This is the fourth installment in a series of blog posts where I discuss some of the tools I use for linguistic analysis. In a previous post I described the tools I use for processing and converting video and audio. This is partly important for using the audio with Praat for acoustic analysis, but also because ultimately I want to transcribe the recordings and analyze the grammatical structure (using the transcriptions) while remaining linked to the recordings. Ideally, such a link would allow me to play back the recording from within the program that I use to analyze the grammatical structure of the language, so I'm not constantly having to open the sound files and scrub to the correct part of the WAV.
Fortunately, this IS possibile, and it currently involves using two larger programs, Transcriber and Toolbox, along with a small tool/script that I wrote for conversion (you can read more details about the script HERE). The original workflow, which my supervisor Alec Coupe taught me, depended on another script developed by Andrew Margetts, which was very useful but required internet access - possibly problematic if one was on fieldwork. Then a few years ago the site went down and conversion became a multi-step process involving emails. So I wrote my own script. But I'm getting ahead of myself.
The first step in this process is getting your audio file transcribed. It is possible to do this in Transcriber (thus the name), and the newly repackaged version for Mac works pretty well (as does 1.5.1 on Windows or in a Windows VirtualBox). You can also use another program to play back the audio (i.e. Audacity, which can slow down the audio for you and let you boost the signal, etc) while transcribing in a text editor. Then you copy/paste the text to Transcriber for time-alignment.
I use Transcriber for time-alignment because it's simple, it allows for extremely short time-stamped windows, and it produces text files (basically) as output. There are other tools such as SayMore (Windows only) that also allow you to transcribe and directly output files for use with Toolbox, but in using it I found that SayMore was a bit of a memory hog and wouldn't let me create time-stamped sections shorter than half a second, which could be problematic for short interjections. And it kept freezing, so that it took a really long time to transcribe a short example.
So with Transcriber I simply open the audio file that I want to time-align, copy the block of text that I've transcribed, and then listen to the audio file, follow along in the text, and insert timestamps ('enter' or 'return' key) at the correct points. Once this is done for the whole file (time-consuming, but not as time-consuming as the transcription) I save the file (a '.trs' format).
Once you have the 'TRS' file, you get to use this converter, which you download to your computer. The converter runs as either a Python script or a Windows executable file that you run in the folder that has your 'TRS' file. When you run it for the first time it asks you for the field names in your Toolbox settings, and creates a configuration file in the folder to store the settings. If it doesn't find the settings file the next time, it will ask again. When it has the settings, it outputs a 'TXT' file for each 'TRS' file in the folder.
Once you've got the 'TXT' file, you can open it up to view it. If you already work with Toolbox, you can see that it is already (mostly) in the correct format. If you don't work with Toolbox, try installing it and go through the tutorials with a new project. Then open the Toolbox-created text file and compare it to the one created by my script from the Transcriber file. You should be able to copy/paste the content of the newly-created text file into your Toolbox project text file with no issues. Now when you open your Toolbox project file in Toolbox, you should be able to see the timestamps and play the sound file (and portions of it) from within Toolbox, provided you put the audio file in the correct location.
This may seem a bit cumbersome, begging the question "Why use Toolbox at all?"
A final note (ELAN)
As I end this post, I should note that many linguists use ELAN for transcription and annotation of video/audio. In fact, that the suggestions made by the 'trs2txt' script are for start and end codes labeled ELANBegin and ELANEnd, respectively, and the code for participants is labeled ELANParticipant, which should facilitate importing into ELAN. Unfortunately, I haven't found ELAN to otherwise be particularly useful for my purposes, primarily because it doesn't have the dictionary linking functions that Toolbox has, at least the last time I looked at it. It does have a Toolbox import function, so I have seen people import a Toolbox file, re-link their video, and output an html web page with embedded video for display on a website (thus the code labels in the script). Maybe you could also transcribe in ELAN, export to Toolbox, do your analysis, and import back to ELAN. For now, though, I think I'll stick with the Transcriber-Toolbox workflow.
This is my first update in a long while, as I notice that this blog hasn’t been updated since 2015. I blame life and the ease of posting short updates on Instagram, Twitter, and Facebook. Since my last post I started a new job, started dating an amazing lady and got married. No raised eyebrows please! It HAS been over a year and a half; things happen.
What this probably means is that updates are not super-likely to come daily or weekly. It’s hard to change habits, particularly when writing on a blog is not on the top of my list (being replaced with research, family, etc..). However, I am working on a few processes to write a bit more frequently, since it’s good practice and I have a lot of thoughts rattling around my brain.
In particular, I enjoy sharing what I know, whether it’s about products I find useful (like the Hanvon that I reviewed in a series of blog posts [Part 1, Part 2, Part 3]) or about processes. The processes I have been developing in the past year are related to data management and workflow, some of which I was taught, some of which I taught myself, and some of which I learned from the internet.
My initial work as a PhD student in linguistics involved Language Documentation and Description, which I hope to continue to do. Now I’m doing more Python programming and data analysis, which I find requires a somewhat different skill set, yet one informs the other. In what I hope will be a series of blog posts, I’ll try to unpack these things, in hopes that they will be useful for other people traveling similar roads.
I'm a linguist and singer-songwriter. I write about life, travel, language and technology.