Moved summary printing to samples printing.

evaluate.py

@@ -8,7 +8,6 @@ import sys
 from multiprocessing import cpu_count
 
 import absl.app
-import numpy as np
 import progressbar
 import tensorflow as tf
 import tensorflow.compat.v1 as tfv1
@@ -17,7 +16,7 @@ from ds_ctcdecoder import ctc_beam_search_decoder_batch, Scorer
 from six.moves import zip
 
 from util.config import Config, initialize_globals
-from util.evaluate_tools import calculate_report, print_report
+from util.evaluate_tools import calculate_and_print_report
 from util.feeding import create_dataset
 from util.flags import create_flags, FLAGS
 from util.logging import create_progressbar, log_error, log_progress
@@ -132,15 +131,8 @@ def evaluate(test_csvs, create_model, try_loading):
             bar.finish()
 
             # Print test summary
-            wer, cer, samples = calculate_report(wav_filenames, ground_truths, predictions, losses)
-            mean_loss = np.mean(losses)
-            print('Test on %s - WER: %f, CER: %f, loss: %f' % (dataset, wer, cer, mean_loss))
-            print('-' * 80)
-
-            # Print some examples
-            print_report(samples)
-
-            return samples
+            test_samples = calculate_and_print_report(wav_filenames, ground_truths, predictions, losses, dataset)
+            return test_samples
 
         samples = []
         for csv, init_op in zip(test_csvs, test_init_ops):

evaluate_tflite.py

@@ -15,7 +15,7 @@ from six.moves import zip, range
 from multiprocessing import JoinableQueue, Process, cpu_count, Manager
 from deepspeech import Model
 
-from util.evaluate_tools import calculate_report, print_report
+from util.evaluate_tools import calculate_and_print_report
 from util.flags import create_flags
 
 r'''
@@ -99,13 +99,7 @@ def main(args, _):
         wavlist.append(msg['wav'])
 
     # Print test summary
-    wer, cer, samples = calculate_report(wav_filenames, ground_truths, predictions, losses)
-    mean_loss = np.mean(losses)
-    print('Test - WER: %f, CER: %f, loss: %f' % (wer, cer, mean_loss))
-    print('-' * 80)
-
-    # Print some examples
-    print_report(samples)
+    _ = calculate_and_print_report(wav_filenames, ground_truths, predictions, losses, args.csv)
 
     if args.dump:
         with open(args.dump + '.txt', 'w') as ftxt, open(args.dump + '.out', 'w') as fout:

util/evaluate_tools.py

@@ -3,6 +3,7 @@
 from __future__ import absolute_import, division, print_function
 
 from multiprocessing.dummy import Pool
+import numpy as np
 
 from attrdict import AttrDict
 
@@ -54,9 +55,9 @@ def process_decode_result(item):
     })
 
 
-def calculate_report(wav_filenames, labels, decodings, losses):
+def calculate_and_print_report(wav_filenames, labels, decodings, losses, dataset):
     r'''
-    This routine will calculate a WER report.
+    This routine will calculate and print a WER report.
     It'll compute the `mean` WER and create ``Sample`` objects of the ``report_count`` top lowest
     loss items from the provided WER results tuple (only items with WER!=0 and ordered by their WER).
     '''
@@ -75,11 +76,19 @@ def calculate_report(wav_filenames, labels, decodings, losses):
     else:
         samples.sort(key=lambda s: s.wer)
 
-    return samples_wer, samples_cer, samples
+    # Print the report
+    print_report(samples, losses, samples_wer, samples_cer, dataset)
+
+    return samples
 
 
-def print_report(samples):
-    """ Print a report with samples of best, median and worst results """
+def print_report(samples, losses, wer, cer, dataset):
+    """ Print a report summary and samples of best, median and worst results """
+
+    # Print summary
+    mean_loss = np.mean(losses)
+    print('Test on %s - WER: %f, CER: %f, loss: %f' % (dataset, wer, cer, mean_loss))
+    print('-' * 80)
 
     best_samples = samples[:FLAGS.report_count]
     worst_samples = samples[-FLAGS.report_count:]