**Vāgdhenu** — *"the wish-cow of speech"* — is a single-speaker **Sanskrit chant (pārāyaṇa) text-to-speech** system that has drawn **465 stars and 109 forks** since its **28 June 2026** release, with the weights pulling **15,025 downloads** on Hugging Face. What separates it from the crowded TTS field is the target: it does not read ślokas aloud, it **chants** them, with metrically-aware durations and a tradition-faithful melodic contour. It is developed and maintained by **Prof. Prathosh at the Indian Institute of Science, Bengaluru**, and the reported quality is **MOS ~4.6** from an expert listener. The most technically interesting claim concerns conjuncts. Sanskrit consonant clusters — including retroflex aspirates such as ṣṭ and ḍḍh — are described as rendering **100% correctly**, and the README is explicit that this is the class of sound earlier architectures could not crack. The route to that result is unusual: Sanskrit text is deliberately **routed through Kannada script** rather than Devanagari, because Devanagari input triggers Hindi schwa-deletion in the underlying model. That kind of orthographic workaround is exactly the sort of practical finding that rarely survives into a published paper but decides whether a system actually works. The stack builds on existing open components rather than training from zero. The backbone is **IndicF5 / F5-TTS**, a flow-matching **DiT** at roughly **337M parameters** using OT-CFM mel-infilling — notably with *no* native duration or pitch head, which is precisely the problem a chant system has to solve. The vocoder is **NVIDIA BigVGAN-v2**, fine-tuned on F5 vocos-mel and described as mandatory because vocos "shivers" on the long vowels that Sanskrit recitation is full of. On prosody, the project reports a candid architectural finding: F5's content fidelity is bulletproof but its prosody is **text-driven, not designable**, so the only working levers are the reference clip (carrying voice, swara, and pace via a "half-reference rule") and a voice-steering fine-tune. The technical report treats this as the central result. The piece most likely to be reused elsewhere is the **text frontend** (`src/prep_text.py`), which handles Devanāgarī→SLP1→Kannada routing, internal visarga sandhi (utva, rutva, lopa, satva), homorganic anusvāra, vocalic-ṝ handling, daṇḍa-final rules, and meter/gaṇa (L/G) detection. That is a substantial piece of Sanskrit computational linguistics shipped as working code, useful well beyond this one model. The project is validated by shipped output rather than benchmarks alone. It produced **MBTN** (Mahābhārata Tātparya Nirṇaya) as 32 YouTube videos totalling **17 hours 34 minutes**, and the **Śrīmad Bhāgavatam** at **16,017 verses** across 12 skandhas as an audio app plus a 31-video three-script karaoke series. Rendering is batch-oriented through a shard JSON, with `src/render_production.py` for one-off verses and a Gradio demo on HF ZeroGPU. The constraints are narrow by design. It is **single-speaker only** — the voice is the author's own — and the ethics note asks that it be used for pārāyaṇa, study, and accessibility, not impersonation. It requires **Python 3.10 and a CUDA 12.1 GPU**, so there is no CPU or Apple-silicon path. Licensing is layered: the code is **Apache-2.0**, but it inherits from AI4Bharat IndicF5 (MIT), NVIDIA BigVGAN-v2, and F5-TTS, with weights redistributed under those terms. Last pushed **19 July 2026**, and the citation BibTeX is still pending an arXiv report.