| --- |
| 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)**. |
|
|
