavierUniform())
-        self.add_parameter('pos_bias_u', pos_bias_u)
-        pos_bias_v = self.create_parameter(
-            (self.h, self.d_k), default_initializer=I.XavierUniform())
-        self.add_parameter('pos_bias_v', pos_bias_v)
-
-    def rel_shift(self, x, zero_triu: bool=False):
-        """Compute relative positinal encoding.
-        Args:
-            x (paddle.Tensor): Input tensor (batch, head, time1, time1).
-            zero_triu (bool): If true, return the lower triangular part of
-                the matrix.
-        Returns:
-            paddle.Tensor: Output tensor. (batch, head, time1, time1)
-        """
-        zero_pad = paddle.zeros(
-            (x.size(0), x.size(1), x.size(2), 1), dtype=x.dtype)
-        x_padded = paddle.cat([zero_pad, x], dim=-1)
-
-        x_padded = x_padded.view(x.size(0), x.size(1), x.size(3) + 1, x.size(2))
-        x = x_padded[:, :, 1:].view_as(x)  # [B, H, T1, T1]
-
-        if zero_triu:
-            ones = paddle.ones((x.size(2), x.size(3)))
-            x = x * paddle.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
-
-        return x
-
-    def forward(self,
-                query: paddle.Tensor,
-                key: paddle.Tensor,
-                value: paddle.Tensor,
-                pos_emb: paddle.Tensor,
-                mask: Optional[paddle.Tensor]):
-        """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
-        Args:
-            query (paddle.Tensor): Query tensor (#batch, time1, size).
-            key (paddle.Tensor): Key tensor (#batch, time2, size).
-            value (paddle.Tensor): Value tensor (#batch, time2, size).
-            pos_emb (paddle.Tensor): Positional embedding tensor
-                (#batch, time1, size).
-            mask (paddle.Tensor): Mask tensor (#batch, 1, time2) or
-                (#batch, time1, time2).
-        Returns:
-            paddle.Tensor: Output tensor (#batch, time1, d_model).
-        """
-        q, k, v = self.forward_qkv(query, key, value)
-        q = q.transpose([0, 2, 1, 3])  # (batch, time1, head, d_k)
-
-        n_batch_pos = pos_emb.size(0)
-        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
-        p = p.transpose([0, 2, 1, 3])  # (batch, head, time1, d_k)
-
-        # (batch, head, time1, d_k)
-        q_with_bias_u = (q + self.pos_bias_u).transpose([0, 2, 1, 3])
-        # (batch, head, time1, d_k)
-        q_with_bias_v = (q + self.pos_bias_v).transpose([0, 2, 1, 3])
-
-        # compute attention score
-        # first compute matrix a and matrix c
-        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
-        # (batch, head, time1, time2)
-        matrix_ac = paddle.matmul(q_with_bias_u, k.transpose([0, 1, 3, 2]))
-
-        # compute matrix b and matrix d
-        # (batch, head, time1, time2)
-        matrix_bd = paddle.matmul(q_with_bias_v, p.transpose([0, 1, 3, 2]))
-        # Remove rel_shift since it is useless in speech recognition,
-        # and it requires special attention for streaming.
-        # matrix_bd = self.rel_shift(matrix_bd)
-
-        scores = (matrix_ac + matrix_bd) / math.sqrt(
-            self.d_k)  # (batch, head, time1, time2)
-
-        return self.forward_attention(v, scores, mask)

paddlespeech/text/speechtask/punctuation_restoration/modules/crf.py

@@ -1,366 +0,0 @@
-# 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.
-import paddle
-from paddle import nn
-
-__all__ = ['CRF']
-
-
-class CRF(nn.Layer):
-    """
-    Linear-chain Conditional Random Field (CRF).
-    
-    Args:
-        nb_labels (int): number of labels in your tagset, including special symbols.
-        bos_tag_id (int): integer representing the beginning of sentence symbol in
-            your tagset.
-        eos_tag_id (int): integer representing the end of sentence symbol in your tagset.
-        pad_tag_id (int, optional): integer representing the pad symbol in your tagset.
-            If None, the model will treat the PAD as a normal tag. Otherwise, the model
-            will apply constraints for PAD transitions.
-        batch_first (bool): Whether the first dimension represents the batch dimension.
-    """
-
-    def __init__(self,
-                 nb_labels: int,
-                 bos_tag_id: int,
-                 eos_tag_id: int,
-                 pad_tag_id: int=None,
-                 batch_first: bool=True):
-        super().__init__()
-
-        self.nb_labels = nb_labels
-        self.BOS_TAG_ID = bos_tag_id
-        self.EOS_TAG_ID = eos_tag_id
-        self.PAD_TAG_ID = pad_tag_id
-        self.batch_first = batch_first
-
-        # initialize transitions from a random uniform distribution between -0.1 and 0.1
-        self.transitions = self.create_parameter(
-            [self.nb_labels, self.nb_labels],
-            default_initializer=nn.initializer.Uniform(-0.1, 0.1))
-        self.init_weights()
-
-    def init_weights(self):
-        # enforce contraints (rows=from, columns=to) with a big negative number
-        # so exp(-10000) will tend to zero
-
-        # no transitions allowed to the beginning of sentence
-        self.transitions[:, self.BOS_TAG_ID] = -10000.0
-        # no transition alloed from the end of sentence
-        self.transitions[self.EOS_TAG_ID, :] = -10000.0
-
-        if self.PAD_TAG_ID is not None:
-            # no transitions from padding
-            self.transitions[self.PAD_TAG_ID, :] = -10000.0
-            # no transitions to padding
-            self.transitions[:, self.PAD_TAG_ID] = -10000.0
-            # except if the end of sentence is reached
-            # or we are already in a pad position
-            self.transitions[self.PAD_TAG_ID, self.EOS_TAG_ID] = 0.0
-            self.transitions[self.PAD_TAG_ID, self.PAD_TAG_ID] = 0.0
-
-    def forward(self,
-                emissions: paddle.Tensor,
-                tags: paddle.Tensor,
-                mask: paddle.Tensor=None) -> paddle.Tensor:
-        """Compute the negative log-likelihood. See `log_likelihood` method."""
-        nll = -self.log_likelihood(emissions, tags, mask=mask)
-        return nll
-
-    def log_likelihood(self, emissions, tags, mask=None):
-        """Compute the probability of a sequence of tags given a sequence of
-        emissions scores.
-
-        Args:
-            emissions (paddle.Tensor): Sequence of emissions for each label.
-                Shape of (batch_size, seq_len, nb_labels) if batch_first is True,
-                (seq_len, batch_size, nb_labels) otherwise.
-            tags (paddle.LongTensor): Sequence of labels.
-                Shape of (batch_size, seq_len) if batch_first is True,
-                (seq_len, batch_size) otherwise.
-            mask (paddle.FloatTensor, optional): Tensor representing valid positions.
-                If None, all positions are considered valid.
-                Shape of (batch_size, seq_len) if batch_first is True,
-                (seq_len, batch_size) otherwise.
-
-        Returns:
-            paddle.Tensor: sum of the log-likelihoods for each sequence in the batch.
-                Shape of ()
-        """
-        # fix tensors order by setting batch as the first dimension
-        if not self.batch_first:
-            emissions = emissions.transpose(0, 1)
-            tags = tags.transpose(0, 1)
-
-        if mask is None:
-            mask = paddle.ones(emissions.shape[:2], dtype=paddle.float)
-
-        scores = self._compute_scores(emissions, tags, mask=mask)
-        partition = self._compute_log_partition(emissions, mask=mask)
-        return paddle.sum(scores - partition)
-
-    def decode(self, emissions, mask=None):
-        """Find the most probable sequence of labels given the emissions using
-        the Viterbi algorithm.
-
-        Args:
-            emissions (paddle.Tensor): Sequence of emissions for each label.
-                Shape (batch_size, seq_len, nb_labels) if batch_first is True,
-                (seq_len, batch_size, nb_labels) otherwise.
-            mask (paddle.FloatTensor, optional): Tensor representing valid positions.
-                If None, all positions are considered valid.
-                Shape (batch_size, seq_len) if batch_first is True,
-                (seq_len, batch_size) otherwise.
-
-        Returns:
-            paddle.Tensor: the viterbi score for the for each batch.
-                Shape of (batch_size,)
-            list of lists: the best viterbi sequence of labels for each batch. [B, T]
-        """
-        # fix tensors order by setting batch as the first dimension
-        if not self.batch_first:
-            emissions = emissions.transpose(0, 1)
-            tags = tags.transpose(0, 1)
-
-        if mask is None:
-            mask = paddle.ones(emissions.shape[:2], dtype=paddle.float)
-
-        scores, sequences = self._viterbi_decode(emissions, mask)
-        return scores, sequences
-
-    def _compute_scores(self, emissions, tags, mask):
-        """Compute the scores for a given batch of emissions with their tags.
-
-        Args:
-            emissions (paddle.Tensor): (batch_size, seq_len, nb_labels)
-            tags (Paddle.LongTensor): (batch_size, seq_len)
-            mask (Paddle.FloatTensor): (batch_size, seq_len)
-
-        Returns:
-            paddle.Tensor: Scores for each batch.
-                Shape of (batch_size,)
-        """
-        batch_size, seq_length = tags.shape
-        scores = paddle.zeros([batch_size])
-
-        # save first and last tags to be used later
-        first_tags = tags[:, 0]
-        last_valid_idx = mask.int().sum(1) - 1
-
-        # TODO(Hui Zhang): not support fancy index. 
-        # last_tags = tags.gather(last_valid_idx.unsqueeze(1), axis=1).squeeze()
-        batch_idx = paddle.arange(batch_size, dtype=last_valid_idx.dtype)
-        gather_last_valid_idx = paddle.stack(
-            [batch_idx, last_valid_idx], axis=-1)
-        last_tags = tags.gather_nd(gather_last_valid_idx)
-
-        # add the transition from BOS to the first tags for each batch
-        # t_scores = self.transitions[self.BOS_TAG_ID, first_tags]
-        t_scores = self.transitions[self.BOS_TAG_ID].gather(first_tags)
-
-        # add the [unary] emission scores for the first tags for each batch
-        # for all batches, the first word, see the correspondent emissions
-        # for the first tags (which is a list of ids):
-        # emissions[:, 0, [tag_1, tag_2, ..., tag_nblabels]]
-        # e_scores = emissions[:, 0].gather(1, first_tags.unsqueeze(1)).squeeze()
-        gather_first_tags_idx = paddle.stack([batch_idx, first_tags], axis=-1)
-        e_scores = emissions[:, 0].gather_nd(gather_first_tags_idx)
-
-        # the scores for a word is just the sum of both scores
-        scores += e_scores + t_scores
-
-        # now lets do this for each remaining word
-        for i in range(1, seq_length):
-
-            # we could: iterate over batches, check if we reached a mask symbol
-            # and stop the iteration, but vecotrizing is faster due to gpu,
-            # so instead we perform an element-wise multiplication
-            is_valid = mask[:, i]
-
-            previous_tags = tags[:, i - 1]
-            current_tags = tags[:, i]
-
-            # calculate emission and transition scores as we did before
-            # e_scores = emissions[:, i].gather(1, current_tags.unsqueeze(1)).squeeze()
-            gather_current_tags_idx = paddle.stack(
-                [batch_idx, current_tags], axis=-1)
-            e_scores = emissions[:, i].gather_nd(gather_current_tags_idx)
-            # t_scores = self.transitions[previous_tags, current_tags]
-            gather_transitions_idx = paddle.stack(
-                [previous_tags, current_tags], axis=-1)
-            t_scores = self.transitions.gather_nd(gather_transitions_idx)
-
-            # apply the mask
-            e_scores = e_scores * is_valid
-            t_scores = t_scores * is_valid
-
-            scores += e_scores + t_scores
-
-        # add the transition from the end tag to the EOS tag for each batch
-        # scores += self.transitions[last_tags, self.EOS_TAG_ID]
-        scores += self.transitions.gather(last_tags)[:, self.EOS_TAG_ID]
-
-        return scores
-
-    def _compute_log_partition(self, emissions, mask):
-        """Compute the partition function in log-space using the forward-algorithm.
-
-        Args:
-            emissions (paddle.Tensor): (batch_size, seq_len, nb_labels)
-            mask (Paddle.FloatTensor): (batch_size, seq_len)
-
-        Returns:
-            paddle.Tensor: the partition scores for each batch.
-                Shape of (batch_size,)
-        """
-        batch_size, seq_length, nb_labels = emissions.shape
-
-        # in the first iteration, BOS will have all the scores
-        alphas = self.transitions[self.BOS_TAG_ID, :].unsqueeze(
-            0) + emissions[:, 0]
-
-        for i in range(1, seq_length):
-            # (bs, nb_labels) -> (bs, 1, nb_labels)
-            e_scores = emissions[:, i].unsqueeze(1)
-
-            # (nb_labels, nb_labels) -> (bs, nb_labels, nb_labels)
-            t_scores = self.transitions.unsqueeze(0)
-
-            # (bs, nb_labels)  -> (bs, nb_labels, 1)
-            a_scores = alphas.unsqueeze(2)
-
-            scores = e_scores + t_scores + a_scores
-            new_alphas = paddle.logsumexp(scores, axis=1)
-
-            # set alphas if the mask is valid, otherwise keep the current values
-            is_valid = mask[:, i].unsqueeze(-1)
-            alphas = is_valid * new_alphas + (1 - is_valid) * alphas
-
-        # add the scores for the final transition
-        last_transition = self.transitions[:, self.EOS_TAG_ID]
-        end_scores = alphas + last_transition.unsqueeze(0)
-
-        # return a *log* of sums of exps
-        return paddle.logsumexp(end_scores, axis=1)
-
-    def _viterbi_decode(self, emissions, mask):
-        """Compute the viterbi algorithm to find the most probable sequence of labels
-        given a sequence of emissions.
-
-        Args:
-            emissions (paddle.Tensor): (batch_size, seq_len, nb_labels)
-            mask (Paddle.FloatTensor): (batch_size, seq_len)
-
-        Returns:
-            paddle.Tensor: the viterbi score for the for each batch.
-                Shape of (batch_size,)
-            list of lists of ints: the best viterbi sequence of labels for each batch
-        """
-        batch_size, seq_length, nb_labels = emissions.shape
-
-        # in the first iteration, BOS will have all the scores and then, the max
-        alphas = self.transitions[self.BOS_TAG_ID, :].unsqueeze(
-            0) + emissions[:, 0]
-
-        backpointers = []
-
-        for i in range(1, seq_length):
-            # (bs, nb_labels) -> (bs, 1, nb_labels)
-            e_scores = emissions[:, i].unsqueeze(1)
-
-            # (nb_labels, nb_labels) -> (bs, nb_labels, nb_labels)
-            t_scores = self.transitions.unsqueeze(0)
-
-            # (bs, nb_labels)  -> (bs, nb_labels, 1)
-            a_scores = alphas.unsqueeze(2)
-
-            # combine current scores with previous alphas
-            scores = e_scores + t_scores + a_scores
-
-            # so far is exactly like the forward algorithm,
-            # but now, instead of calculating the logsumexp,
-            # we will find the highest score and the tag associated with it
-            # max_scores, max_score_tags = paddle.max(scores, axis=1)
-            max_scores = paddle.max(scores, axis=1)
-            max_score_tags = paddle.argmax(scores, axis=1)
-
-            # set alphas if the mask is valid, otherwise keep the current values
-            is_valid = mask[:, i].unsqueeze(-1)
-            alphas = is_valid * max_scores + (1 - is_valid) * alphas
-
-            # add the max_score_tags for our list of backpointers
-            # max_scores has shape (batch_size, nb_labels) so we transpose it to
-            # be compatible with our previous loopy version of viterbi
-            backpointers.append(max_score_tags.t())
-
-        # add the scores for the final transition
-        last_transition = self.transitions[:, self.EOS_TAG_ID]
-        end_scores = alphas + last_transition.unsqueeze(0)
-
-        # get the final most probable score and the final most probable tag
-        # max_final_scores, max_final_tags = paddle.max(end_scores, axis=1)
-        max_final_scores = paddle.max(end_scores, axis=1)
-        max_final_tags = paddle.argmax(end_scores, axis=1)
-
-        # find the best sequence of labels for each sample in the batch
-        best_sequences = []
-        emission_lengths = mask.int().sum(axis=1)
-        for i in range(batch_size):
-
-            # recover the original sentence length for the i-th sample in the batch
-            sample_length = emission_lengths[i].item()
-
-            # recover the max tag for the last timestep
-            sample_final_tag = max_final_tags[i].item()
-
-            # limit the backpointers until the last but one
-            # since the last corresponds to the sample_final_tag
-            sample_backpointers = backpointers[:sample_length - 1]
-
-            # follow the backpointers to build the sequence of labels
-            sample_path = self._find_best_path(i, sample_final_tag,
-                                               sample_backpointers)
-
-            # add this path to the list of best sequences
-            best_sequences.append(sample_path)
-
-        return max_final_scores, best_sequences
-
-    def _find_best_path(self, sample_id, best_tag, backpointers):
-        """Auxiliary function to find the best path sequence for a specific sample.
-
-            Args:
-                sample_id (int): sample index in the range [0, batch_size)
-                best_tag (int): tag which maximizes the final score
-                backpointers (list of lists of tensors): list of pointers with
-                shape (seq_len_i-1, nb_labels, batch_size) where seq_len_i
-                represents the length of the ith sample in the batch
-
-            Returns:
-                list of ints: a list of tag indexes representing the bast path
-        """
-        # add the final best_tag to our best path
-        best_path = [best_tag]
-
-        # traverse the backpointers in backwards
-        for backpointers_t in reversed(backpointers):
-
-            # recover the best_tag at this timestep
-            best_tag = backpointers_t[best_tag][sample_id].item()
-
-            # append to the beginning of the list so we don't need to reverse it later
-            best_path.insert(0, best_tag)
-
-        return best_path

paddlespeech/text/speechtask/punctuation_restoration/training/loss.py

@@ -1,98 +0,0 @@
-# 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.
-import paddle
-import paddle.nn as nn
-import paddle.nn.functional as F
-
-
-class FocalLossHX(nn.Layer):
-    def __init__(self, gamma=0, size_average=True):
-        super(FocalLoss, self).__init__()
-        self.gamma = gamma
-        self.size_average = size_average
-
-    def forward(self, input, target):
-        # print('input')
-        # print(input.shape)
-        # print(target.shape)
-
-        if input.dim() > 2:
-            input = paddle.reshape(
-                input,
-                shape=[input.size(0), input.size(1), -1])  # N,C,H,W => N,C,H*W
-            input = input.transpose(1, 2)  # N,C,H*W => N,H*W,C
-            input = paddle.reshape(
-                input, shape=[-1, input.size(2)])  # N,H*W,C => N*H*W,C
-        target = paddle.reshape(target, shape=[-1])
-
-        logpt = F.log_softmax(input)
-        # print('logpt')
-        # print(logpt.shape)
-        # print(logpt)
-
-        # get true class column from each row
-        all_rows = paddle.arange(len(input))
-        # print(target)
-        log_pt = logpt.numpy()[all_rows.numpy(), target.numpy()]
-
-        pt = paddle.to_tensor(log_pt, dtype='float64').exp()
-        ce = F.cross_entropy(input, target, reduction='none')
-        # print('ce')
-        # print(ce.shape)
-
-        loss = (1 - pt)**self.gamma * ce
-        # print('ce:%f'%ce.mean())
-        # print('fl:%f'%loss.mean())
-        if self.size_average:
-            return loss.mean()
-        else:
-            return loss.sum()
-
-
-class FocalLoss(nn.Layer):
-    """
-    Focal Loss.
-    Code referenced from:
-    https://github.com/clcarwin/focal_loss_pytorch/blob/master/focalloss.py
-    Args:
-        gamma (float): the coefficient of Focal Loss.
-        ignore_index (int64): Specifies a target value that is ignored
-            and does not contribute to the input gradient. Default ``255``.
-    """
-
-    def __init__(self, gamma=2.0):
-        super(FocalLoss, self).__init__()
-        self.gamma = gamma
-
-    def forward(self, logit, label):
-        #####logit = F.softmax(logit)
-        # logit = paddle.reshape(
-        #     logit, [logit.shape[0], logit.shape[1], -1])  # N,C,H,W => N,C,H*W
-        # logit = paddle.transpose(logit, [0, 2, 1])  # N,C,H*W => N,H*W,C
-        # logit = paddle.reshape(logit,
-        #                        [-1, logit.shape[2]])  # N,H*W,C => N*H*W,C
-        label = paddle.reshape(label, [-1, 1])
-        range_ = paddle.arange(0, label.shape[0])
-        range_ = paddle.unsqueeze(range_, axis=-1)
-        label = paddle.cast(label, dtype='int64')
-        label = paddle.concat([range_, label], axis=-1)
-        logpt = F.log_softmax(logit)
-        logpt = paddle.gather_nd(logpt, label)
-
-        pt = paddle.exp(logpt.detach())
-        loss = -1 * (1 - pt)**self.gamma * logpt
-        loss = paddle.mean(loss)
-        # print(loss)
-        # print(logpt)
-        return loss

paddlespeech/text/speechtask/punctuation_restoration/utils/checkpoint.py

@@ -1,304 +0,0 @@
-# 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.
-import glob
-import json
-import os
-import re
-from pathlib import Path
-from typing import Text
-from typing import Union
-
-import paddle
-from paddle import distributed as dist
-from paddle.optimizer import Optimizer
-from speechtask.punctuation_restoration.utils import mp_tools
-# from speechtask.punctuation_restoration.utils.log import Log
-
-# logger = Log(__name__).getlog()
-
-__all__ = ["Checkpoint"]
-
-
-class Checkpoint():
-    def __init__(self,
-                 logger,
-                 kbest_n: int=5,
-                 latest_n: int=1,
-                 metric_type='val_loss'):
-        self.best_records: Mapping[Path, float] = {}
-        self.latest_records = []
-        self.kbest_n = kbest_n
-        self.latest_n = latest_n
-        self._save_all = (kbest_n == -1)
-        self.logger = logger
-        self.metric_type = metric_type
-
-    def add_checkpoint(self,
-                       checkpoint_dir,
-                       tag_or_iteration: Union[int, Text],
-                       model: paddle.nn.Layer,
-                       optimizer: Optimizer=None,
-                       infos: dict=None):
-        """Save checkpoint in best_n and latest_n.
-        Args:
-            checkpoint_dir (str): the directory where checkpoint is saved.
-            tag_or_iteration (int or str): the latest iteration(step or epoch) number or tag.
-            model (Layer):  model to be checkpointed.
-            optimizer (Optimizer, optional): optimizer to be checkpointed.
-            infos (dict or None)):  any info you want to save.
-            metric_type (str, optional): metric type. Defaults to 'val_loss'.
-        """
-        metric_type = self.metric_type
-        if (metric_type not in infos.keys()):
-            self._save_parameters(checkpoint_dir, tag_or_iteration, model,
-                                  optimizer, infos)
-            return
-
-        #save best
-        if self._should_save_best(infos[metric_type]):
-            self._save_best_checkpoint_and_update(
-                infos[metric_type], checkpoint_dir, tag_or_iteration, model,
-                optimizer, infos)
-        #save latest
-        self._save_latest_checkpoint_and_update(
-            checkpoint_dir, tag_or_iteration, model, optimizer, infos)
-
-        if isinstance(tag_or_iteration, int):
-            self._save_checkpoint_record(checkpoint_dir, tag_or_iteration)
-
-    def load_parameters(self,
-                        model,
-                        optimizer=None,
-                        checkpoint_dir=None,
-                        checkpoint_path=None,
-                        record_file="checkpoint_latest"):
-        """Load a last model checkpoint from disk.
-        Args:
-            model (Layer): model to load parameters.
-            optimizer (Optimizer, optional): optimizer to load states if needed.
-                Defaults to None.
-            checkpoint_dir (str, optional): the directory where checkpoint is saved.
-            checkpoint_path (str, optional): if specified, load the checkpoint
-                stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will
-                be ignored. Defaults to None.
-            record_file "checkpoint_latest" or "checkpoint_best"
-        Returns:
-            configs (dict): epoch or step, lr and other meta info should be saved.
-        """
-        configs = {}
-
-        if checkpoint_path is not None:
-            pass
-        elif checkpoint_dir is not None and record_file is not None:
-            # load checkpint from record file
-            checkpoint_record = os.path.join(checkpoint_dir, record_file)
-            iteration = self._load_checkpoint_idx(checkpoint_record)
-            if iteration == -1:
-                return configs
-            checkpoint_path = os.path.join(checkpoint_dir,
-                                           "{}".format(iteration))
-        else:
-            raise ValueError(
-                "At least one of 'checkpoint_path' or 'checkpoint_dir' should be specified!"
-            )
-
-        rank = dist.get_rank()
-
-        params_path = checkpoint_path + ".pdparams"
-        model_dict = paddle.load(params_path)
-        model.set_state_dict(model_dict)
-        self.logger.info(
-            "Rank {}: loaded model from {}".format(rank, params_path))
-
-        optimizer_path = checkpoint_path + ".pdopt"
-        if optimizer and os.path.isfile(optimizer_path):
-            optimizer_dict = paddle.load(optimizer_path)
-            optimizer.set_state_dict(optimizer_dict)
-            self.logger.info("Rank {}: loaded optimizer state from {}".format(
-                rank, optimizer_path))
-
-        info_path = re.sub('.pdparams$', '.json', params_path)
-        if os.path.exists(info_path):
-            with open(info_path, 'r') as fin:
-                configs = json.load(fin)
-        return configs
-
-    def load_latest_parameters(self,
-                               model,
-                               optimizer=None,
-                               checkpoint_dir=None,
-                               checkpoint_path=None):
-        """Load a last model checkpoint from disk.
-        Args:
-            model (Layer): model to load parameters.
-            optimizer (Optimizer, optional): optimizer to load states if needed.
-                Defaults to None.
-            checkpoint_dir (str, optional): the directory where checkpoint is saved.
-            checkpoint_path (str, optional): if specified, load the checkpoint
-                stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will
-                be ignored. Defaults to None.
-        Returns:
-            configs (dict): epoch or step, lr and other meta info should be saved.
-        """
-        return self.load_parameters(model, optimizer, checkpoint_dir,
-                                    checkpoint_path, "checkpoint_latest")
-
-    def load_best_parameters(self,
-                             model,
-                             optimizer=None,
-                             checkpoint_dir=None,
-                             checkpoint_path=None):
-        """Load a last model checkpoint from disk.
-        Args:
-            model (Layer): model to load parameters.
-            optimizer (Optimizer, optional): optimizer to load states if needed.
-                Defaults to None.
-            checkpoint_dir (str, optional): the directory where checkpoint is saved.
-            checkpoint_path (str, optional): if specified, load the checkpoint
-                stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will
-                be ignored. Defaults to None.
-        Returns:
-            configs (dict): epoch or step, lr and other meta info should be saved.
-        """
-        return self.load_parameters(model, optimizer, checkpoint_dir,
-                                    checkpoint_path, "checkpoint_best")
-
-    def _should_save_best(self, metric: float) -> bool:
-        if not self._best_full():
-            return True
-
-        # already full
-        worst_record_path = max(self.best_records, key=self.best_records.get)
-        # worst_record_path = max(self.best_records.iteritems(), key=operator.itemgetter(1))[0]
-        worst_metric = self.best_records[worst_record_path]
-        return metric < worst_metric
-
-    def _best_full(self):
-        return (not self._save_all) and len(self.best_records) == self.kbest_n
-
-    def _latest_full(self):
-        return len(self.latest_records) == self.latest_n
-
-    def _save_best_checkpoint_and_update(self, metric, checkpoint_dir,
-                                         tag_or_iteration, model, optimizer,
-                                         infos):
-        # remove the worst
-        if self._best_full():
-            worst_record_path = max(self.best_records,
-                                    key=self.best_records.get)
-            self.best_records.pop(worst_record_path)
-            if (worst_record_path not in self.latest_records):
-                self.logger.info(
-                    "remove the worst checkpoint: {}".format(worst_record_path))
-                self._del_checkpoint(checkpoint_dir, worst_record_path)
-
-        # add the new one
-        self._save_parameters(checkpoint_dir, tag_or_iteration, model,
-                              optimizer, infos)
-        self.best_records[tag_or_iteration] = metric
-
-    def _save_latest_checkpoint_and_update(
-            self, checkpoint_dir, tag_or_iteration, model, optimizer, infos):
-        # remove the old
-        if self._latest_full():
-            to_del_fn = self.latest_records.pop(0)
-            if (to_del_fn not in self.best_records.keys()):
-                self.logger.info(
-                    "remove the latest checkpoint: {}".format(to_del_fn))
-                self._del_checkpoint(checkpoint_dir, to_del_fn)
-        self.latest_records.append(tag_or_iteration)
-
-        self._save_parameters(checkpoint_dir, tag_or_iteration, model,
-                              optimizer, infos)
-
-    def _del_checkpoint(self, checkpoint_dir, tag_or_iteration):
-        checkpoint_path = os.path.join(checkpoint_dir,
-                                       "{}".format(tag_or_iteration))
-        for filename in glob.glob(checkpoint_path + ".*"):
-            os.remove(filename)
-            self.logger.info("delete file: {}".format(filename))
-
-    def _load_checkpoint_idx(self, checkpoint_record: str) -> int:
-        """Get the iteration number corresponding to the latest saved checkpoint.
-        Args:
-            checkpoint_path (str): the saved path of checkpoint.
-        Returns:
-            int: the latest iteration number. -1 for no checkpoint to load.
-        """
-        if not os.path.isfile(checkpoint_record):
-            return -1
-
-        # Fetch the latest checkpoint index.
-        with open(checkpoint_record, "rt") as handle:
-            latest_checkpoint = handle.readlines()[-1].strip()
-            iteration = int(latest_checkpoint.split(":")[-1])
-        return iteration
-
-    def _save_checkpoint_record(self, checkpoint_dir: str, iteration: int):
-        """Save the iteration number of the latest model to be checkpoint record.
-        Args:
-            checkpoint_dir (str): the directory where checkpoint is saved.
-            iteration (int): the latest iteration number.
-        Returns:
-            None
-        """
-        checkpoint_record_latest = os.path.join(checkpoint_dir,
-                                                "checkpoint_latest")
-        checkpoint_record_best = os.path.join(checkpoint_dir, "checkpoint_best")
-
-        with open(checkpoint_record_best, "w") as handle:
-            for i in self.best_records.keys():
-                handle.write("model_checkpoint_path:{}\n".format(i))
-        with open(checkpoint_record_latest, "w") as handle:
-            for i in self.latest_records:
-                handle.write("model_checkpoint_path:{}\n".format(i))
-
-    @mp_tools.rank_zero_only
-    def _save_parameters(self,
-                         checkpoint_dir: str,
-                         tag_or_iteration: Union[int, str],
-                         model: paddle.nn.Layer,
-                         optimizer: Optimizer=None,
-                         infos: dict=None):
-        """Checkpoint the latest trained model parameters.
-        Args:
-            checkpoint_dir (str): the directory where checkpoint is saved.
-            tag_or_iteration (int or str): the latest iteration(step or epoch) number.
-            model (Layer): model to be checkpointed.
-            optimizer (Optimizer, optional): optimizer to be checkpointed.
-                Defaults to None.
-            infos (dict or None): any info you want to save.
-        Returns:
-            None
-        """
-        checkpoint_path = os.path.join(checkpoint_dir,
-                                       "{}".format(tag_or_iteration))
-
-        model_dict = model.state_dict()
-        params_path = checkpoint_path + ".pdparams"
-        paddle.save(model_dict, params_path)
-        self.logger.info("Saved model to {}".format(params_path))
-
-        if optimizer:
-            opt_dict = optimizer.state_dict()
-            optimizer_path = checkpoint_path + ".pdopt"
-            paddle.save(opt_dict, optimizer_path)
-            self.logger.info(
-                "Saved optimzier state to {}".format(optimizer_path))
-
-        info_path = re.sub('.pdparams$', '.json', params_path)
-        infos = {} if infos is None else infos
-        with open(info_path, 'w') as fout:
-            data = json.dumps(infos)
-            fout.write(data)

paddlespeech/text/speechtask/punctuation_restoration/utils/layer_tools.py

@@ -1,88 +0,0 @@
-# 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.
-import numpy as np
-from paddle import nn
-
-__all__ = [
-    "summary", "gradient_norm", "freeze", "unfreeze", "print_grads",
-    "print_params"
-]
-
-
-def summary(layer: nn.Layer, print_func=print):
-    if print_func is None:
-        return
-    num_params = num_elements = 0
-    for name, param in layer.state_dict().items():
-        if print_func:
-            print_func(
-                "{} | {} | {}".format(name, param.shape, np.prod(param.shape)))
-        num_elements += np.prod(param.shape)
-        num_params += 1
-    if print_func:
-        num_elements = num_elements / 1024**2
-        print_func(
-            f"Total parameters: {num_params}, {num_elements:.2f}M elements.")
-
-
-def print_grads(model, print_func=print):
-    if print_func is None:
-        return
-    for n, p in model.named_parameters():
-        msg = f"param grad: {n}: shape: {p.shape} grad: {p.grad}"
-        print_func(msg)
-
-
-def print_params(model, print_func=print):
-    if print_func is None:
-        return
-    total = 0.0
-    num_params = 0.0
-    for n, p in model.named_parameters():
-        msg = f"{n} | {p.shape} | {np.prod(p.shape)} | {not p.stop_gradient}"
-        total += np.prod(p.shape)
-        num_params += 1
-        if print_func:
-            print_func(msg)
-    if print_func:
-        total = total / 1024**2
-        print_func(f"Total parameters: {num_params}, {total:.2f}M elements.")
-
-
-def gradient_norm(layer: nn.Layer):
-    grad_norm_dict = {}
-    for name, param in layer.state_dict().items():
-        if param.trainable:
-            grad = param.gradient()  # return numpy.ndarray
-            grad_norm_dict[name] = np.linalg.norm(grad) / grad.size
-    return grad_norm_dict
-
-
-def recursively_remove_weight_norm(layer: nn.Layer):
-    for layer in layer.sublayers():
-        try:
-            nn.utils.remove_weight_norm(layer)
-        except ValueError as e:
-            # ther is not weight norm hoom in this layer
-            pass
-
-
-def freeze(layer: nn.Layer):
-    for param in layer.parameters():
-        param.trainable = False
-
-
-def unfreeze(layer: nn.Layer):
-    for param in layer.parameters():
-        param.trainable = True

paddlespeech/text/speechtask/punctuation_restoration/utils/punct_pre.py

@@ -1,163 +0,0 @@
-# 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.
-import os
-import shutil
-
-CHINESE_PUNCTUATION_MAPPING = {
-    'O': '',
-    '，': "，",
-    '。': '。',
-    '？': '？',
-}
-
-
-def process_one_file_chinese(raw_path, save_path):
-    f = open(raw_path, 'r', encoding='utf-8')
-    save_file = open(save_path, 'w', encoding='utf-8')
-    for line in f.readlines():
-        line = line.strip().replace(' ', '').replace(' ', '')
-        for i in line:
-            save_file.write(i + ' ')
-        save_file.write('\n')
-    save_file.close()
-
-
-def process_chinese_pure_senetence(config):
-    ####need raw_path, raw_train_file, raw_dev_file, raw_test_file, punc_file, save_path
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "raw_train_file"])), "train file doesn't exist."
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "raw_dev_file"])), "dev file doesn't exist."
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "raw_test_file"])), "test file doesn't exist."
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "punc_file"])), "punc file doesn't exist."
-
-    train_file = os.path.join(config["raw_path"], config["raw_train_file"])
-    dev_file = os.path.join(config["raw_path"], config["raw_dev_file"])
-    test_file = os.path.join(config["raw_path"], config["raw_test_file"])
-    if not os.path.exists(config["save_path"]):
-        os.makedirs(config["save_path"])
-
-    shutil.copy(
-        os.path.join(config["raw_path"], config["punc_file"]),
-        os.path.join(config["save_path"], config["punc_file"]))
-
-    process_one_file_chinese(train_file,
-                             os.path.join(config["save_path"], "train"))
-    process_one_file_chinese(dev_file, os.path.join(config["save_path"], "dev"))
-    process_one_file_chinese(test_file,
-                             os.path.join(config["save_path"], "test"))
-
-
-def process_one_chinese_pair(raw_path, save_path):
-
-    f = open(raw_path, 'r', encoding='utf-8')
-    save_file = open(save_path, 'w', encoding='utf-8')
-    for line in f.readlines():
-        if (len(line.strip().split()) == 2):
-            word, punc = line.strip().split()
-            save_file.write(word + ' ' + CHINESE_PUNCTUATION_MAPPING[punc])
-            if (punc == "。"):
-                save_file.write("\n")
-            else:
-                save_file.write(" ")
-    save_file.close()
-
-
-def process_chinese_pair(config):
-    ### need raw_path, raw_train_file, raw_dev_file, raw_test_file, punc_file, save_path
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "raw_train_file"])), "train file doesn't exist."
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "raw_dev_file"])), "dev file doesn't exist."
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "raw_test_file"])), "test file doesn't exist."
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "punc_file"])), "punc file doesn't exist."
-
-    train_file = os.path.join(config["raw_path"], config["raw_train_file"])
-    dev_file = os.path.join(config["raw_path"], config["raw_dev_file"])
-    test_file = os.path.join(config["raw_path"], config["raw_test_file"])
-
-    process_one_chinese_pair(train_file,
-                             os.path.join(config["save_path"], "train"))
-    process_one_chinese_pair(dev_file, os.path.join(config["save_path"], "dev"))
-    process_one_chinese_pair(test_file,
-                             os.path.join(config["save_path"], "test"))
-
-    shutil.copy(
-        os.path.join(config["raw_path"], config["punc_file"]),
-        os.path.join(config["save_path"], config["punc_file"]))
-
-
-english_punc = [',', '.', '?']
-ignore_english_punc = ['\"', '/']
-
-
-def process_one_file_english(raw_path, save_path):
-    f = open(raw_path, 'r', encoding='utf-8')
-    save_file = open(save_path, 'w', encoding='utf-8')
-    for line in f.readlines():
-        for i in ignore_english_punc:
-            line = line.replace(i, '')
-        for i in english_punc:
-            line = line.replace(i, ' ' + i)
-        wordlist = line.strip().split(' ')
-        # print(type(wordlist))
-        # print(wordlist)
-        for i in wordlist:
-            save_file.write(i + ' ')
-        save_file.write('\n')
-    save_file.close()
-
-
-def process_english_pure_senetence(config):
-    ####need raw_path, raw_train_file, raw_dev_file, raw_test_file, punc_file, save_path
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "raw_train_file"])), "train file doesn't exist."
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "raw_dev_file"])), "dev file doesn't exist."
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "raw_test_file"])), "test file doesn't exist."
-    assert os.path.exists(
-        os.path.join(config["raw_path"], config[
-            "punc_file"])), "punc file doesn't exist."
-
-    train_file = os.path.join(config["raw_path"], config["raw_train_file"])
-    dev_file = os.path.join(config["raw_path"], config["raw_dev_file"])
-    test_file = os.path.join(config["raw_path"], config["raw_test_file"])
-    if not os.path.exists(config["save_path"]):
-        os.makedirs(config["save_path"])
-
-    shutil.copy(
-        os.path.join(config["raw_path"], config["punc_file"]),
-        os.path.join(config["save_path"], config["punc_file"]))
-
-    process_one_file_english(train_file,
-                             os.path.join(config["save_path"], "train"))
-    process_one_file_english(dev_file, os.path.join(config["save_path"], "dev"))
-    process_one_file_english(test_file,
-                             os.path.join(config["save_path"], "test"))

paddlespeech/text/speechtask/punctuation_restoration/utils/utility.py

@@ -1,81 +0,0 @@
-# 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.
-"""Contains common utility functions."""
-import distutils.util
-import math
-import os
-from typing import List
-
-__all__ = ['print_arguments', 'add_arguments', "log_add"]
-
-
-def print_arguments(args, info=None):
-    """Print argparse's arguments.
-
-    Usage:
-
-    .. code-block:: python
-
-        parser = argparse.ArgumentParser()
-        parser.add_argument("name", default="Jonh", type=str, help="User name.")
-        args = parser.parse_args()
-        print_arguments(args)
-
-    :param args: Input argparse.Namespace for printing.
-    :type args: argparse.Namespace
-    """
-    filename = ""
-    if info:
-        filename = info["__file__"]
-    filename = os.path.basename(filename)
-    print(f"----------- {filename} Configuration Arguments -----------")
-    for arg, value in sorted(vars(args).items()):
-        print("%s: %s" % (arg, value))
-    print("-----------------------------------------------------------")
-
-
-def add_arguments(argname, type, default, help, argparser, **kwargs):
-    """Add argparse's argument.
-
-    Usage:
-
-    .. code-block:: python
-
-        parser = argparse.ArgumentParser()
-        add_argument("name", str, "Jonh", "User name.", parser)
-        args = parser.parse_args()
-    """
-    type = distutils.util.strtobool if type == bool else type
-    argparser.add_argument(
-        "--" + argname,
-        default=default,
-        type=type,
-        help=help + ' Default: %(default)s.',
-        **kwargs)
-
-
-def log_add(args: List[int]) -> float:
-    """Stable log add
-
-    Args:
-        args (List[int]): log scores
-
-    Returns:
-        float: sum of log scores
-    """
-    if all(a == -float('inf') for a in args):
-        return -float('inf')
-    a_max = max(args)
-    lsp = math.log(sum(math.exp(a - a_max) for a in args))
-    return a_max + lsp

paddlespeech/text/training/trainer.py

@@ -25,24 +25,18 @@ from paddle.io import DataLoader
 from sklearn.metrics import classification_report
 from sklearn.metrics import f1_score
 from sklearn.metrics import precision_recall_fscore_support
-from speechtask.punctuation_restoration.io.dataset import PuncDataset
-from speechtask.punctuation_restoration.io.dataset import PuncDatasetFromBertTokenizer
-from speechtask.punctuation_restoration.model.BertBLSTM import BertBLSTMPunc
-from speechtask.punctuation_restoration.model.BertLinear import BertLinearPunc
-from speechtask.punctuation_restoration.model.blstm import BiLSTM
-from speechtask.punctuation_restoration.model.lstm import RnnLm
-from speechtask.punctuation_restoration.utils import layer_tools
-from speechtask.punctuation_restoration.utils import mp_tools
-from speechtask.punctuation_restoration.utils.checkpoint import Checkpoint
-from visualdl import LogWriter
+
+from ...s2t.utils import layer_tools
+from ...s2t.utils import mp_tools
+from ...s2t.utils.checkpoint import Checkpoint
+from ...text.models import ErnieLinear
+from ...text.models.ernie_linear.dataset import PuncDataset
+from ...text.models.ernie_linear.dataset import PuncDatasetFromErnieTokenizer
 
 __all__ = ["Trainer", "Tester"]
 
 DefinedClassifier = {
-    "lstm": RnnLm,
-    "blstm": BiLSTM,
-    "BertLinear": BertLinearPunc,
-    "BertBLSTM": BertBLSTMPunc
+    'ErnieLinear': ErnieLinear,
 }
 
 DefinedLoss = {
@@ -50,74 +44,18 @@ DefinedLoss = {
 }
 
 DefinedDataset = {
-    'PuncCh': PuncDataset,
-    'Bert': PuncDatasetFromBertTokenizer,
+    'Punc': PuncDataset,
+    'Ernie': PuncDatasetFromErnieTokenizer,
 }
 
 
 class Trainer():
-    """
-    An experiment template in order to structure the training code and take 
-    care of saving, loading, logging, visualization stuffs. It"s intended to 
-    be flexible and simple. 
-    
-    So it only handles output directory (create directory for the output, 
-    create a checkpoint directory, dump the config in use and create 
-    visualizer and logger) in a standard way without enforcing any
-    input-output protocols to the model and dataloader. It leaves the main 
-    part for the user to implement their own (setup the model, criterion, 
-    optimizer, define a training step, define a validation function and 
-    customize all the text and visual logs).
-    It does not save too much boilerplate code. The users still have to write 
-    the forward/backward/update mannually, but they are free to add 
-    non-standard behaviors if needed.
-    We have some conventions to follow.
-    1. Experiment should have ``model``, ``optimizer``, ``train_loader`` and 
-    ``valid_loader``, ``config`` and ``args`` attributes.
-    2. The config should have a ``training`` field, which has 
-    ``valid_interval``, ``save_interval`` and ``max_iteration`` keys. It is 
-    used as the trigger to invoke validation, checkpointing and stop of the 
-    experiment.
-    3. There are four methods, namely ``train_batch``, ``valid``, 
-    ``setup_model`` and ``setup_dataloader`` that should be implemented.
-    Feel free to add/overwrite other methods and standalone functions if you 
-    need.
-    
-    Parameters
-    ----------
-    config: yacs.config.CfgNode
-        The configuration used for the experiment.
-    
-    args: argparse.Namespace
-        The parsed command line arguments.
-    Examples
-    --------
-    >>> def main_sp(config, args):
-    >>>     exp = Trainer(config, args)
-    >>>     exp.setup()
-    >>>     exp.run()
-    >>> 
-    >>> config = get_cfg_defaults()
-    >>> parser = default_argument_parser()
-    >>> args = parser.parse_args()
-    >>> if args.config: 
-    >>>     config.merge_from_file(args.config)
-    >>> if args.opts:
-    >>>     config.merge_from_list(args.opts)
-    >>> config.freeze()
-    >>> 
-    >>> if args.ngpu > 1:
-    >>>     dist.spawn(main_sp, args=(config, args), nprocs=args.ngpu)
-    >>> else:
-    >>>     main_sp(config, args)
-    """
-
     def __init__(self, config, args):
         self.config = config
         self.args = args
         self.optimizer = None
-        self.visualizer = None
         self.output_dir = None
+        self.log_dir = None
         self.checkpoint_dir = None
         self.iteration = 0
         self.epoch = 0
@@ -125,14 +63,17 @@ class Trainer():
     def setup(self):
         """Setup the experiment.
         """
+        self.setup_log_dir()
         self.setup_logger()
-        paddle.set_device(self.args.device)
+        if self.args.ngpu > 0:
+            paddle.set_device('gpu')
+        else:
+            paddle.set_device('cpu')
         if self.parallel:
             self.init_parallel()
 
         self.setup_output_dir()
         self.dump_config()
-        self.setup_visualizer()
         self.setup_checkpointer()
 
         self.setup_model()
@@ -140,7 +81,7 @@ class Trainer():
         self.setup_dataloader()
 
         self.iteration = 0
-        self.epoch = 0
+        self.epoch = 1
 
     @property
     def parallel(self):
@@ -169,9 +110,9 @@ class Trainer():
             "epoch": self.epoch,
             "lr": self.optimizer.get_lr()
         })
-        self.checkpointer.add_checkpoint(self.checkpoint_dir, self.iteration
-                                         if tag is None else tag, self.model,
-                                         self.optimizer, infos)
+        self.checkpointer.save_parameters(self.checkpoint_dir, self.iteration
+                                          if tag is None else tag, self.model,
+                                          self.optimizer, infos)
 
     def resume_or_scratch(self):
         """Resume from latest checkpoint at checkpoints in the output 
@@ -239,11 +180,11 @@ class Trainer():
                     msg += "data time: {:>.3f}s, ".format(dataload_time)
                     self.train_batch(batch_index, batch, msg)
                     data_start_time = time.time()
-                t = classification_report(
-                    self.total_label_train,
-                    self.total_predict_train,
-                    target_names=self.punc_list)
-                self.logger.info(t)
+                # t = classification_report(
+                #     self.total_label_train,
+                #     self.total_predict_train,
+                #     target_names=self.punc_list)
+                # self.logger.info(t)
             except Exception as e:
                 self.logger.error(e)
                 raise e
@@ -251,11 +192,6 @@ class Trainer():
             total_loss, F1_score = self.valid()
             self.logger.info("Epoch {} Val info val_loss {}, F1_score {}".
                              format(self.epoch, total_loss, F1_score))
-            if self.visualizer:
-                self.visualizer.add_scalar(
-                    tag='eval/cv_loss', value=cv_loss, step=self.epoch)
-                self.visualizer.add_scalar(
-                    tag='eval/lr', value=self.lr_scheduler(), step=self.epoch)
 
             self.save(
                 tag=self.epoch, infos={"val_loss": total_loss,
@@ -271,6 +207,7 @@ class Trainer():
         try:
             self.train()
         except KeyboardInterrupt:
+            self.logger.info("Training was aborted by keybord interrupt.")
             self.save()
             exit(-1)
         finally:
@@ -281,19 +218,27 @@ class Trainer():
         """Create a directory used for output.
         """
         # output dir
-        output_dir = Path(self.args.output).expanduser()
+        output_dir = Path(self.args.output_dir).expanduser()
         output_dir.mkdir(parents=True, exist_ok=True)
 
         self.output_dir = output_dir
 
+    def setup_log_dir(self):
+        """Create a directory used for logging.
+        """
+        # log dir
+        log_dir = Path(self.args.log_dir).expanduser()
+        log_dir.mkdir(parents=True, exist_ok=True)
+
+        self.log_dir = log_dir
+
     def setup_checkpointer(self):
         """Create a directory used to save checkpoints into.
         
         It is "checkpoints" inside the output directory.
         """
         # checkpoint dir
-        self.checkpointer = Checkpoint(self.logger,
-                                       self.config["checkpoint"]["kbest_n"],
+        self.checkpointer = Checkpoint(self.config["checkpoint"]["kbest_n"],
                                        self.config["checkpoint"]["latest_n"])
 
         checkpoint_dir = self.output_dir / "checkpoints"
@@ -311,38 +256,17 @@ class Trainer():
             level=logging.INFO,
             format=LOG_FORMAT)
         self.logger = logging.getLogger(__name__)
-        # self.logger = logging.getLogger(self.config["training"]["log_path"].strip().split('/')[-1].split('.')[0])
 
-        self.logger.setLevel(logging.INFO)  #设置日志级别
-        sh = logging.StreamHandler()  #往屏幕上输出
-        sh.setFormatter(format_str)  #设置屏幕上显示的格式
-        self.logger.addHandler(sh)  #把对象加到logger里
+        self.logger.setLevel(logging.INFO)
+        sh = logging.StreamHandler()
+        sh.setFormatter(format_str)
+        self.logger.addHandler(sh)
 
         self.logger.info('info')
-        print("setup logger!!!")
 
     @mp_tools.rank_zero_only
     def destory(self):
-        """Close visualizer to avoid hanging after training"""
-        # https://github.com/pytorch/fairseq/issues/2357
-        if self.visualizer:
-            self.visualizer.close()
-
-    @mp_tools.rank_zero_only
-    def setup_visualizer(self):
-        """Initialize a visualizer to log the experiment.
-        
-        The visual log is saved in the output directory.
-        
-        Notes
-        ------
-        Only the main process has a visualizer with it. Use multiple 
-        visualizers in multiprocess to write to a same log file may cause 
-        unexpected behaviors.
-        """
-        # visualizer
-        visualizer = LogWriter(logdir=str(self.output_dir))
-        self.visualizer = visualizer
+        pass
 
     @mp_tools.rank_zero_only
     def dump_config(self):
@@ -363,15 +287,6 @@ class Trainer():
         pred = paddle.argmax(logit, axis=1)
         self.total_label_train.extend(label.numpy().tolist())
         self.total_predict_train.extend(pred.numpy().tolist())
-        # self.total_predict.append(logit.numpy().tolist())
-        # print('--after model----')
-        # # print(label.shape)
-        # # print(pred.shape)
-        # # print('--!!!!!!!!!!!!!----')
-        # print("self.total_label")
-        # print(self.total_label)
-        # print("self.total_predict")
-        # print(self.total_predict)
         loss = self.crit(y, label)
 
         loss.backward()
@@ -388,12 +303,6 @@ class Trainer():
         msg += ", ".join("{}: {:>.6f}".format(k, v)
                          for k, v in losses_np.items())
         self.logger.info(msg)
-        # print(msg)
-
-        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)
         self.iteration += 1
 
     @paddle.no_grad()
@@ -433,12 +342,9 @@ class Trainer():
                 msg += ", ".join("{}: {:>.6f}".format(k, v)
                                  for k, v in valid_dump.items())
                 self.logger.info(msg)
-                # print(msg)
 
         self.logger.info("Rank {} Val info val_loss {}".format(
             dist.get_rank(), total_loss / num_seen_utts))
-        # print("Rank {} Val info val_loss {} acc: {}".format(
-        #     dist.get_rank(), total_loss / num_seen_utts, acc))
         F1_score = f1_score(
             valid_total_label, valid_total_predict, average="macro")
         return total_loss / num_seen_utts, F1_score
@@ -454,8 +360,8 @@ class Trainer():
         if self.parallel:
             model = paddle.DataParallel(model)
 
-        self.logger.info(f"{model}")
-        layer_tools.print_params(model, self.logger.info)
+        # self.logger.info(f"{model}")
+        # layer_tools.print_params(model, self.logger.info)
 
         lr_scheduler = paddle.optimizer.lr.ExponentialDecay(
             learning_rate=config["training"]["lr"],
@@ -473,45 +379,12 @@ class Trainer():
         self.logger.info("Setup model/criterion/optimizer/lr_scheduler!")
 
     def setup_dataloader(self):
-        print("setup_dataloader!!!")
         config = self.config["data"].copy()
-
-        print(config["batch_size"])
-
         train_dataset = DefinedDataset[config["dataset_type"]](
             train_path=config["train_path"], **config["data_params"])
         dev_dataset = DefinedDataset[config["dataset_type"]](
             train_path=config["dev_path"], **config["data_params"])
 
-        # train_dataset = config["dataset_type"](os.path.join(config["save_path"], "train"), 
-        #             os.path.join(config["save_path"], config["vocab_file"]),
-        #             os.path.join(config["save_path"], config["punc_file"]),
-        #             config["seq_len"])
-
-        # dev_dataset = PuncDataset(os.path.join(config["save_path"], "dev"), 
-        #             os.path.join(config["save_path"], config["vocab_file"]),
-        #             os.path.join(config["save_path"], config["punc_file"]),
-        #             config["seq_len"])
-
-        # if self.parallel:
-        #     batch_sampler = SortagradDistributedBatchSampler(
-        #         train_dataset,
-        #         batch_size=config["batch_size"],
-        #         num_replicas=None,
-        #         rank=None,
-        #         shuffle=True,
-        #         drop_last=True,
-        #         sortagrad=config["sortagrad"],
-        #         shuffle_method=config["shuffle_method"])
-        # else:
-        #     batch_sampler = SortagradBatchSampler(
-        #         train_dataset,
-        #         shuffle=True,
-        #         batch_size=config["batch_size"],
-        #         drop_last=True,
-        #         sortagrad=config["sortagrad"],
-        #         shuffle_method=config["shuffle_method"])
-
         self.train_loader = DataLoader(
             train_dataset,
             num_workers=config["num_workers"],
@@ -548,14 +421,12 @@ class Tester(Trainer):
                 pred = paddle.argmax(logit, axis=1)
                 test_total_label.extend(label.numpy().tolist())
                 test_total_predict.extend(pred.numpy().tolist())
-                # print(type(logit))
 
             # logging
         msg = "Test: "
         msg += "epoch: {}, ".format(self.epoch)
         msg += "step: {}, ".format(self.iteration)
         self.logger.info(msg)
-        # print(msg)
         t = classification_report(
             test_total_label, test_total_predict, target_names=self.punc_list)
         print(t)
@@ -579,12 +450,16 @@ class Tester(Trainer):
         try:
             self.test()
         except KeyboardInterrupt:
+            self.logger.info("Testing was aborted by keybord interrupt.")
             exit(-1)
 
     def setup(self):
         """Setup the experiment.
         """
-        paddle.set_device(self.args.device)
+        if self.args.ngpu > 0:
+            paddle.set_device('gpu')
+        else:
+            paddle.set_device('cpu')
         self.setup_logger()
         self.setup_output_dir()
         self.setup_checkpointer()
@@ -620,8 +495,8 @@ class Tester(Trainer):
         """Create a directory used for output.
         """
         # output dir
-        if self.args.output:
-            output_dir = Path(self.args.output).expanduser()
+        if self.args.output_dir:
+            output_dir = Path(self.args.output_dir).expanduser()
             output_dir.mkdir(parents=True, exist_ok=True)
         else:
             output_dir = Path(
@@ -640,12 +515,10 @@ class Tester(Trainer):
             level=logging.INFO,
             format=LOG_FORMAT)
         self.logger = logging.getLogger(__name__)
-        # self.logger = logging.getLogger(self.config["training"]["log_path"].strip().split('/')[-1].split('.')[0])
 
-        self.logger.setLevel(logging.INFO)  #设置日志级别
-        sh = logging.StreamHandler()  #往屏幕上输出
-        sh.setFormatter(format_str)  #设置屏幕上显示的格式
-        self.logger.addHandler(sh)  #把对象加到logger里
+        self.logger.setLevel(logging.INFO)
+        sh = logging.StreamHandler()
+        sh.setFormatter(format_str)
+        self.logger.addHandler(sh)
 
         self.logger.info('info')
-        print("setup test logger!!!")

paddlespeech/text/utils/default_parser.py

@@ -48,7 +48,8 @@ def default_argument_parser():
     parser.add_argument("--config", metavar="FILE", help="path of the config file to overwrite to default config with.")
     parser.add_argument("--dump-config", metavar="FILE", help="dump config to yaml file.")
     # parser.add_argument("--data", metavar="DATA_DIR", help="path to the datatset.")
-    parser.add_argument("--output", metavar="OUTPUT_DIR", help="path to save checkpoint and logs.")
+    parser.add_argument("--output_dir", metavar="OUTPUT_DIR", help="path to save checkpoint.")
+    parser.add_argument("--log_dir", metavar="LOG_DIR", help="path to save logs.")
 
     # load from saved checkpoint
     parser.add_argument("--checkpoint_path", type=str, help="path of the checkpoint to load")