Forced alignment

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Goal and Scope

Forced alignment refers to the process by which orthographic transcriptions are aligned to audio recordings to automatically generate phone level segmentation. While automatic alignment does not yet rival manual alignment, the amount of time gained through forced alignment is often worth the small decrease in accuracy for many projects.

Forced alignment works best on recordings which

  • have one speaker speaking at a time
  • have little environmental noise

but other types of recordings may also be well processed.

Aligning on the BPM

The aligner is an implementation of the Penn forced aligner (Jiahong Yuan), which is based on the HTK speech recognition toolkit. It produces a Praat textgrid file that has word and phone boundaries for the speech in a wav file that you give to the aligner. We used this system in the "voices of Berkeley" project to find vowel midpoints and take formant measurements automatically.

It is implemented on PhonLab BPM using sox and the HTK library of automatic speech recognition software. You may be able to set this up on your home computer, but most people will find it easier to run it through the BPM. Regardless, you will need to register to use the HTK toolkit, at

Getting started with pyalign

For simple alignments involving a single utterance you can call pyalign directly. The multi_align command is used for more complicated situations involving multiple utterances, multiple speakers, or multiple input channels. You should familiarize yourself with pyalign even if you intend to use multi_align since multi_align is just a convenient way to iteratively call pyalign for the individual labels in a TextGrid.

The pyalign command has three required arguments:

  1. Your .wav file. The aligner uses sox to create a copy of your wav file that has all of the properties that are needed for HTK. One thing to keep in mind is that if you specify that you want the 16kHz acoustic models to be used, but you pass an 11.025 kHz file to the aligner the performance will be degraded. Just be sure that the sampling rate of your wav file is at least as fast as the acoustic models you specify.
  2. Your transcript file. The aligner needs to know what words are spoken in the .wav file, and needs to know the order in which they are spoken (and may also need to know about disfluencies, laughter, etc. if they are there). Your transcription must include every single utterance, including false starts, filled pauses such as “um,” “uh,” or any other sort of hesitation. Transcript files may be either .txt files or .TextGrid files (see below)
  3. The output file. This is a text file that can be read into Praat as a textgrid. Praat scripting can then be used to extract phonetic measurements, or you can read the textgrid in a python script (meas_formants for an example) and use the ESPS unix command-line acoustic analysis package to extract phonetic measurements. TextGrid files use the extension .TextGrid.

.txt Transcripts and pyalign

Use the pyalign command to do forced alignment. (The Penn tool is named, and pyalign is a simple wrapper that makes easier to call in the context of the BPM.)

Command-line usage:

> pyalign [options] wave_file transcript_file output_file

where options may include:

 -r sampling_rate -- override which sample rate model to use, one of 8000, 11025, and 16000
 -s start_time    -- start of portion of wavfile to align (in seconds, default 0)
 -e end_time      -- end of portion of wavfile to align (in seconds, defaul to end)

The -r option determines which set of acoustic models to use (I would recommend that you use 16000). Your sound file should have a sampling rate that is equal to or greater than the acoustic model sampling rate.

Adding missing words to the dictionary

Every word in your transcript must exactly match a word in the master dictionary, which is in the file /opt/p2fa/model/dict in the BPM (from the CMU Pronouncing Dictionary). If a word is missing, then the aligner does not have the pronunciation information it requires to complete alignment. You can create your own file named dict.local that contains pronunciations of any missing words.

Refer to /opt/p2fa/model/dict as a model for how to create your dict.local file. The format for each entry line is 1) the orthographic word (in upper case); 2) two space characters; 3) a space-separated list of phones (in upper case). Use the same ARPAbet phoneme set as is used in the CMU Pronouncing Dictionary, and include stress markings for all vowels.


Finally, ensure the last entry is terminated with a line break.

Place the dict.local file in the current working directory when you run pyalign so that the aligner will find it and include its contents.

.TextGrid transcripts and multi_align

TextGrid transcript files may be used with multi_align. Using TextGrid transcripts allows you to align recordings with multiple speakers and have greater control over the specific intervals which are aligned.

In the BPM execute

multi_align --help

to see multi_align's available options. See also the multi_align examples page.

Sharing a dict.local with a Google Drive spreadsheet

For groups of people working together you may find it convenient to maintain dict.local in a spreadsheet in google drive and pull it in with a script. This can be especially convenient if you are collaborating with others, as you can collectively maintain a supplemental dictionary. Here is an example of how to do it, based on Ling113 in spring 2015, using the BPM:

Set up the spreadsheet

  1. Create a google spreadsheet and share it with everyone in your group as an editor.
  2. Also add share rights so that anyone with the link can view the spreadsheet. If you prefer, make the spreadsheet public on the web.
  3. Add records to the spreadsheet by putting the transcription of a word in the first column and the pronunciation in the second. See the Ling113 example.
  4. Open the spreadsheet and look at the URL from your browser's location bar. The Ling113 example looks like this:
  5. Notice the long alphanumeric string after /d/ in your URL. This is the file key.
  6. Also notice the gid value in your url. This will probably be '0', but if you have added multiple sheets it might be different. Make sure your current view is the sheet with the records you want to export.

Create a download script

  1. Choose a name for your script. In our example here we'll call it get_dict_local. In some cases it might be sensible to make it specific to a project, e.g. get_dict_local_myproject.
  2. Create and edit a script file in your path. This works in BCE: sudo gedit /usr/local/bin/get_dict_local. Use the script name you chose in the first step.
  3. Use the Ling113 example script as a base for your download script. Just copy and paste into your editor.
  4. Delete the value of the FILEKEY variable in the Ling113 script (the part between quotation marks) and replace it with the file key you found in your spreadsheet's URL.
  5. Delete the value of the GID variable and replace it with your gid value.
  6. It's a good idea to update the comments in the file to remove references to Ling113 and update with your project name.
  7. Save the changes you made to the script and exit the editor.
  8. Make sure your script is executable. This works in BCE: sudo chmod +x /usr/local/bin/get_dict_local. Make sure you use the script name you chose if it is different than get_dict_local.

Using the script

Using the script is easy. You simply call your script by name at the command line, e.g. get_dict_local and the dict.local file will be created or updated in your current working directory from the contents of your google spreadsheet.


Word not in dictionary

One of the most common errors occurs when a word does not exist in the default dictionary. If this happens, "SKIPPING WORD X" will print in the terminal, where X is the word. The alignment will still occur, but if a word is skipped, this will likely result in other words to be aligned incorrectly. It is thus important to ensure that the aligner does not skip any words, so a local dictionary should be created. If you need a project-specific dictionary (which might include, for example, a set of nonwords, or a set of words in a language other than English) you can create a file that you name “dict.local” that has the same format as /opt/f2a/model/dict but includes your project-specific vocabulary. pyalign looks at both the default dictionary and dict.local to find transcriptions of the words in your transcript file.

SyntaxError: invalid syntax

If your attempt to align ends with the error message SyntaxError: invalid syntax, it probably indicates that you attempted to run directly. Use the pyalign wrapper instead so that the correct Python version interprets the script.

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