Kokoro 82M — ONNX

Kokoro-82M converted to end-to-end ONNX graphs for on-device TTS inference on Android.

Each graph fuses BERT → duration prediction → alignment → prosody → iSTFTNet decoder with fixed-shape I/O. The short-turn graph shares the same external weights as the full graph, so it adds only a small graph file rather than another 155 MB download.

Model

Graph variants

kokoro-e2e-realtime.onnx references the same kokoro-e2e.onnx.data file as the full graph. It avoids decoder work over the unused padded tail on short replies. For text that would exceed its safe output window, split at sentence or word boundaries and synthesize the pieces; speech-core performs that retry automatically.

Files

Tensors

Input

Output (kokoro-e2e.onnx)

For kokoro-e2e-realtime.onnx, the audio output is [1, 1, 72000]; the other input and output tensors are unchanged.

Usage

import numpy as np
import onnxruntime as ort

sess = ort.InferenceSession("kokoro-e2e.onnx")

# Prepare inputs (phoneme IDs from vocab_index.json)
input_ids = np.zeros((1, 128), dtype=np.int64)
input_ids[0, :5] = [0, 60, 46, 79, 0]  # example phonemes
attention_mask = np.zeros((1, 128), dtype=np.int64)
attention_mask[0, :5] = 1

# Load voice embedding (256 floats from .bin file)
voice = np.fromfile("voices/af_heart.bin", dtype=np.float32).reshape(1, 256)

output = sess.run(None, {
    "input_ids": input_ids,
    "attention_mask": attention_mask,
    "ref_s": voice,
    "speed": np.ones(1, dtype=np.float32),
    "random_phases": np.random.rand(1, 9).astype(np.float32),
})

audio = output[0].flatten()[:int(output[1][0])]  # trim to valid length

Precision

Weights are stored as FP16 and cast to FP32 at use, so all computation runs in FP32 and the audio is bit-for-bit equivalent to the FP32 reference — verified by a TTS→ASR round-trip (identical transcriptions across full-length replies) with no length-dependent drift. This halves the download (310 → 155 MB) at no quality cost. Runtime memory is unchanged: ONNX Runtime materializes FP32 weights at session load.

Full-precision weights remain available on the fp32 branch (kokoro-e2e.onnx.data, 310 MB).

Source

Converted from hexgrad/Kokoro-82M using the E2E pipeline with FluidInference-aligned SineGen (segmented cumsum + phase wrapping).

Links

• speech-android — Android SDK
• soniqo.audio — website
• blog — blog

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• Guide: soniqo.audio/guides/kokoro/android
• Docs: soniqo.audio
• GitHub: soniqo/speech-swift