ONNX artifacts for the OuteTTS 1.0 family, laid out for the outetts engine in phoonnx. All models are redistributed unchanged from the official OuteAI and IBM Research repositories. This mirror adds no weights of its own; it only guarantees a stable file layout and stable URLs for phoonnx voice entries.

The two model sizes carry different licenses. The 0.6B is Apache-2.0 and covers 14 languages; phoonnx resolves those 14 to it. The 1B is CC-BY-NC-SA-4.0 - not licensed for commercial use - and is here because it is the only member of the family that covers Arabic, Belarusian, Bengali, Lithuanian, Persian, Portuguese, Swahili, Tamil and Ukrainian. Check the 1B license before you deploy it.

Model authorship and all research credit belong to OuteAI. The audio codec is DAC.speech.v1.0 by IBM Research.

Every export in this mirror was checked against the upstream torch weights on a fixed 1843-token prompt (greedy, 64 decode steps, CPU float32).

The 0.6B float32 export reproduces its torch weights exactly. Every quantized 0.6B export changes greedy decoding, so none is a safe default.

OuteAI/Llama-OuteTTS-1.0-1B-ONNX does not reproduce its own torch weights, even in float32. On the prompt above its last-position logits are off by 12, the logit correlation is 0.48, prefill argmax agrees on only 1580 of 1843 positions, and greedy decoding diverges from OuteAI/Llama-OuteTTS-1.0-1B. The error is already present on a 32-token prompt (0.08 max diff) and never closes (correlation 0.96-0.98 at 512-2048 tokens), so it is a property of the export, not of long-context accumulation. The 0.6B export on the same prompt differs by 3.8e-05. Every quantized 1B export inherits the defect and adds to it.

The 1B/model.onnx in this repository is therefore not OuteAI's file. It was re-exported from the torch checkpoint with scripts/conversion/outetts/export_outetts_onnx.py and verified the same way: max abs logit diff 1.78e-05, mean 3.74e-06, logit correlation 1.00000000, greedy 64/64, and 64/64 again under the windowed repetition penalty OuteTTS actually samples with. The same script reproduces the 0.6B to 1.6e-05, which is how it is checked against a graph already known to be good.

One difference to know about: the re-exported graph returns logits shaped [1, 1, V] - the final row only - where OuteAI's exports return every position. Any consumer that reads the last row, as transformers.js and phoonnx both do, works with either.

Voice ids run outetts/0.6B/<lang> for the 14 languages the 0.6B was trained on and outetts/1B/<lang> for the nine only the 1B covers (ar, be, bn, fa, lt, pt, sw, ta, uk). Every one of them resolves to this repository. The engine drives the LM with a KV cache, then decodes the two DAC codebooks to a waveform.

The LM emits interleaved <|c1_N|><|c2_N|> tokens. dac/decoder_model.onnx takes audio_codes[1, 2, T] and returns audio_values[1, 1, T * 512] at 24 kHz. A speaker profile (speakers/*.json) is an in-context audio prompt: a transcript whose words each carry a duration, three prosody buckets and their DAC codes.

OuteAIs guidance applies unchanged: do not clone a voice without the speakers explicit permission, and do not use this model to impersonate or to produce deceptive content.