Real-time text-to-speech solution for multilingual conferences
PengChengStarling provides a complete solution to address speech-to-text requirements in cross-lingual conferencing scenarios. Compared with traditional solutions, its core advantage is that it supports streaming recognition of 8 languages and inference speed is 7 times faster than Whisper-Large v3.
- Deployment preparation:
- Installation of Linux environment (Ubuntu 18.04+ recommended)
- Clone the project repository and install the dependencies:
git clone https://github.com/yangb05/PengChengStarling
cd PengChengStarling
pip install -r requirements.txt
- Real-time processing configuration:
- Using the Streaming Interface to Process Audio Streams
- Set the sample rate to 16kHz for optimal identification
- Select the corresponding recognition model according to the language of the speaker (supports 8 types of Chinese/English/Russian)
- Optimization Recommendations:
- Fine tuning is available for specific accents:
./train.sh --finetune - Enhancing Inference Efficiency with ONNX Format Deployment
- Enhancing Text Readability with Punctuation Models
- Fine tuning is available for specific accents:
For scenarios requiring higher accuracy, it is recommended that the recordings be processed twice after the meeting in combination with non-streaming reasoning. The complete tool chain provided by this project can effectively solve the speech transcription needs in multilingual scenarios such as multinational enterprises and international conferences.
This answer comes from the articlePengChengStarling: Smaller and Faster Multilingual Speech-to-Text Tool than Whisper-Large v3The
