Kokoro-82M-ONNX

soniqo/Kokoro-82M-ONNX

Kokoro-82M-ONNX is a text-to-speech model from soniqo. Use it when you need text read aloud. It is set up for onnxruntime. The card lists the license as apache-2.0.

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

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At a glance

Task: Text-to-Speech
Library: onnxruntime
License: apache-2.0
Access: Public
Created: Mar 18, 2026
Updated: Oct 23, 2026
SHA: a49a07fb

Base models

hexgrad/Kokoro-82M

Model card

From the Hugging Face model README

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

Property	Value
Parameters	82M
Architecture	Non-autoregressive, single-pass
Format	ONNX (opset 18)
Weights	155 MB (FP16 storage → FP32 compute, shared external data)
Sample rate	24 kHz
Max phonemes	128
Max audio	5s (120,000 samples)
Voices	50+ (256-dim embeddings)
Languages	8 (en, fr, es, ja, zh, ko, hi, pt)

Graph variants

Graph	Audio tensor	Intended use
`kokoro-e2e.onnx`	`[1, 1, 120000]` (5.0 s)	Full-capacity synthesis
`kokoro-e2e-realtime.onnx`	`[1, 1, 72000]` (3.0 s)	Default short voice-agent replies

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

File	Size	Description
`kokoro-e2e.onnx`	3.5 MB	Full-capacity model graph
`kokoro-e2e-realtime.onnx`	2.9 MB	3.0 s short-turn graph; shares the weights file below
`kokoro-e2e.onnx.data`	155 MB	Model weights (external data, FP16 storage)
`vocab_index.json`	2 KB	IPA phoneme → token ID mapping
`us_gold.json`	1.5 MB	Primary pronunciation dictionary
`us_silver.json`	3.2 MB	Fallback pronunciation dictionary
`voices/*.bin`	1 KB each	Voice embeddings (256 × float32)

Tensors

Input

Name	Shape	Type	Description
`input_ids`	`[1, 128]`	int64	Phoneme token IDs (zero-padded)
`attention_mask`	`[1, 128]`	int64	1 for real tokens, 0 for padding
`ref_s`	`[1, 256]`	float32	Voice style embedding
`speed`	`[1]`	float32	Speed factor (1.0 = normal)
`random_phases`	`[1, 9]`	float32	Initial harmonic phases (uniform [0,1))

Output (`kokoro-e2e.onnx`)

Name	Shape	Type	Description
`audio`	`[1, 1, 120000]`	float32	Raw PCM waveform (24 kHz)
`audio_length_samples`	`[1]`	int64	Valid sample count (trim audio to this)
`pred_dur`	`[1, 128]`	float32	Predicted phoneme durations

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).