voidful/tw-speech

TW Speech

Taiwan speech annotations generated with OpenMOSS-Team/MOSS-Audio-8B-Thinking in BF16 without quantization, using voidful/tw-ice-tts-moss-annotation.

Access requires a request and manual approval by the repository owner. The remote release is complete only when publication.json reports remote_upload_complete: true; files may be arriving before that marker exists. Source-specific rights and allowed-use metadata are preserved in canonical_record_json. Some upstream rows carry license_pending_review / private_training_pending_rights_review. This repository does not grant a new open license for the source recordings.

Configuration	Split	Audio windows
tw_tts	train	14,049,932
tw_tts	validation	14,165
earica_speech	train	1,009,291

All 13,687 pinned source files are covered. Every provided audio item, channel and complete nonoverlapping 20-second window is retained, including short final windows. Official source splits are unchanged. The 25 original Earica audio=[] rows have no audio to annotate and are preserved separately in source-issues/earica_speech/train.jsonl.

Parquet shards embed the exact original-rate PCM16 WAV bytes. Their SHA256 is stored in audio_sha256. audio can be loaded directly with Hugging Face Datasets; no external audio paths are required. labels contains the eleven structured annotation fields. canonical_record_json preserves the complete canonical record, source metadata, raw response and model provenance; its audio path is a historical local locator. Use the embedded audio column.

from datasets import load_dataset, Audio
ds = load_dataset("voidful/tw-speech", "tw_tts", split="train", streaming=True, token=True)
ds = ds.cast_column("audio", Audio(decode=False))
example = next(iter(ds))
print(example["labels"])

Annotations are model-generated and have not been manually reviewed. An unavailable transcript remains null with its reason in the canonical record. Original text is preserved separately and was not used as an ASR prompt. Official train/validation source groups can overlap; annotation completion does not certify a leakage-free training split. See provenance.json. manifest.json lists every shard, its row count and exact file checksum.