---
language:
- hi
- ta
- te
- bn
- mr
- gu
- kn
- ml
- or
- pa
- as
- ur
- ne
- sa
- mai
- sd
- kok
- mni
- sat
license: apache-2.0
library_name: transformers.js
pipeline_tag: text-to-audio
tags:
- onnx
- text-to-speech
- tts
- indic
- parler-tts
- onnxruntime-web
base_model: ai4bharat/indic-parler-tts
---

# Indic Parler-TTS — ONNX

This is a verbatim ONNX export of [`ai4bharat/indic-parler-tts`](https://huggingface.co/ai4bharat/indic-parler-tts) for in-browser inference via [onnxruntime-web](https://onnxruntime.ai/) or [transformers.js](https://huggingface.co/docs/transformers.js).

The upstream model is the multilingual Indic-language fine-tune of Parler-TTS ([huggingface/parler-tts](https://github.com/huggingface/parler-tts)) by AI4Bharat. This repo re-exports the same weights as ONNX graphs so they can be loaded into a browser without a Python runtime.

## Files — fp32 + fp16 variants

Both precisions ship in this repo following the
[`onnx-community/dac_44khz-ONNX`](https://huggingface.co/onnx-community/dac_44khz-ONNX)
convention. fp16 graphs use `keep_io_types=True` so they are **drop-in
replacements** for fp32 — consumer code doesn't change tensor dtypes
between sessions when switching variants.

| File | Size | Purpose |
|---|---|---|
| `text_encoder.onnx` + `.data` | 1.30 GB | fp32 — T5 (flan-t5-large) description encoder |
| `text_encoder_fp16.onnx` + `.data` | 651 MB | fp16 — same, fp32 I/O |
| `decoder_model.onnx` + `.data` | 1.98 GB | fp32 — first AR step (codec embed + prompt prefix + cross-attn + 24-layer decoder + fused 9-codebook lm-head) |
| `decoder_model_fp16.onnx` + `.data` | 992 MB | fp16 |
| `decoder_with_past_model.onnx` + `.data` | 1.25 GB | fp32 — subsequent AR steps with past KV cache |
| `decoder_with_past_model_fp16.onnx` + `.data` | 622 MB | fp16 |
| `tokenizer.json`, `tokenizer.model`, `tokenizer_config.json` | 12 MB | LlamaTokenizerFast, vocab 90,714 — used for both description AND prompt |
| `config.json`, `generation_config.json`, `special_tokens_map.json` | small | Model + generation config |

**Bundle totals:**
- **fp16: ~2.3 GB** ← recommended for browser consumers (halves IDB cache + WebGPU memory)
- fp32: ~4.5 GB ← sanity reference; use only for parity testing or non-WebGPU paths

The DAC 44.1 kHz vocoder ([`onnx-community/dac_44khz-ONNX`](https://huggingface.co/onnx-community/dac_44khz-ONNX), referenced separately — pick its `decoder_model_fp16.onnx` at 104 MB) is needed to turn the decoder's codec tokens into audio waveform.

### fp16 parity vs fp32

Conversion via `onnxruntime.transformers.float16.convert_float_to_float16(..., keep_io_types=True)` with the default ORT op-block list. Verified on CPU EP:

| Graph | Max abs diff |
|---|---|
| `text_encoder_fp16.onnx` | 8.6×10⁻³ |
| `decoder_model_fp16.onnx` | 1.8×10⁻² |
| `decoder_with_past_model_fp16.onnx` | (sanity only — finite outputs across full step) |

All within typical fp16 transformer tolerances (≤5×10⁻² is the gate). Audio quality is indistinguishable in practice for VITS-derived TTS.

### Loading by dtype

```js
// Browser, prefer fp16 unless you need bit-exact PyTorch reproduction
const url = (file) =>
  `https://huggingface.co/naklitechie/indic-parler-tts-ONNX/resolve/main/${file}`;
const dtype = 'fp16';   // or 'fp32'
const suffix = dtype === 'fp16' ? '_fp16' : '';
const sess = await ort.InferenceSession.create(
  await fetch(url(`text_encoder${suffix}.onnx`)).then(r => r.arrayBuffer()),
  {
    externalData: [{
      data: await fetch(url(`text_encoder${suffix}.onnx.data`)).then(r => r.arrayBuffer()),
      path: `text_encoder${suffix}.onnx.data`,
    }],
    executionProviders: ['webgpu', 'wasm'],
  }
);
```

## Architecture facts (consumer cheat sheet)

- **Single tokenizer** for description + prompt (the upstream README's mention of a `prompt_tokenizer/` subfolder is a documentation artifact — both inputs use the same `LlamaTokenizerFast`, vocab 90,714).
- **`prompt_cross_attention=False`** — prompt embeddings are *prefixed* to the decoder's input embeddings, not cross-attended. The decoder's self-attention KV cache region therefore covers `(prompt_len + codec_len)` positions after step 1.
- **Decoder**: 24 layers × 16 heads × 64 head_dim (1024 hidden), 9 codebooks, vocab 1088 per codebook, max position 4096.
- **Fused lm_head**: a single `Linear: 1024 → 9 × 1088` projection (not 9 separate heads), reshape + transpose inside the graph.
- **PAD/BOS/EOS/START**: 1024 / 1025 / 1024 / 1025.
- **Sample rate**: 44100 Hz from DAC. ~10 ms per codec token end-to-end.

## I/O signatures

### `text_encoder.onnx`

| Input | Shape | Type |
|---|---|---|
| `input_ids` | `[B, S]` | int64 |
| `attention_mask` | `[B, S]` | int64 |

| Output | Shape | Type |
|---|---|---|
| `last_hidden_state` | `[B, S, 1024]` | float32 |

### `decoder_model.onnx` (first AR step)

| Input | Shape | Type |
|---|---|---|
| `codec_input_ids` | `[B, 9, T]` | int64 |
| `prompt_input_ids` | `[B, P]` | int64 |
| `prompt_attention_mask` | `[B, P]` | int64 |
| `encoder_hidden_states` | `[B, S, 1024]` | float32 |
| `encoder_attention_mask` | `[B, S]` | int64 |

| Output | Shape | Type |
|---|---|---|
| `logits` | `[9*B, P+T, 1088]` | float32 |
| `present.{i}.{decoder.key,decoder.value,encoder.key,encoder.value}` × 24 layers | `[B, 16, P+T or S, 64]` | float32 |

### `decoder_with_past_model.onnx` (subsequent AR steps)

| Input | Shape | Type |
|---|---|---|
| `codec_input_ids` | `[B, 9, 1]` | int64 |
| `attention_mask` | `[B, full_t]` | int64 — covers prompt + prior codec + new |
| `encoder_attention_mask` | `[B, S]` | int64 |
| `cache_position` | `[1]` | int64 — `past_kv_length` scalar |
| `past_key_values.{i}.{decoder.key,decoder.value,encoder.key,encoder.value}` × 24 layers | as in `present` above | float32 |

| Output | Shape | Type |
|---|---|---|
| `logits` | `[9*B, 1, 1088]` | float32 |
| `present.{i}.{...}` × 24 layers | shifted by 1 in self-attn time dim | float32 |

KV layout per layer: `(decoder_self.key, decoder_self.value, encoder_cross.key, encoder_cross.value)` — IT2-compatible.

## End-to-end inference (sketch)

```js
// 1. Encode description
const encOut = await sessText.run({ input_ids, attention_mask });
const enc_h = encOut.last_hidden_state;

// 2. Build initial codec — [B, 9, 1] all START_ID=1025
const codec0 = new BigInt64Array(B * 9 * 1).fill(1025n);

// 3. Build delay-pattern mask (port of parler_tts.build_delay_pattern_mask)
const { initialIds, patternMask } = buildDelayPatternMask({
  bos: 1025, pad: 1024, maxLen: 256, numCodebooks: 9,
});

// 4. First step
let { logits, ...present } = await sessDecNoPast.run({
  codec_input_ids: initialIds,           // [B, 9, T_init]
  prompt_input_ids,
  prompt_attention_mask,
  encoder_hidden_states: enc_h,
  encoder_attention_mask: attention_mask,
});

// 5. Greedy AR loop — apply delay pattern between steps
for (let step = 1; step < maxSteps; step++) {
  const next = applyDelayPatternMask(argmaxLogits(logits), patternMask, step);
  ({ logits, ...present } = await sessDecWithPast.run({
    codec_input_ids: next,
    attention_mask: buildFullAttnMask(promptLen + step),
    encoder_attention_mask,
    cache_position: BigInt(promptLen + step),
    ...present,                          // re-feed all 96 KV tensors
  }));
}

// 6. Decode codec tokens → audio via DAC
//    use onnx-community/dac_44khz-ONNX decoder
```

The `apply_delay_pattern_mask` and `build_delay_pattern_mask` functions need porting to JS — reference the Python source in [`parler_tts/modeling_parler_tts.py`](https://github.com/huggingface/parler-tts/blob/main/parler_tts/modeling_parler_tts.py).

## Validated parity vs PyTorch

- Text encoder: max abs diff `4.72e-6`
- Decoder step 1 (no-past): max abs diff `1.91e-5`
- Decoder step 2 (with-past): max abs diff `1.68e-4`

All well below the typical fp32-sufficient `1e-3` tolerance.

## Citation

If you use this in research or production, please cite the original AI4Bharat work:

```bibtex
@misc{indic-parler-tts,
  title = {Indic Parler-TTS},
  author = {AI4Bharat},
  year = {2024},
  url = {https://huggingface.co/ai4bharat/indic-parler-tts}
}
```

And the upstream Parler-TTS:

```bibtex
@misc{lacombe-etal-2024-parler-tts,
  author = {Yoach Lacombe and Vaibhav Srivastav and Sanchit Gandhi},
  title = {Parler-TTS},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/parler-tts}}
}
```

## License

Apache 2.0 — same as the upstream `ai4bharat/indic-parler-tts`. See [`NOTICE.md`](NOTICE.md) for attribution.