README.md

---
license: mit
language:
  - km
tags:
  - text-to-speech
  - tts
  - khmer
---

# SimpleKhmerTTS

Decoder-only transformer TTS, using [WavTokenizer](https://github.com/jishengpeng/WavTokenizer) as the audio codec and [DDD-Cambodia/khmer-speech-dataset](https://huggingface.co/datasets/DDD-Cambodia/khmer-speech-dataset) as dataset.

## Samples

| Text     | Audio                                                                                                                      |
| -------- | -------------------------------------------------------------------------------------------------------------------------- |
| Sample 1 | <audio controls src="https://huggingface.co/AnotherPotatoCoder/Khmer-TTS/resolve/main/sample_audios/sample_1.wav"></audio> |
| Sample 2 | <audio controls src="https://huggingface.co/AnotherPotatoCoder/Khmer-TTS/resolve/main/sample_audios/sample_2.wav"></audio> |
| Sample 3 | <audio controls src="https://huggingface.co/AnotherPotatoCoder/Khmer-TTS/resolve/main/sample_audios/sample_3.wav"></audio> |
| Sample 4 | <audio controls src="https://huggingface.co/AnotherPotatoCoder/Khmer-TTS/resolve/main/sample_audios/sample_4.wav"></audio> |

## Setup

```bash
git clone https://github.com/FirstPotatoCoder/SimpleKhmerTTS.git
cd SimpleKhmerTTS
pip install -q -r requirements.txt
python scripts/download_weights.py
```

## Run

```bash
python examples/run_inference.py " ភាសាខ្មែរមានអក្សរច្រើន ហើយពិបាកសរសេរបន្តិច។ ភាសានេះមានប្រវត្តិយូរអង្វែង និងជាផ្នែកមួយយ៉ាងសំខាន់នៃវប្បធម៌កម្ពុជា។" --speaker_id 0
```

Or from Python:

```python
from tts.inference import TTSPipeline

pipe = TTSPipeline(
    tts_weights="weights/tts.pt",
    wavtokenizer_weights="weights/wavtokenizer.ckpt",
    wavtokenizer_config="configs/wavtokenizer_config.yaml"
)

pipe.generate(" ភាសាខ្មែរមានអក្សរច្រើន ហើយពិបាកសរសេរបន្តិច។", speaker_id=1, out_path="out.wav")
```

## Limitations

- The model sometimes hallucinates, generating babbling or garbled output on rare or unseen words, making it ill-suited for direct deployment — likely due to the limited amount of training data and codec's limited performance on Khmer audio.
- Works best when generating ~3s to 10s of audio, matching the length distribution of its training data.
- No chunking support yet — the current repo only supports clip-by-clip generation, one sample at a time.

## Credits

- Data: [Khmer Speech Dataset](https://huggingface.co/datasets/DDD-Cambodia/khmer-speech-dataset) by DDD-Cambodia

- Compute: [Kaggle](https://www.kaggle.com) for free T4 GPU access

- Audio tokenizer/codec: [WavTokenizer](https://github.com/jishengpeng/WavTokenizer)
  (vendored under `wavtokenizer/`, trimmed to inference-only code).