[gapeleon]

> Interesting that there isn't a mention of Orpheus as prior art either

Llasa-3b (https://huggingface.co/HKUSTAudio/Llasa-3B) came out before Orpheus (https://huggingface.co/canopylabs/orpheus-3b-0.1-ft).

> it's the exact same thing.

They're very similar, but they're not the exact same thing.

Llasa uses xcodec2, a much simpler, lossless 16khz wav codec. This makes it superior for one-shot voice cloning.

Orpheus' 24khz snac codec is lossy which makes it difficult to use for zero-shot cloning as the reference audio gets degraded during tokenization. You can test this here: https://huggingface.co/spaces/Gapeleon/snac_test

But when finetuned on 50+ audio samples, it produces much cleaner 24khz audio than Llasa, and the snac model is much easier to run on consumer hardware than xcodec2 (87t/s for realtime speech, which can be achieved on an RTX3080 for example)

[oezi]

Do you happen to know why Orpheus and Llasa use Finetuning for voice cloning?

Zonos uses 128-float embeddings for voices and it seems so much nicer. Because you can just mix and match voices without changing the model.

[thot_experiment]

No, you just condition it with text-voice token pairs and then when conditioning further inference w/ text the voice tokens tend to match the pairs further up in the context.

[oezi]

Isn't xcodec2 also lossy? I thought it is also just another neural codec (50 tok/s, single codebook).

What are people using to upsampling back to 44,1 or 48 khz? Anything fancy?

[woodson]

They’re both lossy. They use a VAE-VQ type architecture trained with a combination of losses/discriminators. The differences are mainly the encoder/decoder architecture, the type of bottleneck quantization (RVQ, FSQ, etc.) and of course the training data.