Hui Zhang
3 years ago
commit
2ba3f00bbd
@ -1,15 +0,0 @@
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unset GREP_OPTIONS
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# https://zhuanlan.zhihu.com/p/33050965
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alias nvs='nvidia-smi'
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alias his='history'
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alias jobs='jobs -l'
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alias ports='netstat -tulanp'
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alias wget='wget -c'
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## Colorize the grep command output for ease of use (good for log files)##
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alias grep='grep --color=auto'
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alias egrep='egrep --color=auto'
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alias fgrep='fgrep --color=auto'
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@ -1,468 +0,0 @@
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" Maintainer:
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" Amir Salihefendic — @amix3k
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"
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" Awesome_version:
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" Get this config, nice color schemes and lots of plugins!
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"
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" Install the awesome version from:
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"
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" https://github.com/amix/vimrc
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"
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" Sections:
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" -> General
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" -> VIM user interface
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" -> Colors and Fonts
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" -> Files and backups
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" -> Text, tab and indent related
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" -> Visual mode related
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" -> Moving around, tabs and buffers
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" -> Status line
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" -> Editing mappings
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" -> vimgrep searching and cope displaying
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" -> Spell checking
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" -> Misc
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" -> Helper functions
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"
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" => General
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" Sets how many lines of history VIM has to remember
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set history=500
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" Enable filetype plugins
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filetype plugin on
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filetype indent on
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" Set to auto read when a file is changed from the outside
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set autoread
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au FocusGained,BufEnter * checktime
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" With a map leader it's possible to do extra key combinations
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" like <leader>w saves the current file
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let mapleader = ","
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" Fast saving
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nmap <leader>w :w!<cr>
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" :W sudo saves the file
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" (useful for handling the permission-denied error)
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command! W execute 'w !sudo tee % > /dev/null' <bar> edit!
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" => VIM user interface
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" Set 7 lines to the cursor - when moving vertically using j/k
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set so=7
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" Avoid garbled characters in Chinese language windows OS
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let $LANG='en'
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set langmenu=en
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source $VIMRUNTIME/delmenu.vim
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source $VIMRUNTIME/menu.vim
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" Turn on the Wild menu
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set wildmenu
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" Ignore compiled files
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set wildignore=*.o,*~,*.pyc
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if has("win16") || has("win32")
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set wildignore+=.git\*,.hg\*,.svn\*
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else
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set wildignore+=*/.git/*,*/.hg/*,*/.svn/*,*/.DS_Store
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endif
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"Always show current position
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set ruler
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" Height of the command bar
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set cmdheight=1
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" A buffer becomes hidden when it is abandoned
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set hid
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" Configure backspace so it acts as it should act
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set backspace=eol,start,indent
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set whichwrap+=<,>,h,l
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" Ignore case when searching
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set ignorecase
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" When searching try to be smart about cases
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set smartcase
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" Highlight search results
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set hlsearch
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" Makes search act like search in modern browsers
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set incsearch
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" Don't redraw while executing macros (good performance config)
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set lazyredraw
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" For regular expressions turn magic on
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set magic
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" Show matching brackets when text indicator is over them
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set showmatch
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" How many tenths of a second to blink when matching brackets
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set mat=2
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" No annoying sound on errors
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set noerrorbells
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set novisualbell
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set t_vb=
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set tm=500
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" Properly disable sound on errors on MacVim
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if has("gui_macvim")
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autocmd GUIEnter * set vb t_vb=
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endif
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" Add a bit extra margin to the left
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set foldcolumn=1
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" => Colors and Fonts
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" Enable syntax highlighting
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syntax enable
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" Enable 256 colors palette in Gnome Terminal
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if $COLORTERM == 'gnome-terminal'
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set t_Co=256
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endif
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try
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colorscheme desert
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catch
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endtry
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set background=dark
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" Set extra options when running in GUI mode
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if has("gui_running")
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set guioptions-=T
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set guioptions-=e
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set t_Co=256
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set guitablabel=%M\ %t
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endif
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" Set utf8 as standard encoding and en_US as the standard language
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set encoding=utf8
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set fileencodings=ucs-bom,utf-8,cp936
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set fileencoding=gb2312
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set termencoding=utf-8
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" Use Unix as the standard file type
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set ffs=unix,dos,mac
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" => Files, backups and undo
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" Turn backup off, since most stuff is in SVN, git etc. anyway...
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set nobackup
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set nowb
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set noswapfile
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" => Text, tab and indent related
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" Use spaces instead of tabs
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set expandtab
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" Be smart when using tabs ;)
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set smarttab
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" 1 tab == 4 spaces
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set shiftwidth=4
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set tabstop=4
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" Linebreak on 500 characters
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set lbr
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set tw=500
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set ai "Auto indent
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set si "Smart indent
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set wrap "Wrap lines
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""""""""""""""""""""""""""""""
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" => Visual mode related
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""""""""""""""""""""""""""""""
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" Visual mode pressing * or # searches for the current selection
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" Super useful! From an idea by Michael Naumann
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vnoremap <silent> * :<C-u>call VisualSelection('', '')<CR>/<C-R>=@/<CR><CR>
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vnoremap <silent> # :<C-u>call VisualSelection('', '')<CR>?<C-R>=@/<CR><CR>
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" => Moving around, tabs, windows and buffers
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" Map <Space> to / (search) and Ctrl-<Space> to ? (backwards search)
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map <space> /
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map <C-space> ?
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" Disable highlight when <leader><cr> is pressed
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map <silent> <leader><cr> :noh<cr>
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" Smart way to move between windows
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map <C-j> <C-W>j
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map <C-k> <C-W>k
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map <C-h> <C-W>h
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map <C-l> <C-W>l
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" Close the current buffer
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map <leader>bd :Bclose<cr>:tabclose<cr>gT
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" Close all the buffers
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map <leader>ba :bufdo bd<cr>
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map <leader>l :bnext<cr>
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map <leader>h :bprevious<cr>
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" Useful mappings for managing tabs
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map <leader>tn :tabnew<cr>
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map <leader>to :tabonly<cr>
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map <leader>tc :tabclose<cr>
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map <leader>tm :tabmove
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map <leader>t<leader> :tabnext
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" Let 'tl' toggle between this and the last accessed tab
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let g:lasttab = 1
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nmap <Leader>tl :exe "tabn ".g:lasttab<CR>
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au TabLeave * let g:lasttab = tabpagenr()
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" Opens a new tab with the current buffer's path
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" Super useful when editing files in the same directory
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map <leader>te :tabedit <C-r>=expand("%:p:h")<cr>/
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" Switch CWD to the directory of the open buffer
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map <leader>cd :cd %:p:h<cr>:pwd<cr>
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" Specify the behavior when switching between buffers
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try
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set switchbuf=useopen,usetab,newtab
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set stal=2
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catch
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endtry
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" Return to last edit position when opening files (You want this!)
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au BufReadPost * if line("'\"") > 1 && line("'\"") <= line("$") | exe "normal! g'\"" | endif
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""""""""""""""""""""""""""""""
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" => Status line
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""""""""""""""""""""""""""""""
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" Always show the status line
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set laststatus=2
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" Format the status line
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set statusline=\ %{HasPaste()}%F%m%r%h\ %w\ \ CWD:\ %r%{getcwd()}%h\ \ \ Line:\ %l\ \ Column:\ %c
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" => Editing mappings
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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" Remap VIM 0 to first non-blank character
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map 0 ^
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" Move a line of text using ALT+[jk] or Command+[jk] on mac
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nmap <M-j> mz:m+<cr>`z
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nmap <M-k> mz:m-2<cr>`z
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vmap <M-j> :m'>+<cr>`<my`>mzgv`yo`z
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vmap <M-k> :m'<-2<cr>`>my`<mzgv`yo`z
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if has("mac") || has("macunix")
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nmap <D-j> <M-j>
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nmap <D-k> <M-k>
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vmap <D-j> <M-j>
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vmap <D-k> <M-k>
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endif
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" Delete trailing white space on save, useful for some filetypes ;)
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fun! CleanExtraSpaces()
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let save_cursor = getpos(".")
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let old_query = getreg('/')
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silent! %s/\s\+$//e
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call setpos('.', save_cursor)
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call setreg('/', old_query)
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endfun
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if has("autocmd")
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autocmd BufWritePre *.txt,*.js,*.py,*.wiki,*.sh,*.coffee :call CleanExtraSpaces()
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endif
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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||||
" => Spell checking
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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||||
" Pressing ,ss will toggle and untoggle spell checking
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map <leader>ss :setlocal spell!<cr>
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|
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" Shortcuts using <leader>
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map <leader>sn ]s
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map <leader>sp [s
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map <leader>sa zg
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map <leader>s? z=
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|
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|
||||
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
|
||||
" => Misc
|
||||
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
|
||||
" Remove the Windows ^M - when the encodings gets messed up
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noremap <Leader>m mmHmt:%s/<C-V><cr>//ge<cr>'tzt'm
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|
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" Quickly open a buffer for scribble
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map <leader>q :e ~/buffer<cr>
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||||
|
||||
" Quickly open a markdown buffer for scribble
|
||||
map <leader>x :e ~/buffer.md<cr>
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||||
|
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" Toggle paste mode on and off
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map <leader>pp :setlocal paste!<cr>
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||||
|
||||
|
||||
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
|
||||
" => Helper functions
|
||||
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
|
||||
" Returns true if paste mode is enabled
|
||||
function! HasPaste()
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if &paste
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return 'PASTE MODE '
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endif
|
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return ''
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||||
endfunction
|
||||
|
||||
" Don't close window, when deleting a buffer
|
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command! Bclose call <SID>BufcloseCloseIt()
|
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function! <SID>BufcloseCloseIt()
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let l:currentBufNum = bufnr("%")
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let l:alternateBufNum = bufnr("#")
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|
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if buflisted(l:alternateBufNum)
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buffer #
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else
|
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bnext
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endif
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||||
|
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if bufnr("%") == l:currentBufNum
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new
|
||||
endif
|
||||
|
||||
if buflisted(l:currentBufNum)
|
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execute("bdelete! ".l:currentBufNum)
|
||||
endif
|
||||
endfunction
|
||||
|
||||
function! CmdLine(str)
|
||||
call feedkeys(":" . a:str)
|
||||
endfunction
|
||||
|
||||
function! VisualSelection(direction, extra_filter) range
|
||||
let l:saved_reg = @"
|
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execute "normal! vgvy"
|
||||
|
||||
let l:pattern = escape(@", "\\/.*'$^~[]")
|
||||
let l:pattern = substitute(l:pattern, "\n$", "", "")
|
||||
|
||||
if a:direction == 'gv'
|
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call CmdLine("Ack '" . l:pattern . "' " )
|
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elseif a:direction == 'replace'
|
||||
call CmdLine("%s" . '/'. l:pattern . '/')
|
||||
endif
|
||||
|
||||
let @/ = l:pattern
|
||||
let @" = l:saved_reg
|
||||
endfunction
|
||||
|
||||
|
||||
""""""""""""""""""""""""""""""
|
||||
" => Python section
|
||||
""""""""""""""""""""""""""""""
|
||||
let python_highlight_all = 1
|
||||
au FileType python syn keyword pythonDecorator True None False self
|
||||
|
||||
au BufNewFile,BufRead *.jinja set syntax=htmljinja
|
||||
au BufNewFile,BufRead *.mako set ft=mako
|
||||
|
||||
au FileType python map <buffer> F :set foldmethod=indent<cr>
|
||||
|
||||
au FileType python inoremap <buffer> $r return
|
||||
au FileType python inoremap <buffer> $i import
|
||||
au FileType python inoremap <buffer> $p print
|
||||
au FileType python inoremap <buffer> $f # --- <esc>a
|
||||
au FileType python map <buffer> <leader>1 /class
|
||||
au FileType python map <buffer> <leader>2 /def
|
||||
au FileType python map <buffer> <leader>C ?class
|
||||
au FileType python map <buffer> <leader>D ?def
|
||||
|
||||
|
||||
""""""""""""""""""""""""""""""
|
||||
" => JavaScript section
|
||||
"""""""""""""""""""""""""""""""
|
||||
au FileType javascript call JavaScriptFold()
|
||||
au FileType javascript setl fen
|
||||
au FileType javascript setl nocindent
|
||||
|
||||
au FileType javascript imap <C-t> $log();<esc>hi
|
||||
au FileType javascript imap <C-a> alert();<esc>hi
|
||||
|
||||
au FileType javascript inoremap <buffer> $r return
|
||||
au FileType javascript inoremap <buffer> $f // --- PH<esc>FP2xi
|
||||
|
||||
function! JavaScriptFold()
|
||||
setl foldmethod=syntax
|
||||
setl foldlevelstart=1
|
||||
syn region foldBraces start=/{/ end=/}/ transparent fold keepend extend
|
||||
|
||||
function! FoldText()
|
||||
return substitute(getline(v:foldstart), '{.*', '{...}', '')
|
||||
endfunction
|
||||
setl foldtext=FoldText()
|
||||
endfunction
|
||||
|
||||
|
||||
""""""""""""""""""""""""""""""
|
||||
" => CoffeeScript section
|
||||
"""""""""""""""""""""""""""""""
|
||||
function! CoffeeScriptFold()
|
||||
setl foldmethod=indent
|
||||
setl foldlevelstart=1
|
||||
endfunction
|
||||
au FileType coffee call CoffeeScriptFold()
|
||||
|
||||
au FileType gitcommit call setpos('.', [0, 1, 1, 0])
|
||||
|
||||
|
||||
""""""""""""""""""""""""""""""
|
||||
" => Shell section
|
||||
""""""""""""""""""""""""""""""
|
||||
if exists('$TMUX')
|
||||
if has('nvim')
|
||||
set termguicolors
|
||||
else
|
||||
set term=screen-256color
|
||||
endif
|
||||
endif
|
||||
|
||||
|
||||
""""""""""""""""""""""""""""""
|
||||
" => Twig section
|
||||
""""""""""""""""""""""""""""""
|
||||
autocmd BufRead *.twig set syntax=html filetype=html
|
||||
|
||||
|
||||
""""""""""""""""""""""""""""""
|
||||
" => Markdown
|
||||
""""""""""""""""""""""""""""""
|
||||
let vim_markdown_folding_disabled = 1
|
||||
@ -0,0 +1,5 @@
|
||||
# ASR
|
||||
|
||||
```shell
|
||||
CUDA_VISIBLE_DEVICES=0 ./run.sh
|
||||
```
|
||||
@ -0,0 +1,46 @@
|
||||
# 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 paddle
|
||||
import paddlehub as hub
|
||||
|
||||
# yapf: disable
|
||||
parser = argparse.ArgumentParser(__doc__)
|
||||
parser.add_argument("--device", type=str, default='gpu', choices=['cpu', 'gpu'])
|
||||
parser.add_argument("--wav_en", type=str)
|
||||
parser.add_argument("--wav_zh", type=str)
|
||||
args = parser.parse_args()
|
||||
# yapf: enable
|
||||
|
||||
if __name__ == '__main__':
|
||||
paddle.set_device(args.device)
|
||||
|
||||
s2t_en_model = hub.Module(name='u2_conformer_librispeech')
|
||||
s2t_zh_model = hub.Module(name='u2_conformer_aishell')
|
||||
|
||||
args.wav_en = os.path.abspath(os.path.expanduser(args.wav_en))
|
||||
args.wav_zh = os.path.abspath(os.path.expanduser(args.wav_zh))
|
||||
|
||||
assert os.path.isfile(args.wav_en) and os.path.isfile(
|
||||
args.wav_zh), 'Wav files not exist.'
|
||||
|
||||
print('[S2T][en]Wav: {}'.format(args.wav_en))
|
||||
text_en = s2t_en_model.speech_recognize(args.wav_en)
|
||||
print('[S2T][en]Text: {}'.format(text_en))
|
||||
|
||||
print('[S2T][zh]Wav: {}'.format(args.wav_zh))
|
||||
text_zh = s2t_zh_model.speech_recognize(args.wav_zh)
|
||||
print('[S2T][zh]Text: {}'.format(text_zh))
|
||||
@ -0,0 +1,30 @@
|
||||
#!/bin/bash
|
||||
|
||||
if python -c "import paddlehub" &> /dev/null; then
|
||||
echo 'PaddleHub has already been installed.'
|
||||
else
|
||||
echo 'Installing PaddleHub...'
|
||||
pip install paddlehub -U
|
||||
fi
|
||||
|
||||
mkdir -p data
|
||||
wav_en=data/en.wav
|
||||
wav_zh=data/zh.wav
|
||||
test -e ${wav_en} || wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav -P data
|
||||
test -e ${wav_zh} || wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav -P data
|
||||
|
||||
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
|
||||
if [ ${ngpu} == 0 ];then
|
||||
device=cpu
|
||||
else
|
||||
device=gpu
|
||||
fi
|
||||
|
||||
echo "using ${device}..."
|
||||
|
||||
python3 -u hub_infer.py \
|
||||
--device ${device} \
|
||||
--wav_en ${wav_en} \
|
||||
--wav_zh ${wav_zh}
|
||||
|
||||
exit 0
|
||||
@ -0,0 +1 @@
|
||||
data
|
||||
@ -0,0 +1,13 @@
|
||||
# echo system
|
||||
|
||||
ASR + TTS
|
||||
|
||||
中文：
|
||||
```shell
|
||||
CUDA_VISIBLE_DEVICES=0 ./run.sh 用科技让复杂的世界更简单 . zh
|
||||
```
|
||||
|
||||
英文：
|
||||
```shell
|
||||
CUDA_VISIBLE_DEVICES=0 ./run.sh "Text to speech system converts normal language text into speech." . en
|
||||
```
|
||||
@ -0,0 +1,55 @@
|
||||
# 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 librosa
|
||||
import paddle
|
||||
import paddlehub as hub
|
||||
import soundfile as sf
|
||||
|
||||
# yapf: disable
|
||||
parser = argparse.ArgumentParser(__doc__)
|
||||
parser.add_argument("--lang", type=str, default='zh', choices=['zh', 'en'])
|
||||
parser.add_argument("--device", type=str, default='gpu', choices=['cpu', 'gpu'])
|
||||
parser.add_argument("--text", type=str, nargs='+')
|
||||
parser.add_argument("--output_dir", type=str)
|
||||
args = parser.parse_args()
|
||||
# yapf: enable
|
||||
|
||||
if __name__ == '__main__':
|
||||
paddle.set_device(args.device)
|
||||
|
||||
output_dir = os.path.abspath(os.path.expanduser(args.output_dir))
|
||||
if args.lang == 'zh':
|
||||
t2s_model = hub.Module(name='fastspeech2_baker', output_dir=output_dir)
|
||||
s2t_model = hub.Module(name='u2_conformer_aishell')
|
||||
else:
|
||||
t2s_model = hub.Module(
|
||||
name='fastspeech2_ljspeech', output_dir=output_dir)
|
||||
s2t_model = hub.Module(name='u2_conformer_librispeech')
|
||||
|
||||
if isinstance(args.text, list):
|
||||
args.text = ' '.join(args.text)
|
||||
|
||||
wavs = t2s_model.generate([args.text], device=args.device)
|
||||
print('[T2S]Wav file has been generated: {}'.format(wavs[0]))
|
||||
# convert sr to 16k
|
||||
x, sr = librosa.load(wavs[0])
|
||||
y = librosa.resample(x, sr, 16000)
|
||||
wav_16k = wavs[0].replace('.wav', '_16k.wav')
|
||||
sf.write(wav_16k, y, 16000)
|
||||
print('[S2T]Resample to 16k: {}'.format(wav_16k))
|
||||
text = s2t_model.speech_recognize(wav_16k)
|
||||
print('[S2T]Text recognized from wav file: {}'.format(text))
|
||||
@ -0,0 +1,42 @@
|
||||
#!/bin/bash
|
||||
|
||||
if python -c "import paddlehub" &> /dev/null; then
|
||||
echo 'PaddleHub has already been installed.'
|
||||
else
|
||||
echo 'Installing PaddleHub...'
|
||||
pip install paddlehub -U
|
||||
fi
|
||||
|
||||
if [ $# != 2 -a $# != 3 ];then
|
||||
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} text output_dir [lang]"
|
||||
exit -1
|
||||
fi
|
||||
|
||||
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
|
||||
if [ ${ngpu} == 0 ];then
|
||||
device=cpu
|
||||
else
|
||||
device=gpu
|
||||
fi
|
||||
|
||||
echo "using ${device}..."
|
||||
|
||||
text=$1
|
||||
output_dir=$2
|
||||
if [ $# == 3 ];then
|
||||
lang=$3
|
||||
else
|
||||
lang=zh
|
||||
fi
|
||||
|
||||
if [ ! -d $output_dir ];then
|
||||
mkdir -p $output_dir
|
||||
fi
|
||||
|
||||
python3 -u hub_infer.py \
|
||||
--lang ${lang} \
|
||||
--device ${device} \
|
||||
--text \"${text}\" \
|
||||
--output_dir ${output_dir}
|
||||
|
||||
exit 0
|
||||
|
After Width: | Height: | Size: 441 KiB |
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
|
||||
MODEL=fastspeech2
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}
|
||||
@ -0,0 +1,61 @@
|
||||
#!/bin/bash
|
||||
source path.sh
|
||||
|
||||
gpus=0
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
# with the following command, you can choice the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
mkdir -p download
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# install PaddleGAN
|
||||
git clone https://github.com/PaddlePaddle/PaddleGAN.git
|
||||
pip install -e PaddleGAN/
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# download pretrained PaddleGAN model
|
||||
wget -P download https://paddlegan.bj.bcebos.com/models/wav2lip_hq.pdparams
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# download pretrained tts models and unzip
|
||||
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip
|
||||
unzip -d download download/pwg_baker_ckpt_0.4.zip
|
||||
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip
|
||||
unzip -d download download/fastspeech2_nosil_baker_ckpt_0.4.zip
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# run tts
|
||||
CUDA_VISIBLE_DEVICES=${gpus} \
|
||||
python3 ${BIN_DIR}/synthesize_e2e.py \
|
||||
--fastspeech2-config=download/fastspeech2_nosil_baker_ckpt_0.4/default.yaml \
|
||||
--fastspeech2-checkpoint=download/fastspeech2_nosil_baker_ckpt_0.4/snapshot_iter_76000.pdz \
|
||||
--fastspeech2-stat=download/fastspeech2_nosil_baker_ckpt_0.4/speech_stats.npy \
|
||||
--pwg-config=download/pwg_baker_ckpt_0.4/pwg_default.yaml \
|
||||
--pwg-checkpoint=download/pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
|
||||
--pwg-stat=download/pwg_baker_ckpt_0.4/pwg_stats.npy \
|
||||
--text=sentences.txt \
|
||||
--output-dir=output/wavs \
|
||||
--inference-dir=output/inference \
|
||||
--phones-dict=download/fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt
|
||||
# output/inference is not needed here, which save the static models
|
||||
rm -rf output/inference
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
||||
# We only test one audio here, cause it's slow
|
||||
CUDA_VISIBLE_DEVICES=${gpus} \
|
||||
python3 PaddleGAN/applications/tools/wav2lip.py \
|
||||
--checkpoint_path download/wav2lip_hq.pdparams \
|
||||
--face Lamarr.png \
|
||||
--audio output/wavs/000.wav \
|
||||
--outfile output/tts_lips.mp4 \
|
||||
--face_enhancement
|
||||
fi
|
||||
@ -0,0 +1 @@
|
||||
000 谁知青蛙一落地，竟变成了一位英俊的王子。于是遵照国王的意思，他做了公主的亲密伴侣。
|
||||
|
After Width: | Height: | Size: 1.5 MiB |
@ -0,0 +1,74 @@
|
||||
# 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 pathlib import Path
|
||||
|
||||
import paddle
|
||||
from paddleocr import draw_ocr
|
||||
from paddleocr import PaddleOCR
|
||||
from PIL import Image
|
||||
|
||||
|
||||
def evaluate(args, ocr):
|
||||
img_dir = Path(args.img_dir)
|
||||
output_dir = Path(args.output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
img_out_dir = output_dir / "imgs"
|
||||
img_out_dir.mkdir(parents=True, exist_ok=True)
|
||||
with open(output_dir / "sentences.txt", "w") as wf:
|
||||
for name in os.listdir(img_dir):
|
||||
id = name.split(".")[0]
|
||||
img_path = img_dir / name
|
||||
result = ocr.ocr(str(img_path), cls=True)
|
||||
# draw result
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
boxes = [line[0] for line in result]
|
||||
txts = [line[1][0] for line in result]
|
||||
scores = [line[1][1] for line in result]
|
||||
im_show = draw_ocr(
|
||||
image, boxes, txts, scores, font_path=args.font_path)
|
||||
im_show = Image.fromarray(im_show)
|
||||
paragraph = "".join(txts)
|
||||
# 过滤出中文结果
|
||||
pattern = re.compile(r'[^(\u4e00-\u9fa5)+，。？、]')
|
||||
sentence = re.sub(pattern, '', paragraph)
|
||||
im_show.save(img_out_dir / name)
|
||||
wf.write(id + " " + sentence + "\n")
|
||||
|
||||
|
||||
def main():
|
||||
# parse args and config and redirect to train_sp
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Synthesize with fastspeech2 & parallel wavegan.")
|
||||
parser.add_argument("--img-dir", default="imgs", type=str, help="img_dir.")
|
||||
parser.add_argument(
|
||||
"--output-dir",
|
||||
type=str,
|
||||
default="output",
|
||||
help="output sentences path.")
|
||||
parser.add_argument(
|
||||
"--font-path", type=str, default="simfang.ttf", help="font path")
|
||||
args = parser.parse_args()
|
||||
|
||||
paddle.set_device("gpu")
|
||||
# need to run only once to download and load model into memory
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang='ch')
|
||||
|
||||
evaluate(args, ocr)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
|
||||
MODEL=fastspeech2
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}
|
||||
@ -0,0 +1,50 @@
|
||||
#!/bin/bash
|
||||
source path.sh
|
||||
|
||||
gpus=0
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
# with the following command, you can choice the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
mkdir -p download
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# install PaddleOCR
|
||||
pip install "paddleocr>=2.0.1"
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# download pretrained tts models and unzip
|
||||
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip
|
||||
unzip -d download download/pwg_baker_ckpt_0.4.zip
|
||||
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip
|
||||
unzip -d download download/fastspeech2_nosil_baker_ckpt_0.4.zip
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# run ocr
|
||||
CUDA_VISIBLE_DEVICES=${gpus} \
|
||||
python3 ocr.py --img-dir=imgs --output-dir=output --font-path=simfang.ttf
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# run tts
|
||||
CUDA_VISIBLE_DEVICES=${gpus} \
|
||||
python3 ${BIN_DIR}/synthesize_e2e.py \
|
||||
--fastspeech2-config=download/fastspeech2_nosil_baker_ckpt_0.4/default.yaml \
|
||||
--fastspeech2-checkpoint=download/fastspeech2_nosil_baker_ckpt_0.4/snapshot_iter_76000.pdz \
|
||||
--fastspeech2-stat=download/fastspeech2_nosil_baker_ckpt_0.4/speech_stats.npy \
|
||||
--pwg-config=download/pwg_baker_ckpt_0.4/pwg_default.yaml \
|
||||
--pwg-checkpoint=download/pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
|
||||
--pwg-stat=download/pwg_baker_ckpt_0.4/pwg_stats.npy \
|
||||
--text=output/sentences.txt \
|
||||
--output-dir=output/wavs \
|
||||
--inference-dir=output/inference \
|
||||
--phones-dict=download/fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt
|
||||
# output/inference is not needed here, which save the static models
|
||||
rm -rf output/inference
|
||||
fi
|
||||
Binary file not shown.
@ -0,0 +1,12 @@
|
||||
#!/bin/bash
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
MODEL=fastspeech2
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}
|
||||
@ -0,0 +1,38 @@
|
||||
#!/bin/bash
|
||||
source path.sh
|
||||
|
||||
gpus=0
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
# with the following command, you can choice the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
mkdir -p download
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# download pretrained tts models and unzip
|
||||
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip
|
||||
unzip -d download download/pwg_baker_ckpt_0.4.zip
|
||||
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip
|
||||
unzip -d download download/fastspeech2_nosil_baker_ckpt_0.4.zip
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# run tts
|
||||
CUDA_VISIBLE_DEVICES=${gpus} \
|
||||
python3 style_syn.py \
|
||||
--fastspeech2-config=download/fastspeech2_nosil_baker_ckpt_0.4/default.yaml \
|
||||
--fastspeech2-checkpoint=download/fastspeech2_nosil_baker_ckpt_0.4/snapshot_iter_76000.pdz \
|
||||
--fastspeech2-stat=download/fastspeech2_nosil_baker_ckpt_0.4/speech_stats.npy \
|
||||
--fastspeech2-pitch-stat=download/fastspeech2_nosil_baker_ckpt_0.4/pitch_stats.npy \
|
||||
--fastspeech2-energy-stat=download/fastspeech2_nosil_baker_ckpt_0.4/energy_stats.npy \
|
||||
--pwg-config=download/pwg_baker_ckpt_0.4/pwg_default.yaml \
|
||||
--pwg-checkpoint=download/pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
|
||||
--pwg-stat=download/pwg_baker_ckpt_0.4/pwg_stats.npy \
|
||||
--text=${BIN_DIR}/../sentences.txt \
|
||||
--output-dir=output \
|
||||
--phones-dict=download/fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt
|
||||
fi
|
||||
@ -0,0 +1 @@
|
||||
000 谁知青蛙一落地，竟变成了一位英俊的王子。于是遵照国王的意思，他做了公主的亲密伴侣。
|
||||
@ -0,0 +1,210 @@
|
||||
# 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
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
import soundfile as sf
|
||||
import yaml
|
||||
from yacs.config import CfgNode
|
||||
|
||||
from paddlespeech.t2s.frontend.zh_frontend import Frontend
|
||||
from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
|
||||
from paddlespeech.t2s.models.fastspeech2 import StyleFastSpeech2Inference
|
||||
from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
|
||||
from paddlespeech.t2s.models.parallel_wavegan import PWGInference
|
||||
from paddlespeech.t2s.modules.normalizer import ZScore
|
||||
|
||||
|
||||
def evaluate(args, fastspeech2_config, pwg_config):
|
||||
|
||||
# construct dataset for evaluation
|
||||
sentences = []
|
||||
with open(args.text, 'rt') as f:
|
||||
for line in f:
|
||||
utt_id, sentence = line.strip().split()
|
||||
sentences.append((utt_id, sentence))
|
||||
|
||||
with open(args.phones_dict, "r") as f:
|
||||
phn_id = [line.strip().split() for line in f.readlines()]
|
||||
vocab_size = len(phn_id)
|
||||
print("vocab_size:", vocab_size)
|
||||
|
||||
odim = fastspeech2_config.n_mels
|
||||
model = FastSpeech2(
|
||||
idim=vocab_size, odim=odim, **fastspeech2_config["model"])
|
||||
|
||||
model.set_state_dict(
|
||||
paddle.load(args.fastspeech2_checkpoint)["main_params"])
|
||||
model.eval()
|
||||
|
||||
vocoder = PWGGenerator(**pwg_config["generator_params"])
|
||||
vocoder.set_state_dict(paddle.load(args.pwg_checkpoint)["generator_params"])
|
||||
vocoder.remove_weight_norm()
|
||||
vocoder.eval()
|
||||
print("model done!")
|
||||
|
||||
frontend = Frontend(phone_vocab_path=args.phones_dict)
|
||||
print("frontend done!")
|
||||
|
||||
stat = np.load(args.fastspeech2_stat)
|
||||
mu, std = stat
|
||||
mu = paddle.to_tensor(mu)
|
||||
std = paddle.to_tensor(std)
|
||||
fastspeech2_normalizer = ZScore(mu, std)
|
||||
|
||||
stat = np.load(args.pwg_stat)
|
||||
mu, std = stat
|
||||
mu = paddle.to_tensor(mu)
|
||||
std = paddle.to_tensor(std)
|
||||
pwg_normalizer = ZScore(mu, std)
|
||||
|
||||
fastspeech2_inference = StyleFastSpeech2Inference(
|
||||
fastspeech2_normalizer, model, args.fastspeech2_pitch_stat,
|
||||
args.fastspeech2_energy_stat)
|
||||
fastspeech2_inference.eval()
|
||||
|
||||
pwg_inference = PWGInference(pwg_normalizer, vocoder)
|
||||
pwg_inference.eval()
|
||||
|
||||
output_dir = Path(args.output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
styles = ["normal", "robot", "1.2xspeed", "0.8xspeed", "child_voice"]
|
||||
for style in styles:
|
||||
robot = False
|
||||
durations = None
|
||||
durations_scale = None
|
||||
durations_bias = None
|
||||
pitch = None
|
||||
pitch_scale = None
|
||||
pitch_bias = None
|
||||
energy = None
|
||||
energy_scale = None
|
||||
energy_bias = None
|
||||
if style == "robot":
|
||||
# all tones in phones be `1`
|
||||
# all pitch should be the same, we use mean here
|
||||
robot = True
|
||||
if style == "1.2xspeed":
|
||||
durations_scale = 1 / 1.2
|
||||
if style == "0.8xspeed":
|
||||
durations_scale = 1 / 0.8
|
||||
if style == "child_voice":
|
||||
pitch_scale = 1.3
|
||||
sub_output_dir = output_dir / style
|
||||
sub_output_dir.mkdir(parents=True, exist_ok=True)
|
||||
for utt_id, sentence in sentences:
|
||||
input_ids = frontend.get_input_ids(
|
||||
sentence, merge_sentences=True, robot=robot)
|
||||
phone_ids = input_ids["phone_ids"][0]
|
||||
|
||||
with paddle.no_grad():
|
||||
mel = fastspeech2_inference(
|
||||
phone_ids,
|
||||
durations=durations,
|
||||
durations_scale=durations_scale,
|
||||
durations_bias=durations_bias,
|
||||
pitch=pitch,
|
||||
pitch_scale=pitch_scale,
|
||||
pitch_bias=pitch_bias,
|
||||
energy=energy,
|
||||
energy_scale=energy_scale,
|
||||
energy_bias=energy_bias,
|
||||
robot=robot)
|
||||
wav = pwg_inference(mel)
|
||||
|
||||
sf.write(
|
||||
str(sub_output_dir / (utt_id + ".wav")),
|
||||
wav.numpy(),
|
||||
samplerate=fastspeech2_config.fs)
|
||||
print(f"{style}_{utt_id} done!")
|
||||
|
||||
|
||||
def main():
|
||||
# parse args and config and redirect to train_sp
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Synthesize with fastspeech2 & parallel wavegan.")
|
||||
parser.add_argument(
|
||||
"--fastspeech2-config", type=str, help="fastspeech2 config file.")
|
||||
parser.add_argument(
|
||||
"--fastspeech2-checkpoint",
|
||||
type=str,
|
||||
help="fastspeech2 checkpoint to load.")
|
||||
parser.add_argument(
|
||||
"--fastspeech2-stat",
|
||||
type=str,
|
||||
help="mean and standard deviation used to normalize spectrogram when training fastspeech2."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--fastspeech2-pitch-stat",
|
||||
type=str,
|
||||
help="mean and standard deviation used to normalize pitch when training fastspeech2"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--fastspeech2-energy-stat",
|
||||
type=str,
|
||||
help="mean and standard deviation used to normalize energy when training fastspeech2."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--pwg-config", type=str, help="parallel wavegan config file.")
|
||||
parser.add_argument(
|
||||
"--pwg-checkpoint",
|
||||
type=str,
|
||||
help="parallel wavegan generator parameters to load.")
|
||||
parser.add_argument(
|
||||
"--pwg-stat",
|
||||
type=str,
|
||||
help="mean and standard deviation used to normalize spectrogram when training parallel wavegan."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--phones-dict",
|
||||
type=str,
|
||||
default="phone_id_map.txt",
|
||||
help="phone vocabulary file.")
|
||||
parser.add_argument(
|
||||
"--text",
|
||||
type=str,
|
||||
help="text to synthesize, a 'utt_id sentence' pair per line.")
|
||||
parser.add_argument("--output-dir", type=str, help="output dir.")
|
||||
parser.add_argument(
|
||||
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
|
||||
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.ngpu == 0:
|
||||
paddle.set_device("cpu")
|
||||
elif args.ngpu > 0:
|
||||
paddle.set_device("gpu")
|
||||
else:
|
||||
print("ngpu should >= 0 !")
|
||||
|
||||
with open(args.fastspeech2_config) as f:
|
||||
fastspeech2_config = CfgNode(yaml.safe_load(f))
|
||||
with open(args.pwg_config) as f:
|
||||
pwg_config = CfgNode(yaml.safe_load(f))
|
||||
|
||||
print("========Args========")
|
||||
print(yaml.safe_dump(vars(args)))
|
||||
print("========Config========")
|
||||
print(fastspeech2_config)
|
||||
print(pwg_config)
|
||||
|
||||
evaluate(args, fastspeech2_config, pwg_config)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,11 @@
|
||||
# TTS
|
||||
|
||||
中文：
|
||||
```shell
|
||||
CUDA_VISIBLE_DEVICES=0 ./run.sh 用科技让复杂的世界更简单 . zh
|
||||
```
|
||||
|
||||
英文：
|
||||
```shell
|
||||
CUDA_VISIBLE_DEVICES=0 ./run.sh "Text to speech system converts normal language text into speech." . en
|
||||
```
|
||||
@ -0,0 +1,43 @@
|
||||
# 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 paddle
|
||||
import paddlehub as hub
|
||||
|
||||
# yapf: disable
|
||||
parser = argparse.ArgumentParser(__doc__)
|
||||
parser.add_argument("--lang", type=str, default='zh', choices=['zh', 'en'])
|
||||
parser.add_argument("--device", type=str, default='gpu', choices=['cpu', 'gpu'])
|
||||
parser.add_argument("--text", type=str, nargs='+')
|
||||
parser.add_argument("--output_dir", type=str)
|
||||
args = parser.parse_args()
|
||||
# yapf: enable
|
||||
|
||||
if __name__ == '__main__':
|
||||
paddle.set_device(args.device)
|
||||
|
||||
output_dir = os.path.abspath(os.path.expanduser(args.output_dir))
|
||||
if args.lang == 'zh':
|
||||
t2s_model = hub.Module(name='fastspeech2_baker', output_dir=output_dir)
|
||||
else:
|
||||
t2s_model = hub.Module(
|
||||
name='fastspeech2_ljspeech', output_dir=output_dir)
|
||||
|
||||
if isinstance(args.text, list):
|
||||
args.text = ' '.join(args.text)
|
||||
|
||||
wavs = t2s_model.generate([args.text], device=args.device)
|
||||
print('[T2S]Wav file has been generated: {}'.format(wavs[0]))
|
||||
@ -0,0 +1,42 @@
|
||||
#!/bin/bash
|
||||
|
||||
if python -c "import paddlehub" &> /dev/null; then
|
||||
echo 'PaddleHub has already been installed.'
|
||||
else
|
||||
echo 'Installing PaddleHub...'
|
||||
pip install paddlehub -U
|
||||
fi
|
||||
|
||||
if [ $# != 2 -a $# != 3 ];then
|
||||
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} text output_dir [lang]"
|
||||
exit -1
|
||||
fi
|
||||
|
||||
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
|
||||
if [ ${ngpu} == 0 ];then
|
||||
device=cpu
|
||||
else
|
||||
device=gpu
|
||||
fi
|
||||
|
||||
echo "using ${device}..."
|
||||
|
||||
text=$1
|
||||
output_dir=$2
|
||||
if [ $# == 3 ];then
|
||||
lang=$3
|
||||
else
|
||||
lang=zh
|
||||
fi
|
||||
|
||||
if [ ! -d $output_dir ];then
|
||||
mkdir -p $output_dir
|
||||
fi
|
||||
|
||||
python3 -u hub_infer.py \
|
||||
--lang ${lang} \
|
||||
--device ${device} \
|
||||
--text \"${text}\" \
|
||||
--output_dir ${output_dir}
|
||||
|
||||
exit 0
|
||||
|
Before Width: | Height: | Size: 72 KiB After Width: | Height: | Size: 72 KiB |
@ -0,0 +1,36 @@
|
||||
# The Dependencies
|
||||
|
||||
## By apt-get
|
||||
|
||||
### The base dependencies:
|
||||
|
||||
```
|
||||
bc flac jq vim tig tree pkg-config libsndfile1 libflac-dev libvorbis-dev libboost-dev swig python3-dev
|
||||
```
|
||||
|
||||
### The dependencies of kenlm:
|
||||
|
||||
```
|
||||
build-essential cmake libboost-system-dev libboost-thread-dev libboost-program-options-dev libboost-test-dev libeigen3-dev zlib1g-dev libbz2-dev liblzma-dev gcc-5 g++-5
|
||||
```
|
||||
|
||||
### The dependencies of sox:
|
||||
|
||||
```
|
||||
libvorbis-dev libmp3lame-dev libmad-ocaml-dev
|
||||
```
|
||||
|
||||
|
||||
## By make or setup
|
||||
|
||||
```
|
||||
kenlm
|
||||
sox
|
||||
mfa
|
||||
openblas
|
||||
kaldi
|
||||
sctk
|
||||
AutoLog
|
||||
swig-decoder
|
||||
python_kaldi_features
|
||||
```
|
||||
|
After Width: | Height: | Size: 162 KiB |
|
After Width: | Height: | Size: 66 KiB |
|
After Width: | Height: | Size: 51 KiB |
|
After Width: | Height: | Size: 21 KiB |
|
After Width: | Height: | Size: 50 KiB |
|
After Width: | Height: | Size: 48 KiB |
|
After Width: | Height: | Size: 115 KiB |
|
After Width: | Height: | Size: 123 KiB |
|
After Width: | Height: | Size: 113 KiB |
|
After Width: | Height: | Size: 50 KiB |
|
After Width: | Height: | Size: 31 KiB |
|
After Width: | Height: | Size: 112 KiB |
|
After Width: | Height: | Size: 223 KiB |
|
After Width: | Height: | Size: 140 KiB |
|
After Width: | Height: | Size: 108 KiB |
|
After Width: | Height: | Size: 126 KiB |
|
After Width: | Height: | Size: 47 KiB |
|
After Width: | Height: | Size: 117 KiB |
|
After Width: | Height: | Size: 1.5 MiB |
Binary file not shown.
|
After Width: | Height: | Size: 108 KiB |
|
After Width: | Height: | Size: 224 KiB |
|
After Width: | Height: | Size: 1.5 MiB |
|
After Width: | Height: | Size: 581 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 368 KiB |
@ -0,0 +1,105 @@
|
||||
###########################################################
|
||||
# FEATURE EXTRACTION SETTING #
|
||||
###########################################################
|
||||
|
||||
fs: 24000 # sr
|
||||
n_fft: 2048 # FFT size.
|
||||
n_shift: 300 # Hop size.
|
||||
win_length: 1200 # Window length.
|
||||
# If set to null, it will be the same as fft_size.
|
||||
window: "hann" # Window function.
|
||||
|
||||
# Only used for feats_type != raw
|
||||
|
||||
fmin: 80 # Minimum frequency of Mel basis.
|
||||
fmax: 7600 # Maximum frequency of Mel basis.
|
||||
n_mels: 80 # The number of mel basis.
|
||||
|
||||
# Only used for the model using pitch features (e.g. FastSpeech2)
|
||||
f0min: 80 # Maximum f0 for pitch extraction.
|
||||
f0max: 400 # Minimum f0 for pitch extraction.
|
||||
|
||||
|
||||
###########################################################
|
||||
# DATA SETTING #
|
||||
###########################################################
|
||||
batch_size: 64
|
||||
num_workers: 2
|
||||
|
||||
|
||||
###########################################################
|
||||
# MODEL SETTING #
|
||||
###########################################################
|
||||
model:
|
||||
adim: 384 # attention dimension
|
||||
aheads: 2 # number of attention heads
|
||||
elayers: 4 # number of encoder layers
|
||||
eunits: 1536 # number of encoder ff units
|
||||
dlayers: 4 # number of decoder layers
|
||||
dunits: 1536 # number of decoder ff units
|
||||
positionwise_layer_type: conv1d # type of position-wise layer
|
||||
positionwise_conv_kernel_size: 3 # kernel size of position wise conv layer
|
||||
duration_predictor_layers: 2 # number of layers of duration predictor
|
||||
duration_predictor_chans: 256 # number of channels of duration predictor
|
||||
duration_predictor_kernel_size: 3 # filter size of duration predictor
|
||||
postnet_layers: 5 # number of layers of postnset
|
||||
postnet_filts: 5 # filter size of conv layers in postnet
|
||||
postnet_chans: 256 # number of channels of conv layers in postnet
|
||||
use_masking: True # whether to apply masking for padded part in loss calculation
|
||||
use_scaled_pos_enc: True # whether to use scaled positional encoding
|
||||
encoder_normalize_before: True # whether to perform layer normalization before the input
|
||||
decoder_normalize_before: True # whether to perform layer normalization before the input
|
||||
reduction_factor: 1 # reduction factor
|
||||
init_type: xavier_uniform # initialization type
|
||||
init_enc_alpha: 1.0 # initial value of alpha of encoder scaled position encoding
|
||||
init_dec_alpha: 1.0 # initial value of alpha of decoder scaled position encoding
|
||||
transformer_enc_dropout_rate: 0.2 # dropout rate for transformer encoder layer
|
||||
transformer_enc_positional_dropout_rate: 0.2 # dropout rate for transformer encoder positional encoding
|
||||
transformer_enc_attn_dropout_rate: 0.2 # dropout rate for transformer encoder attention layer
|
||||
transformer_dec_dropout_rate: 0.2 # dropout rate for transformer decoder layer
|
||||
transformer_dec_positional_dropout_rate: 0.2 # dropout rate for transformer decoder positional encoding
|
||||
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
|
||||
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
|
||||
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
|
||||
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
|
||||
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
|
||||
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
|
||||
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
|
||||
stop_gradient_from_pitch_predictor: true # whether to stop the gradient from pitch predictor to encoder
|
||||
energy_predictor_layers: 2 # number of conv layers in energy predictor
|
||||
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
|
||||
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
|
||||
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
|
||||
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
|
||||
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy
|
||||
stop_gradient_from_energy_predictor: false # whether to stop the gradient from energy predictor to encoder
|
||||
spk_embed_dim: 256 # speaker embedding dimension
|
||||
spk_embed_integration_type: concat # speaker embedding integration type
|
||||
|
||||
|
||||
|
||||
###########################################################
|
||||
# UPDATER SETTING #
|
||||
###########################################################
|
||||
updater:
|
||||
use_masking: True # whether to apply masking for padded part in loss calculation
|
||||
|
||||
|
||||
###########################################################
|
||||
# OPTIMIZER SETTING #
|
||||
###########################################################
|
||||
optimizer:
|
||||
optim: adam # optimizer type
|
||||
learning_rate: 0.001 # learning rate
|
||||
|
||||
###########################################################
|
||||
# TRAINING SETTING #
|
||||
###########################################################
|
||||
max_epoch: 200
|
||||
num_snapshots: 5
|
||||
|
||||
|
||||
###########################################################
|
||||
# OTHER SETTING #
|
||||
###########################################################
|
||||
seed: 10086
|
||||
@ -0,0 +1,86 @@
|
||||
#!/bin/bash
|
||||
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
config_path=$1
|
||||
ge2e_ckpt_path=$2
|
||||
|
||||
# gen speaker embedding
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
python3 ${MAIN_ROOT}/paddlespeech/vector/exps/ge2e/inference.py \
|
||||
--input=~/datasets/data_aishell3/train/wav/ \
|
||||
--output=dump/embed \
|
||||
--checkpoint_path=${ge2e_ckpt_path}
|
||||
fi
|
||||
|
||||
# copy from tts3/preprocess
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# get durations from MFA's result
|
||||
echo "Generate durations.txt from MFA results ..."
|
||||
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
|
||||
--inputdir=./aishell3_alignment_tone \
|
||||
--output durations.txt \
|
||||
--config=${config_path}
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# extract features
|
||||
echo "Extract features ..."
|
||||
python3 ${BIN_DIR}/preprocess.py \
|
||||
--dataset=aishell3 \
|
||||
--rootdir=~/datasets/data_aishell3/ \
|
||||
--dumpdir=dump \
|
||||
--dur-file=durations.txt \
|
||||
--config=${config_path} \
|
||||
--num-cpu=20 \
|
||||
--cut-sil=True \
|
||||
--spk_emb_dir=dump/embed
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# get features' stats(mean and std)
|
||||
echo "Get features' stats ..."
|
||||
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--field-name="speech"
|
||||
|
||||
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--field-name="pitch"
|
||||
|
||||
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--field-name="energy"
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
||||
# normalize and covert phone/speaker to id, dev and test should use train's stats
|
||||
echo "Normalize ..."
|
||||
python3 ${BIN_DIR}/normalize.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--dumpdir=dump/train/norm \
|
||||
--speech-stats=dump/train/speech_stats.npy \
|
||||
--pitch-stats=dump/train/pitch_stats.npy \
|
||||
--energy-stats=dump/train/energy_stats.npy \
|
||||
--phones-dict=dump/phone_id_map.txt \
|
||||
--speaker-dict=dump/speaker_id_map.txt
|
||||
|
||||
python3 ${BIN_DIR}/normalize.py \
|
||||
--metadata=dump/dev/raw/metadata.jsonl \
|
||||
--dumpdir=dump/dev/norm \
|
||||
--speech-stats=dump/train/speech_stats.npy \
|
||||
--pitch-stats=dump/train/pitch_stats.npy \
|
||||
--energy-stats=dump/train/energy_stats.npy \
|
||||
--phones-dict=dump/phone_id_map.txt \
|
||||
--speaker-dict=dump/speaker_id_map.txt
|
||||
|
||||
python3 ${BIN_DIR}/normalize.py \
|
||||
--metadata=dump/test/raw/metadata.jsonl \
|
||||
--dumpdir=dump/test/norm \
|
||||
--speech-stats=dump/train/speech_stats.npy \
|
||||
--pitch-stats=dump/train/pitch_stats.npy \
|
||||
--energy-stats=dump/train/energy_stats.npy \
|
||||
--phones-dict=dump/phone_id_map.txt \
|
||||
--speaker-dict=dump/speaker_id_map.txt
|
||||
fi
|
||||
@ -0,0 +1,19 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
ckpt_name=$3
|
||||
|
||||
FLAGS_allocator_strategy=naive_best_fit \
|
||||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
|
||||
python3 ${BIN_DIR}/synthesize.py \
|
||||
--fastspeech2-config=${config_path} \
|
||||
--fastspeech2-checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
|
||||
--fastspeech2-stat=dump/train/speech_stats.npy \
|
||||
--pwg-config=pwg_aishell3_ckpt_0.5/default.yaml \
|
||||
--pwg-checkpoint=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
|
||||
--pwg-stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
|
||||
--test-metadata=dump/test/norm/metadata.jsonl \
|
||||
--output-dir=${train_output_path}/test \
|
||||
--phones-dict=dump/phone_id_map.txt \
|
||||
--voice-cloning=True
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
|
||||
python3 ${BIN_DIR}/train.py \
|
||||
--train-metadata=dump/train/norm/metadata.jsonl \
|
||||
--dev-metadata=dump/dev/norm/metadata.jsonl \
|
||||
--config=${config_path} \
|
||||
--output-dir=${train_output_path} \
|
||||
--ngpu=2 \
|
||||
--phones-dict=dump/phone_id_map.txt \
|
||||
--voice-cloning=True
|
||||
@ -0,0 +1,22 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
ckpt_name=$3
|
||||
ge2e_params_path=$4
|
||||
ref_audio_dir=$5
|
||||
|
||||
FLAGS_allocator_strategy=naive_best_fit \
|
||||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
|
||||
python3 ${BIN_DIR}/voice_cloning.py \
|
||||
--fastspeech2-config=${config_path} \
|
||||
--fastspeech2-checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
|
||||
--fastspeech2-stat=dump/train/speech_stats.npy \
|
||||
--pwg-config=pwg_aishell3_ckpt_0.5/default.yaml \
|
||||
--pwg-checkpoint=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
|
||||
--pwg-stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
|
||||
--ge2e_params_path=${ge2e_params_path} \
|
||||
--text="凯莫瑞安联合体的经济崩溃迫在眉睫。" \
|
||||
--input-dir=${ref_audio_dir} \
|
||||
--output-dir=${train_output_path}/vc_syn \
|
||||
--phones-dict=dump/phone_id_map.txt
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
|
||||
MODEL=fastspeech2
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}
|
||||
@ -0,0 +1,44 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
gpus=0,1
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
conf_path=conf/default.yaml
|
||||
train_output_path=exp/default
|
||||
ckpt_name=snapshot_iter_482.pdz
|
||||
ref_audio_dir=ref_audio
|
||||
|
||||
# not include ".pdparams" here
|
||||
ge2e_ckpt_path=./ge2e_ckpt_0.3/step-3000000
|
||||
|
||||
# include ".pdparams" here
|
||||
ge2e_params_path=${ge2e_ckpt_path}.pdparams
|
||||
|
||||
# with the following command, you can choice the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# prepare data
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/preprocess.sh ${conf_path} ${ge2e_ckpt_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# synthesize, vocoder is pwgan
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# synthesize, vocoder is pwgan
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} ${ge2e_params_path} ${ref_audio_dir} || exit -1
|
||||
fi
|
||||
@ -0,0 +1,146 @@
|
||||
# Parallel WaveGAN with AISHELL-3
|
||||
This example contains code used to train a [parallel wavegan](http://arxiv.org/abs/1910.11480) model with [AISHELL-3](http://www.aishelltech.com/aishell_3).
|
||||
|
||||
AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus which could be used to train multi-speaker Text-to-Speech (TTS) systems.
|
||||
## Dataset
|
||||
### Download and Extract the datasaet
|
||||
Download AISHELL-3.
|
||||
```bash
|
||||
wget https://www.openslr.org/resources/93/data_aishell3.tgz
|
||||
```
|
||||
Extract AISHELL-3.
|
||||
```bash
|
||||
mkdir data_aishell3
|
||||
tar zxvf data_aishell3.tgz -C data_aishell3
|
||||
```
|
||||
### Get MFA result of AISHELL-3 and Extract it
|
||||
We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
|
||||
You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) (use MFA1.x now) of our repo.
|
||||
|
||||
## Get Started
|
||||
Assume the path to the dataset is `~/datasets/data_aishell3`.
|
||||
Assume the path to the MFA result of AISHELL-3 is `./aishell3_alignment_tone`.
|
||||
Run the command below to
|
||||
1. **source path**.
|
||||
2. preprocess the dataset,
|
||||
3. train the model.
|
||||
4. synthesize wavs.
|
||||
- synthesize waveform from `metadata.jsonl`.
|
||||
```bash
|
||||
./run.sh
|
||||
```
|
||||
### Preprocess the dataset
|
||||
```bash
|
||||
./local/preprocess.sh ${conf_path}
|
||||
```
|
||||
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
|
||||
|
||||
```text
|
||||
dump
|
||||
├── dev
|
||||
│ ├── norm
|
||||
│ └── raw
|
||||
├── test
|
||||
│ ├── norm
|
||||
│ └── raw
|
||||
└── train
|
||||
├── norm
|
||||
├── raw
|
||||
└── feats_stats.npy
|
||||
```
|
||||
|
||||
The dataset is split into 3 parts, namely `train`, `dev` and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains log magnitude of mel spectrogram of each utterances, while the norm folder contains normalized spectrogram. The statistics used to normalize the spectrogram is computed from the training set, which is located in `dump/train/feats_stats.npy`.
|
||||
|
||||
Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains id and paths to spectrogam of each utterance.
|
||||
|
||||
### Train the model
|
||||
```bash
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
|
||||
```
|
||||
`./local/train.sh` calls `${BIN_DIR}/train.py`.
|
||||
Here's the complete help message.
|
||||
|
||||
```text
|
||||
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
|
||||
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
|
||||
[--ngpu NGPU] [--verbose VERBOSE] [--batch-size BATCH_SIZE]
|
||||
[--max-iter MAX_ITER] [--run-benchmark RUN_BENCHMARK]
|
||||
[--profiler_options PROFILER_OPTIONS]
|
||||
|
||||
Train a ParallelWaveGAN model.
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--config CONFIG config file to overwrite default config.
|
||||
--train-metadata TRAIN_METADATA
|
||||
training data.
|
||||
--dev-metadata DEV_METADATA
|
||||
dev data.
|
||||
--output-dir OUTPUT_DIR
|
||||
output dir.
|
||||
--ngpu NGPU if ngpu == 0, use cpu.
|
||||
--verbose VERBOSE verbose.
|
||||
|
||||
benchmark:
|
||||
arguments related to benchmark.
|
||||
|
||||
--batch-size BATCH_SIZE
|
||||
batch size.
|
||||
--max-iter MAX_ITER train max steps.
|
||||
--run-benchmark RUN_BENCHMARK
|
||||
runing benchmark or not, if True, use the --batch-size
|
||||
and --max-iter.
|
||||
--profiler_options PROFILER_OPTIONS
|
||||
The option of profiler, which should be in format
|
||||
"key1=value1;key2=value2;key3=value3".
|
||||
```
|
||||
|
||||
1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
|
||||
2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
|
||||
3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
|
||||
4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
|
||||
|
||||
### Synthesize
|
||||
`./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
|
||||
```bash
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
|
||||
```
|
||||
```text
|
||||
usage: synthesize.py [-h] [--config CONFIG] [--checkpoint CHECKPOINT]
|
||||
[--test-metadata TEST_METADATA] [--output-dir OUTPUT_DIR]
|
||||
[--ngpu NGPU] [--verbose VERBOSE]
|
||||
|
||||
Synthesize with parallel wavegan.
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--config CONFIG parallel wavegan config file.
|
||||
--checkpoint CHECKPOINT
|
||||
snapshot to load.
|
||||
--test-metadata TEST_METADATA
|
||||
dev data.
|
||||
--output-dir OUTPUT_DIR
|
||||
output dir.
|
||||
--ngpu NGPU if ngpu == 0, use cpu.
|
||||
--verbose VERBOSE verbose.
|
||||
```
|
||||
|
||||
1. `--config` parallel wavegan config file. You should use the same config with which the model is trained.
|
||||
2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory. If you use the pretrained model, use the `snapshot_iter_1000000.pdz `.
|
||||
3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
|
||||
4. `--output-dir` is the directory to save the synthesized audio files.
|
||||
5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
|
||||
|
||||
## Pretrained Models
|
||||
Pretrained models can be downloaded here [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_aishell3_ckpt_0.5.zip).
|
||||
|
||||
Parallel WaveGAN checkpoint contains files listed below.
|
||||
|
||||
```text
|
||||
pwg_aishell3_ckpt_0.5
|
||||
├── default.yaml # default config used to train parallel wavegan
|
||||
├── feats_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
|
||||
└── snapshot_iter_1000000.pdz # generator parameters of parallel wavegan
|
||||
```
|
||||
## Acknowledgement
|
||||
We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
|
||||
@ -0,0 +1,115 @@
|
||||
# This is the hyperparameter configuration file for Parallel WaveGAN.
|
||||
# Please make sure this is adjusted for the VCTK corpus. If you want to
|
||||
# apply to the other dataset, you might need to carefully change some parameters.
|
||||
# This configuration requires 12 GB GPU memory and takes ~3 days on RTX TITAN.
|
||||
|
||||
###########################################################
|
||||
# FEATURE EXTRACTION SETTING #
|
||||
###########################################################
|
||||
fs: 24000 # Sampling rate.
|
||||
n_fft: 2048 # FFT size. (in samples)
|
||||
n_shift: 300 # Hop size. (in samples)
|
||||
win_length: 1200 # Window length. (in samples)
|
||||
# If set to null, it will be the same as fft_size.
|
||||
window: "hann" # Window function.
|
||||
n_mels: 80 # Number of mel basis.
|
||||
fmin: 80 # Minimum freq in mel basis calculation. (Hz)
|
||||
fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
|
||||
|
||||
###########################################################
|
||||
# GENERATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
generator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_size: 3 # Kernel size of dilated convolution.
|
||||
layers: 30 # Number of residual block layers.
|
||||
stacks: 3 # Number of stacks i.e., dilation cycles.
|
||||
residual_channels: 64 # Number of channels in residual conv.
|
||||
gate_channels: 128 # Number of channels in gated conv.
|
||||
skip_channels: 64 # Number of channels in skip conv.
|
||||
aux_channels: 80 # Number of channels for auxiliary feature conv.
|
||||
# Must be the same as num_mels.
|
||||
aux_context_window: 2 # Context window size for auxiliary feature.
|
||||
# If set to 2, previous 2 and future 2 frames will be considered.
|
||||
dropout: 0.0 # Dropout rate. 0.0 means no dropout applied.
|
||||
use_weight_norm: true # Whether to use weight norm.
|
||||
# If set to true, it will be applied to all of the conv layers.
|
||||
upsample_scales: [4, 5, 3, 5] # Upsampling scales. Prodcut of these must be the same as hop size.
|
||||
|
||||
###########################################################
|
||||
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_size: 3 # Number of output channels.
|
||||
layers: 10 # Number of conv layers.
|
||||
conv_channels: 64 # Number of chnn layers.
|
||||
bias: true # Whether to use bias parameter in conv.
|
||||
use_weight_norm: true # Whether to use weight norm.
|
||||
# If set to true, it will be applied to all of the conv layers.
|
||||
nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv.
|
||||
nonlinear_activation_params: # Nonlinear function parameters
|
||||
negative_slope: 0.2 # Alpha in LeakyReLU.
|
||||
|
||||
###########################################################
|
||||
# STFT LOSS SETTING #
|
||||
###########################################################
|
||||
stft_loss_params:
|
||||
fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss.
|
||||
hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss
|
||||
win_lengths: [600, 1200, 240] # List of window length for STFT-based loss.
|
||||
window: "hann" # Window function for STFT-based loss
|
||||
|
||||
###########################################################
|
||||
# ADVERSARIAL LOSS SETTING #
|
||||
###########################################################
|
||||
lambda_adv: 4.0 # Loss balancing coefficient.
|
||||
|
||||
###########################################################
|
||||
# DATA LOADER SETTING #
|
||||
###########################################################
|
||||
batch_size: 8 # Batch size.
|
||||
batch_max_steps: 24000 # Length of each audio in batch. Make sure dividable by hop_size.
|
||||
pin_memory: true # Whether to pin memory in Pytorch DataLoader.
|
||||
num_workers: 4 # Number of workers in Pytorch DataLoader.
|
||||
remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps.
|
||||
allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory.
|
||||
|
||||
###########################################################
|
||||
# OPTIMIZER & SCHEDULER SETTING #
|
||||
###########################################################
|
||||
generator_optimizer_params:
|
||||
epsilon: 1.0e-6 # Generator's epsilon.
|
||||
weight_decay: 0.0 # Generator's weight decay coefficient.
|
||||
generator_scheduler_params:
|
||||
learning_rate: 0.0001 # Generator's learning rate.
|
||||
step_size: 200000 # Generator's scheduler step size.
|
||||
gamma: 0.5 # Generator's scheduler gamma.
|
||||
# At each step size, lr will be multiplied by this parameter.
|
||||
generator_grad_norm: 10 # Generator's gradient norm.
|
||||
discriminator_optimizer_params:
|
||||
epsilon: 1.0e-6 # Discriminator's epsilon.
|
||||
weight_decay: 0.0 # Discriminator's weight decay coefficient.
|
||||
discriminator_scheduler_params:
|
||||
learning_rate: 0.00005 # Discriminator's learning rate.
|
||||
step_size: 200000 # Discriminator's scheduler step size.
|
||||
gamma: 0.5 # Discriminator's scheduler gamma.
|
||||
# At each step size, lr will be multiplied by this parameter.
|
||||
discriminator_grad_norm: 1 # Discriminator's gradient norm.
|
||||
|
||||
###########################################################
|
||||
# INTERVAL SETTING #
|
||||
###########################################################
|
||||
discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator.
|
||||
train_max_steps: 1000000 # Number of training steps.
|
||||
save_interval_steps: 5000 # Interval steps to save checkpoint.
|
||||
eval_interval_steps: 1000 # Interval steps to evaluate the network.
|
||||
|
||||
###########################################################
|
||||
# OTHER SETTING #
|
||||
###########################################################
|
||||
num_save_intermediate_results: 4 # Number of results to be saved as intermediate results.
|
||||
num_snapshots: 10 # max number of snapshots to keep while training
|
||||
seed: 42 # random seed for paddle, random, and np.random
|
||||
@ -0,0 +1,55 @@
|
||||
#!/bin/bash
|
||||
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
config_path=$1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# get durations from MFA's result
|
||||
echo "Generate durations.txt from MFA results ..."
|
||||
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
|
||||
--inputdir=./aishell3_alignment_tone \
|
||||
--output=durations.txt \
|
||||
--config=${config_path}
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# extract features
|
||||
echo "Extract features ..."
|
||||
python3 ${BIN_DIR}/../preprocess.py \
|
||||
--rootdir=~/datasets/data_aishell3/ \
|
||||
--dataset=aishell3 \
|
||||
--dumpdir=dump \
|
||||
--dur-file=durations.txt \
|
||||
--config=${config_path} \
|
||||
--cut-sil=True \
|
||||
--num-cpu=20
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# get features' stats(mean and std)
|
||||
echo "Get features' stats ..."
|
||||
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--field-name="feats"
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# normalize, dev and test should use train's stats
|
||||
echo "Normalize ..."
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--dumpdir=dump/train/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/dev/raw/metadata.jsonl \
|
||||
--dumpdir=dump/dev/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/test/raw/metadata.jsonl \
|
||||
--dumpdir=dump/test/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
fi
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
ckpt_name=$3
|
||||
|
||||
FLAGS_allocator_strategy=naive_best_fit \
|
||||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
|
||||
python3 ${BIN_DIR}/synthesize.py \
|
||||
--config=${config_path} \
|
||||
--checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
|
||||
--test-metadata=dump/test/norm/metadata.jsonl \
|
||||
--output-dir=${train_output_path}/test
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
|
||||
FLAGS_cudnn_exhaustive_search=true \
|
||||
FLAGS_conv_workspace_size_limit=4000 \
|
||||
python ${BIN_DIR}/train.py \
|
||||
--train-metadata=dump/train/norm/metadata.jsonl \
|
||||
--dev-metadata=dump/dev/norm/metadata.jsonl \
|
||||
--config=${config_path} \
|
||||
--output-dir=${train_output_path} \
|
||||
--ngpu=1
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
|
||||
MODEL=parallelwave_gan
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}
|
||||
@ -0,0 +1,32 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
gpus=0
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
conf_path=conf/default.yaml
|
||||
train_output_path=exp/default
|
||||
ckpt_name=snapshot_iter_5000.pdz
|
||||
|
||||
# with the following command, you can choice the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# prepare data
|
||||
./local/preprocess.sh ${conf_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# synthesize
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in new issue