Install srt_deepl_utilpy today to translate subtitles with DeepL and dramatically speed up your subtitle workflow.
Set up your workspace by creating a python virtual environment and activating it with venvscriptsactivateps1, then install srt_deepl_utilpy and its dependencies. This approach 이용합니다 clean, repeatable builds for every project.
Visit github for the official repo and review the integration with subtitle-extractorpy는, which handles extraction and pre-processing before translation. Pro tip: verify compatibility with your editor before running translations.
Run the pipeline: feed an input.srt, let srt_deepl_utilpy translate, optionally combine whisper로 for speech-to-text alignment, and output a translated file to output.srt. translate operations leverage the DeepL API over a stable python workflow.
For accuracy, store your API key를 securely and tune rate limits; a typical 1,200-line SRT (~60k characters) translates in 3–6 minutes on a mid-range CPU with a stable network. When 번역하면 you get consistent formatting, and you can adjust tone per language pair. The 위해서는 error handling and retry logic should be enabled, 불행랑을 줄여주는 개선 포인트를 확인해 보세요.
이용하는 영상입니다 shows end-to-end flow: input -> subtitle-extractorpy는 -> translation -> srt_deepl_utilpy. This workflow uses whisper로 alignment and makes 처리하는 steps predictable, and the 만들어었던 templates ensure you can reuse across projects. 이용합니다 more, the sample repo includes scripts tied to github actions, making deployment smoother.
Ready to try? Clone the repo from github, configure a venv, set key를 securely in your environment, and run the translation pass on your SRT files today.
Install and configure stable-ts Whisper in Python to extract video subtitles
Install a focused environment and pin the workflow to amd64 hardware by creating a virtual environment with python -m venv venv, then activate using Windows: venvscriptsactivateps1 or Unix: source venv/bin/activate. Install stable-ts, whisper, and PyTorch (torch, torchaudio). If you have a CUDA-enabled GPU, pick the CUDA wheel so recognition runs on the device; otherwise install the CPU variant. Verify that torch.cuda.is_available() reflects the path you chose, or print(False) when running without CUDA. This setup ensures a clean, reproducible process for translating subtitles and printing results to the console or a file. Use a small test video to validate the pipeline and confirm the file outputs before scaling up. The approach keeps the key를 simple and repeatable, and the script handles text and translation results in a compact form.
Предварительные требования
amd64 systems suit this setup, with memory ample enough to load a Whisper model. A 유틸리티 to extract audio from video helps, but you can work directly with an audio stream if needed. Create and run a venv, then install: stable-ts, whisper, torch, torchaudio. On Windows, run venvscriptsactivateps1 to activate the environment. Select a model name like whisper-medium for better accuracy, and keep the file paths clearly named using name conventions to prevent confusion. The process will report vram을 usage and the recognition outcome, and you will see 나타납니다 when the device reports available VRAM. You should also ensure the input and output paths are accessible, with proper read/write permissions. If a GPU path isn’t available, the false로 flag will guide the CPU fallback.
Run and verify
Prepare a small Python script or run a shell flow that loads a video’s audio track and calls translate_subtitles on translate_subtitlesinput_file_name. Pass input_file_name for the media, choose a model, and disable per-word timestamps by setting word_timestampsfalse when you don’t need word-level timing. The script should print the translation result, write it to an SRT or text file, and exit gracefully after processing. Use print to show the text output and store the result in a file named by your naming convention; you can copy the final text to the 클립보드 if needed. If an error occurs, exit with a clear message and log the error for later review. The flow is designed to handle processing of each clip without leftover state, ensuring clean results in subsequent runs. The key outputs include text, translation, and a structured subtitle file, with vram이 usage reflecting the chosen path.
Convert Whisper transcripts into clean SRT: correct indexing, timestamps, and line breaks
Recommendation: use whisper로 transcripts and convert with srt_deepl_utilpy in python3 to ensure correct indexing, timestamps, and clean line breaks. Type the pipeline steps once and reuse translate_subtitlesinput_file_name across runs. If you previously 만들어었던 Whisper output, reprocess it to align with the latest script.
Workflow and concrete steps
Set translate_subtitlesinput_file_name to the base name of your Whisper transcript; this keeps the SRT linked to the proper input file and supports step-by-step auditing.
Create a Python virtual environment and install dependencies: python3 -m venv venv; pip install -r requirements.txt.
Activate the environment on Windows: venvscriptsactivateps1. Then run the conversion with: python3 srt_deepl_utilpy --input translate_subtitlesinput_file_name --output result.srt --word_timestampsfalse. You can also specify a sample output name via --output and adjust the encoding if needed.
Check indexing and timestamps: the first cue must start at 1, and each block should progress sequentially; if a drift occurs, re-run with corrected input or adjust the base offset in your workflow.
Adjust line breaks for readability: keep 1-2 lines per caption and use enter키를 to insert line breaks at natural sentence boundaries without splitting phrases awkwardly.
For translations, use deepl에서 translation as needed and output 한국어로 if required; you can feed the SRT back into the translation pipeline or translate the source text earlier in the flow.
The whisper의 alignment with the audio helps when pairing with audiovideo and 영상입니다; ensure the video file is in sync with the generated SRT by testing on a player.
In heavier tracks, you might isolate audio with demucs and process the resulting file separately before feeding into srt_deepl_utilpy; this is practical when the source file is large (filesnvidia) and demands higher GPU throughput. The script handles such inputs via the translate_subtitlesinput_file_name path and can recognize cprogram style blocks and recognition markers.
Optional enhancements
If you need tighter timing, enable low-latency options and consider using a longer NTP-safe chunking strategy in the input; keep your file and video in the same folder and ensure file permissions allow access to the file file.
Translate SRT with DeepL API: manage API keys, target languages, and request batching
Secure your DeepL API key by loading it from the environment (for example DEEPL_API_KEY) and never hard-code it in code. On Windows, store it under cusers사용자명 or use system variables; configure the runtime so a script can load it without prompts. Use false로 to disable interactive prompts in automated runners.
Prepare the project with python3 and a virtual environment: python3 -m venv venv; activate accordingly (Windows: venv\Scripts\activate, Unix: source venv/bin/activate); then install requests and a small utility stack. subtitle-extractorpy는 설치합니다 to extract text blocks from SRTs, and srt_deepl_utilpy는 Translate SRT with DeepL by batching requests. The workflow keeps timing blocks intact and writes a translated subtitle file while preserving file structure. 텍스트를 추출하고 번역하면 결과는 원본과 같거나 거의 동일한 포맷이 유지됩니다.
Design the batching strategy around DeepL limits: group 20–50 subtitle entries per request, depending on the length of each line. This keeps latency reasonable and avoids timeouts while leveraging parallelization if your environment supports it. recognition 요소와 함께 texts를 모아 translate 호출에 전송하고, 각 응답 텍스트를 원래의 타이밍에 매핑합니다. You can store intermediate results in a folder like filesnvidia to keep assets organized, and run the process in a dedicated venv in a path such as C:\Users\사용자명\Documents\texts or a project directory. sevengivingssubtitle-extractor 경로를 통해 추가 유틸리티를 연결하고, stable-tswhisper를 사용할 필요가 있다면 음성 인식 단계와 함께 병합합니다.
For multi-language output, iterate over target_languages and write separate SRTs with the appropriate language tag. 번역하면 각 파일은 동일한 자막 인덱싱을 유지하고 타임스탬프도 보존됩니다. The script prints progress and logs errors to help diagnose failures during batch translation, ensuring a smooth automated run.
To get started, clone a GitHub repository containing similar tooling and adapt srt_deepl_utilpy to your setup. 예제 구성은 texts 디렉터리의 샘플 파일과 함께 동작합니다. The approach supports large files up to 2gb의 크기까지 확장 가능하도록 스트리밍 처리나 분할 전략을 적용합니다.
| Step | Action | Notes |
|---|---|---|
| 1 | Prepare API key | Load DEEPL_API_KEY from environment; keep it out of code. Example: set in Windows environment or .env. Helps when using github workflows. |
| 2 | Choose target languages | Provide a list like [en, es, fr, ko, ja]. 번역하면 결과가 각 파일에 반영되며, 각 언어별로 파일이 만들어집니다. |
| 3 | Batch strategy | Batch 20–50 subtitle blocks per request; adjust by line length. This balances latency and quota usage; false로 to disable batching if needed. |
| 4 | Prepare script | Use srt_deepl_utilpy with venv; subtitle-extractorpy는 설치합니다; scripts reference filesnvidia and sevengivingssubtitle-extractor paths. |
| 5 | Run and verify | Process emits progress prints; validate output with the original timings; ensure 2gb의 input limits are not exceeded by batching strategy. |
| 6 | Output and reuse | Write translated SRTs next to originals; use text or texts arrays to reuse translated segments in subsequent runs. GitHub-hosted examples help align with your workflow. |
Preserve timing and formatting after translation: re-sync and wrap long lines in SRT
Re-sync each translated cue by keeping the original start times and recalculating end times from the length of the translated text. Use a per-cue duration scale based on characters: translated_chars/original_chars, then clamp the result to a safe range to maintain readability. This approach helps align audio and text after translation, especially when translating the text payload in deepl에서 or via other translate services. For Windows workflows, trigger the process from a venv (activate.ps1 path shown as venvscriptsactivateps1) and preserve paths such as cusers사용자명 to keep outputs organized.
Re-sync with character-based duration adjustment
For every cue, compute: orig_chars = length(original_text), trans_chars = length(translated_text). duration_scale = trans_chars / max(1, orig_chars). new_duration = clamp(original_duration * duration_scale, 1.0, 8.0). Update the end time to start_time + new_duration. If new_duration causes overlap with the next cue, shorten by a small amount (0.2–0.5 seconds) to keep a comfortable gap. Save the result as SRT and verify visually in your editor. Translate the text with deepl에서 for consistency and then apply the re-sync pass, ensuring the wrapped lines reflect the new timing. You’ll see the alignment adjust in the preview as text overlays on the video (영상입니다).
Wrap long lines and respect two-line limits
Wrap translated text so each cue uses at most two lines and each line stays around 42 characters, adjusting for font metrics. Break at natural spaces; avoid mid-word splits, numbers, or time codes. If a single sentence becomes too long, insert a break at a logical boundary without losing punctuation clarity. Use a simple wrapper in your venv venv workflow: wrap translated_text to two lines, then join with a newline inside the SRT text. The wrapped result appears clearly in the final output, making the subtitles easy to read on any audiovideo playback setup. For automation, save outputs to a folder such as cusers사용자명\\Videos\\audiovideo and use 클립보드 to quickly transfer preview text during checks. 번역하면 shows up as properly split lines in your editor and in the player UI.
For large batches, you can automate with a Python script in a venv using commands like install dependencies, then run a processing script that reads files, applies translation, re-syncs durations, and wraps lines. If you work with audio cues, you can add an optional alignment step using whisper로 to refine timing against speech, especially when the source audio lacks clean punctuation. On GPUs, ensure CUDA is available; a system with 2gb의 cuda VRAM or more speeds up processing, and you can leverage filesnvidia paths to organize assets. If you maintain a local library, locate resources under cprogram and load information without disrupting the main project.
Quality check: verify translations, encoding, and subtitle legibility before publishing
Verify translations line-by-line against the source and lock down the glossary before publishing.
Process steps for quality check
- Translation accuracy: compare outputs from deepl에서 with the original texts, confirm consistency with your established glossary, and ensure the translated meaning matches the context. Use subtitle-extractorpy는 to pull lines for review; if a term shows drift, note it in a docx and adjust. If the review reveals identical phrasing, 같은 부분은 같습니다 appears in the log, indicating alignment.
- Glossary and terminology: map every term to a canonical translation (번역하면마다) and verify consistency across all files. Save a trace in docx, and import changes into your workflow so revisions stay synchronized with the translation history in github, texts, and related assets.
- Cross-language checks: ensure proper nouns, product names, and brand terms remain recognizable (예: whisper의 accuracy and deepl에서 translation of proper nouns). Validate that the intended meaning and tone carry through in every language variant.
Technical validation: encoding, formats, and legibility
- Encoding and file integrity: save subtitle files in UTF-8 without BOM when required, and verify non-Latin characters render correctly across players. Use a test script to load files and confirm no garbled characters appear in 영상입니다 captions.
- File formats and compatibility: confirm SRT/WEBVTT structure, correct time stamps, and clean line breaks. If you export to docx for review, ensure the translation blocks align with the original indices and that import paths remain stable on cusers사용자명 and other user profiles.
- Timing and legibility: enforce two lines per subtitle and keep line length under typical reading width. Validate word_timestampsfalse for reduced timestamp noise when the timing is tight; run a quick pass with a waveform tool and airdrop checks to verify alignment with the audio using audiovideo cues.
- Clipboard and workflow checks: copy samples to the 클립보드 to verify quick edits in a local editor, then re-import to the pipeline. Use a standard usage guide to prevent drift between your script and manual edits, and export a test set to docx for team review.
- Environment and automation: reference the venv setup, including 설치합니다 steps and the command sequence venv, 없습니다, venvscriptsactivateps1 for Windows. Run the checks in python environments to ensure consistent results across platforms, and document any type or computing note in the information log.
- Verification workflow: if any issue persists, exit with a clear exit code and a concise message. Use a small script to generate a report that shows translation status, encoding health, and legibility metrics, then push results to github for traceability and auditable history.
Automate the entire pipeline with the DeepL File Translator Helper: batch processing and saving outputs
Install the DeepL File Translator Helper and run a single batch to translate all subtitle files, saving outputs to a dedicated folder automatically. The pipeline uses subtitle-extractorpy는 to extract subtitles from videos, whisper로 to produce transcripts, and deepl에서 to translate text into the target language. Expect large inputs like 2gb의 영상입니다, and batch processing handles multiple files in one run for speed and consistency. If you need guidance, consult the help information available in the docs; when tasks complete, use exit to finish.
Batch processing and file flow
- Prepare inputs by placing translate_subtitlesinput_file_name and its type in the working directory; for example translate_subtitlesinput_file_name.srt (type = srt).
- Run the batch with python, e.g. python -m sevengivingssubtitle-extractor --input translate_subtitlesinput_file_name.srt --output outputs/; after completion, exit automatically.
- The subtitle-extractor pipeline emits text via whisper의 transcription, then deepl에서 translation. The output will be a translated subtitle set with aligned timestamps.
- Outputs are saved per file in outputs/, with names like translate_subtitlesinput_file_name.srt and translate_subtitlesinput_file_name.docx를 이용합니다.
- Manage multiple source files in one run; all related files are stored together as files for easy archival and reuse.
Saving outputs and formats
- Save options include .srt, .vtt, and .docx; docx를 이용합니다 ensures a text-friendly format for editors. Use the translation in 한국어로 or the target language you chose during setup.
- Keep a manifest file (metadata.json) that lists each input, type, language pair, and status so you can re-run without reprocessing. If information is missing, the tool displays help information (없습니다 information).
- All outputs land in an outputs folder; you can archive or move them with a simple script. The pipeline is designed to handle 2gb의 영상입니다 without memory spikes, thanks to chunked processing and efficient I/O.
