The most accurate multilingual text-to-speech, by the numbers
Blog post from Gradium
The text delves into the evaluation of text-to-speech (TTS) models, emphasizing the importance of pronunciation accuracy, which is typically measured by comparing transcribed audio with the original text input. It highlights the challenges of normalization in TTS, such as dealing with homophones and complex numbers, and introduces popular tools like the Whisper English normalizer and Kyutai's tts_longeval French normalizer to aid this process. The text also discusses the Word Error Rate (WER) as a measure of TTS performance, using the MiniMax Multilingual TTS Test Set for benchmarking, and compares the performance of various TTS models, with Gradium emerging as a leader in several languages. Despite the focus on WER, the text argues that a comprehensive TTS system must also ensure low latency, natural-sounding voices, and robust handling of complex linguistic features. It concludes by suggesting that while current benchmarks have reached a saturation point for simpler tasks, future progress in TTS will require more challenging tests and broader measures of robustness.
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