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Open TTS Leaderboard: Scalable Evaluation for Multilingual Text-to-Speech and Voice Cloning
TL;DR
The Open TTS Leaderboard was developed to provide a scalable, objective evaluation of multilingual text-to-speech (TTS) models, addressing the limitations of current arena-based assessments. By utilizing metrics like word error rate (WER) and character error rate (CER), it aims to enhance the representation of open-source models in TTS evaluations.
✦ Why It Matters
Start using the Open TTS Leaderboard to evaluate and compare your TTS models objectively today.
Key Takeaways
- Utilize the Open TTS Leaderboard for objective model evaluations.
- Engage with the community by providing feedback on model outputs.
- Compare TTS models across multiple languages for better insights.
- Leverage metrics like WER and SIM to inform model selection.
How It Works
The Open TTS Leaderboard evaluates models using objective metrics such as word error rate (WER) for intelligibility, inverse real-time factor (RTFx) for speed, and cosine similarity for speaker identity. This allows for rapid comparisons of TTS models without relying solely on human votes, which can be inconsistent and slow to gather.