File size: 3,928 Bytes
4a258e7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | #!/usr/bin/env python3
"""Add the completed ESPnet-format preparation entry to its original data card."""
import argparse
import json
from pathlib import Path
from huggingface_hub import CommitOperationAdd, HfApi, get_token, hf_hub_download
from convert_release import RECIPES
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--dataset", choices=list(RECIPES), required=True)
parser.add_argument(
"--root", type=Path, default=Path("/root/bagpiper-sft-conversion")
)
args = parser.parse_args()
recipe = RECIPES[args.dataset]
api = HfApi(token=get_token())
target_revision = api.repo_info(recipe["target"], repo_type="dataset").sha
status = json.loads(
Path(
hf_hub_download(
recipe["target"],
"STATUS.json",
repo_type="dataset",
revision=target_revision,
)
).read_text()
)
assert (
status["state"] == "complete"
and status.get("validation") == "RELEASE_VALIDATION.json"
)
source_revision = api.repo_info(recipe["source"], repo_type="dataset").sha
card = Path(
hf_hub_download(
recipe["source"], "README.md", repo_type="dataset", revision=source_revision
)
).read_text()
heading = "## Prepare for ESPnet training"
section = f"""{heading}
The complete [ESPnet-format release](https://huggingface.co/datasets/{recipe['target']})
preserves all {status['expected_rows']:,} source rows, ordered text turns, and original
encoded audio bytes. It converts this Parquet schema into three-element dialogue
messages and portable audio files. Source `row_id` becomes the globally unique
ESPnet `example_id`; repeated training examples remain present.
```bash
python -m pip install huggingface_hub zstandard
hf download {recipe['target']} prepare_espnet.py --local-dir tools
python tools/prepare_espnet.py \\
--repo-id {recipe['target']} --data-root /path/to/bagpiper-{args.dataset}-sft
```
The helper verifies archive/audio hashes, resolves local paths, and writes
`prepared/<partition>/{{all,train,valid}}.json` plus `dialogues.jsonl`. Add
`--max-shards 2` for a small trial and repeat `--partition NAME` to select
applications. Audio SHA256 determines the suggested 1% validation split;
`all.json` retains the original training pool. The split is not the paper's
official split. Prepare token-length statistics with the checkpoint-compatible
configuration as shown in the converted release's README.
See its [full release validation](https://huggingface.co/datasets/{recipe['target']}/blob/main/RELEASE_VALIDATION.json)
and [public download/ESPnet reader checks](https://huggingface.co/datasets/{recipe['target']}/blob/main/PUBLIC_DOWNLOAD_VALIDATION.json).
This conversion preserves the original source selection and applicable terms.
"""
if heading in card:
start = card.index(heading)
end = card.find("\n## ", start + len(heading))
card = card[:start] + section + (card[end + 1 :] if end >= 0 else "")
else:
marker = "## Loading the data"
assert marker in card
card = card.replace(marker, section + marker, 1)
output = args.root / "releases" / args.dataset / "SOURCE_README.updated.md"
output.write_text(card)
commit = api.create_commit(
recipe["source"],
repo_type="dataset",
parent_commit=source_revision,
operations=[
CommitOperationAdd(path_in_repo="README.md", path_or_fileobj=output)
],
commit_message="Link completed lossless ESPnet training format and preparation helper",
)
print(
json.dumps(
dict(
source=recipe["source"],
commit=commit.oid,
converted_revision=target_revision,
)
)
)
if __name__ == "__main__":
main()
|