license: cc0-1.0
language:
- en
- hi
- gu
tags:
- thinkspark-v2-350m
- phase1
- mimi
- audio-tokens
- speech
pretty_name: ThinkSpark-v2-350M Phase-1 Free-Audio Corpus
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 | Mimi codebook-0 token id, one per 80ms frame |
energy |
list | log-RMS energy per frame |
f0 |
list | fundamental frequency (Hz), 0 = unvoiced, per frame |
flags |
list | control-flag id per frame (vocab.CONTROL_FLAG_TO_ID) |
agent_state |
list | agent-state id per frame (vocab.AGENT_STATE_TO_ID) |
speaking_mask |
list | 1 = user speaking that frame |
spoken_spans |
list | 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):
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:
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).