Multilingual Hybrid Recognition Solution
Whisper Input achieves hybrid multi-language recognition through the following technologies:
- Dynamic language detection: the system will automatically determine the main language based on audio spectral characteristics (supports 96 languages)
- Hybrid decoding technology: automatically invoke cross-language modeling when foreign words are detected in a statement (needs to be set in .env)
MULTILINGUAL=true) - Terminology optimization: add a custom glossary (in the format of JSON array) in config.json to improve the recognition rate of domain-specific terminology
Practical Examples
Take a mixed Chinese and English scene for example:
- Modify the .env file:
PRIMARY_LANG=zh(Set main language to Chinese) - Adding supplementary dictionaries: create in the project directory
custom_words.jsonWrite common English terminology - Enable Mixing Mode: Settings
HYBRID_TRANSLATION=trueRealize real-time language switching - Test effect: Read aloud Chinese passages containing specialized English terms, and the system will automatically keep the terms as they are in the original output.
Performance Optimization Recommendations
- Network latency-sensitive scenarios: SiliconFlow's SenseVoiceSmall model is recommended (40% response rate improvement)
- Long audio processing: Segmented inputs (≤30 seconds recommended for a single session) can avoid model distraction
This answer comes from the articleWhisper Input: a free and high-speed voice-to-text transcription service using GroqThe
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