---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: en_text
    dtype: string
  - name: tr_text
    dtype: string
  - name: hi_text
    dtype: string
  - name: source
    dtype: string
  splits:
  - name: train
    num_bytes: 23340593
    num_examples: 65662
  download_size: 10073692
  dataset_size: 23340593
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
language:
- tr
- hi
- en
license: cc-by-sa-4.0
task_categories:
- translation
tags:
- parallel-text
- turkish
- hindi
- flores
- opus-100
pretty_name: TR↔HI Parallel Text
size_categories:
- 10K<n<100K
---

# TR↔HI Parallel Text

**65,662** aligned text triples — English pivot plus Turkish and Hindi
(`en_text` / `tr_text` / `hi_text`), each tagged with its `source`.

This is the **text layer** the speech corpora were synthesised from: these
sentences were sent to TTS to produce
[`tr-hi-parallel-speech-v2`](https://huggingface.co/datasets/tiny-aya-translate/tr-hi-parallel-speech-v2),
which was then Mimi-encoded into
[`tr-hi-mimi-encoded`](https://huggingface.co/datasets/tiny-aya-translate/tr-hi-mimi-encoded).

Text-only, ~10 MB, no audio. Sources include FLORES, OPUS-100, and
machine-translated conversational data — check `source` per row, since the
licence terms differ by origin (FLORES is share-alike).

## Where this sits

The v0.3 speech-to-speech pipeline, end to end:

```
tr-hi-parallel-text          text triples (en pivot -> tr / hi)
        |  TTS
tr-hi-parallel-speech-v2     synthetic speech + QC signals
        |  Mimi encode
tr-hi-mimi-encoded           8-codebook tokens + word alignments
        |  Stage-2 training
tr-hi-s2st-v0.3              the released model
```

| | |
|---|---|
| **Model** | [`tr-hi-s2st-v0.3`](https://huggingface.co/tiny-aya-translate/tr-hi-s2st-v0.3) |
| **Text** | [`tr-hi-parallel-text`](https://huggingface.co/datasets/tiny-aya-translate/tr-hi-parallel-text) |
| **Speech** | [`tr-hi-parallel-speech-v2`](https://huggingface.co/datasets/tiny-aya-translate/tr-hi-parallel-speech-v2) · [`-v3`](https://huggingface.co/datasets/tiny-aya-translate/tr-hi-parallel-speech-v3) |
| **Encoded** | [`tr-hi-mimi-encoded`](https://huggingface.co/datasets/tiny-aya-translate/tr-hi-mimi-encoded) |
| **Eval sets** | [`fleurs-tr-hi-mimi-encoded`](https://huggingface.co/datasets/tiny-aya-translate/fleurs-tr-hi-mimi-encoded) · [`lahaja-eval`](https://huggingface.co/datasets/tiny-aya-translate/lahaja-eval) · [`cv-tr-eval`](https://huggingface.co/datasets/tiny-aya-translate/cv-tr-eval) |

## Code

| repo | what it does |
|---|---|
| [`model`](https://github.com/tiny-aya-simultaneous-translation/model) | Stage-2 training, evaluation harness and TPU launch tooling |

## Project

**TinyAya Stage 2** — Turkish⇄Hindi speech-to-speech translation with a text
inner-monologue: a LoRA-adapted Cohere2 backbone driving a **frozen** Moshi depth
decoder over Mimi codes.

The v0.3 run covered **76,250 steps / 2.07 epochs** on a Cloud TPU v6e-16
(best val composite **2.8199** @ step 76,000). Read honestly: the text
inner-monologue **learns to translate** (free-run chrF++ ~25.7 / 25.1), while
**intelligible audio synthesis remains the frontier** (ASR-chrF++ 3.7 / 9.6
against a 92.1 / 86.6 ground-truth-audio ceiling) — bounded by the frozen depth
decoder, not by translation understanding.

- **Results:** [v0.3 evaluation report](https://github.com/tiny-aya-simultaneous-translation/model/blob/main/docs/v0.3-eval-report.md)
- **Training run:** [W&B `xzcb60bl`](https://wandb.ai/cataluna84/tinyaya-stage2-tpu/runs/xzcb60bl) · [emergence report](https://wandb.ai/cataluna84/tinyaya-stage2-tpu/reports/TinyAya-v0.3-Emergence-and-Data-Efficiency--VmlldzoxNzU1OTU1NQ==)
- **Blog:** [Adapting Moshi for Low-Resource Speech Translation](https://labscommunity.cohere.com/blog/2026/adapting-moshi-low-resource-speech-translation/)

Compute for the v0.3 run was provided by **Google's TPU Research Cloud (TRC)**.