# Inference Performance

## Test Scripts

```python
from towhee import ops
import time

decode = ops.audio_decode.ffmpeg()
audio = [x[0] for x in decode('path/to/test.wav')]

op = ops.audio_embedding.nnfp()
# op = ops.audio_embedding.nnfp(model_path='path/to/torchscript/model.pt')
# op = ops.audio_embedding.nnfp(model_path='path/to/model.onnx')


start = time.time()
for _ in range(100):
	embs = op(audio)
	assert(embs.shape == (10, 128))
end = time.time()

print((end-start) / 100)
```

## Results

- Device: MacOS, 2.3 GHz Quad-Core Intel Core i7, 8 CPUs
- Input: 10s audio, loop for 100 times

| inference method | mem usage | avg time |
| -- | -- | -- |
| pytorch | 0.3G | 0.451s |
| torchscript | 0.3G | 0.470s |
| onnx | 0.3G | 0.378s |

- Device: MacOS, 2.3 GHz Quad-Core Intel Core i7, 8 CPUs
- Input: 188s audio, loop for 100 times

| inference method | mem usage | avg time |
| -- | -- | -- |
| pytorch | 2.6G | 8.162s |
| torchscript | 2.8G | 7.507s |
| onnx | 1.7G | 6.769s |