Processing non-English audio requires special preprocessing and modeling adjustments:
Multi-language support program
- Model Tuning: Replacing the default ASR module with the multilingual Wav2Vec2 model on Hugging Face
- phoneme alignment: For tonal languages (e.g., Chinese), enable the
use_phonemes: trueparameters - character set configuration: set in config.yaml
character_set: unicodeSupport for non-Latin characters
Practical operation process
- Prepare 50+ minutes of target language training data
- (of a computer) run
python train.py --lang=zh-CNConducting transfer learning - Output translation using tools such as OpenNMT (when English subtitles are required)
language-specific techniques
- Japanese/Korean: enabledmorpheme_segmentationParametric improvement of clauses
- Arabic: setupright_to_left: trueReorienting text
- Dialect processing: adding local noise samples from 3% enhances robustness
alternative
When the result is still unsatisfactory, you can use Whisper to generate the initial subtitles first, and then use this tool for speaker annotation and timestamp calibration.
This answer comes from the articleSimple Subtitling: an open source tool for automatically generating video subtitles and speaker identificationThe
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