Reverse ordered loss again.
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				| @ -65,8 +65,9 @@ def calculate_report(wav_filenames, labels, decodings, losses): | |||||||
|     # Getting the WER and CER from the accumulated edit distances and lengths |     # Getting the WER and CER from the accumulated edit distances and lengths | ||||||
|     samples_wer, samples_cer = wer_cer_batch(samples) |     samples_wer, samples_cer = wer_cer_batch(samples) | ||||||
| 
 | 
 | ||||||
|     # Order the remaining items by their loss (lowest loss on top) |     # Reversed because the worst WER with the best loss is to identify systemic issues, where the acoustic model is confident, | ||||||
|     samples.sort(key=lambda s: s.loss) |     # yet the result is completely off the mark. This can point to transcription errors and stuff like that. | ||||||
|  |     samples.sort(key=lambda s: s.loss, reverse=True) | ||||||
| 
 | 
 | ||||||
|     # Then order by ascending WER/CER |     # Then order by ascending WER/CER | ||||||
|     if FLAGS.utf8: |     if FLAGS.utf8: | ||||||
|  | |||||||
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