Label audio data using your existing models

This example comes from the Edge Impulse Linux Inferencing Python SDK 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 [email protected]:edgeimpulse/example-active-learning-linux-python-sdk.git

1. Install the dependencies:

pip3 install -r requirements.txt

1. Grab your the API key of the project you want to upload the inferred results raw data:

EI API KEY

1. 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'

1. Download your modelfile.eim:

edge-impulse-linux-runner --download modelfile.eim

1. 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

• <audio_device_ID, optional>

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
...

results