A wrapper for Audeering's wav2vector-based dimensional speech emotion recognition (arousal, dominance, and valence).
input: any audio file readable by torchaudio at any sample rate (will be resampled to 16000 Hz on the fly)
output: score of valence, arousal, and dominance in a range [0, 1]
I recommend using a virtual environment to run this script. You can use either venv or conda. I prefer
to use (Mini) conda now over venv. Here is the example.
conda create -n w2v2-vad python=3.8
conda activate w2v2-vad
pip3 install -r requirements.txt
python3 predict_vad_w2v2.py input.wav
Positional: input file at any sample rate
Optional:
-s split, `chunks` or `full`, default is full.
-d duration, duration in seconds (if the split is chunks, must be specified)
bagus@L140MU:w2v2-vad$ python3 predict_vad_w2v2.py bagus-test_16000.wav
Arousal, dominance, and valence #0: [[0.32293236 0.41639617 0.5942142 ]]
bagus@L140MU:w2v2-vad$ python3 predict_vad_w2v2.py bagus-test_16000.wav -s chunks -d 2
Arousal, dominance, and valence #0: [[0.3404813 0.42247295 0.35256445]]
Arousal, dominance, and valence #1: [[0.22009875 0.322832 0.51018834]]
Arousal, dominance, and valence #2: [[0.3478799 0.4332775 0.45645887]]
Arousal, dominance, and valence #3: [[0.29967275 0.4038131 0.4949872 ]]
Arousal, dominance, and valence #4: [[0.24804251 0.33543587 0.50990975]]
Arousal, dominance, and valence #5: [[0.38564402 0.43214017 0.37035757]]
https://github.com/audeering/w2v2-how-to
All credit goes to Audeering.