AutoPrep has proven its efficiency and reliability in experiments conducted on the open-sourced WenetSpeech and the self-collected AutoPrepWild corpora. The processed data can be used directly in various tasks such as Text-to-Speech (TTS), Speaker Verification (SV), and Automatic Speech Recognition (ASR) model training. Furthermore, AutoPrep has shown significant improvements in the mean opinion score (MOS) and speaker similarity MOS (SMOS) score, demonstrating its effectiveness in enhancing speech quality. The framework represents Tencent AI Lab's commitment to advancing research and development in speech technology.
Key takeaways:
- Tencent AI Lab has launched AutoPrep, a preprocessing framework designed for in-the-wild speech data, offering automated preprocessing and high-quality annotation.
- AutoPrep addresses the challenges of processing large-scale speech data, such as background noise, speech overlapping, incomplete transcriptions, and missing speaker labels.
- The framework has shown significant improvements in speech quality and effectiveness in speech technology applications, including Text-to-Speech (TTS) synthesis.
- AutoPrep represents a significant advancement in speech technology, providing a solution to the challenges of processing unstructured speech data and setting the stage for future developments in the field.