numpy to keras ops #2126
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numpy to keras ops #2126
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mattdangerw
reviewed
Mar 20, 2025
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| try: | ||
| import tensorflow as tf | ||
| import keras.ops as ops |
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No reason to import keras conditionally. We don't want to assume tf is install, we can assume keras is installed.
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| return config | ||
| return config No newline at end of file |
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don't remove training newline
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| def _get_rfftfreq_keras(self): # Inside the class definition |
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what is this comment support to mean?
| @@ -92,25 +108,15 @@ def _get_mel_filters(self): | |||
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| # TODO: Convert to TensorFlow ops (if possible). | |||
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We should remove this TODO I think.
| logstep * (mels[log_t] - min_log_mel) | ||
| ) | ||
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| freqs = ops.where(log_t, min_log_hz * ops.exp(logstep * (mels - min_log_mel)), freqs) # using tf.where for conditional replacement |
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remove the comment, and reformat to 80 chars
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| fdiff = np.diff(mel_f) | ||
| ramps = np.subtract.outer(mel_f, fftfreqs) | ||
| fdiff = mel_f[1:] - mel_f[:-1] #keras diff. |
sachinprasadhs
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WIP
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Closing this since there is a continuation PR created for this |
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The mel_filters function was successfully migrated to Keras ops, replacing NumPy. All tests now pass, ensuring consistent performance within the Keras framework.