| --- |
| configs: |
| - config_name: th |
| data_files: |
| - split: train |
| path: corpus/th.train.clean.jsonl |
| - config_name: audio-aadonis |
| data_files: |
| - split: train |
| path: audio/aadonis/*/part-*.parquet |
| - config_name: audio-common_voice16 |
| data_files: |
| - split: train |
| path: audio/common_voice16/*/part-*.parquet |
| - config_name: audio-fleurs |
| data_files: |
| - split: train |
| path: audio/fleurs/*/part-*.parquet |
| - config_name: audio-gigaspeech2 |
| data_files: |
| - split: train |
| path: audio/gigaspeech2/*/part-*.parquet |
| - config_name: audio-worldspeech |
| data_files: |
| - split: train |
| path: audio/worldspeech/*/part-*.parquet |
| --- |
| |
| # th high-quality speech corpus |
|
|
| This dataset contains the complete cleaned Thai corpus and its source-partitioned audio. |
|
|
| All audio is FLAC PCM16. Source sample rate and channel count are preserved. Every packaged item passed a source-to-FLAC bit-identical PCM round trip and a separate full-release decode/hash validation. |
|
|
| Corpus rows use portable locators of the form `audio/<subset>/<group>/part-N.parquet#row=N`. |
|
|
| ## Download only selected subsets |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| repo_id="ACCOUNT/th", |
| repo_type="dataset", |
| allow_patterns=[ |
| "corpus/*", |
| "audio/aadonis/*/part-*.parquet", |
| ], |
| ) |
| ``` |
|
|
| Or load an individual configuration: |
|
|
| ```python |
| from datasets import load_dataset |
| corpus = load_dataset("ACCOUNT/th", "th") |
| audio = load_dataset("ACCOUNT/th", "audio-aadonis") |
| ``` |
|
|
| ## Release totals |
|
|
| - Unique audio: 11,762,414 |
| - Audio hours: 13,430.474 |
| - Parquet size: 0.914450 TB (0.831687 TiB) |
|
|
| See `release.json` for exact per-subset counts, hours, bytes, and corpus checksums. |
|
|
