kokoro-thai-q8-onnx

Kokoro-Thai INT8 (Refined Q8 ONNX)

This repository provides an optimized, high-fidelity INT8 quantized ONNX checkpoint for the Thai Kokoro TTS model (FastThaiG2P lineage).

Optimized and packaged for Text To Speech on Steam.

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Key Highlights

• 33% Smaller Disk Footprint: Reduced from 310.5 MB (FP32) down to 208.4 MB (INT8).
• 22.5% Lower RAM Usage: Saves 105.3 MB of system RAM during load and reduces peak working-set memory by 118.1 MB.
• 100% Timing Parity (0.0 ms Duration Drift): Evaluated across 534 rigorous test cases spanning all 44 Thai consonants, digits (0–9, ๐–๙), minimal tone pairs, decimals, currency formats, multi-lingual English loanwords, and massive 475-token paragraphs. The duration predictor matched FP32 timing with exact 0-frame drift (0.0 ms) across all 534 cases.
• Tonal Pitch Integrity: Mean fundamental frequency ($F_0$) Pearson correlation $r = 0.9907$ with pitch RMSE $< 4.2\text{ Hz}$ (well below human ear pitch discrimination thresholds of $3\text{--}5\text{ Hz}$).
• Whisper ASR Verified: 100% transcription parity with baseline FP32 when verified on local Whisper large-v3-turbo.
• Native CPU Acceleration: Optimized for x86_64 AVX2 / VNNI execution on the standard ONNX Runtime CPU Execution Provider.

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Quantization Architecture & Policy

Naive static quantization of Kokoro typically ruins duration modeling and introduces vocoder distortion. This model follows the upstream Kokoro quantization policy:

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Benchmark Profile (ONNX Runtime CPU - 4 Threads)

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File Manifest & Integrity

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Usage Example (Python ONNX Runtime)

import onnxruntime as ort
import numpy as np
import soundfile as sf

# Load session on CPU with 4 threads
opts = ort.SessionOptions()
opts.intra_op_num_threads = 4
session = ort.InferenceSession("kokoro_thai_q8.onnx", opts, providers=["CPUExecutionProvider"])

# Load voice style tensor (shape: [1, 256])
style = np.load("thai_som.npy")[0].astype(np.float32)

# Token IDs from FastThaiG2P frontend with BOS (0) and EOS (0)
# Example token sequence for 'ใช่ครับ'
input_ids = np.array([[0, 61, 65, 140, 172, 55, 43, 62, 173, 50, 43, 173, 0]], dtype=np.int64)

# Run inference
outputs = session.run(None, {
    "input_ids": input_ids,
    "ref_s": style,
    "speed": np.array([1.0], dtype=np.float32)
})

audio = outputs[0].squeeze()
sf.write("output.wav", audio, 24000)

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Provenance & Attribution

• Application: Text To Speech on Steam
• Base Acoustic Model: Derived from FastThaiG2P v0.3.0 (Apache-2.0, AWS Labs).
• Voice Pack (thai_som.npy): Derived from Som TTS (CC BY).
• Pronunciation Lexicon & IPA Data: Merged from Kaikki.org / Wiktionary (CC BY-SA 3.0 / GFDL) and validated phonetic dictionaries.
• License: Apache-2.0.