A Telugu text-to-speech voice, finetuned from Orpheus. Voice prefix: telugu_female.

Eval loss 3.1809, matching the male run's 3.1779. Trained on 19,859 clips (~48 h), of which only 4 were dropped -- the benefit of a corpus with ground-truth transcripts rather than ASR labels.

Measured median F0 of generated speech: 218.5 Hz, against the male model's 136.9 Hz with no overlap between the two sets. Voice identity was verified by measurement rather than assumed, because a generic prefix on an earlier Tamil model silently drifted toward the wrong gender.

See also the matching male voice and the GGUF build.

Orpheus emits SNAC audio-codec tokens which a SNAC vocoder decodes to 24 kHz mono PCM. Prompt format is {voice}: {text}; the voice is a literal text prefix learned at training time, so this model has no zero-shot cloning and takes no audio input.

Audio token layout: control tokens 128257-128262, audio offset 128266, 7 tokens per frame each offset by (position_in_frame 0-6) * 4096.

Recommended sampling: temperature=0.3, repetition_penalty=1.3. Output is quiet (peaks ~0.2-0.4) -- peak-normalise or level it downstream.

Derived from the SYSPIN Telugu corpus. The mirror this was trained from does not declare a licence; anyone intending commercial use should confirm terms with the upstream corpus holders (IISc SPIRE Lab / Bhashini) rather than relying on this repo.

Runs with llama.cpp / llama-cpp-python. You still need a SNAC vocoder (hubertsiuzdak/snac_24khz) to turn the emitted codec tokens into audio -- the GGUF alone produces tokens, not waveforms.