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anuj-inavlabs/kupe-thinkspark-270m-phase1-data

ThinkSpark-v2-350M — Phase-1 free-audio training data Pre-encoded Mimi cb0 (12.5Hz semantic) tokens + per-frame energy/f0 prosody, packaged for Phase 1 of ThinkSpark-v2-350M — teaching a 270M-parameter Gemma-3-based full-duplex floor-controller that a stream of Mimi audio tokens carries language + prosody, before Phase 2 teaches it to referee turn-taking. Sourced from free/open corpora (LibriSpeech, AI4Bharat Kathbath/Shrutilipi, IndicTTS, Google FLEURS) via… See the full description on the dataset page: https://huggingface.co/datasets/anuj-inavlabs/kupe-thinkspark-270m-phase1-data.

sourceHugging Facecc0-1.0updated 26d agoView on Hugging Face

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ThinkSpark-v2-350M — Phase-1 free-audio training data

Pre-encoded Mimi cb0 (12.5Hz semantic) tokens + per-frame energy/f0 prosody, packaged for Phase 1 of ThinkSpark-v2-350M — teaching a 270M-parameter Gemma-3-based full-duplex floor-controller that a stream of Mimi audio tokens carries language + prosody, before Phase 2 teaches it to referee turn-taking.

Sourced from free/open corpora (LibriSpeech, AI4Bharat Kathbath/Shrutilipi, IndicTTS, Google FLEURS) via scripts/P1_00_sequential.py / scripts/P1_00_pipeline.py in the kupe-thinkspark-v2-270m repo — see that repo's configs/phase1_corpus.yaml for the exact per-language source mix, weights, and citations.

Layout

Each language has its own handful of self-contained Parquet shards:

data/en/en-shard-00000.parquet
data/en/en-shard-00001.parquet
...
data/hi/hi-shard-00000.parquet
...
data/gu/gu-shard-00000.parquet
...

Every row is one clip's full frame record — audio tokens included directly as columns, no separate file to join against:

column	type	meaning
`scenario_id`	string	stable clip id
`behaviour`	string	always `"phase1_free_audio"` here
`language`	string	`en` / `hi` / `gu`
`domain`	string	source dataset id (e.g. `librispeech`, `kathbath`)
`agent_text`	string	always empty for Phase-1 (no agent turn)
`user_text`	string	the clip's transcript
`num_frames` / `audio_frames`	int	frame count (also `len(cb0)`)
`cb0`	list<int64>	Mimi codebook-0 token id, one per 80ms frame
`energy`	list<float32>	log-RMS energy per frame
`f0`	list<float32>	fundamental frequency (Hz), 0 = unvoiced, per frame
`flags`	list<int32>	control-flag id per frame (vocab.CONTROLFLAGTO_ID)
`agent_state`	list<int32>	agent-state id per frame (vocab.AGENTSTATETO_ID)
`speaking_mask`	list<int32>	1 = user speaking that frame
`spoken_spans`	list<string>	JSON-encoded spans (empty for Phase-1)

Loading

Straight into a training machine's local layout (recommended — no extra code needed, matches what scripts/06_train_phase1.py in the source repo reads directly):

bash

python scripts/19_fetch_training_data.py --phase1-repo anuj-inavlabs/kupe-thinkspark-270m-phase1-data

Or directly via datasets/pandas/pyarrow if you just want to inspect it:

python

from datasets import load_dataset
ds = load_dataset("anuj-inavlabs/kupe-thinkspark-270m-phase1-data", data_files="data/en/*.parquet", split="train")

License

CC0 — free/open source audio only (see the source repo's configs/phase1_corpus.yaml for each source's own license/citation).