dump decode result as jsonlines

deepspeech/exps/deepspeech2/model.py

@@ -18,6 +18,7 @@ from collections import defaultdict
 from contextlib import nullcontext
 from pathlib import Path
 from typing import Optional
+import jsonlines
 
 import numpy as np
 import paddle
@@ -305,9 +306,10 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             len_refs += len_ref
             num_ins += 1
             if fout:
-                fout.write(utt + " " + result + "\n")
+                fout.write({"utt": utt, "ref", target, "hyp": result})
-            logger.info("\nTarget Transcription: %s\nOutput Transcription: %s" %
+            logger.info(f"Utt: {utt}")
-                        (target, result))
+            logger.info(f"Ref: {target}")
+            logger.info(f"Hyp: {result}")
             logger.info("Current error rate [%s] = %f" %
                         (cfg.error_rate_type, error_rate_func(target, result)))
 
@@ -350,7 +352,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         cfg = self.config
         error_rate_type = None
         errors_sum, len_refs, num_ins = 0.0, 0, 0
-        with open(self.args.result_file, 'w') as fout:
+        with jsonlines.open(self.args.result_file, 'w') as fout:
             for i, batch in enumerate(self.test_loader):
                 utts, audio, audio_len, texts, texts_len = batch
                 metrics = self.compute_metrics(utts, audio, audio_len, texts,

deepspeech/exps/u2/model.py

@@ -21,6 +21,7 @@ from collections import OrderedDict
 from contextlib import nullcontext
 from pathlib import Path
 from typing import Optional
+import jsonlines
 
 import numpy as np
 import paddle
@@ -466,9 +467,10 @@ class U2Tester(U2Trainer):
             len_refs += len_ref
             num_ins += 1
             if fout:
-                fout.write(utt + " " + result + "\n")
+                fout.write({"utt": utt, "ref", target, "hyp": result})
-            logger.info("\nTarget Transcription: %s\nOutput Transcription: %s" %
+            logger.info(f"Utt: {utt}")
-                        (target, result))
+            logger.info(f"Ref: {target}")
+            logger.info(f"Hyp: {result}")
             logger.info("One example error rate [%s] = %f" %
                         (cfg.error_rate_type, error_rate_func(target, result)))
 
@@ -493,7 +495,7 @@ class U2Tester(U2Trainer):
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         num_frames = 0.0
         num_time = 0.0
-        with open(self.args.result_file, 'w') as fout:
+        with jsonlines.open(self.args.result_file, 'w') as fout:
             for i, batch in enumerate(self.test_loader):
                 metrics = self.compute_metrics(*batch, fout=fout)
                 num_frames += metrics['num_frames']

deepspeech/exps/u2_kaldi/model.py

@@ -20,6 +20,7 @@ from collections import defaultdict
 from contextlib import nullcontext
 from pathlib import Path
 from typing import Optional
+import jsonlines
 
 import numpy as np
 import paddle
@@ -445,9 +446,10 @@ class U2Tester(U2Trainer):
             len_refs += len_ref
             num_ins += 1
             if fout:
-                fout.write(utt + " " + result + "\n")
+                fout.write({"utt": utt, "ref", target, "hyp": result})
-            logger.info("\nTarget Transcription: %s\nOutput Transcription: %s" %
+            logger.info(f"Utt: {utt}")
-                        (target, result))
+            logger.info(f"Ref: {target}")
+            logger.info(f"Hyp: {result}")
             logger.info("One example error rate [%s] = %f" %
                         (cfg.error_rate_type, error_rate_func(target, result)))
 
@@ -472,7 +474,7 @@ class U2Tester(U2Trainer):
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         num_frames = 0.0
         num_time = 0.0
-        with open(self.args.result_file, 'w') as fout:
+        with jsonlines.open(self.args.result_file, 'w') as fout:
             for i, batch in enumerate(self.test_loader):
                 metrics = self.compute_metrics(*batch, fout=fout)
                 num_frames += metrics['num_frames']

deepspeech/exps/u2_st/model.py

@@ -20,6 +20,7 @@ from collections import defaultdict
 from contextlib import nullcontext
 from pathlib import Path
 from typing import Optional
+import jsonlines
 
 import numpy as np
 import paddle
@@ -479,8 +480,10 @@ class U2STTester(U2STTrainer):
             len_refs += len(target.split())
             num_ins += 1
             if fout:
-                fout.write(utt + " " + result + "\n")
+                fout.write({"utt": utt, "ref", target, "hyp": result})
-            logger.info("\nReference: %s\nHypothesis: %s" % (target, result))
+            logger.info(f"Utt: {utt}")
+            logger.info(f"Ref: {target}")
+            logger.info(f"Hyp: {result}")
             logger.info("One example BLEU = %s" %
                         (bleu_func([result], [[target]]).prec_str))
 
@@ -508,7 +511,7 @@ class U2STTester(U2STTrainer):
         len_refs, num_ins = 0, 0
         num_frames = 0.0
         num_time = 0.0
-        with open(self.args.result_file, 'w') as fout:
+        with jsonlines.open(self.args.result_file, 'w') as fout:
             for i, batch in enumerate(self.test_loader):
                 metrics = self.compute_translation_metrics(
                     *batch, bleu_func=bleu_func, fout=fout)