Pathways to the realization of multilingual conference transcription
To handle multilingual scenarios such as mixing Chinese and English, a step-by-step configuration is required:
- Model preparation phase::
- Download whisper-large-v3 multilingual model (~3GB)
- Setting in .env
MODEL_ID=openai/whisper-large-v3 - Installing the langdetect library for language detection
- Runtime Configuration::
- Modify transcribe_task.py:
task='translate' - Set fallback_language='en' (default output English)
- Add language_detection_threshold=0.7 parameter
- Modify transcribe_task.py:
- <strong]Special treatment::
- Additional settings are required for languages such as CJK:
initial_prompt='以下是中文内容：' - Enabling the sentence_splitter module for mixed statements
- Additional settings are required for languages such as CJK:
The advanced solution can integrate the languageID feature of Azure Speech Services to realize dynamic language switching. Tests show that this solution has an accuracy of 78% for recognizing mixed Chinese and English content.
This answer comes from the articleOpen source tool for real-time speech to textThe
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