> ## Documentation Index
> Fetch the complete documentation index at: https://docs.edgeimpulse.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Label audio data using your existing model
> Use an existing Edge Impulse model to run inference on raw audio data and automatically upload the labeled results back to your project.
This example comes from the [Edge Impulse Linux Inferencing Python SDK](https://github.com/edgeimpulse/linux-sdk-python/) that has been slightly modify to upload the raw data back to Edge Impulse based on the inference results.
To run the example:
1. Clone this repository:
```
git clone git@github.com:edgeimpulse/example-active-learning-linux-python-sdk.git
```
2. Install the dependencies:
```
pip3 install -r requirements.txt
```
3. Grab your the API key of the project you want to upload the inferred results raw data:
4. Past the new key in the `EI_API_KEY` variable in the `audio-classify-export.py` file. Alternatively, load it from your environment variable:
```
export EI_API_KEY='ei_xxxx'
```
5. Download your modelfile.eim:
```
edge-impulse-linux-runner --download modelfile.eim
```
6. Run the script:
```
python3 audio-classify-export.py modelfile.eim yes,no 0.6 0.8
```
Here are the arguments you can set:
* `modelfile.eim`, path the model.eim
* `yes,no`, labels to upload, separated by comas, no space
* `0.6`, low confidence threshold
* `0.8`, high confidence threshold
* ``
In a keyword spotting model, it can give the following results:
```
python3 audio-classify-export.py modelfile.eim yes,no 0.6 0.8
['modelfile.eim', 'yes,no', '0.6', '0.8']
{'model_parameters': {'axis_count': 1, 'frequency': 16000, 'has_anomaly': 0, 'image_channel_count': 0, 'image_input_frames': 0, 'image_input_height': 0, 'image_input_width': 0, 'input_features_count': 16000, 'interval_ms': 0.0625, 'label_count': 4, 'labels': ['no', 'noise', 'unknown', 'yes'], 'model_type': 'classification', 'sensor': 1, 'slice_size': 4000, 'use_continuous_mode': True}, 'project': {'deploy_version': 29, 'id': 10487, 'name': 'Keywords Detection', 'owner': 'Demo Team'}}
Loaded runner for "Demo Team / Keywords Detection"
0 --> MacBook Pro Microphone
2 --> Microsoft Teams Audio
3 --> Descript Loopback Recorder
4 --> ZoomAudioDevice
Type the id of the audio device you want to use:
0
selected Audio device: 0
Result (0 ms.) no: 0.18 noise: 0.16 unknown: 0.20 yes: 0.46
Result (0 ms.) no: 0.13 noise: 0.58 unknown: 0.22 yes: 0.07
Result (0 ms.) no: 0.00 noise: 0.89 unknown: 0.10 yes: 0.01
Result (0 ms.) no: 0.00 noise: 0.01 unknown: 0.04 yes: 0.95
Result (0 ms.) no: 0.00 noise: 0.82 unknown: 0.10 yes: 0.07
Result (0 ms.) no: 0.02 noise: 0.77 unknown: 0.13 yes: 0.08
Result (0 ms.) no: 0.01 noise: 0.14 unknown: 0.26 yes: 0.59
Result (0 ms.) no: 0.07 noise: 0.76 unknown: 0.15 yes: 0.01
Result (0 ms.) no: 0.04 noise: 0.24 unknown: 0.11 yes: 0.61 Uploading sample to Edge Impulse...
Successfully uploaded audio to Edge Impulse.
Result (0 ms.) no: 0.02 noise: 0.93 unknown: 0.04 yes: 0.00
Result (0 ms.) no: 0.01 noise: 0.67 unknown: 0.32 yes: 0.01
Result (0 ms.) no: 0.02 noise: 0.18 unknown: 0.23 yes: 0.57
Result (0 ms.) no: 0.07 noise: 0.70 unknown: 0.22 yes: 0.01
Result (0 ms.) no: 0.03 noise: 0.83 unknown: 0.12 yes: 0.02
Result (0 ms.) no: 0.24 noise: 0.44 unknown: 0.21 yes: 0.11
Result (0 ms.) no: 0.23 noise: 0.25 unknown: 0.42 yes: 0.10
Result (0 ms.) no: 0.04 noise: 0.76 unknown: 0.18 yes: 0.02
Result (0 ms.) no: 0.16 noise: 0.67 unknown: 0.12 yes: 0.05
Result (0 ms.) no: 0.12 noise: 0.81 unknown: 0.06 yes: 0.01
Result (0 ms.) no: 0.54 noise: 0.24 unknown: 0.12 yes: 0.10
Result (0 ms.) no: 0.01 noise: 0.91 unknown: 0.05 yes: 0.03
Result (0 ms.) no: 0.65 Uploading sample to Edge Impulse...
Successfully uploaded audio to Edge Impulse.
noise: 0.08 unknown: 0.17 yes: 0.10
Result (0 ms.) no: 0.00 noise: 0.96 unknown: 0.03 yes: 0.00
Result (0 ms.) no: 0.04 noise: 0.80 unknown: 0.13 yes: 0.03
Result (0 ms.) no: 0.03 noise: 0.27 unknown: 0.16 yes: 0.54
Result (0 ms.) no: 0.05 noise: 0.66 unknown: 0.15 yes: 0.14
Result (0 ms.) no: 0.08 noise: 0.74 unknown: 0.14 yes: 0.04
Result (0 ms.) no: 0.01 noise: 0.87 unknown: 0.11 yes: 0.02
Result (0 ms.) no: 0.01 noise: 0.87 unknown: 0.06 yes: 0.06
...
```
