diff --git a/examples/export.py b/examples/export.py
new file mode 100644
index 0000000..3e42c00
--- /dev/null
+++ b/examples/export.py
@@ -0,0 +1,148 @@
+import argparse
+import os
+import torch
+import onnx
+import onnxruntime as ort
+import sounddevice as sd
+
+from kokoro import KModel, KPipeline
+from kokoro.model import KModelForONNX
+
+def export_onnx(model, output):
+    onnx_file = output + "/" + "kokoro.onnx"
+
+    input_ids = torch.randint(1, 100, (48,)).numpy()
+    input_ids = torch.LongTensor([[0, *input_ids, 0]])
+    style = torch.randn(1, 256)
+    speed = torch.randint(1, 10, (1,)).int()
+
+    torch.onnx.export(
+        model, 
+        args = (input_ids, style, speed), 
+        f = onnx_file, 
+        export_params = True, 
+        verbose = True, 
+        input_names = [ 'input_ids', 'style', 'speed' ], 
+        output_names = [ 'waveform', 'duration' ],
+        opset_version = 17, 
+        dynamic_axes = {
+            'input_ids': { 1: 'input_ids_len' }, 
+            'waveform': { 0: 'num_samples' }, 
+        }, 
+        do_constant_folding = True, 
+    )
+
+    print('export kokoro.onnx ok!')
+
+    onnx_model = onnx.load(onnx_file)
+    onnx.checker.check_model(onnx_model)
+    print('onnx check ok!')
+
+def load_input_ids(pipeline, text):
+    if pipeline.lang_code in 'ab':
+        _, tokens = pipeline.g2p(text)
+        for gs, ps, tks in pipeline.en_tokenize(tokens):
+            if not ps:
+                continue
+    else:
+        ps, _ = pipeline.g2p(text)
+
+    if len(ps) > 510:
+        ps = ps[:510]
+
+    input_ids = list(filter(lambda i: i is not None, map(lambda p: pipeline.model.vocab.get(p), ps)))
+    print(f"text: {text} -> phonemes: {ps} -> input_ids: {input_ids}")
+    input_ids = torch.LongTensor([[0, *input_ids, 0]]).to(pipeline.model.device)
+    return ps, input_ids
+
+def load_voice(pipeline, voice, phonemes):
+    pack = pipeline.load_voice(voice).to('cpu')
+    return pack[len(phonemes) - 1]
+
+def load_sample(model):
+    pipeline = KPipeline(lang_code='a', model=model.kmodel, device='cpu')
+    text = '''
+    In today's fast-paced tech world, building software applications has never been easier — thanks to AI-powered coding assistants.'
+    '''
+    text = '''
+    The sky above the port was the color of television, tuned to a dead channel.
+    '''
+    voice = 'checkpoints/voices/af_heart.pt'
+
+    pipeline = KPipeline(lang_code='z', model=model.kmodel, device='cpu')
+    text = '''
+    2月15日晚，猫眼专业版数据显示，截至发稿，《哪吒之魔童闹海》（或称《哪吒2》）今日票房已达7.8亿元，累计票房（含预售）超过114亿元。
+    '''
+    voice = 'checkpoints/voices/zf_xiaoxiao.pt'
+
+    phonemes, input_ids = load_input_ids(pipeline, text)
+    style = load_voice(pipeline, voice, phonemes)
+    speed = torch.IntTensor([1])
+
+    return input_ids, style, speed
+
+def inference_onnx(model, output):
+    onnx_file = output + "/" + "kokoro.onnx"
+    session = ort.InferenceSession(onnx_file)
+
+    input_ids, style, speed = load_sample(model)
+
+    outputs = session.run(None, {
+        'input_ids': input_ids.numpy(), 
+        'style': style.numpy(), 
+        'speed': speed.numpy(), 
+    })
+
+    output = torch.from_numpy(outputs[0])
+    print(f'output: {output.shape}')
+    print(output)
+
+    audio = output.numpy()
+    sd.play(audio, 24000)
+    sd.wait()
+
+def check_model(model):
+    input_ids, style, speed = load_sample(model)
+    output, duration = model(input_ids, style, speed)
+
+    print(f'output: {output.shape}')
+    print(f'duration: {duration.shape}')
+    print(output)
+
+    audio = output.numpy()
+    sd.play(audio, 24000)
+    sd.wait()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Export kokoro Model to ONNX", add_help=True)
+    parser.add_argument("--inference", "-t", help="test kokoro.onnx model", action="store_true")
+    parser.add_argument("--check", "-m", help="check kokoro model", action="store_true")
+    parser.add_argument(
+        "--config_file", "-c", type=str, default="checkpoints/config.json", help="path to config file"
+    )
+    parser.add_argument(
+        "--checkpoint_path", "-p", type=str, default="checkpoints/kokoro-v1_0.pth", help="path to checkpoint file"
+    )
+    parser.add_argument(
+        "--output_dir", "-o", type=str, default="onnx", help="output directory"
+    )
+
+    args = parser.parse_args()
+
+    # cfg
+    config_file = args.config_file  # change the path of the model config file
+    checkpoint_path = args.checkpoint_path  # change the path of the model
+    output_dir = args.output_dir
+    
+    # make dir
+    os.makedirs(output_dir, exist_ok=True)
+
+    kmodel = KModel(config=config_file, model=checkpoint_path, disable_complex=True)
+    model = KModelForONNX(kmodel).eval()
+
+    if args.inference:
+        inference_onnx(model, output_dir)
+    elif args.check:
+        check_model(model)
+    else:
+        export_onnx(model, output_dir)
diff --git a/kokoro/model.py b/kokoro/model.py
index 51e835b..a7622ba 100644
--- a/kokoro/model.py
+++ b/kokoro/model.py
@@ -148,4 +148,4 @@ class KModelForONNX(torch.nn.Module):
         speed: float = 1
     ) -> tuple[torch.FloatTensor, torch.LongTensor]:
         waveform, duration = self.kmodel.forward_with_tokens(input_ids, ref_s, speed)
-        return waveform
+        return waveform, duration