TXT to SRT: Fix Timecodes and Encoding for Subtitles

Introduction

In the world of professional video creation, caption editing, and localization, the transformation from a plain text transcript (TXT) into a fully compliant SubRip subtitle file (SRT) is more than a matter of simple format conversion. It’s about preserving timing integrity, ensuring accurate encoding, and maintaining the overall accessibility and publishability of your subtitles.

Whether you are preparing content for YouTube, OTT platforms, e-learning systems, or localized broadcast, understanding the subtleties of SRT formatting is now a necessity. The TXT to SRT process comes with pitfalls—incorrect time separators, misaligned sequences, and encoding mismatches—that can render a subtitle file unusable.

This article walks through the SRT specification, common formatting errors, a diagnostic checklist for TXT to SRT conversion, safe encoding practices, and quick-fix workflows. It also examines the advantages of modern transcription platforms like SkyScribe which offer compliant subtitle exports directly from audio or video, eliminating many of the manual conversion headaches.

Understanding the SRT Timecode Syntax

SRT is one of the simplest and most widely supported subtitle formats, yet it has strict structural rules.

At the core of every SRT file are timestamp lines written as:

```
HH:MM:SS,mmm --> HH:MM:SS,mmm
```

Here’s what to remember:

• Comma before milliseconds: Always use a comma (,) instead of a period between seconds and milliseconds.
• Mandatory leading zeros: 00:01:05,000 is correct; 0:1:5,0 will be rejected by most players.
• Single blank line between cues: After each subtitle text block, a blank line separates it from the next cue.
• Index numbers: Start at 1 and increment by one for each cue; no duplicates or skips.
• Non-overlapping cues: Start times must precede end times without overlap.

A common misconception arises from confusion with WebVTT syntax, which can look similar (00:00:02.000) but uses a period in place of a comma. Copying these directly into SRT often triggers rejection by the player or platform.

Common TXT to SRT Format Mismatches

TXT exports from transcription tools, AI scripts, and meeting recorders often need structural adjustments before they can serve as valid SRT. Here are recurring failure modes seen in production workflows:

1. Incorrect separators
   Periods instead of commas in milliseconds: 00:05:10.345 → must be 00:05:10,345.
2. Frame-based timecodes
   SMPTE-style format HH:MM:SS:FF often appears in EDL/XML exports and needs conversion to milliseconds.
3. Missing leading zeros or hours field
   Files with 1:05,000 instead of 00:01:05,000 fail on strict parsers.
4. Malformed cue structure
   Missing arrow spaces (--> vs ` --> `) or blank lines between cues.
5. Mixed outputs in merged files
   Combined transcripts from multiple sources might blend formats, leading to inconsistent syntax within the same file.

Platforms like LocalIT’s SRT guide underline the importance of adhering exactly to the spec for successful uploads.

A Step-by-Step Diagnostic Checklist

Before attempting any conversion, diagnose the structure of your TXT export to understand what changes are required.

Step 1: Identify Existing Time Format

Check whether timestamps follow SRT (HH:MM:SS,mmm), WebVTT (HH:MM:SS.mmm), SMPTE (HH:MM:SS:FF), or decimal seconds (SS.mmm). Each style requires a different conversion approach.

Step 2: Distinguish Frame-Based vs Clock Time

Frames (FF) must be converted to milliseconds based on video frame rate—SRT doesn’t understand frames.

Step 3: Normalize Millisecond Separators

Apply pattern-based find-and-replace to fix . to , only in timestamp positions, avoiding accidental changes in subtitle text.

Step 4: Validate Cue Structure

Every cue should have:

• Sequential index starting with 1
• Correct timestamp range
• Subtitle text lines (one or two)
• One blank line after the text

Step 5: Check Overlaps and Gaps

Ensure that no cue’s time range overlaps the next; precise alignment improves readability and platform compatibility.

Pattern-based fixes can be done by hand, through scripts, or directly inside advanced tools. Manual cleanup is error-prone, so many professionals prefer small targeted changes inside an environment that supports automated transcript resegmentation to tidy structure across the entire file.

Encoding Pitfalls: UTF-8 vs ANSI

Encoding errors often lurk unnoticed until subtitles are published.

Modern platforms expect UTF-8 encoding for subtitle files—particularly when multi-language content is involved. Saving SRTs in ANSI can cause accented characters, Asian scripts, or RTL languages to display incorrectly, showing as � or garbled characters.

Causes of Encoding Failure

• Default save settings in OS text editors (ANSI instead of UTF-8)
• Rich-text editors introducing smart quotes or non-breaking spaces
• Round-tripping between platforms with differing default encodings

Best Practices

• Explicitly select UTF-8 when saving/exporting SRTs
• Avoid editing SRTs in MS Word or other rich-text editors
• Keep the file format plain text throughout its lifecycle

AI transcription platforms that enable direct UTF-8 SRT export save hours of troubleshooting. In SkyScribe, every transcript—whether generated from an uploaded file or a YouTube link—is timestamped correctly and can be exported in UTF-8 without manual intervention, preventing these encoding issues entirely.

Quick-Fix Patterns and Minimal Resegmentation

Search/Replace for Separators

Run a targeted regex replace to convert period-based milliseconds:
Pattern: (\d{2}:\d{2}:\d{2})\.(\d{3}) → Replace with \1,\2

Normalize Hours and Leading Zeros

If the duration is under an hour, add a 00: prefix to timecodes without an hours field.

Minimal Resegmentation

Preserve original timestamps as ground truth. Only split or merge cues for readability, without altering sync points. Tools and workflows offering one-click cleanup functions are valuable here—SkyScribe’s AI editing cleanup allows automatic removal of filler words, punctuation fixes, and timestamp standardization while leaving the essential timing intact.

The Case for Streamlined TXT to SRT Workflows

Converting TXT to SRT manually is feasible, but time-consuming and prone to inconsistencies. The risks intensify with multilingual projects, strict platform validators, and tight deadlines.

A streamlined workflow:

1. Use an origin tool that supports direct SRT export in UTF-8 and adheres to the correct spec.
2. Ensure timestamps are recorded with millisecond commas and correct zero-padding from the start.
3. Apply automated resegmentation or cleanup rather than hand-editing every cue.
4. Avoid intermediate conversions (TXT → DOCX → CSV → SRT) that increase opportunities for drift.

By adopting link-or-upload transcription platforms like SkyScribe, video creators and localization teams can bypass the downloader-plus-cleanup cycle entirely. This reduces the risk of timecode drift, encoding loss, and misstructured cues—delivering SRT files that are immediately ready for publishing.

Conclusion

For video creators, caption editors, and localization specialists, getting TXT to SRT right is about mastering the SRT specification, catching and correcting subtle timecode mismatches, and preserving encoding integrity across the workflow.

The difference between a compliant SRT and a rejected upload often comes down to tiny details: a comma vs. a period, a missing zero, an incorrect encoding flag. By following a structured diagnostic checklist and adopting workflows that generate ready-to-use SRT directly—such as those supported by SkyScribe—professionals can ensure their subtitles are platform-compliant, globally legible, and perfectly timed.

FAQ

Q1: Why do commas matter in SRT timecodes?
SRT requires commas before milliseconds (00:01:23,456) as part of its specification. Periods are invalid and common in other formats like WebVTT.

Q2: How can I tell if a timestamp is frame-based or millisecond-based?
Frame-based timestamps end with a frame count (:FF), usually below 30 depending on the frame rate, and must be converted to milliseconds for SRT.

Q3: What’s the fastest way to fix separator issues in a large subtitle file?
Use a regex-based search/replace targeting only timestamps’ milliseconds portion, ensuring you don’t affect periods in the subtitle text.

Q4: Why do my SRT files show garbled characters after upload?
They are likely saved in ANSI encoding; exporting them in UTF-8 resolves display issues for accents, non-Latin scripts, and special characters.

Q5: Can transcription tools export directly to compliant SRT?
Many modern tools can, but choosing one that produces fully compliant, timestamp-accurate, UTF-8 SRTs—like SkyScribe—avoids the common pitfalls of manual TXT to SRT conversion.