espnet/Bagpiper_SFT_Data_ESPnet

Bagpiper SFT Data in ESPnet format

This release converts espnet/Bagpiper_SFT_Data at revision 7dec4d68dc30e442368914619668fcd695a897f7 into portable, lossless ESPnet dialogue bundles. Check STATUS.json: the release is usable in full only when state is complete. An explicit prefix trial is possible while conversion is in progress.

Each .tar.zst contains original encoded audio, three-element dialogue messages, and source provenance. Audio is deduplicated by SHA-256 within each ordered conversion stream; identical files across streams are verified and merged during extraction. Repeated training examples remain present. All text, message roles, and order are preserved. The original globally unique row_id becomes ESPnet example_id. No audio resampling or lossy transcoding is performed.

Prepare local training inputs

python -m pip install huggingface_hub zstandard
hf download espnet/Bagpiper_SFT_Data_ESPnet prepare_espnet.py --local-dir tools
python tools/prepare_espnet.py --repo-id espnet/Bagpiper_SFT_Data_ESPnet --data-root /path/to/bagpiper-sft

For a small prefix trial, add --max-shards 2 --allow-incomplete. Repeat --partition NAME to select application partitions. The helper verifies archive SHA-256, extracts safely, resolves audio paths, checks duplicate IDs and linkage, and writes prepared/NAME/{all,train,valid}.json plus dialogues.jsonl.

The default validation fraction is 1%, chosen deterministically from the audio SHA-256. The same audio always has the same split, including across source shards and partitions. Use all.json to retain the original complete training pool. This suggested split is not the paper's official split. --valid-fraction 0 assigns every row to train.

From the ESPnet repository root, collect exact token lengths with the checkpoint-compatible training config, then pass the generated manifests to the recipe:

python -m espnet2.speechlm.bin.prepare_length_stats \
  --train-config /path/to/checkpoint-compatible.yaml \
  --train-unregistered-specifier 'dialogue:NAME_train:/path/to/prepared/NAME/train.json' \
  --valid-unregistered-specifier 'dialogue:NAME_valid:/path/to/prepared/NAME/valid.json' \
  --output-dir /path/to/stats

Use distinct dataset names for each partition and split. Do not select arbitrary archive subsets: later bundles can reference audio deduplicated into earlier bundles. The helper selects consecutive prefixes and handles this dependency.

Provenance and terms

SOURCE_MANIFEST.json pins every source shard and checksum. STATUS.json records conversion/upload checksums and counts. provenance/ retains original row IDs, source subsets/revisions, audio hashes and source annotations. CONVERSION.md and the executable converter document the release process.

This is a format conversion of the linked source dataset and does not grant new rights to its audio or annotations. Source terms and the source dataset's disclosed limitations apply. The general Bagpiper source release is the recipe-v1 pool; conversion does not make it the paper-v2 selected pool.

ESPnet recipes: Bagpiper · Bagpiper-TTS.