Hey everyone, I've been diving into text-to-speech tools to help with my workflow. I use Speechify sometimes for articles and reports, but I had this random idea the other night.
I write a ton of SQL comments and basic documentation for my dashboards. Reading them back silently, I often miss awkward phrasing or typos. Has anyone tried using Speechify specifically for proofreading technical writing? I'm thinking things like:
```sql
-- Calculates the rolling 7-day average for revenue, exclunding refunds
SELECT
date,
AVG(daily_revenue) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_revenue_7day
FROM cleaned_transactions
WHERE refund_flag = 0;
```
Hearing "exclunding" read out loud would probably catch that error immediately. But I'm wondering how it handles:
* Common coding abbreviations like "i.e.", "e.g.", or "Fig."
* Inline code snippets or variable names within a sentence.
* The overall flow and clarity of a longer README file.
I'm curious if it's more distracting than helpful, or if it's become a key part of anyone's review process. Also, does the voice stumble over complex table names or function syntax? Trying to decide if it's worth setting up a specific workflow for this.
I tried something similar with NaturalReader on my API docs. It stumbled over abbreviations like "e.g." and "No." all the time, saying "egg" and "number" instead. Made it useless for my use case. You need a tool that lets you customize pronunciation rules, but most TTS tools for general text don't have that level of control. Speechify might be the same.
Check if they have a free trial long enough to test on your actual comments. The flow for longer docs is okay, but the voice can get monotonous and you stop listening.
Good idea. I've used this exact trick for email copy and even support article drafts. Hearing it aloud catches those weird autocorrect fails and clunky phrases you gloss over visually.
On the technical side, most tools struggle with the abbreviations. I've found Speechify handles "i.e." as "that is" now, but it will absolutely butcher variable names or inline code. For table names like `stg_customer_transactions_2024`, it'll try to pronounce it as a word, which is a mess. You end up tuning it out.
Try pasting just the prose sentences into the tool, skipping the actual code blocks. It's less seamless, but you'll actually listen for the errors instead of the weird pronunciations.
Always A/B test.
Yep, that's the exact trade-off. The "egg" problem is real with a lot of these tools.
Your point about the voice getting monotonous is key, too. I find that even if the pronunciation is perfect, a flat, robotic reading voice makes my brain tune out after a few minutes, and then I'm not really proofreading anymore. Some tools offer a few more expressive voices, but they often cost extra.
Have you found any workarounds for the abbreviation issue, or did you just abandon the TTS approach for technical docs?
Raise the signal, lower the noise.
You're spot on about the voice monotony. It turns a useful proofreading step into background noise. I have to switch voices every 20 minutes or so to stay engaged, which is a hassle.
For the abbreviation issue, I've taken to doing a quick find-and-replace in my draft before I listen. Swap "e.g." for "for example," "i.e." for "that is." It's an extra step, but it's faster than fighting the mispronunciations. I abandoned TTS for inline code or variable names completely, though. That's a lost cause.
Curious, do you ever use it for reviewing the actual logic flow in a comment, or just for basic grammar and typos?
Oh, that's a really smart idea for catching those visual typos. I haven't used Speechify, but I tried a different TTS tool for proofreading some basic chatbot response scripts.
It worked great for finding repeated words, but it totally mangled any placeholder variables like `{customer_name}`. For a README, I'd be worried about it trying to pronounce every single backtick or code fence. Does Speechify just skip over those when it hits a code block, or does it try to read everything?
Following this to see if anyone's found a tool that works.
Ask me in a year
I tried that exact workflow for a Jenkins pipeline Groovy script I had to document last month. It's a double-edged sword.
You're right that it'll catch "exclunding" instantly. Where it falls apart completely is on anything resembling a path, flag, or variable. For your example, it would likely choke on "refund_flag = 0", trying to say "refund underscore flag equals zero" or some other verbose mess. Same for table names. Once it starts spelling out underscores and parentheses, your brain disengages from proofreading prose and starts wincing at the audio.
My compromise is a clunky two-pass system. First, I extract all the plain English sentences from the comments/docs into a scratch file. Run TTS on that. Then, I visually scan the actual code blocks for typos in identifiers. It adds steps, but it's still faster than the three silent re-reads I'd normally do before spotting a missing "the". For abbreviations, I just pre-expand them - "for example" instead of "e.g.". It's manual, but reliable.
So yes, it can be a key part of the process, but only if you're willing to surgically isolate the human-language parts. Letting it loose on raw source files is an exercise in frustration.
Speed up your build
Two-pass system? That's a lot of manual labor just to make a closed-source tool you don't control kinda work.
The real failure is needing to pre-process your text. If a tool can't handle the common syntax where you use it, it's not fit for purpose. It's a proofreading crutch that adds its own new class of errors - you might forget to add something back after your "scratch file" step.
Just use a simple linter or a spellchecker in your editor for the typos. For awkward phrasing, you're still better off with a rubber duck.
—aB
I've run this exact experiment on data pipeline documentation and can confirm your hypothesis about catching typos like "exclunding" is correct. The auditory disconnect is surprisingly effective for spotting those visual-glaze errors.
However, the practicality breaks down on your second and third points. For inline code or variable names, it's a disaster. A sentence like "The `dim_customer` table is refreshed nightly" becomes "The dim underscore customer table is refreshed nightly," which forces your cognitive load away from prose flow and onto parsing the audio carnage. The voice will also treat common abbreviations as words unless the engine has been specifically trained otherwise, which most general TTS tools haven't.
My adapted workflow is to use a regex pattern to strip out anything in backticks or code blocks before feeding the text to the TTS engine. It's an extra preprocessing step, but it isolates the natural language sentences you actually want to hear. You still need a separate visual scan for the code blocks themselves, as the tool provides zero value there. It becomes a specialized filter, not a comprehensive solution.
Data doesn't lie, but folks sometimes do.
Good instinct on using auditory feedback to catch those visual typos - it really does work for that specific problem.
You're hitting on the core trade-off. The tools are excellent for finding misspelled words in plain sentences, but they fail completely on the technical grammar of documentation itself. A sentence that reads fine can sound like nonsense when a voice reads "backtick dim underscore customer backtick" aloud. The cognitive switch from evaluating prose to deciphering mangled syntax often breaks the proofreading flow entirely.
Several replies mention pre-processing steps like find-and-replace or extracting text. That can work if you're disciplined, but it adds friction. The real test is whether that friction is less than the pain of missing a typo. For me, it's only worth it on very high-stakes, public-facing docs. For internal SQL comments, a quick visual scan plus a basic spellcheck plugin usually gets me 95% of the way there with less overhead.
Keep it constructive.
I get the frustration with extra steps, but I think there's still a case for it. A linter catches "exclunding," but it won't tell you the sentence *sounds* awkward. The two-pass thing is definitely clunky, but for a critical doc that's going to a client, I'll take the friction over a silly typo.
You're right about the new class of errors though. That's why I never edit the original file, I only listen to the scratch copy. But yeah, if the tool can't handle the native format, it's a stopgap at best. Still, sometimes a stopgap is what you need for one final sanity check.
Trust the trial period.
Totally works for catching the odd typo like "exclunding". That's the sweet spot.
But for your specific questions, it's rough. Abbreviations like "i.e." will often get mispronounced. Inline code makes the voice stutter on every backtick. And a long README will be a slog of it trying to pronounce `refund_flag = 0` letter by letter.
My hack? I paste just the comment text into a blank doc and listen to that. It's an extra step, but hearing the plain English helps me spot clunky phrases. I still have to visually check the code bits, though.
That's a really practical compromise, extracting just the comment text. I've been considering a similar approach for our email campaign documentation, which is full of Liquid tags and merge fields.
> But a long README will be a slog
This is my main hesitation. For a short doc, the extra step might be fine. But for a sprawling internal wiki page, manually extracting the prose feels like it would take longer than just a careful visual read. Do you find you only use this method for final drafts of smaller pieces, or does it scale for you?
That's a helpful tip about skipping code blocks. I tried this with some email campaign documentation and you're right, it's way easier to focus on the sentences when the tool isn't trying to say `{{customer.first_name}}` out loud.
I'm curious, when you say it handles "i.e." as "that is" now, does it also handle "e.g." correctly? That one trips me up in drafts all the time.
It depends on your engine, and it's often configurable. But that's the problem, you're now configuring a text-to-speech engine instead of just proofreading.
Manually extracting the clean prose is the only reliable method. Otherwise, you're at the mercy of the tool's tokenizer. One week "e.g." is fine, next update it reads the letters.
-- old school