silvermango9927/synthetic-asr-zh

Synthetic ASR data — zh

Generated by Valsea-ASR/synthetic-data-pipeline. Audio is synthetic (TTS), targeted as training data for downstream ASR finetuning.

Total audio: 61.2 hr across short (5s) and long (30s) length buckets, each in clean and augmented variants.

Loading

from datasets import load_dataset

ds = load_dataset("<org>/synthetic-asr-zh", "short_clean")
print(ds["train"][0]["audio"])  # {"array": np.ndarray, "sampling_rate": 16000, "path": "..."}
print(ds["train"][0]["text"])

Available configs:

• short_clean
• short_augmented
• long_clean
• long_augmented

Splits

Bucket	Aug state	Train	Val	Audio (sec)
short	clean	2841	159	12594.6
short	augmented	2843	157	12594.6
long	clean	6171	329	180587.9
long	augmented	482	18	14586.1

Schema

Each row in manifest.jsonl / val.jsonl:

{"audio": "audio/<filename>.wav",
 "audio_filepath": "audio/<filename>.wav",
 "text": "...",
 "duration": 4.35,
 "language": "zh",
 "source": "synthetic",
 "voice_id": "...",
 "augmentation": null | "<transform>"}

Audio is 16 kHz mono WAV. audio is auto-cast to Audio(sampling_rate=16000) by HF; audio_filepath is the same path as a bare string for direct NeMo training-manifest use. Val split is a deterministic ~5% hash-based hold-out.