---
license: apache-2.0
language: [as, bn, brx, doi, en, gu, hi, kn, kok, mai, ml, mni, mr, ne, or, pa, sa, sat, sd, ta, te, ur]
library_name: onnx
pipeline_tag: text-to-speech
tags: [onnx, text-to-speech, tts, indic, parler-tts, phoonnx]
base_model: ai4bharat/indic-parler-tts
---

# phoonnx — Indic Parler-TTS (ONNX)

ONNX export of [`ai4bharat/indic-parler-tts`](https://huggingface.co/ai4bharat/indic-parler-tts)
for the [phoonnx](https://github.com/OpenVoiceOS/phoonnx) `indic_parler` engine.

Indic Parler-TTS speaks 20 Indic languages and English. You select the voice with a
natural-language description ("Rohit's voice is clear and expressive..."), not with a
speaker id or a reference clip.

## Graphs

| File | Size | Purpose |
|---|---|---|
| `text_encoder.onnx` | 1.4 GB | Flan-T5 encoder over the voice description |
| `decoder_prefill.onnx` | 2.1 GB | First AR step; emits self-attention **and** cross-attention KV |
| `decoder_decode.onnx` | 1.3 GB | Later AR steps; consumes both caches, emits self-attention KV only |
| `dac_decoder.onnx` | 217 MB | DAC 44.1 kHz codec decoder (9 codebooks) |
| `tokenizer.json` | | prompt tokenizer (the text you want spoken) |
| `description_tokenizer.json` | | description tokenizer (Flan-T5 vocabulary) |
| `config.json` | | self-describing phoonnx config (`engine: indic_parler`) |

The two tokenizers are **different vocabularies**. Upstream is explicit about this: one
tokenizer for the prompt, one for the description.

## Architecture

    description --> Flan-T5 encoder --> encoder states --> cross-attention (all 24 layers)
    prompt      --> embed_prompts   --> prepended to the decoder input embeddings
    decoder     --> 9 delayed DAC codebooks --> DAC decoder --> 44.1 kHz mono

Cross-attention keys and values do not change while decoding, so `decoder_prefill`
computes them once and `decoder_decode` reads them back unchanged.

## Parity

Every graph is float32 and was checked against the PyTorch model
(`parler_tts.ParlerTTSForConditionalGeneration`, eager attention) on ser9 CPU:

| Check | Result |
|---|---|
| Text encoder, max abs diff (hi / en / ta) | 3.9e-07 / 6.5e-06 / 3.2e-07 |
| Prefill KV, max abs diff (self K/V, cross K/V) | 8.1e-06 / 4.5e-06 / 2.4e-06 / 1.2e-06 |
| Greedy codes, 60 steps | identical, all 3 languages |
| Waveform correlation vs torch | 0.9999999917 / 1.0000000000 / 1.0000000000 |
| Waveform RMSE vs torch | 1.3e-08 / 5.0e-08 / 1.5e-08 |

No quantised variants are published: the quantised graphs were not verified, and
phoonnx does not ship unverified exports.

## Notice

Upstream model: `ai4bharat/indic-parler-tts` by [AI4Bharat](https://ai4bharat.iitm.ac.in/),
built on [`huggingface/parler-tts`](https://github.com/huggingface/parler-tts) by Yoach
Lacombe, Vaibhav Srivastav and Sanchit Gandhi. Apache-2.0, preserved from upstream.
The DAC codec is Descript's, as vendored by upstream.

The weights are unmodified: the export wraps the PyTorch modules and traces them. No
surgery, no quantisation.