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|
--- |
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dataset_info: |
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features: |
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|
- name: id |
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|
dtype: string |
|
|
- name: category |
|
|
dtype: string |
|
|
- name: text |
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dtype: string |
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splits: |
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- name: all |
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num_bytes: 91813 |
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|
num_examples: 290 |
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|
- name: easy |
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|
num_bytes: 9124 |
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|
num_examples: 50 |
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|
- name: medium |
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|
num_bytes: 20234 |
|
|
num_examples: 50 |
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|
- name: hard |
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|
num_bytes: 27971 |
|
|
num_examples: 50 |
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|
- name: scbx |
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|
num_bytes: 17314 |
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|
num_examples: 50 |
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|
- name: name |
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|
num_bytes: 10118 |
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|
num_examples: 50 |
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|
- name: other |
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|
num_bytes: 7052 |
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num_examples: 40 |
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download_size: 103240 |
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dataset_size: 183626 |
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configs: |
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- config_name: default |
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data_files: |
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- split: all |
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path: data/all-* |
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|
- split: easy |
|
|
path: data/easy-* |
|
|
- split: medium |
|
|
path: data/medium-* |
|
|
- split: hard |
|
|
path: data/hard-* |
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|
- split: scbx |
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|
path: data/scbx-* |
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|
- split: name |
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|
path: data/name-* |
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- split: other |
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path: data/other-* |
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--- |
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# Thai-TTS-Intelligibility-Eval |
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**Thai-TTS-Intelligibility-Eval** is a curated evaluation set for measuring **intelligibility** of Thai Text-to-Speech (TTS) systems. |
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All 290 items are short, challenging phrases that commonly trip up phoneme-to-grapheme converters, prosody models, or pronunciation lexicons. |
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It is **not** intended for training; use it purely for benchmarking and regression tests. |
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## Dataset Summary |
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| Split | #Utterances | Description | |
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|---------|-------------|-------------------------------------------------------------| |
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| `easy` | 50 | Everyday phrases that most TTS systems should read correctly| |
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| `medium`| 50 | More challening than easy | |
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| `hard` | 50 | Hard phrases, e.g., mixed Thai and English and unique names | |
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| `scbx` | 50 | SCBX-specific terminology, products, and names | |
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| `name` | 50 | Synthetic Thai personal names (mixed Thai & foreign roots) | |
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| `other` | 40 | Miscellaneous edge-cases not covered above | |
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| **Total** | **290** | | |
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Each record contains: |
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- **`id`** `string` Unique identifier |
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- **`text`** `string` sentence/phrase |
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- **`category`** `string` One of *easy, medium, hard, scbx, name, other* |
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## Loading With 🤗 `datasets` |
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```python |
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from datasets import load_dataset |
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ds = load_dataset( |
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"scb10x/thai-tts-intelligiblity-eval", |
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) |
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ds_scbx = ds["scbx"] |
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print(ds[0]) |
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# {'id': '53ef39464d9c1e6f', 'text': '...', 'category': 'scbx'} |
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``` |
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## Intended Use |
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1. **Objective evaluation** |
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- *Compute WER/CER* between automatic transcripts of your TTS output and the gold reference text. |
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- Code: https://github.com/scb-10x/thai-tts-eval/tree/main/intelligibility |
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2. **Subjective evaluation** |
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- Conduct human listening tests (MOS, ABX, etc.)—the dataset is small enough for quick rounds. |
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- Future work |
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4. **Regression testing** |
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- Track intelligibility across model versions with a fixed set of hard sentences. |
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- Future work |
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## CER Evaluation Results |
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- CER: lower is better |
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| System | All | Easy | Medium | Hard | SCBX | Name | Other | |
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|-----------------------------------|------|-------|--------|------|------|-------|-------| |
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| Azure Premwadee | 9.39 | 2.87 | 2.92 | 13.80| 10.44| 13.07 | 7.57 | |
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| `facebook-mms-tts-tha` | 28.47| 10.31 | 12.40 | 38.83| 36.04| 26.33 | 30.83 | |
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| `VIZINTZOR-MMS-TTS-THAI-FEMALEV1` | 27.42| 13.30 | 13.13 | 30.92| 34.76| 25.53 | 54.60 | |
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