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

Eval loss 3.1779 -- the best figure recorded in this project, below the Tamil full-finetune baseline of 3.235 that had needed a rented A100. This one trained on a single consumer 16 GB card at no GPU cost.

Measured median F0 of generated speech: 136.9 Hz.

See also the matching female 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.