IndexError in music-arousal-valence predict.py

[shaolo1]

After scanning my collection I have found a number of tracks that get an IndexError processing the results.

I added a few prints that I thought might be helpful

code

        embeddings = self.embeddings[embedding_type](waveform)
        print(f'{embeddings=}')

        classifier_name = f"{dataset}-{embedding_type}"
        results = self.classifiers[classifier_name](embeddings)
        print(f'1 {results=}')
        results = np.mean(results.squeeze(), axis=0)
        print(f'2 {results=}')

        # Manual normalization (1, 9) -> (-1, 1)
        results = (results - 5) / 4
        print(f'3 {results=}')

        valence = results[0]
        arousal = results[1]

results

embeddings=array([[-9.79215622e-01, -8.83984268e-01, -1.24399066e-01,
        -1.76216686e+00,  2.23284006e+00, -2.35090113e+00,
        -1.51178837e-01, -7.01047301e-01, -1.43495429e+00,
        -2.37940550e-02, -1.29274404e+00, -1.91006780e+00,
         1.25425029e+00, -8.58444750e-01,  7.34418631e-01,
         1.33782005e+00,  2.59214449e+00,  7.50851035e-01,
        -1.70553470e+00,  2.75724173e-01,  7.39336312e-01,
        -1.39294171e+00,  1.54597783e+00,  4.18036556e+00,
        -6.21451616e-01,  4.44779444e+00,  1.75153589e+00,
        -1.52528954e+00,  2.38282728e+00,  7.09380984e-01,
         2.29190707e-01,  1.83067608e+00, -4.18588161e-01,
         2.74203825e+00,  1.44375741e+00,  4.74760234e-01,
         1.68121433e+00,  1.47874069e+00, -1.43617809e-01,
         1.61118841e+00, -4.04867458e+00, -1.74851203e+00,
        -7.01254368e-01,  8.15650105e-01, -3.72627974e-01,
        -3.70025277e-01, -1.27522266e+00, -3.15851212e-01,
         3.06479597e+00,  2.72586560e+00, -1.56302571e+00,
         4.67827857e-01, -1.73909569e+00,  1.84981191e+00,
        -4.66434956e-02, -2.83670783e-01,  2.95574927e+00,
        -1.03319979e+00, -6.25443459e-03,  1.73981488e+00,
         1.23516762e+00, -1.13622689e+00, -1.46244502e+00,
         2.78344154e-01, -1.81369394e-01, -2.91738105e+00,
        -9.67344344e-01, -3.99259138e+00, -2.59917498e+00,
         9.12960827e-01,  3.73457408e+00,  1.10781193e+00,
         2.50818825e+00,  9.99556780e-02,  8.17857623e-01,
         2.07035232e+00,  8.15089345e-01,  4.53723907e-01,
         2.33492875e+00,  1.09855568e+00,  7.04147816e-01,
         7.99394250e-01,  1.43385303e+00,  2.09725070e+00,
        -2.20253325e+00, -2.23348546e+00,  2.42416933e-01,
         8.75000894e-01,  7.81318426e-01, -2.33874941e+00,
        -1.38941491e+00,  2.43245935e+00,  5.70881069e-01,
        -1.18041801e+00, -2.64751124e+00,  1.97756720e+00,
         3.16218066e+00, -1.19582140e+00, -2.14283633e+00,
        -2.38000154e-01, -2.88674641e+00, -3.46466994e+00,
         8.75189066e-01,  1.96835589e+00, -3.10026336e+00,
         8.83012056e-01, -6.35378361e-01,  9.42563534e-01,
         2.52133560e+00,  9.96280730e-01, -9.30942893e-02,
         9.47340965e-01,  3.68399858e-01,  2.75828791e+00,
        -3.09385014e+00, -2.02378774e+00,  1.70357120e+00,
        -2.36064720e+00,  1.22432148e+00,  2.61093092e+00,
        -4.17023540e-01,  4.29861546e-02,  1.84735775e+00,
        -2.60978580e+00,  1.00775516e+00,  2.10353518e+00,
        -2.91596794e+00, -1.38390124e-01,  1.19646978e+00,
        -3.24083090e+00,  1.05771589e+00,  3.88351679e-01,
         2.92800188e-01, -1.96619999e+00,  1.95784867e+00,
        -4.13798034e-01,  1.47642946e+00,  1.82268894e+00,
        -2.04837084e+00,  3.72619486e+00,  5.20634508e+00,
        -1.47109365e+00, -1.74831533e+00, -2.20906281e+00,
         1.04206157e+00,  2.17300224e+00, -1.77130532e+00,
         3.63481402e-01,  1.55684721e+00,  1.12421358e+00,
         6.73773527e-01, -2.70564651e+00, -1.60699368e+00,
        -5.89830041e-01, -7.11880744e-01, -7.72383094e-01,
        -4.74024773e+00, -7.64594853e-01, -3.01190495e-01,
         3.00760567e-02,  3.28799963e-01, -9.20998335e-01,
        -5.90195060e-01, -2.10354662e+00, -8.08288455e-02,
        -3.00726771e-01, -2.94073272e+00, -6.67897463e-02,
         4.05205935e-01, -1.01796746e-01, -6.07753038e-01,
         1.34296894e-01,  2.52990663e-01,  1.05131876e+00,
        -1.03456736e-01,  2.22371054e+00, -1.65700173e+00,
         4.42044616e-01, -1.74274778e+00,  2.11299562e+00,
         1.25178683e+00, -3.84630859e-01,  6.38607979e+00,
        -5.62303066e-02,  2.39617169e-01,  5.12941265e+00,
        -3.73447478e-01,  2.76882744e+00, -1.02909255e+00,
         8.72482896e-01, -3.09532136e-03,  1.65725648e+00,
        -1.45965195e+00,  4.96256053e-02,  1.95450640e+00,
         1.29016256e+00,  4.35873270e-02,  1.59944141e+00,
         2.08828044e+00,  1.44816399e+00]], dtype=float32)
1 results=array([[4.485778 , 4.6124716]], dtype=float32)
2 results=4.5491247
3 results=-0.11271882057189941
exception invalid index to scalar variable.
Traceback (most recent call last):
  File "test.py", line 35, in _run
    valence, arousal = predictor.predict(Path(url),  None, "msd-musicnn", "emomusic")
  File "music-arousal-valence/predict.py", line 122, in predict
    valence = results[0]
IndexError: invalid index to scalar variable.