diff --git "a/nohup.out" "b/nohup.out"
--- "a/nohup.out"
+++ "b/nohup.out"
@@ -43494,3 +43494,11472 @@ huggingface/tokenizers: The current process just got forked, after parallelism h
 To disable this warning, you can either:
 	- Avoid using `tokenizers` before the fork if possible
 	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+
+
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+  return jax.tree_map(
+
+
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+  return jax.tree_map(lambda x: x[0], tree)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+
                                                                                                                                                                  

+
                                                        

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+
                                                      

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+
Training...:  87% 3841/4393 [5:11:42<36:54,  4.01s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:14:50<35:02:00, 21020.10s/it]
+
Training...:  87% 3841/4393 [5:11:46<36:54,  4.01s/it]
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+
Training...:  88% 3866/4393 [5:13:36<50:30,  5.75s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:16:47<35:02:00, 21020.10s/it]
+
Training...:  88% 3866/4393 [5:13:43<50:30,  5.75s/it]
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+
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+
Training...:  89% 3891/4393 [5:15:39<31:47,  3.80s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:18:46<35:02:00, 21020.10s/it]
+
Training...:  89% 3891/4393 [5:15:42<31:47,  3.80s/it]
+
Training...:  89% 3892/4393 [5:15:42<31:05,  3.72s/it]
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+
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+
Training...:  89% 3916/4393 [5:17:34<45:54,  5.78s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:20:44<35:02:00, 21020.10s/it]
+
Training...:  89% 3916/4393 [5:17:40<45:54,  5.78s/it]
+
Training...:  89% 3917/4393 [5:17:40<47:37,  6.00s/it]
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+
Training...:  90% 3941/4393 [5:19:37<28:46,  3.82s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:22:44<35:02:00, 21020.10s/it]
+
Training...:  90% 3941/4393 [5:19:40<28:46,  3.82s/it]
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+
Training...:  90% 3966/4393 [5:21:32<40:45,  5.73s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:24:42<35:02:00, 21020.10s/it]
+
Training...:  90% 3966/4393 [5:21:38<40:45,  5.73s/it]
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Training...:  90% 3967/4393 [5:21:38<41:58,  5.91s/it]
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Training...:  91% 3991/4393 [5:23:34<25:09,  3.76s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:26:41<35:02:00, 21020.10s/it]
+
Training...:  91% 3991/4393 [5:23:37<25:09,  3.76s/it]
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Training...:  91% 3992/4393 [5:23:37<24:31,  3.67s/it]
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Training...:  91% 4016/4393 [5:25:28<35:56,  5.72s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:28:38<35:02:00, 21020.10s/it]
+
Training...:  91% 4016/4393 [5:25:34<35:56,  5.72s/it]
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Training...:  91% 4017/4393 [5:25:34<37:05,  5.92s/it]
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+
Training...:  92% 4041/4393 [5:27:31<22:33,  3.85s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:30:38<35:02:00, 21020.10s/it]
+
Training...:  92% 4041/4393 [5:27:34<22:33,  3.85s/it]
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+
Training...:  93% 4066/4393 [5:29:28<32:45,  6.01s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:32:38<35:02:00, 21020.10s/it]
+
Training...:  93% 4066/4393 [5:29:34<32:45,  6.01s/it]
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+
Training...:  93% 4091/4393 [5:31:37<20:05,  3.99s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:34:45<35:02:00, 21020.10s/it]
+
Training...:  93% 4091/4393 [5:31:41<20:05,  3.99s/it]
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+
Training...:  94% 4116/4393 [5:33:32<26:33,  5.75s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:36:43<35:02:00, 21020.10s/it]
+
Training...:  94% 4116/4393 [5:33:39<26:33,  5.75s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:38:45<35:02:00, 21020.10s/it]
+
Training...:  94% 4141/4393 [5:35:41<16:42,  3.98s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:40:43<35:02:00, 21020.10s/it]
+
Training...:  95% 4166/4393 [5:37:39<21:51,  5.78s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:42:46<35:02:00, 21020.10s/it]
+
Training...:  95% 4191/4393 [5:39:42<13:35,  4.04s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:44:44<35:02:00, 21020.10s/it]
+
Training...:  96% 4216/4393 [5:41:40<16:58,  5.75s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:46:45<35:02:00, 21020.10s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:48:43<35:02:00, 21020.10s/it]
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Training...:  98% 4291/4393 [5:47:36<06:34,  3.87s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:50:44<35:02:00, 21020.10s/it]
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Training...:  98% 4291/4393 [5:47:40<06:34,  3.87s/it]
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Training...:  98% 4316/4393 [5:49:31<07:22,  5.75s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:52:41<35:02:00, 21020.10s/it]
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Training...:  98% 4316/4393 [5:49:37<07:22,  5.75s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:54:42<35:02:00, 21020.10s/it]
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Training...:  99% 4341/4393 [5:51:38<03:23,  3.92s/it]
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Training...:  99% 4366/4393 [5:53:29<02:33,  5.68s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:56:39<35:02:00, 21020.10s/it]
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Training...:  99% 4366/4393 [5:53:35<02:33,  5.68s/it]
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Training...: 100% 4391/4393 [5:55:18<00:04,  2.46s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  50% 6/12 [40:58:24<35:02:00, 21020.10s/it]
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Training...: 100% 4391/4393 [5:55:20<00:04,  2.46s/it]
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Training...: 100% 4393/4393 [5:55:21<00:00,  4.85s/it]
+
Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [40:58:25<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:00:48<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:02:23<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:04:46<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:06:23<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:08:45<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:10:23<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:12:46<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:14:24<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:16:46<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:18:24<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:20:46<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:22:23<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:24:45<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:26:22<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:28:44<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:30:21<29:19:56, 21119.31s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:32:43<29:19:56, 21119.31s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:34:20<29:19:56, 21119.31s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:36:43<29:19:56, 21119.31s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:38:21<29:19:56, 21119.31s/it]
+
Training...:  11% 498/4393 [39:53<2:24:36,  2.23s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:40:47<29:19:56, 21119.31s/it]
+
Training...:  12% 523/4393 [42:19<6:08:44,  5.72s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:42:24<29:19:56, 21119.31s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:44:46<29:19:56, 21119.31s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:46:24<29:19:56, 21119.31s/it]
+
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:48:49<29:19:56, 21119.31s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:50:27<29:19:56, 21119.31s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:52:50<29:19:56, 21119.31s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:54:27<29:19:56, 21119.31s/it]
+
Training...:  16% 698/4393 [55:59<2:12:19,  2.15s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:56:49<29:19:56, 21119.31s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [41:58:25<29:19:56, 21119.31s/it]
+
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:00:47<29:19:56, 21119.31s/it]
+
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:02:24<29:19:56, 21119.31s/it]
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:04:45<29:19:56, 21119.31s/it]
+
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:06:22<29:19:56, 21119.31s/it]
+
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:08:43<29:19:56, 21119.31s/it]
+
Training...:  20% 873/4393 [1:10:15<5:22:22,  5.50s/it]
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:10:21<29:19:56, 21119.31s/it]
+
Training...:  20% 898/4393 [1:11:53<2:12:29,  2.27s/it]
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:12:43<29:19:56, 21119.31s/it]
+
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:14:20<29:19:56, 21119.31s/it]
+
Training...:  22% 948/4393 [1:15:52<2:04:12,  2.16s/it]
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:16:42<29:19:56, 21119.31s/it]
+
Training...:  22% 973/4393 [1:18:15<5:16:02,  5.54s/it]
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:18:21<29:19:56, 21119.31s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:20:43<29:19:56, 21119.31s/it]
+
Training...:  23% 1023/4393 [1:22:15<5:07:07,  5.47s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:22:20<29:19:56, 21119.31s/it]
+
Training...:  24% 1048/4393 [1:23:52<1:58:49,  2.13s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:24:41<29:19:56, 21119.31s/it]
+
Training...:  24% 1073/4393 [1:26:13<5:05:06,  5.51s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:26:16<29:19:56, 21119.31s/it]
+
Training...:  25% 1098/4393 [1:27:48<1:56:45,  2.13s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:28:37<29:19:56, 21119.31s/it]
+
Training...:  26% 1123/4393 [1:30:10<4:57:00,  5.45s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:30:12<29:19:56, 21119.31s/it]
+
Training...:  26% 1148/4393 [1:31:44<1:51:57,  2.07s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:32:34<29:19:56, 21119.31s/it]
+
Training...:  27% 1173/4393 [1:34:06<4:58:19,  5.56s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:34:11<29:19:56, 21119.31s/it]
+
Training...:  27% 1198/4393 [1:35:43<1:53:58,  2.14s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:36:32<29:19:56, 21119.31s/it]
+
Training...:  28% 1223/4393 [1:38:04<4:48:17,  5.46s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:38:07<29:19:56, 21119.31s/it]
+
Training...:  28% 1248/4393 [1:39:39<1:49:14,  2.08s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:40:29<29:19:56, 21119.31s/it]
+
Training...:  29% 1273/4393 [1:42:02<4:45:06,  5.48s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:42:06<29:19:56, 21119.31s/it]
+
Training...:  30% 1298/4393 [1:43:39<1:55:00,  2.23s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:44:29<29:19:56, 21119.31s/it]
+
Training...:  30% 1323/4393 [1:46:02<4:42:43,  5.53s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:46:06<29:19:56, 21119.31s/it]
+
Training...:  31% 1348/4393 [1:47:39<1:49:37,  2.16s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:48:29<29:19:56, 21119.31s/it]
+
Training...:  31% 1373/4393 [1:50:01<4:36:55,  5.50s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:50:07<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:52:29<29:19:56, 21119.31s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:54:06<29:19:56, 21119.31s/it]
+
Training...:  33% 1448/4393 [1:55:39<1:48:29,  2.21s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:56:29<29:19:56, 21119.31s/it]
+
Training...:  34% 1473/4393 [1:58:01<4:32:10,  5.59s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [42:58:07<29:19:56, 21119.31s/it]
+
Training...:  34% 1498/4393 [1:59:40<1:46:27,  2.21s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:00:32<29:19:56, 21119.31s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:02:09<29:19:56, 21119.31s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:04:31<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:06:07<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:08:31<29:19:56, 21119.31s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:10:07<29:19:56, 21119.31s/it]
+
Training...:  38% 1648/4393 [2:11:40<1:38:51,  2.16s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:12:33<29:19:56, 21119.31s/it]
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Training...:  38% 1673/4393 [2:14:05<4:20:21,  5.74s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:14:11<29:19:56, 21119.31s/it]
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Training...:  39% 1698/4393 [2:15:43<1:40:36,  2.24s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:16:33<29:19:56, 21119.31s/it]
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Training...:  39% 1723/4393 [2:18:05<4:06:39,  5.54s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:18:11<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:20:34<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:22:11<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:24:32<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:26:09<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:28:31<29:19:56, 21119.31s/it]
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Training...:  43% 1881/4393 [2:30:39<3:34:14,  5.12s/it]
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Training...:  43% 1882/4393 [2:30:44<3:31:21,  5.05s/it]
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Training...:  43% 1883/4393 [2:30:48<3:28:03,  4.97s/it]
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Training...:  43% 1884/4393 [2:30:53<3:24:25,  4.89s/it]
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Training...:  43% 1885/4393 [2:30:58<3:20:44,  4.80s/it]
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Training...:  43% 1886/4393 [2:31:02<3:16:03,  4.69s/it]
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Training...:  43% 1887/4393 [2:31:07<3:11:25,  4.58s/it]
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Training...:  43% 1888/4393 [2:31:11<3:05:45,  4.45s/it]
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Training...:  43% 1889/4393 [2:31:15<2:58:28,  4.28s/it]
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Training...:  43% 1890/4393 [2:31:18<2:50:27,  4.09s/it]
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Training...:  43% 1891/4393 [2:31:22<2:41:24,  3.87s/it]
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Training...:  43% 1892/4393 [2:31:25<2:33:10,  3.67s/it]
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Training...:  43% 1893/4393 [2:31:28<2:24:18,  3.46s/it]
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Training...:  43% 1894/4393 [2:31:30<2:14:59,  3.24s/it]
+
Training...:  43% 1895/4393 [2:31:33<2:04:07,  2.98s/it]
+
Training...:  43% 1896/4393 [2:31:35<1:53:08,  2.72s/it]
+
Training...:  43% 1897/4393 [2:31:37<1:42:21,  2.46s/it]
+
Training...:  43% 1898/4393 [2:31:38<1:31:39,  2.20s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:30:08<29:19:56, 21119.31s/it]
+
Training...:  43% 1898/4393 [2:31:40<1:31:39,  2.20s/it]
+
Training...:  43% 1899/4393 [2:31:40<1:24:42,  2.04s/it]
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Training...:  43% 1900/4393 [2:31:41<1:12:17,  1.74s/it]
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Training...:  43% 1901/4393 [2:31:47<2:09:43,  3.12s/it]
+
Training...:  43% 1902/4393 [2:31:54<2:48:50,  4.07s/it]
+
Training...:  43% 1903/4393 [2:32:00<3:14:14,  4.68s/it]
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Training...:  43% 1904/4393 [2:32:06<3:31:12,  5.09s/it]
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Training...:  43% 1905/4393 [2:32:12<3:42:58,  5.38s/it]
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Training...:  43% 1906/4393 [2:32:18<3:49:34,  5.54s/it]
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Training...:  43% 1907/4393 [2:32:24<3:55:35,  5.69s/it]
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Training...:  43% 1908/4393 [2:32:30<3:59:22,  5.78s/it]
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Training...:  43% 1909/4393 [2:32:36<4:01:07,  5.82s/it]
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Training...:  43% 1910/4393 [2:32:42<4:01:00,  5.82s/it]
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Training...:  44% 1911/4393 [2:32:47<4:01:03,  5.83s/it]
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Training...:  44% 1912/4393 [2:32:53<4:00:14,  5.81s/it]
+
Training...:  44% 1913/4393 [2:32:59<3:59:32,  5.80s/it]
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Training...:  44% 1914/4393 [2:33:05<4:00:42,  5.83s/it]
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Training...:  44% 1915/4393 [2:33:11<4:01:09,  5.84s/it]
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Training...:  44% 1916/4393 [2:33:16<3:58:51,  5.79s/it]
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Training...:  44% 1919/4393 [2:33:33<3:53:59,  5.67s/it]
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Training...:  44% 1920/4393 [2:33:39<3:50:37,  5.60s/it]
+
Training...:  44% 1921/4393 [2:33:44<3:49:50,  5.58s/it]
+
Training...:  44% 1922/4393 [2:33:50<3:48:08,  5.54s/it]
+
Training...:  44% 1923/4393 [2:33:55<3:46:49,  5.51s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:32:29<29:19:56, 21119.31s/it]
+
Training...:  44% 1923/4393 [2:34:01<3:46:49,  5.51s/it]
+
Training...:  44% 1924/4393 [2:34:01<3:54:17,  5.69s/it]
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Training...:  44% 1925/4393 [2:34:07<3:49:37,  5.58s/it]
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Training...:  44% 1926/4393 [2:34:12<3:44:58,  5.47s/it]
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Training...:  44% 1927/4393 [2:34:17<3:42:14,  5.41s/it]
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Training...:  44% 1928/4393 [2:34:22<3:38:54,  5.33s/it]
+
Training...:  44% 1929/4393 [2:34:27<3:36:03,  5.26s/it]
+
Training...:  44% 1930/4393 [2:34:32<3:32:19,  5.17s/it]
+
Training...:  44% 1931/4393 [2:34:37<3:29:33,  5.11s/it]
+
Training...:  44% 1932/4393 [2:34:42<3:26:44,  5.04s/it]
+
Training...:  44% 1933/4393 [2:34:47<3:25:45,  5.02s/it]
+
Training...:  44% 1934/4393 [2:34:52<3:20:48,  4.90s/it]
+
Training...:  44% 1935/4393 [2:34:56<3:15:41,  4.78s/it]
+
Training...:  44% 1936/4393 [2:35:01<3:10:13,  4.65s/it]
+
Training...:  44% 1937/4393 [2:35:05<3:05:51,  4.54s/it]
+
Training...:  44% 1938/4393 [2:35:09<3:00:09,  4.40s/it]
+
Training...:  44% 1939/4393 [2:35:13<2:54:08,  4.26s/it]
+
Training...:  44% 1940/4393 [2:35:17<2:47:09,  4.09s/it]
+
Training...:  44% 1941/4393 [2:35:20<2:40:02,  3.92s/it]
+
Training...:  44% 1942/4393 [2:35:23<2:31:21,  3.71s/it]
+
Training...:  44% 1943/4393 [2:35:26<2:22:17,  3.48s/it]
+
Training...:  44% 1944/4393 [2:35:29<2:12:29,  3.25s/it]
+
Training...:  44% 1945/4393 [2:35:31<2:02:19,  3.00s/it]
+
Training...:  44% 1946/4393 [2:35:33<1:51:48,  2.74s/it]
+
Training...:  44% 1947/4393 [2:35:35<1:41:09,  2.48s/it]
+
Training...:  44% 1948/4393 [2:35:37<1:30:01,  2.21s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:34:06<29:19:56, 21119.31s/it]
+
Training...:  44% 1948/4393 [2:35:39<1:30:01,  2.21s/it]
+
Training...:  44% 1949/4393 [2:35:39<1:23:00,  2.04s/it]
+
Training...:  44% 1950/4393 [2:35:40<1:10:00,  1.72s/it]
+
Training...:  44% 1951/4393 [2:35:46<2:07:10,  3.12s/it]
+
Training...:  44% 1952/4393 [2:35:52<2:46:08,  4.08s/it]
+
Training...:  44% 1953/4393 [2:35:58<3:11:04,  4.70s/it]
+
Training...:  44% 1954/4393 [2:36:04<3:27:37,  5.11s/it]
+
Training...:  45% 1955/4393 [2:36:11<3:39:21,  5.40s/it]
+
Training...:  45% 1956/4393 [2:36:17<3:46:44,  5.58s/it]
+
Training...:  45% 1957/4393 [2:36:22<3:50:57,  5.69s/it]
+
Training...:  45% 1958/4393 [2:36:28<3:53:37,  5.76s/it]
+
Training...:  45% 1959/4393 [2:36:34<3:56:51,  5.84s/it]
+
Training...:  45% 1960/4393 [2:36:40<3:59:18,  5.90s/it]
+
Training...:  45% 1961/4393 [2:36:46<3:59:19,  5.90s/it]
+
Training...:  45% 1962/4393 [2:36:52<3:57:52,  5.87s/it]
+
Training...:  45% 1963/4393 [2:36:58<3:57:03,  5.85s/it]
+
Training...:  45% 1964/4393 [2:37:04<3:54:50,  5.80s/it]
+
Training...:  45% 1965/4393 [2:37:09<3:53:49,  5.78s/it]
+
Training...:  45% 1966/4393 [2:37:15<3:51:25,  5.72s/it]
+
Training...:  45% 1967/4393 [2:37:21<3:50:13,  5.69s/it]
+
Training...:  45% 1968/4393 [2:37:26<3:48:37,  5.66s/it]
+
Training...:  45% 1969/4393 [2:37:32<3:48:06,  5.65s/it]
+
Training...:  45% 1970/4393 [2:37:37<3:45:55,  5.59s/it]
+
Training...:  45% 1971/4393 [2:37:43<3:45:05,  5.58s/it]
+
Training...:  45% 1972/4393 [2:37:48<3:43:23,  5.54s/it]
+
Training...:  45% 1973/4393 [2:37:54<3:42:16,  5.51s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:36:27<29:19:56, 21119.31s/it]
+
Training...:  45% 1973/4393 [2:38:00<3:42:16,  5.51s/it]
+
Training...:  45% 1974/4393 [2:38:00<3:49:22,  5.69s/it]
+
Training...:  45% 1975/4393 [2:38:05<3:44:25,  5.57s/it]
+
Training...:  45% 1976/4393 [2:38:10<3:40:33,  5.48s/it]
+
Training...:  45% 1977/4393 [2:38:16<3:37:39,  5.41s/it]
+
Training...:  45% 1978/4393 [2:38:21<3:34:22,  5.33s/it]
+
Training...:  45% 1979/4393 [2:38:26<3:31:26,  5.26s/it]
+
Training...:  45% 1980/4393 [2:38:31<3:28:14,  5.18s/it]
+
Training...:  45% 1981/4393 [2:38:36<3:25:59,  5.12s/it]
+
Training...:  45% 1982/4393 [2:38:41<3:23:49,  5.07s/it]
+
Training...:  45% 1983/4393 [2:38:46<3:22:06,  5.03s/it]
+
Training...:  45% 1984/4393 [2:38:51<3:19:34,  4.97s/it]
+
Training...:  45% 1985/4393 [2:38:55<3:16:00,  4.88s/it]
+
Training...:  45% 1986/4393 [2:39:00<3:10:48,  4.76s/it]
+
Training...:  45% 1987/4393 [2:39:04<3:05:05,  4.62s/it]
+
Training...:  45% 1988/4393 [2:39:08<2:58:43,  4.46s/it]
+
Training...:  45% 1989/4393 [2:39:12<2:52:03,  4.29s/it]
+
Training...:  45% 1990/4393 [2:39:16<2:43:53,  4.09s/it]
+
Training...:  45% 1991/4393 [2:39:19<2:34:58,  3.87s/it]
+
Training...:  45% 1992/4393 [2:39:22<2:26:00,  3.65s/it]
+
Training...:  45% 1993/4393 [2:39:25<2:17:20,  3.43s/it]
+
Training...:  45% 1994/4393 [2:39:28<2:07:30,  3.19s/it]
+
Training...:  45% 1995/4393 [2:39:30<1:56:21,  2.91s/it]
+
Training...:  45% 1996/4393 [2:39:32<1:45:41,  2.65s/it]
+
Training...:  45% 1997/4393 [2:39:34<1:35:19,  2.39s/it]
+
Training...:  45% 1998/4393 [2:39:35<1:24:55,  2.13s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:38:04<29:19:56, 21119.31s/it]
+
Training...:  45% 1998/4393 [2:39:37<1:24:55,  2.13s/it]
+
Training...:  46% 1999/4393 [2:39:37<1:18:33,  1.97s/it]
+
Training...:  46% 2000/4393 [2:39:38<1:06:56,  1.68s/it]
+
Training...:  46% 2001/4393 [2:39:44<2:01:51,  3.06s/it]
+
Training...:  46% 2002/4393 [2:39:50<2:39:45,  4.01s/it]
+
Training...:  46% 2003/4393 [2:39:56<3:05:16,  4.65s/it]
+
Training...:  46% 2004/4393 [2:40:03<3:21:50,  5.07s/it]
+
Training...:  46% 2005/4393 [2:40:09<3:33:13,  5.36s/it]
+
Training...:  46% 2006/4393 [2:40:15<3:40:15,  5.54s/it]
+
Training...:  46% 2007/4393 [2:40:20<3:45:32,  5.67s/it]
+
Training...:  46% 2008/4393 [2:40:26<3:49:01,  5.76s/it]
+
Training...:  46% 2009/4393 [2:40:33<3:52:33,  5.85s/it]
+
Training...:  46% 2010/4393 [2:40:39<3:54:34,  5.91s/it]
+
Training...:  46% 2011/4393 [2:40:44<3:54:03,  5.90s/it]
+
Training...:  46% 2012/4393 [2:40:50<3:52:29,  5.86s/it]
+
Training...:  46% 2013/4393 [2:40:56<3:51:51,  5.85s/it]
+
Training...:  46% 2014/4393 [2:41:02<3:50:30,  5.81s/it]
+
Training...:  46% 2015/4393 [2:41:07<3:49:14,  5.78s/it]
+
Training...:  46% 2016/4393 [2:41:13<3:47:16,  5.74s/it]
+
Training...:  46% 2017/4393 [2:41:19<3:45:39,  5.70s/it]
+
Training...:  46% 2018/4393 [2:41:24<3:43:55,  5.66s/it]
+
Training...:  46% 2019/4393 [2:41:30<3:42:11,  5.62s/it]
+
Training...:  46% 2020/4393 [2:41:35<3:40:12,  5.57s/it]
+
Training...:  46% 2021/4393 [2:41:41<3:38:53,  5.54s/it]
+
Training...:  46% 2022/4393 [2:41:46<3:37:50,  5.51s/it]
+
Training...:  46% 2023/4393 [2:41:52<3:36:29,  5.48s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:40:25<29:19:56, 21119.31s/it]
+
Training...:  46% 2023/4393 [2:41:58<3:36:29,  5.48s/it]
+
Training...:  46% 2024/4393 [2:41:58<3:45:15,  5.71s/it]
+
Training...:  46% 2025/4393 [2:42:03<3:42:59,  5.65s/it]
+
Training...:  46% 2026/4393 [2:42:09<3:38:25,  5.54s/it]
+
Training...:  46% 2027/4393 [2:42:14<3:34:46,  5.45s/it]
+
Training...:  46% 2028/4393 [2:42:19<3:30:33,  5.34s/it]
+
Training...:  46% 2029/4393 [2:42:24<3:27:18,  5.26s/it]
+
Training...:  46% 2030/4393 [2:42:29<3:24:09,  5.18s/it]
+
Training...:  46% 2031/4393 [2:42:34<3:21:34,  5.12s/it]
+
Training...:  46% 2032/4393 [2:42:39<3:18:49,  5.05s/it]
+
Training...:  46% 2033/4393 [2:42:44<3:16:28,  5.00s/it]
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Training...:  46% 2034/4393 [2:42:48<3:12:54,  4.91s/it]
+
Training...:  46% 2035/4393 [2:42:53<3:09:12,  4.81s/it]
+
Training...:  46% 2036/4393 [2:42:58<3:05:24,  4.72s/it]
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Training...:  46% 2037/4393 [2:43:02<3:00:22,  4.59s/it]
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Training...:  46% 2038/4393 [2:43:06<2:54:59,  4.46s/it]
+
Training...:  46% 2039/4393 [2:43:10<2:48:55,  4.31s/it]
+
Training...:  46% 2040/4393 [2:43:14<2:41:36,  4.12s/it]
+
Training...:  46% 2041/4393 [2:43:17<2:32:50,  3.90s/it]
+
Training...:  46% 2042/4393 [2:43:20<2:23:07,  3.65s/it]
+
Training...:  47% 2043/4393 [2:43:23<2:13:10,  3.40s/it]
+
Training...:  47% 2044/4393 [2:43:25<2:02:49,  3.14s/it]
+
Training...:  47% 2045/4393 [2:43:28<1:52:39,  2.88s/it]
+
Training...:  47% 2046/4393 [2:43:30<1:42:50,  2.63s/it]
+
Training...:  47% 2047/4393 [2:43:32<1:33:04,  2.38s/it]
+
Training...:  47% 2048/4393 [2:43:33<1:22:51,  2.12s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:42:02<29:19:56, 21119.31s/it]
+
Training...:  47% 2048/4393 [2:43:35<1:22:51,  2.12s/it]
+
Training...:  47% 2049/4393 [2:43:35<1:16:35,  1.96s/it]
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Training...:  47% 2050/4393 [2:43:36<1:05:21,  1.67s/it]
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Training...:  47% 2051/4393 [2:43:42<2:00:08,  3.08s/it]
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Training...:  47% 2052/4393 [2:43:48<2:37:57,  4.05s/it]
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Training...:  47% 2053/4393 [2:43:54<3:01:42,  4.66s/it]
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Training...:  47% 2054/4393 [2:44:00<3:17:28,  5.07s/it]
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Training...:  47% 2055/4393 [2:44:06<3:27:57,  5.34s/it]
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Training...:  47% 2056/4393 [2:44:12<3:34:46,  5.51s/it]
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Training...:  47% 2057/4393 [2:44:18<3:39:46,  5.64s/it]
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Training...:  47% 2058/4393 [2:44:24<3:42:25,  5.72s/it]
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Training...:  47% 2059/4393 [2:44:30<3:45:09,  5.79s/it]
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Training...:  47% 2060/4393 [2:44:36<3:46:24,  5.82s/it]
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Training...:  47% 2061/4393 [2:44:42<3:46:12,  5.82s/it]
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Training...:  47% 2062/4393 [2:44:48<3:47:48,  5.86s/it]
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Training...:  47% 2063/4393 [2:44:54<3:48:05,  5.87s/it]
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Training...:  47% 2064/4393 [2:44:59<3:45:41,  5.81s/it]
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Training...:  47% 2065/4393 [2:45:05<3:44:32,  5.79s/it]
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Training...:  47% 2066/4393 [2:45:11<3:42:58,  5.75s/it]
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Training...:  47% 2067/4393 [2:45:16<3:41:55,  5.72s/it]
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Training...:  47% 2068/4393 [2:45:22<3:39:29,  5.66s/it]
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Training...:  47% 2069/4393 [2:45:27<3:37:34,  5.62s/it]
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Training...:  47% 2070/4393 [2:45:33<3:35:13,  5.56s/it]
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Training...:  47% 2071/4393 [2:45:38<3:33:49,  5.53s/it]
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Training...:  47% 2072/4393 [2:45:44<3:31:19,  5.46s/it]
+
Training...:  47% 2073/4393 [2:45:49<3:30:39,  5.45s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:44:23<29:19:56, 21119.31s/it]
+
Training...:  47% 2073/4393 [2:45:55<3:30:39,  5.45s/it]
+
Training...:  47% 2074/4393 [2:45:55<3:37:39,  5.63s/it]
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Training...:  47% 2075/4393 [2:46:00<3:33:08,  5.52s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:45:59<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:48:26<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:50:06<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:52:28<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:54:07<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:56:29<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [43:58:07<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:00:27<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:02:05<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:04:27<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:06:05<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:08:27<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:10:04<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:12:24<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:14:00<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:16:22<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:17:58<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:20:20<29:19:56, 21119.31s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:21:57<29:19:56, 21119.31s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:24:19<29:19:56, 21119.31s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:25:56<29:19:56, 21119.31s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:28:17<29:19:56, 21119.31s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:29:53<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:32:15<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:33:54<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:36:15<29:19:56, 21119.31s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:37:54<29:19:56, 21119.31s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:40:15<29:19:56, 21119.31s/it]
+
Training...:  63% 2773/4393 [3:41:48<2:28:33,  5.50s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:41:54<29:19:56, 21119.31s/it]
+
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:44:14<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:45:54<29:19:56, 21119.31s/it]
+
Training...:  65% 2848/4393 [3:47:26<58:37,  2.28s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:48:15<29:19:56, 21119.31s/it]
+
Training...:  65% 2873/4393 [3:49:47<2:18:34,  5.47s/it]
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Training...:  66% 2898/4393 [3:51:25<55:33,  2.23s/it]  
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:49:54<29:19:56, 21119.31s/it]
+
Training...:  66% 2898/4393 [3:51:26<55:33,  2.23s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:52:16<29:19:56, 21119.31s/it]
+
Training...:  67% 2923/4393 [3:53:48<2:14:08,  5.48s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:53:53<29:19:56, 21119.31s/it]
+
Training...:  67% 2948/4393 [3:55:25<52:45,  2.19s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:56:15<29:19:56, 21119.31s/it]
+
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [44:57:54<29:19:56, 21119.31s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:00:15<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:01:52<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:04:14<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:05:50<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:08:12<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:09:50<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:12:11<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:13:49<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:16:11<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:17:49<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:20:10<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:21:49<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:24:10<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:25:46<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:28:08<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:29:46<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:32:07<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:33:45<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:36:07<29:19:56, 21119.31s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:37:44<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:40:06<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:41:42<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:44:04<29:19:56, 21119.31s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:45:42<29:19:56, 21119.31s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:48:04<29:19:56, 21119.31s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:49:40<29:19:56, 21119.31s/it]
+
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:52:02<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:53:40<29:19:56, 21119.31s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:56:01<29:19:56, 21119.31s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:57:36<29:19:56, 21119.31s/it]
+
Training...:  85% 3748/4393 [4:59:09<22:24,  2.08s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [45:59:58<29:19:56, 21119.31s/it]
+
Training...:  86% 3773/4393 [5:01:30<56:40,  5.49s/it]
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Training...:  86% 3798/4393 [5:03:05<20:41,  2.09s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:01:34<29:19:56, 21119.31s/it]
+
Training...:  86% 3798/4393 [5:03:06<20:41,  2.09s/it]
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+
Training...:  87% 3823/4393 [5:05:22<52:25,  5.52s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:03:55<29:19:56, 21119.31s/it]
+
Training...:  87% 3823/4393 [5:05:28<52:25,  5.52s/it]
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+
Training...:  88% 3848/4393 [5:07:05<20:44,  2.28s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:05:35<29:19:56, 21119.31s/it]
+
Training...:  88% 3848/4393 [5:07:07<20:44,  2.28s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:07:57<29:19:56, 21119.31s/it]
+
Training...:  88% 3873/4393 [5:09:29<47:48,  5.52s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:09:34<29:19:56, 21119.31s/it]
+
Training...:  89% 3898/4393 [5:11:06<17:32,  2.13s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:11:55<29:19:56, 21119.31s/it]
+
Training...:  89% 3923/4393 [5:13:28<42:54,  5.48s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:13:33<29:19:56, 21119.31s/it]
+
Training...:  90% 3948/4393 [5:15:05<16:22,  2.21s/it]
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+
Training...:  90% 3973/4393 [5:17:20<38:14,  5.46s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:15:54<29:19:56, 21119.31s/it]
+
Training...:  90% 3973/4393 [5:17:26<38:14,  5.46s/it]
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+
Training...:  91% 3998/4393 [5:19:00<13:45,  2.09s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:17:30<29:19:56, 21119.31s/it]
+
Training...:  91% 3998/4393 [5:19:02<13:45,  2.09s/it]
+
Training...:  91% 3999/4393 [5:19:02<12:42,  1.94s/it]
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+
Training...:  92% 4023/4393 [5:21:17<34:15,  5.55s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:19:51<29:19:56, 21119.31s/it]
+
Training...:  92% 4023/4393 [5:21:23<34:15,  5.55s/it]
+
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+
Training...:  92% 4048/4393 [5:22:58<12:13,  2.13s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:21:27<29:19:56, 21119.31s/it]
+
Training...:  92% 4048/4393 [5:23:00<12:13,  2.13s/it]
+
Training...:  92% 4049/4393 [5:23:00<11:21,  1.98s/it]
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+
Training...:  93% 4073/4393 [5:25:16<29:42,  5.57s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:23:49<29:19:56, 21119.31s/it]
+
Training...:  93% 4073/4393 [5:25:22<29:42,  5.57s/it]
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+
Training...:  93% 4098/4393 [5:26:59<11:17,  2.30s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:25:28<29:19:56, 21119.31s/it]
+
Training...:  93% 4098/4393 [5:27:01<11:17,  2.30s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:27:50<29:19:56, 21119.31s/it]
+
Training...:  94% 4123/4393 [5:29:22<24:55,  5.54s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:29:27<29:19:56, 21119.31s/it]
+
Training...:  94% 4148/4393 [5:31:00<08:59,  2.20s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:31:49<29:19:56, 21119.31s/it]
+
Training...:  95% 4173/4393 [5:33:21<20:09,  5.50s/it]
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+
Training...:  96% 4198/4393 [5:34:56<07:02,  2.17s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:33:26<29:19:56, 21119.31s/it]
+
Training...:  96% 4198/4393 [5:34:58<07:02,  2.17s/it]
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Training...:  96% 4223/4393 [5:37:14<15:36,  5.51s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:35:48<29:19:56, 21119.31s/it]
+
Training...:  96% 4223/4393 [5:37:20<15:36,  5.51s/it]
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Training...:  97% 4248/4393 [5:38:56<05:23,  2.23s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:37:25<29:19:56, 21119.31s/it]
+
Training...:  97% 4248/4393 [5:38:58<05:23,  2.23s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:39:46<29:19:56, 21119.31s/it]
+
Training...:  97% 4273/4393 [5:41:19<10:56,  5.47s/it]
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Training...:  98% 4298/4393 [5:42:53<03:17,  2.07s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:41:22<29:19:56, 21119.31s/it]
+
Training...:  98% 4298/4393 [5:42:55<03:17,  2.07s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:43:44<29:19:56, 21119.31s/it]
+
Training...:  98% 4323/4393 [5:45:16<06:23,  5.47s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:45:23<29:19:56, 21119.31s/it]
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Training...:  99% 4348/4393 [5:46:55<01:46,  2.36s/it]
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Training...: 100% 4373/4393 [5:49:11<01:47,  5.36s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  58% 7/12 [46:47:44<29:19:56, 21119.31s/it]
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Training...: 100% 4373/4393 [5:49:17<01:47,  5.36s/it]
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Training...: 100% 4393/4393 [5:50:25<00:00,  4.79s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [46:48:52<23:25:59, 21089.92s/it]
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+
                                                   

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [46:49:32<23:25:59, 21089.92s/it]
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+
                                                    

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [46:51:51<23:25:59, 21089.92s/it]
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+
                                                    

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [46:53:31<23:25:59, 21089.92s/it]
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+
                                                    

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [46:55:49<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [46:57:28<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [46:59:47<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:01:27<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:03:45<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:05:23<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:07:42<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:09:21<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:11:40<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:13:20<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:15:39<23:25:59, 21089.92s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:17:20<23:25:59, 21089.92s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:19:39<23:25:59, 21089.92s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:21:20<23:25:59, 21089.92s/it]
+
Training...:   9% 405/4393 [32:25<5:56:38,  5.37s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:23:39<23:25:59, 21089.92s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:25:18<23:25:59, 21089.92s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:27:37<23:25:59, 21089.92s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:29:17<23:25:59, 21089.92s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:31:36<23:25:59, 21089.92s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:33:15<23:25:59, 21089.92s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:35:34<23:25:59, 21089.92s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:37:12<23:25:59, 21089.92s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:39:31<23:25:59, 21089.92s/it]
+
Training...:  14% 630/4393 [50:37<5:24:33,  5.18s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:41:10<23:25:59, 21089.92s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:43:29<23:25:59, 21089.92s/it]
+
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:45:08<23:25:59, 21089.92s/it]
+
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:47:27<23:25:59, 21089.92s/it]
+
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:49:06<23:25:59, 21089.92s/it]
+
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:51:25<23:25:59, 21089.92s/it]
+
Training...:  18% 780/4393 [1:02:30<5:10:31,  5.16s/it]
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:53:03<23:25:59, 21089.92s/it]
+
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:55:22<23:25:59, 21089.92s/it]
+
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:56:59<23:25:59, 21089.92s/it]
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [47:59:17<23:25:59, 21089.92s/it]
+
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:00:56<23:25:59, 21089.92s/it]
+
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:03:14<23:25:59, 21089.92s/it]
+
Training...:  21% 930/4393 [1:14:20<4:56:09,  5.13s/it]
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+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:04:53<23:25:59, 21089.92s/it]
+
Training...:  22% 955/4393 [1:15:59<5:09:31,  5.40s/it]
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+
Training...:  22% 980/4393 [1:18:12<4:51:10,  5.12s/it]
                                                                                                                                                                     

+
                                                       

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:07:12<23:25:59, 21089.92s/it]
+
Training...:  22% 980/4393 [1:18:18<4:51:10,  5.12s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:08:51<23:25:59, 21089.92s/it]
+
Training...:  23% 1005/4393 [1:19:57<5:00:52,  5.33s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:11:10<23:25:59, 21089.92s/it]
+
Training...:  23% 1030/4393 [1:22:15<4:46:54,  5.12s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:12:50<23:25:59, 21089.92s/it]
+
Training...:  24% 1055/4393 [1:23:55<4:57:03,  5.34s/it]
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Training...:  25% 1080/4393 [1:26:08<4:43:43,  5.14s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:15:09<23:25:59, 21089.92s/it]
+
Training...:  25% 1080/4393 [1:26:14<4:43:43,  5.14s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:16:49<23:25:59, 21089.92s/it]
+
Training...:  25% 1105/4393 [1:27:54<4:54:08,  5.37s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:19:08<23:25:59, 21089.92s/it]
+
Training...:  26% 1130/4393 [1:30:13<4:39:19,  5.14s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:20:47<23:25:59, 21089.92s/it]
+
Training...:  26% 1155/4393 [1:31:52<4:48:26,  5.34s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:23:06<23:25:59, 21089.92s/it]
+
Training...:  27% 1180/4393 [1:34:11<4:36:43,  5.17s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:24:45<23:25:59, 21089.92s/it]
+
Training...:  27% 1205/4393 [1:35:50<4:43:29,  5.34s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:27:03<23:25:59, 21089.92s/it]
+
Training...:  28% 1230/4393 [1:38:08<4:29:44,  5.12s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:28:43<23:25:59, 21089.92s/it]
+
Training...:  29% 1255/4393 [1:39:48<4:40:00,  5.35s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:31:01<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:32:38<23:25:59, 21089.92s/it]
+
Training...:  30% 1305/4393 [1:43:44<4:33:14,  5.31s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:34:56<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:36:34<23:25:59, 21089.92s/it]
+
Training...:  31% 1355/4393 [1:47:39<4:35:34,  5.44s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:38:52<23:25:59, 21089.92s/it]
+
Training...:  31% 1380/4393 [1:49:58<4:17:40,  5.13s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:40:33<23:25:59, 21089.92s/it]
+
Training...:  32% 1405/4393 [1:51:38<4:27:26,  5.37s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:42:51<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:44:31<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:46:50<23:25:59, 21089.92s/it]
+
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:48:29<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:50:48<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:52:28<23:25:59, 21089.92s/it]
+
Training...:  35% 1555/4393 [2:03:33<4:12:58,  5.35s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:54:47<23:25:59, 21089.92s/it]
+
Training...:  36% 1580/4393 [2:05:52<4:02:44,  5.18s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:56:27<23:25:59, 21089.92s/it]
+
Training...:  37% 1605/4393 [2:07:33<4:08:07,  5.34s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [48:58:46<23:25:59, 21089.92s/it]
+
Training...:  37% 1630/4393 [2:09:51<3:56:35,  5.14s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:00:25<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:02:44<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:06:41<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:08:19<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:10:36<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:12:15<23:25:59, 21089.92s/it]
+
Training...:  41% 1805/4393 [2:23:20<3:53:00,  5.40s/it]
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Training...:  42% 1830/4393 [2:25:33<3:40:18,  5.16s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:14:33<23:25:59, 21089.92s/it]
+
Training...:  42% 1830/4393 [2:25:39<3:40:18,  5.16s/it]
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Training...:  42% 1855/4393 [2:27:11<3:45:25,  5.33s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:16:13<23:25:59, 21089.92s/it]
+
Training...:  42% 1855/4393 [2:27:18<3:45:25,  5.33s/it]
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Training...:  43% 1876/4393 [2:29:10<3:41:15,  5.27s/it]
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Training...:  43% 1879/4393 [2:29:25<3:34:38,  5.12s/it]
+
Training...:  43% 1880/4393 [2:29:30<3:32:30,  5.07s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:18:30<23:25:59, 21089.92s/it]
+
Training...:  43% 1880/4393 [2:29:36<3:32:30,  5.07s/it]
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Training...:  43% 1881/4393 [2:29:36<3:40:08,  5.26s/it]
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Training...:  43% 1901/4393 [2:30:44<2:06:44,  3.05s/it]
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Training...:  43% 1903/4393 [2:30:56<3:12:45,  4.64s/it]
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Training...:  43% 1904/4393 [2:31:02<3:28:54,  5.04s/it]
+
Training...:  43% 1905/4393 [2:31:08<3:40:47,  5.32s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:20:09<23:25:59, 21089.92s/it]
+
Training...:  43% 1905/4393 [2:31:15<3:40:47,  5.32s/it]
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Training...:  43% 1906/4393 [2:31:15<3:58:22,  5.75s/it]
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Training...:  43% 1910/4393 [2:31:38<4:02:31,  5.86s/it]
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Training...:  44% 1930/4393 [2:33:28<3:32:53,  5.19s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:22:28<23:25:59, 21089.92s/it]
+
Training...:  44% 1930/4393 [2:33:33<3:32:53,  5.19s/it]
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Training...:  44% 1931/4393 [2:33:33<3:39:12,  5.34s/it]
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Training...:  45% 1955/4393 [2:35:07<3:36:36,  5.33s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:24:09<23:25:59, 21089.92s/it]
+
Training...:  45% 1955/4393 [2:35:14<3:36:36,  5.33s/it]
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Training...:  45% 1980/4393 [2:37:27<3:27:33,  5.16s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:26:27<23:25:59, 21089.92s/it]
+
Training...:  45% 1980/4393 [2:37:33<3:27:33,  5.16s/it]
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Training...:  45% 1993/4393 [2:38:22<2:18:54,  3.47s/it]
+
Training...:  45% 1994/4393 [2:38:25<2:09:45,  3.25s/it]
+
Training...:  45% 1995/4393 [2:38:27<1:59:13,  2.98s/it]
+
Training...:  45% 1996/4393 [2:38:29<1:48:20,  2.71s/it]
+
Training...:  45% 1997/4393 [2:38:31<1:37:17,  2.44s/it]
+
Training...:  45% 1998/4393 [2:38:32<1:26:54,  2.18s/it]
+
Training...:  46% 1999/4393 [2:38:34<1:16:38,  1.92s/it]
+
Training...:  46% 2000/4393 [2:38:35<1:06:14,  1.66s/it]
+
Training...:  46% 2001/4393 [2:38:41<2:01:37,  3.05s/it]
+
Training...:  46% 2002/4393 [2:38:47<2:39:44,  4.01s/it]
+
Training...:  46% 2003/4393 [2:38:54<3:06:31,  4.68s/it]
+
Training...:  46% 2004/4393 [2:39:00<3:25:58,  5.17s/it]
+
Training...:  46% 2005/4393 [2:39:06<3:37:08,  5.46s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:28:07<23:25:59, 21089.92s/it]
+
Training...:  46% 2005/4393 [2:39:13<3:37:08,  5.46s/it]
+
Training...:  46% 2006/4393 [2:39:13<3:52:04,  5.83s/it]
+
Training...:  46% 2007/4393 [2:39:19<3:53:35,  5.87s/it]
+
Training...:  46% 2008/4393 [2:39:25<3:53:44,  5.88s/it]
+
Training...:  46% 2009/4393 [2:39:30<3:53:11,  5.87s/it]
+
Training...:  46% 2010/4393 [2:39:36<3:53:09,  5.87s/it]
+
Training...:  46% 2011/4393 [2:39:42<3:52:47,  5.86s/it]
+
Training...:  46% 2012/4393 [2:39:48<3:51:11,  5.83s/it]
+
Training...:  46% 2013/4393 [2:39:54<3:50:24,  5.81s/it]
+
Training...:  46% 2014/4393 [2:39:59<3:49:05,  5.78s/it]
+
Training...:  46% 2015/4393 [2:40:05<3:48:11,  5.76s/it]
+
Training...:  46% 2016/4393 [2:40:11<3:45:53,  5.70s/it]
+
Training...:  46% 2017/4393 [2:40:16<3:44:26,  5.67s/it]
+
Training...:  46% 2018/4393 [2:40:22<3:44:04,  5.66s/it]
+
Training...:  46% 2019/4393 [2:40:27<3:43:46,  5.66s/it]
+
Training...:  46% 2020/4393 [2:40:33<3:43:27,  5.65s/it]
+
Training...:  46% 2021/4393 [2:40:39<3:41:00,  5.59s/it]
+
Training...:  46% 2022/4393 [2:40:44<3:38:09,  5.52s/it]
+
Training...:  46% 2023/4393 [2:40:49<3:36:31,  5.48s/it]
+
Training...:  46% 2024/4393 [2:40:55<3:33:37,  5.41s/it]
+
Training...:  46% 2025/4393 [2:41:00<3:31:44,  5.37s/it]
+
Training...:  46% 2026/4393 [2:41:05<3:30:13,  5.33s/it]
+
Training...:  46% 2027/4393 [2:41:10<3:27:45,  5.27s/it]
+
Training...:  46% 2028/4393 [2:41:15<3:25:25,  5.21s/it]
+
Training...:  46% 2029/4393 [2:41:20<3:23:15,  5.16s/it]
+
Training...:  46% 2030/4393 [2:41:25<3:20:43,  5.10s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:30:26<23:25:59, 21089.92s/it]
+
Training...:  46% 2030/4393 [2:41:31<3:20:43,  5.10s/it]
+
Training...:  46% 2031/4393 [2:41:31<3:27:09,  5.26s/it]
+
Training...:  46% 2032/4393 [2:41:36<3:22:45,  5.15s/it]
+
Training...:  46% 2033/4393 [2:41:41<3:19:23,  5.07s/it]
+
Training...:  46% 2034/4393 [2:41:45<3:15:02,  4.96s/it]
+
Training...:  46% 2035/4393 [2:41:50<3:10:25,  4.85s/it]
+
Training...:  46% 2036/4393 [2:41:54<3:04:43,  4.70s/it]
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Training...:  46% 2037/4393 [2:41:59<3:00:08,  4.59s/it]
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Training...:  46% 2038/4393 [2:42:03<2:54:56,  4.46s/it]
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Training...:  46% 2039/4393 [2:42:07<2:48:27,  4.29s/it]
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Training...:  46% 2040/4393 [2:42:10<2:41:07,  4.11s/it]
+
Training...:  46% 2041/4393 [2:42:14<2:33:04,  3.90s/it]
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Training...:  46% 2042/4393 [2:42:17<2:24:10,  3.68s/it]
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Training...:  47% 2043/4393 [2:42:20<2:15:34,  3.46s/it]
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Training...:  47% 2044/4393 [2:42:23<2:06:17,  3.23s/it]
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Training...:  47% 2045/4393 [2:42:25<1:56:57,  2.99s/it]
+
Training...:  47% 2046/4393 [2:42:27<1:46:56,  2.73s/it]
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Training...:  47% 2047/4393 [2:42:29<1:36:03,  2.46s/it]
+
Training...:  47% 2048/4393 [2:42:31<1:26:02,  2.20s/it]
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Training...:  47% 2049/4393 [2:42:32<1:15:47,  1.94s/it]
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Training...:  47% 2050/4393 [2:42:33<1:04:55,  1.66s/it]
+
Training...:  47% 2051/4393 [2:42:39<1:58:24,  3.03s/it]
+
Training...:  47% 2052/4393 [2:42:45<2:34:52,  3.97s/it]
+
Training...:  47% 2053/4393 [2:42:51<2:59:55,  4.61s/it]
+
Training...:  47% 2054/4393 [2:42:57<3:15:31,  5.02s/it]
+
Training...:  47% 2055/4393 [2:43:03<3:26:59,  5.31s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:32:05<23:25:59, 21089.92s/it]
+
Training...:  47% 2055/4393 [2:43:10<3:26:59,  5.31s/it]
+
Training...:  47% 2056/4393 [2:43:10<3:43:52,  5.75s/it]
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Training...:  47% 2057/4393 [2:43:16<3:48:14,  5.86s/it]
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Training...:  47% 2058/4393 [2:43:22<3:50:01,  5.91s/it]
+
Training...:  47% 2059/4393 [2:43:28<3:49:50,  5.91s/it]
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Training...:  47% 2060/4393 [2:43:34<3:48:37,  5.88s/it]
+
Training...:  47% 2061/4393 [2:43:40<3:48:41,  5.88s/it]
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Training...:  47% 2062/4393 [2:43:46<3:48:24,  5.88s/it]
+
Training...:  47% 2063/4393 [2:43:52<3:47:49,  5.87s/it]
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Training...:  47% 2064/4393 [2:43:57<3:45:53,  5.82s/it]
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Training...:  47% 2065/4393 [2:44:03<3:44:17,  5.78s/it]
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Training...:  47% 2066/4393 [2:44:09<3:42:45,  5.74s/it]
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Training...:  47% 2067/4393 [2:44:14<3:40:48,  5.70s/it]
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Training...:  47% 2068/4393 [2:44:20<3:39:17,  5.66s/it]
+
Training...:  47% 2069/4393 [2:44:25<3:38:06,  5.63s/it]
+
Training...:  47% 2070/4393 [2:44:31<3:35:51,  5.58s/it]
+
Training...:  47% 2071/4393 [2:44:36<3:33:53,  5.53s/it]
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Training...:  47% 2072/4393 [2:44:42<3:31:49,  5.48s/it]
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Training...:  47% 2073/4393 [2:44:47<3:30:31,  5.44s/it]
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Training...:  47% 2074/4393 [2:44:52<3:28:56,  5.41s/it]
+
Training...:  47% 2075/4393 [2:44:58<3:27:56,  5.38s/it]
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Training...:  47% 2076/4393 [2:45:03<3:25:59,  5.33s/it]
+
Training...:  47% 2077/4393 [2:45:08<3:23:32,  5.27s/it]
+
Training...:  47% 2078/4393 [2:45:13<3:20:27,  5.20s/it]
+
Training...:  47% 2079/4393 [2:45:18<3:18:50,  5.16s/it]
+
Training...:  47% 2080/4393 [2:45:23<3:15:57,  5.08s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:34:23<23:25:59, 21089.92s/it]
+
Training...:  47% 2080/4393 [2:45:29<3:15:57,  5.08s/it]
+
Training...:  47% 2081/4393 [2:45:29<3:21:51,  5.24s/it]
+
Training...:  47% 2082/4393 [2:45:33<3:17:01,  5.12s/it]
+
Training...:  47% 2083/4393 [2:45:38<3:12:12,  4.99s/it]
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Training...:  47% 2084/4393 [2:45:43<3:08:27,  4.90s/it]
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Training...:  47% 2085/4393 [2:45:47<3:04:11,  4.79s/it]
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Training...:  47% 2086/4393 [2:45:52<2:58:46,  4.65s/it]
+
Training...:  48% 2087/4393 [2:45:56<2:52:54,  4.50s/it]
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Training...:  48% 2088/4393 [2:46:00<2:46:19,  4.33s/it]
+
Training...:  48% 2089/4393 [2:46:03<2:38:19,  4.12s/it]
+
Training...:  48% 2090/4393 [2:46:07<2:30:13,  3.91s/it]
+
Training...:  48% 2091/4393 [2:46:10<2:22:42,  3.72s/it]
+
Training...:  48% 2092/4393 [2:46:13<2:15:08,  3.52s/it]
+
Training...:  48% 2093/4393 [2:46:16<2:06:18,  3.29s/it]
+
Training...:  48% 2094/4393 [2:46:18<1:56:53,  3.05s/it]
+
Training...:  48% 2095/4393 [2:46:21<1:47:35,  2.81s/it]
+
Training...:  48% 2096/4393 [2:46:23<1:37:54,  2.56s/it]
+
Training...:  48% 2097/4393 [2:46:24<1:28:21,  2.31s/it]
+
Training...:  48% 2098/4393 [2:46:26<1:18:57,  2.06s/it]
+
Training...:  48% 2099/4393 [2:46:27<1:09:38,  1.82s/it]
+
Training...:  48% 2100/4393 [2:46:28<1:00:18,  1.58s/it]
+
Training...:  48% 2101/4393 [2:46:34<1:53:49,  2.98s/it]
+
Training...:  48% 2102/4393 [2:46:41<2:32:27,  3.99s/it]
+
Training...:  48% 2103/4393 [2:46:47<2:57:24,  4.65s/it]
+
Training...:  48% 2104/4393 [2:46:53<3:12:41,  5.05s/it]
+
Training...:  48% 2105/4393 [2:46:59<3:24:07,  5.35s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:36:00<23:25:59, 21089.92s/it]
+
Training...:  48% 2105/4393 [2:47:06<3:24:07,  5.35s/it]
+
Training...:  48% 2106/4393 [2:47:06<3:40:10,  5.78s/it]
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Training...:  48% 2107/4393 [2:47:12<3:43:29,  5.87s/it]
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Training...:  48% 2111/4393 [2:47:35<3:42:23,  5.85s/it]
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Training...:  48% 2112/4393 [2:47:41<3:42:22,  5.85s/it]
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Training...:  48% 2119/4393 [2:48:21<3:32:26,  5.61s/it]
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Training...:  48% 2120/4393 [2:48:26<3:30:30,  5.56s/it]
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Training...:  48% 2121/4393 [2:48:31<3:28:34,  5.51s/it]
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Training...:  48% 2124/4393 [2:48:47<3:23:49,  5.39s/it]
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Training...:  48% 2126/4393 [2:48:58<3:19:50,  5.29s/it]
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Training...:  48% 2128/4393 [2:49:08<3:15:51,  5.19s/it]
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Training...:  48% 2129/4393 [2:49:13<3:13:49,  5.14s/it]
+
Training...:  48% 2130/4393 [2:49:18<3:11:36,  5.08s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:38:18<23:25:59, 21089.92s/it]
+
Training...:  48% 2130/4393 [2:49:24<3:11:36,  5.08s/it]
+
Training...:  49% 2131/4393 [2:49:24<3:18:19,  5.26s/it]
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Training...:  49% 2155/4393 [2:50:55<3:20:15,  5.37s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:39:56<23:25:59, 21089.92s/it]
+
Training...:  49% 2155/4393 [2:51:02<3:20:15,  5.37s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:42:14<23:25:59, 21089.92s/it]
+
Training...:  50% 2180/4393 [2:53:20<3:08:01,  5.10s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:43:52<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:46:11<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:47:49<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:50:07<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:51:45<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:54:04<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:55:44<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:58:02<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [49:59:43<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:02:02<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:03:40<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:05:58<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:07:37<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:09:55<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:11:36<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:13:54<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:15:34<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:17:52<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:19:38<23:25:59, 21089.92s/it]
+
Training...:  60% 2655/4393 [3:30:43<2:43:53,  5.66s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:22:00<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:23:40<23:25:59, 21089.92s/it]
+
Training...:  62% 2705/4393 [3:34:46<2:31:11,  5.37s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:26:05<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:27:46<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:30:04<23:25:59, 21089.92s/it]
+
Training...:  63% 2780/4393 [3:41:10<2:17:47,  5.13s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:31:44<23:25:59, 21089.92s/it]
+
Training...:  64% 2805/4393 [3:42:49<2:21:41,  5.35s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:34:02<23:25:59, 21089.92s/it]
+
Training...:  64% 2830/4393 [3:45:07<2:12:59,  5.11s/it]
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Training...:  65% 2855/4393 [3:46:37<2:16:04,  5.31s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:35:39<23:25:59, 21089.92s/it]
+
Training...:  65% 2855/4393 [3:46:44<2:16:04,  5.31s/it]
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Training...:  66% 2880/4393 [3:48:57<2:11:05,  5.20s/it]
                                                                                                                                                                     

+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:37:57<23:25:59, 21089.92s/it]
+
Training...:  66% 2880/4393 [3:49:03<2:11:05,  5.20s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:39:37<23:25:59, 21089.92s/it]
+
Training...:  66% 2905/4393 [3:50:43<2:13:39,  5.39s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:41:56<23:25:59, 21089.92s/it]
+
Training...:  67% 2930/4393 [3:53:01<2:05:11,  5.13s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:43:35<23:25:59, 21089.92s/it]
+
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:45:53<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:47:32<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:49:49<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:51:27<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:53:44<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:55:22<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:57:40<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [50:59:20<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:01:38<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:03:17<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:05:36<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:07:14<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:09:32<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:11:10<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:13:29<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:15:07<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:17:26<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:19:04<23:25:59, 21089.92s/it]
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Training...:  78% 3405/4393 [4:30:09<1:28:38,  5.38s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:21:23<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:23:02<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:25:20<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:26:59<23:25:59, 21089.92s/it]
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Training...:  80% 3505/4393 [4:38:04<1:19:21,  5.36s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:29:18<23:25:59, 21089.92s/it]
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Training...:  80% 3530/4393 [4:40:23<1:14:07,  5.15s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:30:58<23:25:59, 21089.92s/it]
+
Training...:  81% 3555/4393 [4:42:03<1:15:28,  5.40s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:33:16<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:34:54<23:25:59, 21089.92s/it]
+
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:37:12<23:25:59, 21089.92s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:38:51<23:25:59, 21089.92s/it]
+
Training...:  83% 3655/4393 [4:49:56<1:05:41,  5.34s/it]
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:41:09<23:25:59, 21089.92s/it]
+
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+
                                                        

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:42:50<23:25:59, 21089.92s/it]
+
Training...:  84% 3705/4393 [4:53:55<1:01:43,  5.38s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:45:08<23:25:59, 21089.92s/it]
+
Training...:  85% 3730/4393 [4:56:14<56:31,  5.12s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:46:46<23:25:59, 21089.92s/it]
+
Training...:  85% 3755/4393 [4:57:51<57:58,  5.45s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:49:05<23:25:59, 21089.92s/it]
+
Training...:  86% 3780/4393 [5:00:10<52:49,  5.17s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:50:43<23:25:59, 21089.92s/it]
+
Training...:  87% 3805/4393 [5:01:48<52:12,  5.33s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:53:02<23:25:59, 21089.92s/it]
+
Training...:  87% 3830/4393 [5:04:07<48:24,  5.16s/it]
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+
Training...:  88% 3855/4393 [5:05:39<47:53,  5.34s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:54:40<23:25:59, 21089.92s/it]
+
Training...:  88% 3855/4393 [5:05:46<47:53,  5.34s/it]
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+
Training...:  88% 3880/4393 [5:07:58<44:26,  5.20s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:56:59<23:25:59, 21089.92s/it]
+
Training...:  88% 3880/4393 [5:08:04<44:26,  5.20s/it]
+
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+
Training...:  89% 3905/4393 [5:09:37<43:56,  5.40s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [51:58:38<23:25:59, 21089.92s/it]
+
Training...:  89% 3905/4393 [5:09:44<43:56,  5.40s/it]
+
Training...:  89% 3906/4393 [5:09:44<47:08,  5.81s/it]
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+
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+
Training...:  89% 3930/4393 [5:11:56<39:36,  5.13s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:00:57<23:25:59, 21089.92s/it]
+
Training...:  89% 3930/4393 [5:12:02<39:36,  5.13s/it]
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+
Training...:  90% 3955/4393 [5:13:36<39:18,  5.38s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:02:38<23:25:59, 21089.92s/it]
+
Training...:  90% 3955/4393 [5:13:43<39:18,  5.38s/it]
+
Training...:  90% 3956/4393 [5:13:43<42:18,  5.81s/it]
+
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+
Training...:  91% 3980/4393 [5:15:55<35:05,  5.10s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:04:55<23:25:59, 21089.92s/it]
+
Training...:  91% 3980/4393 [5:16:01<35:05,  5.10s/it]
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Training...:  91% 3981/4393 [5:16:01<36:06,  5.26s/it]
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+
Training...:  91% 4005/4393 [5:17:33<34:25,  5.32s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:06:35<23:25:59, 21089.92s/it]
+
Training...:  91% 4005/4393 [5:17:40<34:25,  5.32s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:08:53<23:25:59, 21089.92s/it]
+
Training...:  92% 4030/4393 [5:19:58<31:01,  5.13s/it]
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+
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+
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:10:31<23:25:59, 21089.92s/it]
+
Training...:  92% 4055/4393 [5:21:36<29:58,  5.32s/it]
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+
Training...:  93% 4080/4393 [5:23:48<26:36,  5.10s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:12:48<23:25:59, 21089.92s/it]
+
Training...:  93% 4080/4393 [5:23:54<26:36,  5.10s/it]
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+
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:14:27<23:25:59, 21089.92s/it]
+
Training...:  93% 4105/4393 [5:25:33<25:39,  5.34s/it]
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+
Training...:  94% 4130/4393 [5:27:45<22:32,  5.14s/it]
                                                                                                                                                                     

+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:16:46<23:25:59, 21089.92s/it]
+
Training...:  94% 4130/4393 [5:27:51<22:32,  5.14s/it]
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+
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:18:23<23:25:59, 21089.92s/it]
+
Training...:  95% 4155/4393 [5:29:28<21:11,  5.34s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:20:41<23:25:59, 21089.92s/it]
+
Training...:  95% 4180/4393 [5:31:47<18:22,  5.18s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:22:20<23:25:59, 21089.92s/it]
+
Training...:  96% 4205/4393 [5:33:25<16:41,  5.33s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:24:38<23:25:59, 21089.92s/it]
+
Training...:  96% 4230/4393 [5:35:43<13:49,  5.09s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:26:16<23:25:59, 21089.92s/it]
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Training...:  97% 4255/4393 [5:37:22<12:18,  5.35s/it]
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Training...:  97% 4280/4393 [5:39:34<09:40,  5.14s/it]
                                                                                                                                                                     

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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:28:34<23:25:59, 21089.92s/it]
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Training...:  97% 4280/4393 [5:39:40<09:40,  5.14s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:30:13<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:32:31<23:25:59, 21089.92s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:34:10<23:25:59, 21089.92s/it]
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+
                                                      

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  67% 8/12 [52:36:24<23:25:59, 21089.92s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:36:57<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:38:18<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:40:27<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:42:16<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:44:25<17:31:17, 21025.84s/it]
+
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:46:14<17:31:17, 21025.84s/it]
+
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:48:23<17:31:17, 21025.84s/it]
+
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:50:11<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:52:20<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:54:10<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:56:19<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [52:58:07<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [53:00:16<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [53:02:04<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [53:04:14<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [53:06:03<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [53:08:13<17:31:17, 21025.84s/it]
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Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [53:10:02<17:31:17, 21025.84s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [53:12:11<17:31:17, 21025.84s/it]
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+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [53:14:00<17:31:17, 21025.84s/it]
+
Training...:  11% 462/4393 [37:00<6:26:59,  5.91s/it]Step... (39550 | Loss: 0.013957838527858257, Learning Rate: 2.1113133698236197e-05, Gradient Norm: 0.25816094875335693)
+Step... (39575 | Loss: 0.003900324460119009, Learning Rate: 2.1062624000478536e-05, Gradient Norm: 0.15411750972270966)
+Step... (39600 | Loss: 0.004219492897391319, Learning Rate: 2.101212157867849e-05, Gradient Norm: 0.1492801159620285)
+Step... (39625 | Loss: 0.007128617260605097, Learning Rate: 2.0961617337889038e-05, Gradient Norm: 0.21547576785087585)
+Step... (39650 | Loss: 0.012958021834492683, Learning Rate: 2.091110945912078e-05, Gradient Norm: 0.28005728125572205)
+Step... (39675 | Loss: 0.006572413258254528, Learning Rate: 2.086060521833133e-05, Gradient Norm: 0.3049915134906769)
+Step... (39700 | Loss: 0.008246519602835178, Learning Rate: 2.0810102796531282e-05, Gradient Norm: 0.2068808376789093)
+Step... (39725 | Loss: 0.004701581783592701, Learning Rate: 2.075959309877362e-05, Gradient Norm: 0.23778201639652252)
+Step... (39750 | Loss: 0.010084073059260845, Learning Rate: 2.0709090676973574e-05, Gradient Norm: 0.1955907642841339)
+Step... (39775 | Loss: 0.0037632507737725973, Learning Rate: 2.0658588255173527e-05, Gradient Norm: 0.18372221291065216)
+Step... (39800 | Loss: 0.009699777700006962, Learning Rate: 2.0608078557415865e-05, Gradient Norm: 0.19054998457431793)
+Step... (39825 | Loss: 0.0035127829760313034, Learning Rate: 2.0557576135615818e-05, Gradient Norm: 0.17126810550689697)
+Step... (39850 | Loss: 0.007305247243493795, Learning Rate: 2.050707371381577e-05, Gradient Norm: 0.15133488178253174)
+Step... (39875 | Loss: 0.007563646882772446, Learning Rate: 2.045656401605811e-05, Gradient Norm: 0.2595842778682709)
+Step... (39900 | Loss: 0.009252386167645454, Learning Rate: 2.0406061594258063e-05, Gradient Norm: 0.16340221464633942)
+Step... (39925 | Loss: 0.009359311312437057, Learning Rate: 2.0355557353468612e-05, Gradient Norm: 0.15072058141231537)
+Step... (39950 | Loss: 0.005342613440006971, Learning Rate: 2.0305049474700354e-05, Gradient Norm: 0.1631428301334381)
+Step... (39975 | Loss: 0.008378183469176292, Learning Rate: 2.0254545233910903e-05, Gradient Norm: 0.23551413416862488)
+Step... (40000 | Loss: 0.007560828700661659, Learning Rate: 2.0204042812110856e-05, Gradient Norm: 0.2140130251646042)
+
+
+
Evaluating ...:   0% 0/85 [00:00<?, ?it/s]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
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+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/training/common_utils.py:51: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  device_metrics = jax.tree_map(lambda x: x[0], device_metrics)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/training/common_utils.py:45: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(stack_args, *forest)
+run_flax_speech_recognition_seq2seq.py:1392: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
+
                                                                                                                                                                     

+
                                                     

Step... (30000/50000 | Eval Loss: 0.9876799583435059 | Eval wer: 0.04759016212639241 | Eval cer: 0.035079873533571844 |):  75% 9/12 [53:21:16<17:31:17, 21025.84s/it]
+
Training...:  11% 462/4393 [44:16<6:26:59,  5.91s/it]run_flax_speech_recognition_seq2seq.py:1425: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
+Configuration saved in /home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/config.json
+tcmalloc: large alloc 2586787840 bytes == 0x46a526000 @  0x7f7cba873680 0x7f7cba893bdd 0x7f7b690721ff 0x7f7b6908142c 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6907c164 0x7f7b6907c91e 0x505166 0x56bbfa 0x569dba 0x5f6eb3 0x56cc1f 0x569dba 0x5f6eb3 0x56cc1f 0x5f6cd6 0x56bacd 0x569dba 0x50bca0 0x56cc1f 0x569dba 0x5f6eb3 0x56bacd 0x569dba 0x5f6eb3
+Model weights saved in /home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/flax_model.msgpack
+tokenizer config file saved in ./tokenizer_config.json
+Special tokens file saved in ./special_tokens_map.json
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+Adding files tracked by Git LFS: ['wandb/run-20220828_085247-2hx8pk65/logs/debug-internal.log']. This may take a bit of time if the files are large.
+WARNING:huggingface_hub.repository:Adding files tracked by Git LFS: ['wandb/run-20220828_085247-2hx8pk65/logs/debug-internal.log']. This may take a bit of time if the files are large.
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)