E2E/Streaming Transformer/Conformer ASR (#578)
* add cmvn and label smoothing loss layer
* add layer for transformer
* add glu and conformer conv
* add torch compatiable hack, mask funcs
* not hack size since it exists
* add test; attention
* add attention, common utils, hack paddle
* add audio utils
* conformer batch padding mask bug fix #223
* fix typo, python infer fix rnn mem opt name error and batchnorm1d, will be available at 2.0.2
* fix ci
* fix ci
* add encoder
* refactor egs
* add decoder
* refactor ctc, add ctc align, refactor ckpt, add warmup lr scheduler, cmvn utils
* refactor docs
* add fix
* fix readme
* fix bugs, refactor collator, add pad_sequence, fix ckpt bugs
* fix docstring
* refactor data feed order
* add u2 model
* refactor cmvn, test
* add utils
* add u2 config
* fix bugs
* fix bugs
* fix autograd maybe has problem when using inplace operation
* refactor data, build vocab; add format data
* fix text featurizer
* refactor build vocab
* add fbank, refactor feature of speech
* refactor audio feat
* refactor data preprare
* refactor data
* model init from config
* add u2 bins
* flake8
* can train
* fix bugs, add coverage, add scripts
* test can run
* fix data
* speed perturb with sox
* add spec aug
* fix for train
* fix train logitc
* fix logger
* log valid loss, time dataset process
* using np for speed perturb, remove some debug log of grad clip
* fix logger
* fix build vocab
* fix logger name
* using module logger as default
* fix
* fix install
* reorder imports
* fix board logger
* fix logger
* kaldi fbank and mfcc
* fix cmvn and print prarams
* fix add_eos_sos and cmvn
* fix cmvn compute
* fix logger and cmvn
* fix subsampling, label smoothing loss, remove useless
* add notebook test
* fix log
* fix tb logger
* multi gpu valid
* fix log
* fix log
* fix config
* fix compute cmvn, need paddle 2.1
* add cmvn notebook
* fix layer tools
* fix compute cmvn
* add rtf
* fix decoding
* fix layer tools
* fix log, add avg script
* more avg and test info
* fix dataset pickle problem; using 2.1 paddle; num_workers can > 0; ckpt save in exp dir;fix setup.sh;
* add vimrc
* refactor tiny script, add transformer and stream conf
* spm demo; librisppech scripts and confs
* fix log
* add librispeech scripts
* refactor data pipe; fix conf; fix u2 default params
* fix bugs
* refactor aishell scripts
* fix test
* fix cmvn
* fix s0 scripts
* fix ds2 scripts and bugs
* fix dev & test dataset filter
* fix dataset filter
* filter dev
* fix ckpt path
* filter test, since librispeech will cause OOM, but all test wer will be worse, since mismatch train with test
* add comment
* add syllable doc
* fix ds2 configs
* add doc
* add pypinyin tools
* fix decoder using blank_id=0
* mmseg with pybind11
* format code
4 years ago
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.. _develop:
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开发文档
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========
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准备开发环境
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-------------
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::
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$ virtualenv venv
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$ . venv/bin/activate
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(venv) $ pip install -U -r requirements_dev.txt
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(venv) $ pip install -e .
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(venv) $ pre-commit install
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TODO: 把这个步骤放到一个 make 命令中。
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.. note::
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推荐在 Python 3.6+ 环境下进行开发。
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测试
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------
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可以通过 ``make test`` 命令在当前 Python 版本下运行单元测试: ::
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(venv) $ make test
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可以通过 ``tox`` 测试程序在多个 Python 版本下的单元测试结果（这一步也可以在提 PR 的时候通过 CI 来运行）: ::
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(venv) $ tox
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.. note::
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如果对测试有疑问或者有些测试实在无法通过，可以先提交 PR 大家一起来看看。
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目录结构
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--------
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关键文件和目录 ::
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$ tree -L 2
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├── CHANGELOG.rst # 更新日志
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├── Makefile
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├── README.rst
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├── docs # 文档
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├── gen_phrases_dict.py # 生成 phrases_dict.py 的脚本
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├── gen_pinyin_dict.py # 生成 pinyin_dict.py 的脚本
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├── phrase-pinyin-data # gen_phrases_dict.py 使用的数据源
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├── pinyin-data # gen_pinyin_dict.py 使用的数据源
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├── pypinyin # pypinyin 模块源代码
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│ ├── __init__.py
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│ ├── __main__.py # 命令行程序的入口
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│ ├── compat.py
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│ ├── constants.py
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│ ├── contrib # 目前包含了一个分词模块
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│ ├── core.py # pypinyin 模块的核心逻辑
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│ ├── phonetic_symbol.py
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│ ├── phrases_dict.py # 词组的拼音数据，由 gen_phrases_dict.py 生成
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│ ├── pinyin_dict.py # 单个汉字的拼音数据，由 gen_pinyin_dict.py 生成
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│ ├── runner.py # 命令行程序的主逻辑
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│ ├── standard.py # strict=True 时的拼音转换逻辑
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│ ├── style # 各种拼音风格在 style 目录下实现
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│ ├── utils.py
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├── pytest.ini
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├── requirements_dev.txt
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├── setup.cfg
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├── setup.py
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├── tests
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├── tox.ini
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实现思路/主逻辑
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----------------
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主逻辑:
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1. 对输入的字符串按是否是汉字进行分词（``seg``）
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2. 对分词结果的每个词条进行获取词条拼音的逻辑
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1. 检查词条是否是汉字，不是汉字则走处理没有拼音数据的逻辑（``handle_nopinyin``）
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2. 检查词条是否在 ``PHRASES_DICT`` 中，如果在直接取 ``PHRASES_DICT`` 中这个词条的拼音数据
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3. 如果词条不在 ``PHRASES_DICT`` 中，遍历词条包含的字符，每个字符进行 ``single_pinyin`` 逻辑处理
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3. ``single_pinyin`` 的逻辑：
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1. 检查字符是否在 ``PINYIN_DICT`` 中，如果在的话，取 ``PINYIN_DICT`` 中这个字符的拼音数据
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2. 如果不在的话，走 ``handle_nopinyin`` 逻辑
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4. ``handle_nopinyin`` 逻辑: 根据 ``errors`` 参数的值返回不同的结果。
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5. 对上面的步骤获得的拼音数据按指定的拼音风格进行转换。
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* ``PHRASES_DICT``：词组拼音数据
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* ``PINYIN_DICT``: 单个汉字的拼音数据
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TODO: 画流程图
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发布新版本
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----------
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1. 切分到 develop 分支
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2. rebase master 分支的代码: ``make rebase_master``
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3. 通过 ``make gen_data`` 生成最新的数据文件
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4. 通过 ``make test`` 跑测试
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5. 更新 CHANGELOG
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6. 提交代码
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7. 检查 develop 分支的 CI 结果
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8. 切换到 master 分支
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9. 合并 develop 分支代码: ``make merge_dev``
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10. 更新版本号:
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* 大改动(1.1.x -> 1.2.x)：``make bump_minor``
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* 小改动(1.1.1 -> 1.1.2)：``make bump_patch``
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11. 发布到 test pypi: ``make publish_test``
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12. 安装和测试发布到 test pypi 上的版本
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13. 发布到 pypi: ``make publish``
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14. 安装和测试发布到 pypi 上的版本
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15. 提交 master 分支代码，更新 develop 分支代码，进入下一个开发阶段：``make start_next``
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