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You can generate editable first-pass subtitles for a video library in Subtitle Edit by choosing a speech-to-text engine that runs on your computer, selecting a model and language, reviewing the transcript against the audio, and exporting corrected subtitle files. The important qualification is that Subtitle Edit offers both local and online engines: the workflow is local only when you explicitly select a local backend such as Whisper CPP or a documented local Faster-Whisper option.
For a small collection, process one video at a time. For a large library, validate the engine, model, language, filename pattern and subtitle format on a small sample before using batch mode. Keep the original media untouched and record the settings used so you can reproduce or revise the results later.
What does “local AI” mean for subtitle generation?
Local AI means the audio is transcribed by an engine running on your own computer rather than uploaded to a hosted transcription service. A desktop editor does not automatically make every engine local. Subtitle Edit lists local engines alongside online choices, so inspect the engine selection and configuration before starting.
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Local processing can be useful when footage is confidential, internet access is limited, or you need a workflow that does not depend on a recurring service. It also shifts responsibility to your machine: you must install or obtain the backend and model, provide enough storage, and accept that processing time and results depend on the chosen model and hardware.
What do I need before I start?
Prepare the following before opening a large batch:
- Video files that play correctly and have understandable audio.
- A current Subtitle Edit installation compatible with your operating system.
- A locally runnable speech-to-text backend, such as Whisper CPP or another engine documented as local in Subtitle Edit’s current documentation.
- The model files required by that backend, with enough disk space for the model and temporary working data.
- The spoken-language choice for each group of videos. Automatic language detection exists for some engines, but it is not universal.
- A destination where generated subtitle files can be written without overwriting the source videos.
- A short test set representing the difficult material in your library: names, numbers, accents, music, multiple speakers and fast dialogue.
There is no single machine specification or universal speed promise that applies to every engine and model. Larger models generally need more disk space and take longer, while they may improve recognition. Treat that as a trade-off to test on your own hardware, not as a guaranteed accuracy ranking.
How do I choose a genuinely local engine?
Open Subtitle Edit’s speech-to-text feature and distinguish local backends from hosted services before selecting a model. The engine name, configuration panel and documentation should make clear where the audio is processed. If the option requires an account or sends media to a remote endpoint, it is not the local workflow described here.
Whisper CPP
Whisper CPP is a local Whisper implementation listed by Subtitle Edit. It is a practical starting point when you want a self-contained desktop workflow, but the supported model formats, acceleration options and installation details depend on the current build and platform.
Faster-Whisper and CTranslate2-based options
Subtitle Edit lists Purfview Faster Whisper XXL and Whisper CTranslate2 among its engines. Faster-Whisper deployments can use different CPU or GPU backends and model packages; check the engine’s configuration and current project notes rather than assuming a particular accelerator is available.
WhisperX
WhisperX is another listed option and is relevant when detailed word-level timing is important. Its alignment and optional speaker features can introduce additional dependencies, so verify the current setup requirements before choosing it for a repeatable library job.
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LocalAI backends
LocalAI separately documents local audio-to-text backends including whisper.cpp, faster-whisper and WhisperX, with timestamped transcription segments. Its documentation treats diarization as an optional capability with additional requirements; speaker labels should not be assumed merely because a backend can produce timestamps. See the LocalAI audio-to-text documentation when you are evaluating that architecture.
Selection criteria that matter in practice
| Criterion | What to check | Why it matters |
|---|---|---|
| Operating-system support | Whether the current engine build supports your Windows, macOS or Linux environment | An otherwise suitable engine may not install or accelerate correctly on your platform. |
| CPU/GPU backend | Which processor or accelerator paths the engine can use on your machine | Backend compatibility affects setup and processing time. |
| Model options | Available model sizes, formats and language coverage | Models trade storage and processing time against recognition quality. |
| Timing detail | Whether segment or word-level timestamps and alignment are available | Fine-grained timing can reduce manual cue adjustment, but may require extra components. |
| Installation complexity | Required runtimes, model downloads and configuration steps | A simpler installation is easier to reproduce across editing computers. |
| Data path | Confirmation that audio stays on the local machine | Only a local backend satisfies an offline or privacy-sensitive requirement. |
The documentation does not provide a controlled speed or accuracy comparison across these engines. Select one that supports your platform and language, then measure the review effort on representative clips.
How do I generate subtitles for one video in Subtitle Edit?
Use this sequence for a controlled first run. The labels below follow Subtitle Edit’s documented interface; wording can change between releases.
- Open the media. In Subtitle Edit, open the video you want to transcribe. Confirm that the correct audio track plays and that the timeline displays the expected duration.
- Start speech recognition. Choose Video → Speech to text. This opens the transcription workflow documented in Subtitle Edit’s Speech to Text feature page.
- Select a local engine. Choose Whisper CPP, Purfview Faster Whisper XXL, Whisper CTranslate2, WhisperX or another engine explicitly documented as local. Do not select an online option simply because it appears in the same menu.
- Configure the backend. Point the engine to its required executable, runtime or model location when prompted. Confirm that the selected model is installed locally and that any CPU/GPU setting matches your computer.
- Choose the model. Start with a model appropriate for your available storage and the language complexity of the footage. Larger models may improve recognition but generally require more storage and processing time; test rather than assuming the largest model is always best.
- Set the spoken language. Choose the actual language when known. Automatic language detection is available for some engines but not all, and mixed-language footage can still require manual correction.
- Run transcription. Start the job and wait for Subtitle Edit to create timed subtitle cues. Do not edit or overwrite the source video while the job runs.
- Save the draft. Save the generated subtitle project or file with a name that distinguishes it from the unreviewed output, such as a language-coded draft filename.
The result is a timed transcript, not a publication-ready caption track. Recognition errors are especially common around proper names, numbers, specialist vocabulary, overlapping speech, punctuation and line breaks.
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Play the video while reading every cue. A reliable correction pass checks both what the words say and when each cue appears on screen.
Correct the text against the audio
- Replay names, places, product terms and technical vocabulary at reduced speed when necessary.
- Check all numbers, dates, units, abbreviations and negative words; a single missing word can reverse the meaning.
- Fix punctuation and capitalization, but follow the style required by your target platform or house guide.
- Mark speakers or non-speech events only when your captioning specification calls for them.
- Separate overlapping dialogue carefully. Automatic transcription may merge speakers or assign words to the wrong cue.
Check cue boundaries and reading flow
- Make sure a cue starts after the speaker begins and ends before the next unrelated shot or speaker.
- Break long sentences at natural phrase boundaries rather than in the middle of a name or grammatical unit.
- Keep lines readable and avoid leaving a one-word “orphan” on a second line when a better break is possible.
- Look for cues that flash briefly, remain on screen too long, or overlap in a way your player cannot display.
Use post-processing carefully
Subtitle Edit provides post-processing options for tasks such as timing adjustment, casing, merging lines and punctuation. Apply these operations to a copy or save a checkpoint first. Automated formatting can improve consistency, but it cannot decide whether a specialist term, speaker change or intentional pause is correct.
For word-level alignment or speaker separation, verify that the selected engine actually supports the feature and that its additional components are installed. Timestamped segments alone do not identify speakers.
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How do I process an entire video library in batch mode?
Batch processing is efficient only after a small sample has passed your quality checks. Subtitle Edit documents batch transcription and saving .srt files beside the source videos.
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- Record the configuration. Write down the engine, model, language, backend settings, Subtitle Edit version and date. This record makes later corrections reproducible.
- Run the sample in batch mode. Use Subtitle Edit’s batch speech-to-text workflow and keep the output location separate from the originals.
- Inspect filenames. Confirm that each subtitle file matches the intended video and that any language code is placed according to your naming convention. Subtitle Edit’s documentation describes adding a language code to output filenames.
- Open the files in your target player or publishing tool. Verify that timing, character encoding, line breaks and language metadata display correctly.
- Adjust the settings if needed. If names, punctuation or timing require excessive manual work, change the model, language setting or post-processing choices and repeat the sample.
- Scale up only after acceptance. Run the remaining library with the approved configuration, monitoring disk space and keeping a log of failed or skipped files.
Saving SRT beside each video is convenient for many players, but it is not a universal delivery requirement. Some platforms require another subtitle format or a separate upload step, so confirm the format expected by your final destination before processing thousands of files.
Which subtitle format should I export?
Use SRT when your player or publishing system accepts it and you want a simple, widely supported text format. Subtitle Edit’s batch documentation specifically describes SRT output beside source videos.
| Use case | Practical choice | Verification |
|---|---|---|
| Local playback and general archive | SRT with a clear language-coded filename | Open it beside the video in the players used by your team. |
| Platform upload | The exact caption format required by that platform | Upload a short sample and inspect line wrapping, timing and character display. |
| Further editing | Keep the editable Subtitle Edit project or an equivalent working copy in addition to the delivery file | Reopen the project and confirm that timing and text remain editable. |
Do not treat a successful export as proof that every player will render the file identically. Test the actual playback or publishing path, especially for accented characters, right-to-left languages and long lines.
How should I preserve originals and make the workflow reproducible?
Use a non-destructive folder layout. Keep the original video files read-only or in a separate directory, write drafts to a working folder, and place reviewed exports in a delivery folder. Never rely on a subtitle filename alone to identify which settings produced it.
A useful record for each batch includes:
- Video filename and source location.
- Subtitle Edit version and operating system.
- Engine name and backend version, if shown.
- Model name or file identifier.
- Spoken-language setting and whether automatic detection was used.
- Any diarization, alignment or post-processing options.
- Review status, reviewer and date.
- Output format and final filename.
When a later model produces better text, you can regenerate only the affected files and compare them with the reviewed versions instead of losing the original corrections.
What problems commonly occur, and how do I fix them?
The engine is missing or will not start
Check that the backend executable, runtime and model path are valid for your operating system. Reopen the engine configuration, verify permissions and consult the current Subtitle Edit documentation for supported components. If a GPU backend fails, try the documented CPU path for a small clip to separate an installation problem from an accelerator problem.
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The transcript is in the wrong language
Set the spoken language explicitly and rerun a short sample. Do not assume automatic detection is available for the selected engine, and avoid using detection on mixed-language footage without reviewing the result.
Names and technical terms are repeatedly wrong
Test a larger or otherwise different model if your machine can support it, then correct the output manually. Keep a terminology list for reviewers; the reviewed documentation does not establish a universal accuracy benchmark or a guaranteed improvement for any particular model.
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Cues are badly timed or too long
Check whether the engine provides only segment timestamps or supports finer alignment. Use Subtitle Edit’s timing and line-merging post-processing conservatively, then replay the affected passages. Word-level alignment can require an engine such as WhisperX and additional setup.
Several people are speaking at once
Overlapping speech is a manual-review case. Split or annotate the dialogue according to your captioning rules, and do not infer speaker identity from timestamps alone. Optional diarization may be available in some local architectures, but it has separate requirements.
Batch output is missing or mismatched
Run one file manually and inspect the destination folder. Confirm that the source filenames are unique, the output directory is writable, and the language-code naming option is configured as intended. A small sample catches these errors before they affect the full library.
The subtitle looks correct in the editor but not in the player
Check the exported format, text encoding, filename pairing and player-specific line-length rules. Open the exact exported file—not only the project—in the target player and test a clip with accented characters and long dialogue.
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No. Local speech recognition is a productive first pass, but the generated file should not be treated as publication-ready without listening review. The available documentation does not provide a validated accuracy benchmark for a particular language, model or hardware setup.
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At minimum, review names, numbers, specialist vocabulary, overlapping speech, punctuation, line breaks and cue timing. For public-facing captions, also apply the accessibility, speaker-identification and non-speech-description rules required by your organization or distribution platform.
What is the most reliable workflow for a large library?
A dependable library process is staged: select a local engine, test it on representative videos, correct and approve the sample, then batch transcribe the remainder while preserving originals and recording settings. This approach avoids scaling an incorrect language choice, filename pattern or backend configuration across hundreds of files.
- Choose the local engine and model that your computer can run consistently.
- Transcribe a varied sample through Video → Speech to text.
- Measure the human correction effort rather than relying on an unverified accuracy claim.
- Approve naming, language codes, output format and folder structure.
- Run batch mode, monitor failures and retain the configuration log.
- Perform a final spot check in the actual playback or publishing environment.
Frequently Asked Questions
Can I generate subtitles with no internet connection at all?
After the editor, backend and model have been installed locally, transcription can run without sending audio to a hosted service. Initial downloads, software updates and documentation access may still require connectivity, so install and verify the components before taking a computer offline.
Will a local model work for more than one spoken language?
Language coverage depends on the selected model and engine. For a multilingual archive, group files by language where possible and test each group separately instead of assuming one model or automatic detection will handle every recording equally well.
Can local transcription identify each speaker automatically?
Not necessarily. Basic speech-to-text produces words and timestamps, while diarization is a separate capability that may require another backend or additional dependencies. Treat speaker labels as unverified until you compare them with the audio.
How can I estimate storage needs before a batch run?
Check the model’s documented file size and leave additional working space for temporary data and exported subtitles. A small trial run reveals how much the specific backend writes on your system; do not calculate capacity from video duration alone.
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