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Tacotron 2 + HiFi-GAN (LJ Speech)
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Tacotron 2 + HiFi-GAN (LJ Speech) is an open-weights text-to-speech model from VITS study (public implementations), released June 2021.
Catalogue identity notes
Exact ljspeech system in VITS Table 1; HiFi-GAN fine-tuning is a distinct vocoder configuration. Published implementation/checkpoint links in §3.4; no generic Tacotron or Glow-TTS score substitution.
Score within the VITS 2021 LJ Speech human MOS comparison set only.
$ how it compares
Scored text-to-speech models released closest to Tacotron 2 + HiFi-GAN (LJ Speech), out of 148 with a score.
| # | model | score | where |
|---|---|---|---|
| 81 | 50±16 | comparison only | |
| 82 | 50±16 | comparison only | |
| 124 | 34±5 | local | |
| 135 | 28±6 | comparison only | |
| 142 | 19±6 | comparison only |
see all text-to-speech models · find the ones that fit your machine
$ what it's good at
other tests1 not in a task · 65% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| VITS 2021 LJ Speech human MOS | 3.77 / 5 ✎ | █····█·█·█·█ beats 0/4 | 65% |
$ where to run it
No verified API or download recorded yet · add one
Weights and documentation: github.com/NVIDIA ↗
$ how the score was worked out · VITS: ICML 2021 original list… 65%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
| 1 | 65% | |
| prior · pull toward the middle | 35% | |
| weighted average | = 23.4 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json