Khmer TTS — Female (VITS, ONNX)
A single-speaker female Khmer text-to-speech voice. VITS (end-to-end),
grapheme-based (no phonemizer), exported to ONNX so it runs fully offline
on-device — in the browser via onnxruntime-web and on mobile via
onnxruntime-react-native. Built for iAny,
where it reads Khmer news aloud in the Radio feature.
Files
| File | What |
|---|---|
khmer_tts_v3.onnx | The voice to use. Tuned export: length_scale 1.15 (a touch slower/clearer), noise_scale 0.5, noise_scale_w 0.6 (less timing jitter). These are baked into the graph. |
tts_meta.json | Grapheme vocabulary + inference metadata (see below). |
config.json | Coqui-TTS training/model config. |
best_model.pth | PyTorch checkpoint (for re-export / continued training). |
khmer_tts*.onnx (others) | Earlier / alternate-speed exports, kept for comparison. |
tts_meta.json fields:
{
"vocab": ["…"], // grapheme -> id is the index into this array
"add_blank": true, // interleave the blank token between graphemes
"blank": "@", // the blank symbol
"sample_rate": 22050
}
Inference (ONNX Runtime)
The graph takes token ids and returns float PCM:
- Inputs:
x=int64[1, T](grapheme ids),x_lengths=int64[1](= T) - Output:
y=float32waveform, mono,22050 Hz
Tokenize by mapping each character to its index in vocab; if add_blank is
true, start with the blank id and put a blank id between every grapheme.
import json, numpy as np, onnxruntime as ort, soundfile as sf
meta = json.load(open("tts_meta.json"))
id_of = {c: i for i, c in enumerate(meta["vocab"])}
blank = id_of[meta["blank"]]
def to_ids(text):
ids = [id_of[c] for c in text if c in id_of]
if meta["add_blank"]:
out = [blank]
for i in ids: out += [i, blank]
return out
return ids
sess = ort.InferenceSession("khmer_tts_v3.onnx")
ids = np.array([to_ids("សួស្ដី ពិភពលោក")], dtype=np.int64)
xlen = np.array([ids.shape[1]], dtype=np.int64)
y = sess.run(None, {"x": ids, "x_lengths": xlen})[0]
sf.write("out.wav", np.asarray(y).squeeze(), meta["sample_rate"])
The same tokenize → run → play flow is used in the browser (Web Audio) and in React Native (expo-av); see the iAny source for reference implementations.
Training
VITS trained from scratch on a single female Khmer speaker (~727 h DDD-Cambodia read-speech corpus), then continued training for more steps/data (the "v2" line). The v3 ONNX is the same model with tuned inference constants.
License & attribution
CC-BY-SA-4.0. This voice is trained on the DDD-Cambodia speech corpus, which is licensed CC-BY-SA-4.0. ShareAlike carries over, so the voice — and any derivative of it — must:
- credit the DDD-Cambodia corpus, and
- be shared under CC-BY-SA-4.0 as well.
Model architecture: VITS (Kim et al., 2021), trained with the Coqui TTS toolkit.