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tiny-aya-translate/hinglish-casual

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. from… See the full description on the dataset page: https://huggingface.co/datasets/tiny-aya-translate/hinglish-casual.

sourceHugging Faceotherupdated 2mo agoView on Hugging Face

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Dataset Card

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`.

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`	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
—Training run: W&B `xzcb60bl` · emergence report
—Blog: Adapting Moshi for Low-Resource Speech Translation

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