Use Penn Forced Aligner (P2FA)

3.1 Installation

For Mac users, you will need Xcode from the App Store and install HTK 3.4 http://htk.eng.cam.ac.uk, prior to the installation of P2FA.

The Penn Forced Aligner (P2FA) can be downloaded from here. They have a version for American English and one for Mandarin Chinese, though there’s only scant documentation. The installation of P2FA can be a bit of hassle. Fortunately we have detailed instructions:

• For Mac users: Check Will Styler’s post. The installation is the same for English and Mandarin versions.

• For Windows users: Check Cong Zhang’s post.

I also rewrite a bit of the python code for the Mandarin version and make it a Python 3 script since P2FA was originally written in Python 2 which is getting outdated. If you have installed Xcode and HTK 3.4 successfully, you can check out my Github repository P2FA_Mandarin_py3 for an enhanced Python 3 script for Mandarin alignment.

There is also FAVE, a up-to-date implementation of the P2FA with pre-trained acoustic models of American English.

3.2 Pronunciation Dictionary

Before running the aligner, we need to make sure that the pronunciation dictionary /P2FA_Mandarin/run/model/dict contains all the characters appeared in our transcripts. Again, Bash Shell commands can help us with that.

First we obtain a wordlist from our transcripts. Continuing with the previous example list.txt in section 2.3, we make a copy of it, and in Terminal we navigate to this directory.

$ cut -d " " -f2- list.txt|tr ' ' '\n'|sed '/^$/d'|sort|uniq -c|sed 's/^ *//'|sort -r -n > wordlist.txt

(If there are trailing white spaces after each line, we will have some blank lines after replacing a space with \n a new line. So we delete the blank lines sed '/^$/d'. uniq works after you sort them first.)

This command generates a wordlist.txt file in which each unique Chinese character is lining up as a single column. The command also gives you the corresponding frequency count of each character in the first column. Then we want to compare it against the dictionary. We can also make a copy of the dict file dict copy (so that we don’t ruin the original dict file by accident). If the character in wordlist.txt is also in the dictionary, then the corresponding dictionary line is extracted.

$ cut -d ' ' -f 2 wordlist.txt | sed 's/^/^/'| sed 's/$/ /' >tmp.txt 

(-d ' ': this flag specifies the delimiter is a space. Put the column of characters into regular expression format for locating the beginning of a line^)

$ egrep --file=tmp.txt dict\ copy > words_phones.txt

There are some duplicated rows in the dict file. So we could do the following:

$ cat words_phones.txt|uniq -c|sed 's/^ *//' >words_phones2.txt

The idea is to sort the Chinese characters the same way in wordlist.txt and words_phones2.txt so that we can use the join command to see the record(s) that do not match.

$ sort -k 2 wordlist.txt >tmp1.txt

The problem is that even if we sort the column of characters in the grepped dict file words_phones2.txt, the sorting result is influenced by the third field of letters. So we decided to extract only the column of the Chinese characters of words_phones2.txt and sort it.

$ awk '{print $2}' words_phones2.txt|sort> tmp2.txt

Then we find out whether there are any characters in tmp1.txt but missing in tmp2.txt:

$ join -v 1 -1 2 -2 1 tmp1.txt tmp2.txt >missingwords.txt

(-v 1: this flag displays the non-matching records of the file 1. The following -1 2 -2 1: file 1, second column or field; file 2, first column.) This missingwords.txt lists the missing Chinese characters and you can manually add them to the original dict file in the /model.

Inspired by: Corpus Phonetics Tutorial by Eleanor Chodroff.

3.3 Running P2FA

Running P2FA is easy when you have all the input files prepared as required. Here is a checklist:

• All .wav files are in 16KHz, 16-bit, mono channel
• Each .wav file has a .txttranscript file with a matching filename
• The pronunciation dictionary in the P2FA model has been updated
• All the files has been put in the same directory /P2FA_Mandarin/run

You just need one single line in the Terminal calling the Calign2textgrid.py and filling in relevant arguments: .wav file path, .txt file path, (output) .Textgrid file path. This script returns the short form .Textgrid file.

If you want to run the aligner for all of the audio files in a directory, you can make use of a loop structure:

$ for i in *.wav; do python Calign2textgrid.py $i $i.txt $i.TextGrid; done

In the Github repository /P2FA_Mandarin there’s also Calign2mlf.py, which returns the output in .mlf with table-like form, as shown in the following example:

#!MLF!#
"/tmp/xuchenzi_27944.rec"
0 8500000 sp 3079.143311 sp
8500000 8800000 n -1.651408 你
8800000 9700000 i 73.151802

I also made a Python script mlf2textgrid.py to convert files in .mlf to .Textgrid (short form).