# 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)
```

## Performance (Default model)

- Device: MacOS, 2.3 GHz Quad-Core Intel Core i7, 8 CPUs
- Input: 10s stereo audio, shape (2, 441344), sr 44100, loop for 100 times

| inference method | mem usage | avg time |
| -- | -- | -- |
| pytorch | 0.3G | 0.160s |
| torchscript | 0.3G | 0.167s |
| onnx | 0.4G | 0.093s |

- Device: MacOS, 2.3 GHz Quad-Core Intel Core i7, 8 CPUs
- Input: 188s stereo audio, shape (2, 8328408), sr 44100, loop for 100 times

| inference method | mem usage | avg time |
| -- | -- | -- |
| pytorch | 2.7G | 2.29s |
| torchscript | 2.5G | 2.30s |
| onnx | 1.3G | 1.77s |

- Device: MacOS, 2.3 GHz Quad-Core Intel Core i7, 8 CPUs
- Input: 600s mono audio, shape (1, 9600000), sr 16000, loop for 20 times

| inference method | mem usage | avg time |
| -- | -- | -- |
| pytorch | 4.9G | 9.04s |
| torchscript | 4.9G | 9.302s |
| onnx | 2.7G | 4.586s |