---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: utterance
    dtype: string
  - name: utterance_latin
    dtype: string
  - name: style_metadata
    dtype: string
  - name: speaker
    dtype: string
  - name: duration
    dtype: float32
  - name: audio
    dtype: audio
  - name: word_count
    dtype: int32
  splits:
  - name: train
    num_bytes: 31017252549
    num_examples: 33275
  download_size: 29048443091
  dataset_size: 31017252549
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
language:
- hi
- en
license: other
license_name: see-upstream
task_categories:
- automatic-speech-recognition
tags:
- hinglish
- code-switching
- casual-speech
pretty_name: Hinglish Casual Speech
---

# Hinglish Casual Speech

**33,275** casual Hindi-English code-switched utterances (~31 GB) with audio,
transcripts in both Devanagari and Latin script (`utterance` /
`utterance_latin`), speaker ids, style metadata and durations. Full schema is in
the YAML header above.

Collected during the TinyAya programme to probe **code-switched** speech, which
neither the FLORES-derived text nor the TTS corpora cover. It is **not** part of
the v0.3 Stage-2 training set — that is
[`tr-hi-mimi-encoded`](https://huggingface.co/datasets/tiny-aya-translate/tr-hi-mimi-encoded).

```python
from datasets import load_dataset
ds = load_dataset("tiny-aya-translate/hinglish-casual", split="train", streaming=True)
```

Streaming is recommended at this size. **Licence:** upstream terms apply and are
not restated here — verify before redistribution.

## Code

| repo | what it does |
|---|---|
| [`sound-quality-check`](https://github.com/tiny-aya-simultaneous-translation/sound-quality-check) | 4-stage speech-dataset quality control |

## 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)**.