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kronosta/piper.en_US-trinsfer-medium
piper.en_US-trinsfer-medium is a machine learning model from kronosta. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This is a multispeaker piper model containing 22 speakers for my worldbuilding project Trinsfer/The Dimensional Stack, where most characters have "voice claims" based on YouTubers' voices concatenated and repitched be…
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Updated Oct 26, 2026
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From the Hugging Face model README
This is a multispeaker piper model containing 22 speakers for my worldbuilding project Trinsfer/The Dimensional Stack, where most characters have "voice claims" based on YouTubers' voices concatenated and repitched before being fed into AI to mix them into new voices.
This model is not particularly natural, but it is comprehensible. It's only been trained for ~35 epochs, and since ~20 or so it hasn't improved much. The law of diminishing returns is quite strong when finetuning piper, it seems, and that means if you're satisfied with mere comprehensibility and timbre accuracy like I am, then you really don't need to finetune your piper model for more than a few hours on a medium grade GPU (I have a 4060 Ti; I used 300 max phoneme ids and batch size 16).
It is finetuned from the ljspeech-medium checkpoint, making it not affected by the whole lessac license problem where almost all models are legally stuck to "research purposes only". In fact, I think it might be the first multi-speaker English model on medium quality to not have this problem (the libritts model is high quality which is a bit slower even on a beefy gaming laptop).
To train this, I actually downgraded to the old rhasspy piper implementation to allow finetuning a single-speaker model into a multi-speaker one; the OHF-voice implementation doesn't have that option even though it really should (and in rhasspy the option is EXTREMELY simple, just a few lines of code).
The speakers: