Hugging Face's logo

AigizK
/
bashkir-whisper-small

Automatic Speech Recognition Transformers PyTorch
Bashkir whisper whisper-event generated_from_trainer hf-asr-leaderboard Eval Results Inference Endpoints
Model card Files Community
AigizK commited on
Commit
3d2797a
•
1 Parent(s): 77e2595

30000 steps

Browse files
Files changed (16) hide show
  1. README.md +93 -0
  2. added_tokens.json +109 -0
  3. all_results.json +12 -0
  4. config.json +41 -0
  5. eval_results.json +8 -0
  6. merges.txt +0 -0
  7. normalizer.json +1742 -0
  8. preprocessor_config.json +0 -0
  9. pytorch_model.bin +3 -0
  10. run.sh +37 -0
  11. special_tokens_map.json +133 -0
  12. tokenizer_config.json +36 -0
  13. train_results.json +7 -0
  14. trainer_state.json +1960 -0
  15. training_args.bin +3 -0
  16. vocab.json +0 -0
README.md ADDED
@@ -0,0 +1,93 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - ba
4
+ license: apache-2.0
5
+ tags:
6
+ - whisper-event
7
+ - generated_from_trainer
8
+ datasets:
9
+ - mozilla-foundation/common_voice_11_0
10
+ metrics:
11
+ - wer
12
+ model-index:
13
+ - name: Whisper Small Bashkir
14
+ results:
15
+ - task:
16
+ name: Automatic Speech Recognition
17
+ type: automatic-speech-recognition
18
+ dataset:
19
+ name: mozilla-foundation/common_voice_11_0 ba
20
+ type: mozilla-foundation/common_voice_11_0
21
+ config: ba
22
+ split: test
23
+ args: ba
24
+ metrics:
25
+ - name: Wer
26
+ type: wer
27
+ value: 15.072300680807968
28
+ ---
29
+
30
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
31
+ should probably proofread and complete it, then remove this comment. -->
32
+
33
+ # Whisper Small Bashkir
34
+
35
+ This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ba dataset.
36
+ It achieves the following results on the evaluation set:
37
+ - Loss: 0.2589
38
+ - Wer: 15.0723
39
+
40
+ ## Model description
41
+
42
+ More information needed
43
+
44
+ ## Intended uses & limitations
45
+
46
+ More information needed
47
+
48
+ ## Training and evaluation data
49
+
50
+ More information needed
51
+
52
+ ## Training procedure
53
+
54
+ ### Training hyperparameters
55
+
56
+ The following hyperparameters were used during training:
57
+ - learning_rate: 1e-05
58
+ - train_batch_size: 16
59
+ - eval_batch_size: 8
60
+ - seed: 42
61
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
62
+ - lr_scheduler_type: linear
63
+ - lr_scheduler_warmup_steps: 500
64
+ - training_steps: 30000
65
+ - mixed_precision_training: Native AMP
66
+
67
+ ### Training results
68
+
69
+ | Training Loss | Epoch | Step | Validation Loss | Wer |
70
+ |:-------------:|:-----:|:-----:|:---------------:|:-------:|
71
+ | 0.1637 | 1.01 | 2000 | 0.2555 | 26.4682 |
72
+ | 0.1375 | 2.01 | 4000 | 0.2223 | 21.5394 |
73
+ | 0.0851 | 3.02 | 6000 | 0.2086 | 19.6725 |
74
+ | 0.0573 | 4.02 | 8000 | 0.2178 | 18.4280 |
75
+ | 0.036 | 5.03 | 10000 | 0.2312 | 17.8248 |
76
+ | 0.0238 | 6.04 | 12000 | 0.2621 | 17.4096 |
77
+ | 0.0733 | 7.04 | 14000 | 0.2120 | 16.5656 |
78
+ | 0.0111 | 8.05 | 16000 | 0.2682 | 16.2291 |
79
+ | 0.0155 | 9.05 | 18000 | 0.2677 | 15.9242 |
80
+ | 0.0041 | 10.06 | 20000 | 0.3178 | 15.9534 |
81
+ | 0.0023 | 12.01 | 22000 | 0.3218 | 16.0536 |
82
+ | 0.0621 | 13.01 | 24000 | 0.2313 | 15.6169 |
83
+ | 0.0022 | 14.02 | 26000 | 0.2887 | 15.1083 |
84
+ | 0.0199 | 15.02 | 28000 | 0.2553 | 15.1848 |
85
+ | 0.0083 | 16.03 | 30000 | 0.2589 | 15.0723 |
86
+
87
+
88
+ ### Framework versions
89
+
90
+ - Transformers 4.26.0.dev0
91
+ - Pytorch 1.13.0+cu117
92
+ - Datasets 2.7.1.dev0
93
+ - Tokenizers 0.13.2
added_tokens.json ADDED
@@ -0,0 +1,109 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "<|af|>": 50327,
3
+ "<|am|>": 50334,
4
+ "<|ar|>": 50272,
5
+ "<|as|>": 50350,
6
+ "<|az|>": 50304,
7
+ "<|ba|>": 50355,
8
+ "<|be|>": 50330,
9
+ "<|bg|>": 50292,
10
+ "<|bn|>": 50302,
11
+ "<|bo|>": 50347,
12
+ "<|br|>": 50309,
13
+ "<|bs|>": 50315,
14
+ "<|ca|>": 50270,
15
+ "<|cs|>": 50283,
16
+ "<|cy|>": 50297,
17
+ "<|da|>": 50285,
18
+ "<|de|>": 50261,
19
+ "<|el|>": 50281,
20
+ "<|endoftext|>": 50257,
21
+ "<|en|>": 50259,
22
+ "<|es|>": 50262,
23
+ "<|et|>": 50307,
24
+ "<|eu|>": 50310,
25
+ "<|fa|>": 50300,
26
+ "<|fi|>": 50277,
27
+ "<|fo|>": 50338,
28
+ "<|fr|>": 50265,
29
+ "<|gl|>": 50319,
30
+ "<|gu|>": 50333,
31
+ "<|haw|>": 50352,
32
+ "<|ha|>": 50354,
33
+ "<|hi|>": 50276,
34
+ "<|hr|>": 50291,
35
+ "<|ht|>": 50339,
36
+ "<|hu|>": 50286,
37
+ "<|hy|>": 50312,
38
+ "<|id|>": 50275,
39
+ "<|is|>": 50311,
40
+ "<|it|>": 50274,
41
+ "<|iw|>": 50279,
42
+ "<|ja|>": 50266,
43
+ "<|jw|>": 50356,
44
+ "<|ka|>": 50329,
45
+ "<|kk|>": 50316,
46
+ "<|km|>": 50323,
47
+ "<|kn|>": 50306,
48
+ "<|ko|>": 50264,
49
+ "<|la|>": 50294,
50
+ "<|lb|>": 50345,
51
+ "<|ln|>": 50353,
52
+ "<|lo|>": 50336,
53
+ "<|lt|>": 50293,
54
+ "<|lv|>": 50301,
55
+ "<|mg|>": 50349,
56
+ "<|mi|>": 50295,
57
+ "<|mk|>": 50308,
58
+ "<|ml|>": 50296,
59
+ "<|mn|>": 50314,
60
+ "<|mr|>": 50320,
61
+ "<|ms|>": 50282,
62
+ "<|mt|>": 50343,
63
+ "<|my|>": 50346,
64
+ "<|ne|>": 50313,
65
+ "<|nl|>": 50271,
66
+ "<|nn|>": 50342,
67
+ "<|nocaptions|>": 50362,
68
+ "<|notimestamps|>": 50363,
69
+ "<|no|>": 50288,
70
+ "<|oc|>": 50328,
71
+ "<|pa|>": 50321,
72
+ "<|pl|>": 50269,
73
+ "<|ps|>": 50340,
74
+ "<|pt|>": 50267,
75
+ "<|ro|>": 50284,
76
+ "<|ru|>": 50263,
77
+ "<|sa|>": 50344,
78
+ "<|sd|>": 50332,
79
+ "<|si|>": 50322,
80
+ "<|sk|>": 50298,
81
+ "<|sl|>": 50305,
82
+ "<|sn|>": 50324,
83
+ "<|so|>": 50326,
84
+ "<|sq|>": 50317,
85
+ "<|sr|>": 50303,
86
+ "<|startoflm|>": 50360,
87
+ "<|startofprev|>": 50361,
88
+ "<|startoftranscript|>": 50258,
89
+ "<|su|>": 50357,
90
+ "<|sv|>": 50273,
91
+ "<|sw|>": 50318,
92
+ "<|ta|>": 50287,
93
+ "<|te|>": 50299,
94
+ "<|tg|>": 50331,
95
+ "<|th|>": 50289,
96
+ "<|tk|>": 50341,
97
+ "<|tl|>": 50348,
98
+ "<|transcribe|>": 50359,
99
+ "<|translate|>": 50358,
100
+ "<|tr|>": 50268,
101
+ "<|tt|>": 50351,
102
+ "<|uk|>": 50280,
103
+ "<|ur|>": 50290,
104
+ "<|uz|>": 50337,
105
+ "<|vi|>": 50278,
106
+ "<|yi|>": 50335,
107
+ "<|yo|>": 50325,
108
+ "<|zh|>": 50260
109
+ }
all_results.json ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "epoch": 16.03,
3
+ "eval_loss": 0.2589367628097534,
4
+ "eval_runtime": 6960.2959,
5
+ "eval_samples_per_second": 2.089,
6
+ "eval_steps_per_second": 0.261,
7
+ "eval_wer": 15.072300680807968,
8
+ "train_loss": 0.07104161073341966,
9
+ "train_runtime": 196903.7961,
10
+ "train_samples_per_second": 2.438,
11
+ "train_steps_per_second": 0.152
12
+ }
config.json ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "openai/whisper-small",
3
+ "activation_dropout": 0.0,
4
+ "activation_function": "gelu",
5
+ "architectures": [
6
+ "WhisperForConditionalGeneration"
7
+ ],
8
+ "attention_dropout": 0.0,
9
+ "begin_suppress_tokens": [
10
+ 220,
11
+ 50257
12
+ ],
13
+ "bos_token_id": 50257,
14
+ "d_model": 768,
15
+ "decoder_attention_heads": 12,
16
+ "decoder_ffn_dim": 3072,
17
+ "decoder_layerdrop": 0.0,
18
+ "decoder_layers": 12,
19
+ "decoder_start_token_id": 50258,
20
+ "dropout": 0.0,
21
+ "encoder_attention_heads": 12,
22
+ "encoder_ffn_dim": 3072,
23
+ "encoder_layerdrop": 0.0,
24
+ "encoder_layers": 12,
25
+ "eos_token_id": 50257,
26
+ "forced_decoder_ids": null,
27
+ "init_std": 0.02,
28
+ "is_encoder_decoder": true,
29
+ "max_length": 448,
30
+ "max_source_positions": 1500,
31
+ "max_target_positions": 448,
32
+ "model_type": "whisper",
33
+ "num_hidden_layers": 12,
34
+ "num_mel_bins": 80,
35
+ "pad_token_id": 50257,
36
+ "scale_embedding": false,
37
+ "torch_dtype": "float32",
38
+ "transformers_version": "4.26.0.dev0",
39
+ "use_cache": false,
40
+ "vocab_size": 51865
41
+ }
eval_results.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "epoch": 16.03,
3
+ "eval_loss": 0.2589367628097534,
4
+ "eval_runtime": 6960.2959,
5
+ "eval_samples_per_second": 2.089,
6
+ "eval_steps_per_second": 0.261,
7
+ "eval_wer": 15.072300680807968
8
+ }
merges.txt ADDED
The diff for this file is too large to render. See raw diff
normalizer.json ADDED
@@ -0,0 +1,1742 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "accessorise": "accessorize",
3
+ "accessorised": "accessorized",
4
+ "accessorises": "accessorizes",
5
+ "accessorising": "accessorizing",
6
+ "acclimatisation": "acclimatization",
7
+ "acclimatise": "acclimatize",
8
+ "acclimatised": "acclimatized",
9
+ "acclimatises": "acclimatizes",
10
+ "acclimatising": "acclimatizing",
11
+ "accoutrements": "accouterments",
12
+ "aeon": "eon",
13
+ "aeons": "eons",
14
+ "aerogramme": "aerogram",
15
+ "aerogrammes": "aerograms",
16
+ "aeroplane": "airplane",
17
+ "aeroplanes": "airplanes",
18
+ "aesthete": "esthete",
19
+ "aesthetes": "esthetes",
20
+ "aesthetic": "esthetic",
21
+ "aesthetically": "esthetically",
22
+ "aesthetics": "esthetics",
23
+ "aetiology": "etiology",
24
+ "ageing": "aging",
25
+ "aggrandisement": "aggrandizement",
26
+ "agonise": "agonize",
27
+ "agonised": "agonized",
28
+ "agonises": "agonizes",
29
+ "agonising": "agonizing",
30
+ "agonisingly": "agonizingly",
31
+ "almanack": "almanac",
32
+ "almanacks": "almanacs",
33
+ "aluminium": "aluminum",
34
+ "amortisable": "amortizable",
35
+ "amortisation": "amortization",
36
+ "amortisations": "amortizations",
37
+ "amortise": "amortize",
38
+ "amortised": "amortized",
39
+ "amortises": "amortizes",
40
+ "amortising": "amortizing",
41
+ "amphitheatre": "amphitheater",
42
+ "amphitheatres": "amphitheaters",
43
+ "anaemia": "anemia",
44
+ "anaemic": "anemic",
45
+ "anaesthesia": "anesthesia",
46
+ "anaesthetic": "anesthetic",
47
+ "anaesthetics": "anesthetics",
48
+ "anaesthetise": "anesthetize",
49
+ "anaesthetised": "anesthetized",
50
+ "anaesthetises": "anesthetizes",
51
+ "anaesthetising": "anesthetizing",
52
+ "anaesthetist": "anesthetist",
53
+ "anaesthetists": "anesthetists",
54
+ "anaesthetize": "anesthetize",
55
+ "anaesthetized": "anesthetized",
56
+ "anaesthetizes": "anesthetizes",
57
+ "anaesthetizing": "anesthetizing",
58
+ "analogue": "analog",
59
+ "analogues": "analogs",
60
+ "analyse": "analyze",
61
+ "analysed": "analyzed",
62
+ "analyses": "analyzes",
63
+ "analysing": "analyzing",
64
+ "anglicise": "anglicize",
65
+ "anglicised": "anglicized",
66
+ "anglicises": "anglicizes",
67
+ "anglicising": "anglicizing",
68
+ "annualised": "annualized",
69
+ "antagonise": "antagonize",
70
+ "antagonised": "antagonized",
71
+ "antagonises": "antagonizes",
72
+ "antagonising": "antagonizing",
73
+ "apologise": "apologize",
74
+ "apologised": "apologized",
75
+ "apologises": "apologizes",
76
+ "apologising": "apologizing",
77
+ "appal": "appall",
78
+ "appals": "appalls",
79
+ "appetiser": "appetizer",
80
+ "appetisers": "appetizers",
81
+ "appetising": "appetizing",
82
+ "appetisingly": "appetizingly",
83
+ "arbour": "arbor",
84
+ "arbours": "arbors",
85
+ "archaeologically": "archeologically",
86
+ "archaeologist": "archeologist",
87
+ "archaeologists": "archeologists",
88
+ "archaeology": "archeology</span>",
89
+ "archeological": "archaeological",
90
+ "ardour": "ardor",
91
+ "armour": "armor",
92
+ "armoured": "armored",
93
+ "armourer": "armorer",
94
+ "armourers": "armorers",
95
+ "armouries": "armories",
96
+ "armoury": "armory",
97
+ "artefact": "artifact",
98
+ "artefacts": "artifacts",
99
+ "authorise": "authorize",
100
+ "authorised": "authorized",
101
+ "authorises": "authorizes",
102
+ "authorising": "authorizing",
103
+ "axe": "ax",
104
+ "backpedalled": "backpedaled",
105
+ "backpedalling": "backpedaling",
106
+ "bannister": "banister",
107
+ "bannisters": "banisters",
108
+ "baptise": "baptize",
109
+ "baptised": "baptized",
110
+ "baptises": "baptizes",
111
+ "baptising": "baptizing",
112
+ "bastardise": "bastardize",
113
+ "bastardised": "bastardized",
114
+ "bastardises": "bastardizes",
115
+ "bastardising": "bastardizing",
116
+ "battleax": "battleaxe",
117
+ "baulk": "balk",
118
+ "baulked": "balked",
119
+ "baulking": "balking",
120
+ "baulks": "balks",
121
+ "bedevilled": "bedeviled",
122
+ "bedevilling": "bedeviling",
123
+ "behaviour": "behavior",
124
+ "behavioural": "behavioral",
125
+ "behaviourism": "behaviorism",
126
+ "behaviourist": "behaviorist",
127
+ "behaviourists": "behaviorists",
128
+ "behaviours": "behaviors",
129
+ "behove": "behoove",
130
+ "behoved": "behooved",
131
+ "behoves": "behooves",
132
+ "bejewelled": "bejeweled",
133
+ "belabour": "belabor",
134
+ "belaboured": "belabored",
135
+ "belabouring": "belaboring",
136
+ "belabours": "belabors",
137
+ "bevelled": "beveled",
138
+ "bevvies": "bevies",
139
+ "bevvy": "bevy",
140
+ "biassed": "biased",
141
+ "biassing": "biasing",
142
+ "bingeing": "binging",
143
+ "bougainvillaea": "bougainvillea",
144
+ "bougainvillaeas": "bougainvilleas",
145
+ "bowdlerise": "bowdlerize",
146
+ "bowdlerised": "bowdlerized",
147
+ "bowdlerises": "bowdlerizes",
148
+ "bowdlerising": "bowdlerizing",
149
+ "breathalyse": "breathalyze",
150
+ "breathalysed": "breathalyzed",
151
+ "breathalyser": "breathalyzer",
152
+ "breathalysers": "breathalyzers",
153
+ "breathalyses": "breathalyzes",
154
+ "breathalysing": "breathalyzing",
155
+ "brutalise": "brutalize",
156
+ "brutalised": "brutalized",
157
+ "brutalises": "brutalizes",
158
+ "brutalising": "brutalizing",
159
+ "busses": "buses",
160
+ "bussing": "busing",
161
+ "caesarean": "cesarean",
162
+ "caesareans": "cesareans",
163
+ "calibre": "caliber",
164
+ "calibres": "calibers",
165
+ "calliper": "caliper",
166
+ "callipers": "calipers",
167
+ "callisthenics": "calisthenics",
168
+ "canalise": "canalize",
169
+ "canalised": "canalized",
170
+ "canalises": "canalizes",
171
+ "canalising": "canalizing",
172
+ "cancelation": "cancellation",
173
+ "cancelations": "cancellations",
174
+ "cancelled": "canceled",
175
+ "cancelling": "canceling",
176
+ "candour": "candor",
177
+ "cannibalise": "cannibalize",
178
+ "cannibalised": "cannibalized",
179
+ "cannibalises": "cannibalizes",
180
+ "cannibalising": "cannibalizing",
181
+ "canonise": "canonize",
182
+ "canonised": "canonized",
183
+ "canonises": "canonizes",
184
+ "canonising": "canonizing",
185
+ "capitalise": "capitalize",
186
+ "capitalised": "capitalized",
187
+ "capitalises": "capitalizes",
188
+ "capitalising": "capitalizing",
189
+ "caramelise": "caramelize",
190
+ "caramelised": "caramelized",
191
+ "caramelises": "caramelizes",
192
+ "caramelising": "caramelizing",
193
+ "carbonise": "carbonize",
194
+ "carbonised": "carbonized",
195
+ "carbonises": "carbonizes",
196
+ "carbonising": "carbonizing",
197
+ "carolled": "caroled",
198
+ "carolling": "caroling",
199
+ "catalogue": "catalog",
200
+ "catalogued": "cataloged",
201
+ "catalogues": "catalogs",
202
+ "cataloguing": "cataloging",
203
+ "catalyse": "catalyze",
204
+ "catalysed": "catalyzed",
205
+ "catalyses": "catalyzes",
206
+ "catalysing": "catalyzing",
207
+ "categorise": "categorize",
208
+ "categorised": "categorized",
209
+ "categorises": "categorizes",
210
+ "categorising": "categorizing",
211
+ "cauterise": "cauterize",
212
+ "cauterised": "cauterized",
213
+ "cauterises": "cauterizes",
214
+ "cauterising": "cauterizing",
215
+ "cavilled": "caviled",
216
+ "cavilling": "caviling",
217
+ "centigramme": "centigram",
218
+ "centigrammes": "centigrams",
219
+ "centilitre": "centiliter",
220
+ "centilitres": "centiliters",
221
+ "centimetre": "centimeter",
222
+ "centimetres": "centimeters",
223
+ "centralise": "centralize",
224
+ "centralised": "centralized",
225
+ "centralises": "centralizes",
226
+ "centralising": "centralizing",
227
+ "centre": "center",
228
+ "centred": "centered",
229
+ "centrefold": "centerfold",
230
+ "centrefolds": "centerfolds",
231
+ "centrepiece": "centerpiece",
232
+ "centrepieces": "centerpieces",
233
+ "centres": "centers",
234
+ "channelled": "channeled",
235
+ "channelling": "channeling",
236
+ "characterise": "characterize",
237
+ "characterised": "characterized",
238
+ "characterises": "characterizes",
239
+ "characterising": "characterizing",
240
+ "cheque": "check",
241
+ "chequebook": "checkbook",
242
+ "chequebooks": "checkbooks",
243
+ "chequered": "checkered",
244
+ "cheques": "checks",
245
+ "chilli": "chili",
246
+ "chimaera": "chimera",
247
+ "chimaeras": "chimeras",
248
+ "chiselled": "chiseled",
249
+ "chiselling": "chiseling",
250
+ "circularise": "circularize",
251
+ "circularised": "circularized",
252
+ "circularises": "circularizes",
253
+ "circularising": "circularizing",
254
+ "civilise": "civilize",
255
+ "civilised": "civilized",
256
+ "civilises": "civilizes",
257
+ "civilising": "civilizing",
258
+ "clamour": "clamor",
259
+ "clamoured": "clamored",
260
+ "clamouring": "clamoring",
261
+ "clamours": "clamors",
262
+ "clangour": "clangor",
263
+ "clarinettist": "clarinetist",
264
+ "clarinettists": "clarinetists",
265
+ "collectivise": "collectivize",
266
+ "collectivised": "collectivized",
267
+ "collectivises": "collectivizes",
268
+ "collectivising": "collectivizing",
269
+ "colonisation": "colonization",
270
+ "colonise": "colonize",
271
+ "colonised": "colonized",
272
+ "coloniser": "colonizer",
273
+ "colonisers": "colonizers",
274
+ "colonises": "colonizes",
275
+ "colonising": "colonizing",
276
+ "colour": "color",
277
+ "colourant": "colorant",
278
+ "colourants": "colorants",
279
+ "coloured": "colored",
280
+ "coloureds": "coloreds",
281
+ "colourful": "colorful",
282
+ "colourfully": "colorfully",
283
+ "colouring": "coloring",
284
+ "colourize": "colorize",
285
+ "colourized": "colorized",
286
+ "colourizes": "colorizes",
287
+ "colourizing": "colorizing",
288
+ "colourless": "colorless",
289
+ "colours": "colors",
290
+ "commercialise": "commercialize",
291
+ "commercialised": "commercialized",
292
+ "commercialises": "commercializes",
293
+ "commercialising": "commercializing",
294
+ "compartmentalise": "compartmentalize",
295
+ "compartmentalised": "compartmentalized",
296
+ "compartmentalises": "compartmentalizes",
297
+ "compartmentalising": "compartmentalizing",
298
+ "computerise": "computerize",
299
+ "computerised": "computerized",
300
+ "computerises": "computerizes",
301
+ "computerising": "computerizing",
302
+ "conceptualise": "conceptualize",
303
+ "conceptualised": "conceptualized",
304
+ "conceptualises": "conceptualizes",
305
+ "conceptualising": "conceptualizing",
306
+ "connexion": "connection",
307
+ "connexions": "connections",
308
+ "contextualise": "contextualize",
309
+ "contextualised": "contextualized",
310
+ "contextualises": "contextualizes",
311
+ "contextualising": "contextualizing",
312
+ "cosier": "cozier",
313
+ "cosies": "cozies",
314
+ "cosiest": "coziest",
315
+ "cosily": "cozily",
316
+ "cosiness": "coziness",
317
+ "cosy": "cozy",
318
+ "councillor": "councilor",
319
+ "councillors": "councilors",
320
+ "counselled": "counseled",
321
+ "counselling": "counseling",
322
+ "counsellor": "counselor",
323
+ "counsellors": "counselors",
324
+ "crenelated": "crenellated",
325
+ "criminalise": "criminalize",
326
+ "criminalised": "criminalized",
327
+ "criminalises": "criminalizes",
328
+ "criminalising": "criminalizing",
329
+ "criticise": "criticize",
330
+ "criticised": "criticized",
331
+ "criticises": "criticizes",
332
+ "criticising": "criticizing",
333
+ "crueller": "crueler",
334
+ "cruellest": "cruelest",
335
+ "crystallisation": "crystallization",
336
+ "crystallise": "crystallize",
337
+ "crystallised": "crystallized",
338
+ "crystallises": "crystallizes",
339
+ "crystallising": "crystallizing",
340
+ "cudgelled": "cudgeled",
341
+ "cudgelling": "cudgeling",
342
+ "customise": "customize",
343
+ "customised": "customized",
344
+ "customises": "customizes",
345
+ "customising": "customizing",
346
+ "cypher": "cipher",
347
+ "cyphers": "ciphers",
348
+ "decentralisation": "decentralization",
349
+ "decentralise": "decentralize",
350
+ "decentralised": "decentralized",
351
+ "decentralises": "decentralizes",
352
+ "decentralising": "decentralizing",
353
+ "decriminalisation": "decriminalization",
354
+ "decriminalise": "decriminalize",
355
+ "decriminalised": "decriminalized",
356
+ "decriminalises": "decriminalizes",
357
+ "decriminalising": "decriminalizing",
358
+ "defence": "defense",
359
+ "defenceless": "defenseless",
360
+ "defences": "defenses",
361
+ "dehumanisation": "dehumanization",
362
+ "dehumanise": "dehumanize",
363
+ "dehumanised": "dehumanized",
364
+ "dehumanises": "dehumanizes",
365
+ "dehumanising": "dehumanizing",
366
+ "demeanour": "demeanor",
367
+ "demilitarisation": "demilitarization",
368
+ "demilitarise": "demilitarize",
369
+ "demilitarised": "demilitarized",
370
+ "demilitarises": "demilitarizes",
371
+ "demilitarising": "demilitarizing",
372
+ "demobilisation": "demobilization",
373
+ "demobilise": "demobilize",
374
+ "demobilised": "demobilized",
375
+ "demobilises": "demobilizes",
376
+ "demobilising": "demobilizing",
377
+ "democratisation": "democratization",
378
+ "democratise": "democratize",
379
+ "democratised": "democratized",
380
+ "democratises": "democratizes",
381
+ "democratising": "democratizing",
382
+ "demonise": "demonize",
383
+ "demonised": "demonized",
384
+ "demonises": "demonizes",
385
+ "demonising": "demonizing",
386
+ "demoralisation": "demoralization",
387
+ "demoralise": "demoralize",
388
+ "demoralised": "demoralized",
389
+ "demoralises": "demoralizes",
390
+ "demoralising": "demoralizing",
391
+ "denationalisation": "denationalization",
392
+ "denationalise": "denationalize",
393
+ "denationalised": "denationalized",
394
+ "denationalises": "denationalizes",
395
+ "denationalising": "denationalizing",
396
+ "deodorise": "deodorize",
397
+ "deodorised": "deodorized",
398
+ "deodorises": "deodorizes",
399
+ "deodorising": "deodorizing",
400
+ "depersonalise": "depersonalize",
401
+ "depersonalised": "depersonalized",
402
+ "depersonalises": "depersonalizes",
403
+ "depersonalising": "depersonalizing",
404
+ "deputise": "deputize",
405
+ "deputised": "deputized",
406
+ "deputises": "deputizes",
407
+ "deputising": "deputizing",
408
+ "desensitisation": "desensitization",
409
+ "desensitise": "desensitize",
410
+ "desensitised": "desensitized",
411
+ "desensitises": "desensitizes",
412
+ "desensitising": "desensitizing",
413
+ "destabilisation": "destabilization",
414
+ "destabilise": "destabilize",
415
+ "destabilised": "destabilized",
416
+ "destabilises": "destabilizes",
417
+ "destabilising": "destabilizing",
418
+ "dialled": "dialed",
419
+ "dialling": "dialing",
420
+ "dialogue": "dialog",
421
+ "dialogues": "dialogs",
422
+ "diarrhoea": "diarrhea",
423
+ "digitise": "digitize",
424
+ "digitised": "digitized",
425
+ "digitises": "digitizes",
426
+ "digitising": "digitizing",
427
+ "disc": "disk",
428
+ "discolour": "discolor",
429
+ "discoloured": "discolored",
430
+ "discolouring": "discoloring",
431
+ "discolours": "discolors",
432
+ "discs": "disks",
433
+ "disembowelled": "disemboweled",
434
+ "disembowelling": "disemboweling",
435
+ "disfavour": "disfavor",
436
+ "dishevelled": "disheveled",
437
+ "dishonour": "dishonor",
438
+ "dishonourable": "dishonorable",
439
+ "dishonourably": "dishonorably",
440
+ "dishonoured": "dishonored",
441
+ "dishonouring": "dishonoring",
442
+ "dishonours": "dishonors",
443
+ "disorganisation": "disorganization",
444
+ "disorganised": "disorganized",
445
+ "distil": "distill",
446
+ "distils": "distills",
447
+ "dramatisation": "dramatization",
448
+ "dramatisations": "dramatizations",
449
+ "dramatise": "dramatize",
450
+ "dramatised": "dramatized",
451
+ "dramatises": "dramatizes",
452
+ "dramatising": "dramatizing",
453
+ "draught": "draft",
454
+ "draughtboard": "draftboard",
455
+ "draughtboards": "draftboards",
456
+ "draughtier": "draftier",
457
+ "draughtiest": "draftiest",
458
+ "draughts": "drafts",
459
+ "draughtsman": "draftsman",
460
+ "draughtsmanship": "draftsmanship",
461
+ "draughtsmen": "draftsmen",
462
+ "draughtswoman": "draftswoman",
463
+ "draughtswomen": "draftswomen",
464
+ "draughty": "drafty",
465
+ "drivelled": "driveled",
466
+ "drivelling": "driveling",
467
+ "duelled": "dueled",
468
+ "duelling": "dueling",
469
+ "economise": "economize",
470
+ "economised": "economized",
471
+ "economises": "economizes",
472
+ "economising": "economizing",
473
+ "editorialise": "editorialize",
474
+ "editorialised": "editorialized",
475
+ "editorialises": "editorializes",
476
+ "editorialising": "editorializing",
477
+ "edoema": "edema",
478
+ "empathise": "empathize",
479
+ "empathised": "empathized",
480
+ "empathises": "empathizes",
481
+ "empathising": "empathizing",
482
+ "emphasise": "emphasize",
483
+ "emphasised": "emphasized",
484
+ "emphasises": "emphasizes",
485
+ "emphasising": "emphasizing",
486
+ "enamelled": "enameled",
487
+ "enamelling": "enameling",
488
+ "enamoured": "enamored",
489
+ "encyclopaedia": "encyclopedia",
490
+ "encyclopaedias": "encyclopedias",
491
+ "encyclopaedic": "encyclopedic",
492
+ "endeavour": "endeavor",
493
+ "endeavoured": "endeavored",
494
+ "endeavouring": "endeavoring",
495
+ "endeavours": "endeavors",
496
+ "energise": "energize",
497
+ "energised": "energized",
498
+ "energises": "energizes",
499
+ "energising": "energizing",
500
+ "enrol": "enroll",
501
+ "enrols": "enrolls",
502
+ "enthral": "enthrall",
503
+ "enthrals": "enthralls",
504
+ "epaulette": "epaulet",
505
+ "epaulettes": "epaulets",
506
+ "epicentre": "epicenter",
507
+ "epicentres": "epicenters",
508
+ "epilogue": "epilog",
509
+ "epilogues": "epilogs",
510
+ "epitomise": "epitomize",
511
+ "epitomised": "epitomized",
512
+ "epitomises": "epitomizes",
513
+ "epitomising": "epitomizing",
514
+ "equalisation": "equalization",
515
+ "equalise": "equalize",
516
+ "equalised": "equalized",
517
+ "equaliser": "equalizer",
518
+ "equalisers": "equalizers",
519
+ "equalises": "equalizes",
520
+ "equalising": "equalizing",
521
+ "eulogise": "eulogize",
522
+ "eulogised": "eulogized",
523
+ "eulogises": "eulogizes",
524
+ "eulogising": "eulogizing",
525
+ "evangelise": "evangelize",
526
+ "evangelised": "evangelized",
527
+ "evangelises": "evangelizes",
528
+ "evangelising": "evangelizing",
529
+ "exorcise": "exorcize",
530
+ "exorcised": "exorcized",
531
+ "exorcises": "exorcizes",
532
+ "exorcising": "exorcizing",
533
+ "extemporisation": "extemporization",
534
+ "extemporise": "extemporize",
535
+ "extemporised": "extemporized",
536
+ "extemporises": "extemporizes",
537
+ "extemporising": "extemporizing",
538
+ "externalisation": "externalization",
539
+ "externalisations": "externalizations",
540
+ "externalise": "externalize",
541
+ "externalised": "externalized",
542
+ "externalises": "externalizes",
543
+ "externalising": "externalizing",
544
+ "factorise": "factorize",
545
+ "factorised": "factorized",
546
+ "factorises": "factorizes",
547
+ "factorising": "factorizing",
548
+ "faecal": "fecal",
549
+ "faeces": "feces",
550
+ "familiarisation": "familiarization",
551
+ "familiarise": "familiarize",
552
+ "familiarised": "familiarized",
553
+ "familiarises": "familiarizes",
554
+ "familiarising": "familiarizing",
555
+ "fantasise": "fantasize",
556
+ "fantasised": "fantasized",
557
+ "fantasises": "fantasizes",
558
+ "fantasising": "fantasizing",
559
+ "favour": "favor",
560
+ "favourable": "favorable",
561
+ "favourably": "favorably",
562
+ "favoured": "favored",
563
+ "favouring": "favoring",
564
+ "favourite": "favorite",
565
+ "favourites": "favorites",
566
+ "favouritism": "favoritism",
567
+ "favours": "favors",
568
+ "feminise": "feminize",
569
+ "feminised": "feminized",
570
+ "feminises": "feminizes",
571
+ "feminising": "feminizing",
572
+ "fertilisation": "fertilization",
573
+ "fertilise": "fertilize",
574
+ "fertilised": "fertilized",
575
+ "fertiliser": "fertilizer",
576
+ "fertilisers": "fertilizers",
577
+ "fertilises": "fertilizes",
578
+ "fertilising": "fertilizing",
579
+ "fervour": "fervor",
580
+ "fibre": "fiber",
581
+ "fibreglass": "fiberglass",
582
+ "fibres": "fibers",
583
+ "fictionalisation": "fictionalization",
584
+ "fictionalisations": "fictionalizations",
585
+ "fictionalise": "fictionalize",
586
+ "fictionalised": "fictionalized",
587
+ "fictionalises": "fictionalizes",
588
+ "fictionalising": "fictionalizing",
589
+ "fillet": "filet",
590
+ "filleted": "fileted",
591
+ "filleting": "fileting",
592
+ "fillets": "filets",
593
+ "finalisation": "finalization",
594
+ "finalise": "finalize",
595
+ "finalised": "finalized",
596
+ "finalises": "finalizes",
597
+ "finalising": "finalizing",
598
+ "flautist": "flutist",
599
+ "flautists": "flutists",
600
+ "flavour": "flavor",
601
+ "flavoured": "flavored",
602
+ "flavouring": "flavoring",
603
+ "flavourings": "flavorings",
604
+ "flavourless": "flavorless",
605
+ "flavours": "flavors",
606
+ "flavoursome": "flavorsome",
607
+ "flyer / flier": "flier / flyer",
608
+ "foetal": "fetal",
609
+ "foetid": "fetid",
610
+ "foetus": "fetus",
611
+ "foetuses": "fetuses",
612
+ "formalisation": "formalization",
613
+ "formalise": "formalize",
614
+ "formalised": "formalized",
615
+ "formalises": "formalizes",
616
+ "formalising": "formalizing",
617
+ "fossilisation": "fossilization",
618
+ "fossilise": "fossilize",
619
+ "fossilised": "fossilized",
620
+ "fossilises": "fossilizes",
621
+ "fossilising": "fossilizing",
622
+ "fraternisation": "fraternization",
623
+ "fraternise": "fraternize",
624
+ "fraternised": "fraternized",
625
+ "fraternises": "fraternizes",
626
+ "fraternising": "fraternizing",
627
+ "fulfil": "fulfill",
628
+ "fulfilment": "fulfillment",
629
+ "fulfils": "fulfills",
630
+ "funnelled": "funneled",
631
+ "funnelling": "funneling",
632
+ "gage": "gauge",
633
+ "gaged": "gauged",
634
+ "gages": "gauges",
635
+ "gaging": "gauging",
636
+ "galvanise": "galvanize",
637
+ "galvanised": "galvanized",
638
+ "galvanises": "galvanizes",
639
+ "galvanising": "galvanizing",
640
+ "gambolled": "gamboled",
641
+ "gambolling": "gamboling",
642
+ "gaol": "jail",
643
+ "gaolbird": "jailbird",
644
+ "gaolbirds": "jailbirds",
645
+ "gaolbreak": "jailbreak",
646
+ "gaolbreaks": "jailbreaks",
647
+ "gaoled": "jailed",
648
+ "gaoler": "jailer",
649
+ "gaolers": "jailers",
650
+ "gaoling": "jailing",
651
+ "gaols": "jails",
652
+ "gasses": "gases",
653
+ "generalisation": "generalization",
654
+ "generalisations": "generalizations",
655
+ "generalise": "generalize",
656
+ "generalised": "generalized",
657
+ "generalises": "generalizes",
658
+ "generalising": "generalizing",
659
+ "ghettoise": "ghettoize",
660
+ "ghettoised": "ghettoized",
661
+ "ghettoises": "ghettoizes",
662
+ "ghettoising": "ghettoizing",
663
+ "gipsies": "gypsies",
664
+ "glamor": "glamour",
665
+ "glamorise": "glamorize",
666
+ "glamorised": "glamorized",
667
+ "glamorises": "glamorizes",
668
+ "glamorising": "glamorizing",
669
+ "globalisation": "globalization",
670
+ "globalise": "globalize",
671
+ "globalised": "globalized",
672
+ "globalises": "globalizes",
673
+ "globalising": "globalizing",
674
+ "glueing": "gluing",
675
+ "goitre": "goiter",
676
+ "goitres": "goiters",
677
+ "gonorrhoea": "gonorrhea",
678
+ "gramme": "gram",
679
+ "grammes": "grams",
680
+ "gravelled": "graveled",
681
+ "grey": "gray",
682
+ "greyed": "grayed",
683
+ "greying": "graying",
684
+ "greyish": "grayish",
685
+ "greyness": "grayness",
686
+ "greys": "grays",
687
+ "grovelled": "groveled",
688
+ "grovelling": "groveling",
689
+ "groyne": "groin",
690
+ "groynes": "groins",
691
+ "gruelling": "grueling",
692
+ "gruellingly": "gruelingly",
693
+ "gryphon": "griffin",
694
+ "gryphons": "griffins",
695
+ "gynaecological": "gynecological",
696
+ "gynaecologist": "gynecologist",
697
+ "gynaecologists": "gynecologists",
698
+ "gynaecology": "gynecology",
699
+ "haematological": "hematological",
700
+ "haematologist": "hematologist",
701
+ "haematologists": "hematologists",
702
+ "haematology": "hematology",
703
+ "haemoglobin": "hemoglobin",
704
+ "haemophilia": "hemophilia",
705
+ "haemophiliac": "hemophiliac",
706
+ "haemophiliacs": "hemophiliacs",
707
+ "haemorrhage": "hemorrhage",
708
+ "haemorrhaged": "hemorrhaged",
709
+ "haemorrhages": "hemorrhages",
710
+ "haemorrhaging": "hemorrhaging",
711
+ "haemorrhoids": "hemorrhoids",
712
+ "harbour": "harbor",
713
+ "harboured": "harbored",
714
+ "harbouring": "harboring",
715
+ "harbours": "harbors",
716
+ "harmonisation": "harmonization",
717
+ "harmonise": "harmonize",
718
+ "harmonised": "harmonized",
719
+ "harmonises": "harmonizes",
720
+ "harmonising": "harmonizing",
721
+ "homoeopath": "homeopath",
722
+ "homoeopathic": "homeopathic",
723
+ "homoeopaths": "homeopaths",
724
+ "homoeopathy": "homeopathy",
725
+ "homogenise": "homogenize",
726
+ "homogenised": "homogenized",
727
+ "homogenises": "homogenizes",
728
+ "homogenising": "homogenizing",
729
+ "honour": "honor",
730
+ "honourable": "honorable",
731
+ "honourably": "honorably",
732
+ "honoured": "honored",
733
+ "honouring": "honoring",
734
+ "honours": "honors",
735
+ "hospitalisation": "hospitalization",
736
+ "hospitalise": "hospitalize",
737
+ "hospitalised": "hospitalized",
738
+ "hospitalises": "hospitalizes",
739
+ "hospitalising": "hospitalizing",
740
+ "humanise": "humanize",
741
+ "humanised": "humanized",
742
+ "humanises": "humanizes",
743
+ "humanising": "humanizing",
744
+ "humour": "humor",
745
+ "humoured": "humored",
746
+ "humouring": "humoring",
747
+ "humourless": "humorless",
748
+ "humours": "humors",
749
+ "hybridise": "hybridize",
750
+ "hybridised": "hybridized",
751
+ "hybridises": "hybridizes",
752
+ "hybridising": "hybridizing",
753
+ "hypnotise": "hypnotize",
754
+ "hypnotised": "hypnotized",
755
+ "hypnotises": "hypnotizes",
756
+ "hypnotising": "hypnotizing",
757
+ "hypothesise": "hypothesize",
758
+ "hypothesised": "hypothesized",
759
+ "hypothesises": "hypothesizes",
760
+ "hypothesising": "hypothesizing",
761
+ "idealisation": "idealization",
762
+ "idealise": "idealize",
763
+ "idealised": "idealized",
764
+ "idealises": "idealizes",
765
+ "idealising": "idealizing",
766
+ "idolise": "idolize",
767
+ "idolised": "idolized",
768
+ "idolises": "idolizes",
769
+ "idolising": "idolizing",
770
+ "immobilisation": "immobilization",
771
+ "immobilise": "immobilize",
772
+ "immobilised": "immobilized",
773
+ "immobiliser": "immobilizer",
774
+ "immobilisers": "immobilizers",
775
+ "immobilises": "immobilizes",
776
+ "immobilising": "immobilizing",
777
+ "immortalise": "immortalize",
778
+ "immortalised": "immortalized",
779
+ "immortalises": "immortalizes",
780
+ "immortalising": "immortalizing",
781
+ "immunisation": "immunization",
782
+ "immunise": "immunize",
783
+ "immunised": "immunized",
784
+ "immunises": "immunizes",
785
+ "immunising": "immunizing",
786
+ "impanelled": "impaneled",
787
+ "impanelling": "impaneling",
788
+ "imperilled": "imperiled",
789
+ "imperilling": "imperiling",
790
+ "individualise": "individualize",
791
+ "individualised": "individualized",
792
+ "individualises": "individualizes",
793
+ "individualising": "individualizing",
794
+ "industrialise": "industrialize",
795
+ "industrialised": "industrialized",
796
+ "industrialises": "industrializes",
797
+ "industrialising": "industrializing",
798
+ "inflexion": "inflection",
799
+ "inflexions": "inflections",
800
+ "initialise": "initialize",
801
+ "initialised": "initialized",
802
+ "initialises": "initializes",
803
+ "initialising": "initializing",
804
+ "initialled": "initialed",
805
+ "initialling": "initialing",
806
+ "instal": "install",
807
+ "instalment": "installment",
808
+ "instalments": "installments",
809
+ "instals": "installs",
810
+ "instil": "instill",
811
+ "instils": "instills",
812
+ "institutionalisation": "institutionalization",
813
+ "institutionalise": "institutionalize",
814
+ "institutionalised": "institutionalized",
815
+ "institutionalises": "institutionalizes",
816
+ "institutionalising": "institutionalizing",
817
+ "intellectualise": "intellectualize",
818
+ "intellectualised": "intellectualized",
819
+ "intellectualises": "intellectualizes",
820
+ "intellectualising": "intellectualizing",
821
+ "internalisation": "internalization",
822
+ "internalise": "internalize",
823
+ "internalised": "internalized",
824
+ "internalises": "internalizes",
825
+ "internalising": "internalizing",
826
+ "internationalisation": "internationalization",
827
+ "internationalise": "internationalize",
828
+ "internationalised": "internationalized",
829
+ "internationalises": "internationalizes",
830
+ "internationalising": "internationalizing",
831
+ "ionisation": "ionization",
832
+ "ionise": "ionize",
833
+ "ionised": "ionized",
834
+ "ioniser": "ionizer",
835
+ "ionisers": "ionizers",
836
+ "ionises": "ionizes",
837
+ "ionising": "ionizing",
838
+ "italicise": "italicize",
839
+ "italicised": "italicized",
840
+ "italicises": "italicizes",
841
+ "italicising": "italicizing",
842
+ "itemise": "itemize",
843
+ "itemised": "itemized",
844
+ "itemises": "itemizes",
845
+ "itemising": "itemizing",
846
+ "jeopardise": "jeopardize",
847
+ "jeopardised": "jeopardized",
848
+ "jeopardises": "jeopardizes",
849
+ "jeopardising": "jeopardizing",
850
+ "jewelled": "jeweled",
851
+ "jeweller": "jeweler",
852
+ "jewellers": "jewelers",
853
+ "jewellery": "jewelry",
854
+ "judgement": "judgment",
855
+ "kilogramme": "kilogram",
856
+ "kilogrammes": "kilograms",
857
+ "kilometre": "kilometer",
858
+ "kilometres": "kilometers",
859
+ "labelled": "labeled",
860
+ "labelling": "labeling",
861
+ "labour": "labor",
862
+ "laboured": "labored",
863
+ "labourer": "laborer",
864
+ "labourers": "laborers",
865
+ "labouring": "laboring",
866
+ "labours": "labors",
867
+ "lacklustre": "lackluster",
868
+ "legalisation": "legalization",
869
+ "legalise": "legalize",
870
+ "legalised": "legalized",
871
+ "legalises": "legalizes",
872
+ "legalising": "legalizing",
873
+ "legitimise": "legitimize",
874
+ "legitimised": "legitimized",
875
+ "legitimises": "legitimizes",
876
+ "legitimising": "legitimizing",
877
+ "leukaemia": "leukemia",
878
+ "levelled": "leveled",
879
+ "leveller": "leveler",
880
+ "levellers": "levelers",
881
+ "levelling": "leveling",
882
+ "libelled": "libeled",
883
+ "libelling": "libeling",
884
+ "libellous": "libelous",
885
+ "liberalisation": "liberalization",
886
+ "liberalise": "liberalize",
887
+ "liberalised": "liberalized",
888
+ "liberalises": "liberalizes",
889
+ "liberalising": "liberalizing",
890
+ "licence": "license",
891
+ "licenced": "licensed",
892
+ "licences": "licenses",
893
+ "licencing": "licensing",
894
+ "likeable": "likable",
895
+ "lionisation": "lionization",
896
+ "lionise": "lionize",
897
+ "lionised": "lionized",
898
+ "lionises": "lionizes",
899
+ "lionising": "lionizing",
900
+ "liquidise": "liquidize",
901
+ "liquidised": "liquidized",
902
+ "liquidiser": "liquidizer",
903
+ "liquidisers": "liquidizers",
904
+ "liquidises": "liquidizes",
905
+ "liquidising": "liquidizing",
906
+ "litre": "liter",
907
+ "litres": "liters",
908
+ "localise": "localize",
909
+ "localised": "localized",
910
+ "localises": "localizes",
911
+ "localising": "localizing",
912
+ "louvre": "louver",
913
+ "louvred": "louvered",
914
+ "louvres": "louvers",
915
+ "lustre": "luster",
916
+ "magnetise": "magnetize",
917
+ "magnetised": "magnetized",
918
+ "magnetises": "magnetizes",
919
+ "magnetising": "magnetizing",
920
+ "manoeuvrability": "maneuverability",
921
+ "manoeuvrable": "maneuverable",
922
+ "manoeuvre": "maneuver",
923
+ "manoeuvred": "maneuvered",
924
+ "manoeuvres": "maneuvers",
925
+ "manoeuvring": "maneuvering",
926
+ "manoeuvrings": "maneuverings",
927
+ "marginalisation": "marginalization",
928
+ "marginalise": "marginalize",
929
+ "marginalised": "marginalized",
930
+ "marginalises": "marginalizes",
931
+ "marginalising": "marginalizing",
932
+ "marshalled": "marshaled",
933
+ "marshalling": "marshaling",
934
+ "marvelled": "marveled",
935
+ "marvelling": "marveling",
936
+ "marvellous": "marvelous",
937
+ "marvellously": "marvelously",
938
+ "materialisation": "materialization",
939
+ "materialise": "materialize",
940
+ "materialised": "materialized",
941
+ "materialises": "materializes",
942
+ "materialising": "materializing",
943
+ "maximisation": "maximization",
944
+ "maximise": "maximize",
945
+ "maximised": "maximized",
946
+ "maximises": "maximizes",
947
+ "maximising": "maximizing",
948
+ "meagre": "meager",
949
+ "mechanisation": "mechanization",
950
+ "mechanise": "mechanize",
951
+ "mechanised": "mechanized",
952
+ "mechanises": "mechanizes",
953
+ "mechanising": "mechanizing",
954
+ "mediaeval": "medieval",
955
+ "memorialise": "memorialize",
956
+ "memorialised": "memorialized",
957
+ "memorialises": "memorializes",
958
+ "memorialising": "memorializing",
959
+ "memorise": "memorize",
960
+ "memorised": "memorized",
961
+ "memorises": "memorizes",
962
+ "memorising": "memorizing",
963
+ "mesmerise": "mesmerize",
964
+ "mesmerised": "mesmerized",
965
+ "mesmerises": "mesmerizes",
966
+ "mesmerising": "mesmerizing",
967
+ "metabolise": "metabolize",
968
+ "metabolised": "metabolized",
969
+ "metabolises": "metabolizes",
970
+ "metabolising": "metabolizing",
971
+ "metre": "meter",
972
+ "metres": "meters",
973
+ "mhm": "hmm",
974
+ "micrometre": "micrometer",
975
+ "micrometres": "micrometers",
976
+ "militarise": "militarize",
977
+ "militarised": "militarized",
978
+ "militarises": "militarizes",
979
+ "militarising": "militarizing",
980
+ "milligramme": "milligram",
981
+ "milligrammes": "milligrams",
982
+ "millilitre": "milliliter",
983
+ "millilitres": "milliliters",
984
+ "millimetre": "millimeter",
985
+ "millimetres": "millimeters",
986
+ "miniaturisation": "miniaturization",
987
+ "miniaturise": "miniaturize",
988
+ "miniaturised": "miniaturized",
989
+ "miniaturises": "miniaturizes",
990
+ "miniaturising": "miniaturizing",
991
+ "minibusses": "minibuses",
992
+ "minimise": "minimize",
993
+ "minimised": "minimized",
994
+ "minimises": "minimizes",
995
+ "minimising": "minimizing",
996
+ "misbehaviour": "misbehavior",
997
+ "misdemeanour": "misdemeanor",
998
+ "misdemeanours": "misdemeanors",
999
+ "misspelt": "misspelled",
1000
+ "mitre": "miter",
1001
+ "mitres": "miters",
1002
+ "mm": "hmm",
1003
+ "mmm": "hmm",
1004
+ "mobilisation": "mobilization",
1005
+ "mobilise": "mobilize",
1006
+ "mobilised": "mobilized",
1007
+ "mobilises": "mobilizes",
1008
+ "mobilising": "mobilizing",
1009
+ "modelled": "modeled",
1010
+ "modeller": "modeler",
1011
+ "modellers": "modelers",
1012
+ "modelling": "modeling",
1013
+ "modernise": "modernize",
1014
+ "modernised": "modernized",
1015
+ "modernises": "modernizes",
1016
+ "modernising": "modernizing",
1017
+ "moisturise": "moisturize",
1018
+ "moisturised": "moisturized",
1019
+ "moisturiser": "moisturizer",
1020
+ "moisturisers": "moisturizers",
1021
+ "moisturises": "moisturizes",
1022
+ "moisturising": "moisturizing",
1023
+ "monologue": "monolog",
1024
+ "monologues": "monologs",
1025
+ "monopolisation": "monopolization",
1026
+ "monopolise": "monopolize",
1027
+ "monopolised": "monopolized",
1028
+ "monopolises": "monopolizes",
1029
+ "monopolising": "monopolizing",
1030
+ "moralise": "moralize",
1031
+ "moralised": "moralized",
1032
+ "moralises": "moralizes",
1033
+ "moralising": "moralizing",
1034
+ "motorised": "motorized",
1035
+ "mould": "mold",
1036
+ "moulded": "molded",
1037
+ "moulder": "molder",
1038
+ "mouldered": "moldered",
1039
+ "mouldering": "moldering",
1040
+ "moulders": "molders",
1041
+ "mouldier": "moldier",
1042
+ "mouldiest": "moldiest",
1043
+ "moulding": "molding",
1044
+ "mouldings": "moldings",
1045
+ "moulds": "molds",
1046
+ "mouldy": "moldy",
1047
+ "moult": "molt",
1048
+ "moulted": "molted",
1049
+ "moulting": "molting",
1050
+ "moults": "molts",
1051
+ "moustache": "mustache",
1052
+ "moustached": "mustached",
1053
+ "moustaches": "mustaches",
1054
+ "moustachioed": "mustachioed",
1055
+ "multicoloured": "multicolored",
1056
+ "nationalisation": "nationalization",
1057
+ "nationalisations": "nationalizations",
1058
+ "nationalise": "nationalize",
1059
+ "nationalised": "nationalized",
1060
+ "nationalises": "nationalizes",
1061
+ "nationalising": "nationalizing",
1062
+ "naturalisation": "naturalization",
1063
+ "naturalise": "naturalize",
1064
+ "naturalised": "naturalized",
1065
+ "naturalises": "naturalizes",
1066
+ "naturalising": "naturalizing",
1067
+ "neighbour": "neighbor",
1068
+ "neighbourhood": "neighborhood",
1069
+ "neighbourhoods": "neighborhoods",
1070
+ "neighbouring": "neighboring",
1071
+ "neighbourliness": "neighborliness",
1072
+ "neighbourly": "neighborly",
1073
+ "neighbours": "neighbors",
1074
+ "neutralisation": "neutralization",
1075
+ "neutralise": "neutralize",
1076
+ "neutralised": "neutralized",
1077
+ "neutralises": "neutralizes",
1078
+ "neutralising": "neutralizing",
1079
+ "normalisation": "normalization",
1080
+ "normalise": "normalize",
1081
+ "normalised": "normalized",
1082
+ "normalises": "normalizes",
1083
+ "normalising": "normalizing",
1084
+ "odour": "odor",
1085
+ "odourless": "odorless",
1086
+ "odours": "odors",
1087
+ "oesophagus": "esophagus",
1088
+ "oesophaguses": "esophaguses",
1089
+ "oestrogen": "estrogen",
1090
+ "offence": "offense",
1091
+ "offences": "offenses",
1092
+ "omelette": "omelet",
1093
+ "omelettes": "omelets",
1094
+ "optimise": "optimize",
1095
+ "optimised": "optimized",
1096
+ "optimises": "optimizes",
1097
+ "optimising": "optimizing",
1098
+ "organisation": "organization",
1099
+ "organisational": "organizational",
1100
+ "organisations": "organizations",
1101
+ "organise": "organize",
1102
+ "organised": "organized",
1103
+ "organiser": "organizer",
1104
+ "organisers": "organizers",
1105
+ "organises": "organizes",
1106
+ "organising": "organizing",
1107
+ "orthopaedic": "orthopedic",
1108
+ "orthopaedics": "orthopedics",
1109
+ "ostracise": "ostracize",
1110
+ "ostracised": "ostracized",
1111
+ "ostracises": "ostracizes",
1112
+ "ostracising": "ostracizing",
1113
+ "outmanoeuvre": "outmaneuver",
1114
+ "outmanoeuvred": "outmaneuvered",
1115
+ "outmanoeuvres": "outmaneuvers",
1116
+ "outmanoeuvring": "outmaneuvering",
1117
+ "overemphasise": "overemphasize",
1118
+ "overemphasised": "overemphasized",
1119
+ "overemphasises": "overemphasizes",
1120
+ "overemphasising": "overemphasizing",
1121
+ "oxidisation": "oxidization",
1122
+ "oxidise": "oxidize",
1123
+ "oxidised": "oxidized",
1124
+ "oxidises": "oxidizes",
1125
+ "oxidising": "oxidizing",
1126
+ "paederast": "pederast",
1127
+ "paederasts": "pederasts",
1128
+ "paediatric": "pediatric",
1129
+ "paediatrician": "pediatrician",
1130
+ "paediatricians": "pediatricians",
1131
+ "paediatrics": "pediatrics",
1132
+ "paedophile": "pedophile",
1133
+ "paedophiles": "pedophiles",
1134
+ "paedophilia": "pedophilia",
1135
+ "palaeolithic": "paleolithic",
1136
+ "palaeontologist": "paleontologist",
1137
+ "palaeontologists": "paleontologists",
1138
+ "palaeontology": "paleontology",
1139
+ "panelled": "paneled",
1140
+ "panelling": "paneling",
1141
+ "panellist": "panelist",
1142
+ "panellists": "panelists",
1143
+ "paralyse": "paralyze",
1144
+ "paralysed": "paralyzed",
1145
+ "paralyses": "paralyzes",
1146
+ "paralysing": "paralyzing",
1147
+ "parcelled": "parceled",
1148
+ "parcelling": "parceling",
1149
+ "parlour": "parlor",
1150
+ "parlours": "parlors",
1151
+ "particularise": "particularize",
1152
+ "particularised": "particularized",
1153
+ "particularises": "particularizes",
1154
+ "particularising": "particularizing",
1155
+ "passivisation": "passivization",
1156
+ "passivise": "passivize",
1157
+ "passivised": "passivized",
1158
+ "passivises": "passivizes",
1159
+ "passivising": "passivizing",
1160
+ "pasteurisation": "pasteurization",
1161
+ "pasteurise": "pasteurize",
1162
+ "pasteurised": "pasteurized",
1163
+ "pasteurises": "pasteurizes",
1164
+ "pasteurising": "pasteurizing",
1165
+ "patronise": "patronize",
1166
+ "patronised": "patronized",
1167
+ "patronises": "patronizes",
1168
+ "patronising": "patronizing",
1169
+ "patronisingly": "patronizingly",
1170
+ "pedalled": "pedaled",
1171
+ "pedalling": "pedaling",
1172
+ "pedestrianisation": "pedestrianization",
1173
+ "pedestrianise": "pedestrianize",
1174
+ "pedestrianised": "pedestrianized",
1175
+ "pedestrianises": "pedestrianizes",
1176
+ "pedestrianising": "pedestrianizing",
1177
+ "penalise": "penalize",
1178
+ "penalised": "penalized",
1179
+ "penalises": "penalizes",
1180
+ "penalising": "penalizing",
1181
+ "pencilled": "penciled",
1182
+ "pencilling": "penciling",
1183
+ "personalise": "personalize",
1184
+ "personalised": "personalized",
1185
+ "personalises": "personalizes",
1186
+ "personalising": "personalizing",
1187
+ "pharmacopoeia": "pharmacopeia",
1188
+ "pharmacopoeias": "pharmacopeias",
1189
+ "philosophise": "philosophize",
1190
+ "philosophised": "philosophized",
1191
+ "philosophises": "philosophizes",
1192
+ "philosophising": "philosophizing",
1193
+ "philtre": "filter",
1194
+ "philtres": "filters",
1195
+ "phoney": "phony",
1196
+ "plagiarise": "plagiarize",
1197
+ "plagiarised": "plagiarized",
1198
+ "plagiarises": "plagiarizes",
1199
+ "plagiarising": "plagiarizing",
1200
+ "plough": "plow",
1201
+ "ploughed": "plowed",
1202
+ "ploughing": "plowing",
1203
+ "ploughman": "plowman",
1204
+ "ploughmen": "plowmen",
1205
+ "ploughs": "plows",
1206
+ "ploughshare": "plowshare",
1207
+ "ploughshares": "plowshares",
1208
+ "polarisation": "polarization",
1209
+ "polarise": "polarize",
1210
+ "polarised": "polarized",
1211
+ "polarises": "polarizes",
1212
+ "polarising": "polarizing",
1213
+ "politicisation": "politicization",
1214
+ "politicise": "politicize",
1215
+ "politicised": "politicized",
1216
+ "politicises": "politicizes",
1217
+ "politicising": "politicizing",
1218
+ "popularisation": "popularization",
1219
+ "popularise": "popularize",
1220
+ "popularised": "popularized",
1221
+ "popularises": "popularizes",
1222
+ "popularising": "popularizing",
1223
+ "pouffe": "pouf",
1224
+ "pouffes": "poufs",
1225
+ "practise": "practice",
1226
+ "practised": "practiced",
1227
+ "practises": "practices",
1228
+ "practising": "practicing",
1229
+ "praesidium": "presidium",
1230
+ "praesidiums": "presidiums",
1231
+ "pressurisation": "pressurization",
1232
+ "pressurise": "pressurize",
1233
+ "pressurised": "pressurized",
1234
+ "pressurises": "pressurizes",
1235
+ "pressurising": "pressurizing",
1236
+ "pretence": "pretense",
1237
+ "pretences": "pretenses",
1238
+ "primaeval": "primeval",
1239
+ "prioritisation": "prioritization",
1240
+ "prioritise": "prioritize",
1241
+ "prioritised": "prioritized",
1242
+ "prioritises": "prioritizes",
1243
+ "prioritising": "prioritizing",
1244
+ "privatisation": "privatization",
1245
+ "privatisations": "privatizations",
1246
+ "privatise": "privatize",
1247
+ "privatised": "privatized",
1248
+ "privatises": "privatizes",
1249
+ "privatising": "privatizing",
1250
+ "professionalisation": "professionalization",
1251
+ "professionalise": "professionalize",
1252
+ "professionalised": "professionalized",
1253
+ "professionalises": "professionalizes",
1254
+ "professionalising": "professionalizing",
1255
+ "programme": "program",
1256
+ "programmes": "programs",
1257
+ "prologue": "prolog",
1258
+ "prologues": "prologs",
1259
+ "propagandise": "propagandize",
1260
+ "propagandised": "propagandized",
1261
+ "propagandises": "propagandizes",
1262
+ "propagandising": "propagandizing",
1263
+ "proselytise": "proselytize",
1264
+ "proselytised": "proselytized",
1265
+ "proselytiser": "proselytizer",
1266
+ "proselytisers": "proselytizers",
1267
+ "proselytises": "proselytizes",
1268
+ "proselytising": "proselytizing",
1269
+ "psychoanalyse": "psychoanalyze",
1270
+ "psychoanalysed": "psychoanalyzed",
1271
+ "psychoanalyses": "psychoanalyzes",
1272
+ "psychoanalysing": "psychoanalyzing",
1273
+ "publicise": "publicize",
1274
+ "publicised": "publicized",
1275
+ "publicises": "publicizes",
1276
+ "publicising": "publicizing",
1277
+ "pulverisation": "pulverization",
1278
+ "pulverise": "pulverize",
1279
+ "pulverised": "pulverized",
1280
+ "pulverises": "pulverizes",
1281
+ "pulverising": "pulverizing",
1282
+ "pummelled": "pummel",
1283
+ "pummelling": "pummeled",
1284
+ "pyjama": "pajama",
1285
+ "pyjamas": "pajamas",
1286
+ "pzazz": "pizzazz",
1287
+ "quarrelled": "quarreled",
1288
+ "quarrelling": "quarreling",
1289
+ "radicalise": "radicalize",
1290
+ "radicalised": "radicalized",
1291
+ "radicalises": "radicalizes",
1292
+ "radicalising": "radicalizing",
1293
+ "rancour": "rancor",
1294
+ "randomise": "randomize",
1295
+ "randomised": "randomized",
1296
+ "randomises": "randomizes",
1297
+ "randomising": "randomizing",
1298
+ "rationalisation": "rationalization",
1299
+ "rationalisations": "rationalizations",
1300
+ "rationalise": "rationalize",
1301
+ "rationalised": "rationalized",
1302
+ "rationalises": "rationalizes",
1303
+ "rationalising": "rationalizing",
1304
+ "ravelled": "raveled",
1305
+ "ravelling": "raveling",
1306
+ "realisable": "realizable",
1307
+ "realisation": "realization",
1308
+ "realisations": "realizations",
1309
+ "realise": "realize",
1310
+ "realised": "realized",
1311
+ "realises": "realizes",
1312
+ "realising": "realizing",
1313
+ "recognisable": "recognizable",
1314
+ "recognisably": "recognizably",
1315
+ "recognisance": "recognizance",
1316
+ "recognise": "recognize",
1317
+ "recognised": "recognized",
1318
+ "recognises": "recognizes",
1319
+ "recognising": "recognizing",
1320
+ "reconnoitre": "reconnoiter",
1321
+ "reconnoitred": "reconnoitered",
1322
+ "reconnoitres": "reconnoiters",
1323
+ "reconnoitring": "reconnoitering",
1324
+ "refuelled": "refueled",
1325
+ "refuelling": "refueling",
1326
+ "regularisation": "regularization",
1327
+ "regularise": "regularize",
1328
+ "regularised": "regularized",
1329
+ "regularises": "regularizes",
1330
+ "regularising": "regularizing",
1331
+ "remodelled": "remodeled",
1332
+ "remodelling": "remodeling",
1333
+ "remould": "remold",
1334
+ "remoulded": "remolded",
1335
+ "remoulding": "remolding",
1336
+ "remoulds": "remolds",
1337
+ "reorganisation": "reorganization",
1338
+ "reorganisations": "reorganizations",
1339
+ "reorganise": "reorganize",
1340
+ "reorganised": "reorganized",
1341
+ "reorganises": "reorganizes",
1342
+ "reorganising": "reorganizing",
1343
+ "revelled": "reveled",
1344
+ "reveller": "reveler",
1345
+ "revellers": "revelers",
1346
+ "revelling": "reveling",
1347
+ "revitalise": "revitalize",
1348
+ "revitalised": "revitalized",
1349
+ "revitalises": "revitalizes",
1350
+ "revitalising": "revitalizing",
1351
+ "revolutionise": "revolutionize",
1352
+ "revolutionised": "revolutionized",
1353
+ "revolutionises": "revolutionizes",
1354
+ "revolutionising": "revolutionizing",
1355
+ "rhapsodise": "rhapsodize",
1356
+ "rhapsodised": "rhapsodized",
1357
+ "rhapsodises": "rhapsodizes",
1358
+ "rhapsodising": "rhapsodizing",
1359
+ "rigour": "rigor",
1360
+ "rigours": "rigors",
1361
+ "ritualised": "ritualized",
1362
+ "rivalled": "rivaled",
1363
+ "rivalling": "rivaling",
1364
+ "romanticise": "romanticize",
1365
+ "romanticised": "romanticized",
1366
+ "romanticises": "romanticizes",
1367
+ "romanticising": "romanticizing",
1368
+ "rumour": "rumor",
1369
+ "rumoured": "rumored",
1370
+ "rumours": "rumors",
1371
+ "sabre": "saber",
1372
+ "sabres": "sabers",
1373
+ "saltpetre": "saltpeter",
1374
+ "sanitise": "sanitize",
1375
+ "sanitised": "sanitized",
1376
+ "sanitises": "sanitizes",
1377
+ "sanitising": "sanitizing",
1378
+ "satirise": "satirize",
1379
+ "satirised": "satirized",
1380
+ "satirises": "satirizes",
1381
+ "satirising": "satirizing",
1382
+ "saviour": "savior",
1383
+ "saviours": "saviors",
1384
+ "savour": "savor",
1385
+ "savoured": "savored",
1386
+ "savouries": "savories",
1387
+ "savouring": "savoring",
1388
+ "savours": "savors",
1389
+ "savoury": "savory",
1390
+ "scandalise": "scandalize",
1391
+ "scandalised": "scandalized",
1392
+ "scandalises": "scandalizes",
1393
+ "scandalising": "scandalizing",
1394
+ "sceptic": "skeptic",
1395
+ "sceptical": "skeptical",
1396
+ "sceptically": "skeptically",
1397
+ "scepticism": "skepticism",
1398
+ "sceptics": "skeptics",
1399
+ "sceptre": "scepter",
1400
+ "sceptres": "scepters",
1401
+ "scrutinise": "scrutinize",
1402
+ "scrutinised": "scrutinized",
1403
+ "scrutinises": "scrutinizes",
1404
+ "scrutinising": "scrutinizing",
1405
+ "secularisation": "secularization",
1406
+ "secularise": "secularize",
1407
+ "secularised": "secularized",
1408
+ "secularises": "secularizes",
1409
+ "secularising": "secularizing",
1410
+ "sensationalise": "sensationalize",
1411
+ "sensationalised": "sensationalized",
1412
+ "sensationalises": "sensationalizes",
1413
+ "sensationalising": "sensationalizing",
1414
+ "sensitise": "sensitize",
1415
+ "sensitised": "sensitized",
1416
+ "sensitises": "sensitizes",
1417
+ "sensitising": "sensitizing",
1418
+ "sentimentalise": "sentimentalize",
1419
+ "sentimentalised": "sentimentalized",
1420
+ "sentimentalises": "sentimentalizes",
1421
+ "sentimentalising": "sentimentalizing",
1422
+ "sepulchre": "sepulcher",
1423
+ "sepulchres": "sepulchers",
1424
+ "serialisation": "serialization",
1425
+ "serialisations": "serializations",
1426
+ "serialise": "serialize",
1427
+ "serialised": "serialized",
1428
+ "serialises": "serializes",
1429
+ "serialising": "serializing",
1430
+ "sermonise": "sermonize",
1431
+ "sermonised": "sermonized",
1432
+ "sermonises": "sermonizes",
1433
+ "sermonising": "sermonizing",
1434
+ "sheikh": "sheik",
1435
+ "shovelled": "shoveled",
1436
+ "shovelling": "shoveling",
1437
+ "shrivelled": "shriveled",
1438
+ "shrivelling": "shriveling",
1439
+ "signalise": "signalize",
1440
+ "signalised": "signalized",
1441
+ "signalises": "signalizes",
1442
+ "signalising": "signalizing",
1443
+ "signalled": "signaled",
1444
+ "signalling": "signaling",
1445
+ "smoulder": "smolder",
1446
+ "smouldered": "smoldered",
1447
+ "smouldering": "smoldering",
1448
+ "smoulders": "smolders",
1449
+ "snivelled": "sniveled",
1450
+ "snivelling": "sniveling",
1451
+ "snorkelled": "snorkeled",
1452
+ "snorkelling": "snorkeling",
1453
+ "snowplough": "snowplow",
1454
+ "snowploughs": "snowplow",
1455
+ "socialisation": "socialization",
1456
+ "socialise": "socialize",
1457
+ "socialised": "socialized",
1458
+ "socialises": "socializes",
1459
+ "socialising": "socializing",
1460
+ "sodomise": "sodomize",
1461
+ "sodomised": "sodomized",
1462
+ "sodomises": "sodomizes",
1463
+ "sodomising": "sodomizing",
1464
+ "solemnise": "solemnize",
1465
+ "solemnised": "solemnized",
1466
+ "solemnises": "solemnizes",
1467
+ "solemnising": "solemnizing",
1468
+ "sombre": "somber",
1469
+ "specialisation": "specialization",
1470
+ "specialisations": "specializations",
1471
+ "specialise": "specialize",
1472
+ "specialised": "specialized",
1473
+ "specialises": "specializes",
1474
+ "specialising": "specializing",
1475
+ "spectre": "specter",
1476
+ "spectres": "specters",
1477
+ "spiralled": "spiraled",
1478
+ "spiralling": "spiraling",
1479
+ "splendour": "splendor",
1480
+ "splendours": "splendors",
1481
+ "squirrelled": "squirreled",
1482
+ "squirrelling": "squirreling",
1483
+ "stabilisation": "stabilization",
1484
+ "stabilise": "stabilize",
1485
+ "stabilised": "stabilized",
1486
+ "stabiliser": "stabilizer",
1487
+ "stabilisers": "stabilizers",
1488
+ "stabilises": "stabilizes",
1489
+ "stabilising": "stabilizing",
1490
+ "standardisation": "standardization",
1491
+ "standardise": "standardize",
1492
+ "standardised": "standardized",
1493
+ "standardises": "standardizes",
1494
+ "standardising": "standardizing",
1495
+ "stencilled": "stenciled",
1496
+ "stencilling": "stenciling",
1497
+ "sterilisation": "sterilization",
1498
+ "sterilisations": "sterilizations",
1499
+ "sterilise": "sterilize",
1500
+ "sterilised": "sterilized",
1501
+ "steriliser": "sterilizer",
1502
+ "sterilisers": "sterilizers",
1503
+ "sterilises": "sterilizes",
1504
+ "sterilising": "sterilizing",
1505
+ "stigmatisation": "stigmatization",
1506
+ "stigmatise": "stigmatize",
1507
+ "stigmatised": "stigmatized",
1508
+ "stigmatises": "stigmatizes",
1509
+ "stigmatising": "stigmatizing",
1510
+ "storey": "story",
1511
+ "storeys": "stories",
1512
+ "subsidisation": "subsidization",
1513
+ "subsidise": "subsidize",
1514
+ "subsidised": "subsidized",
1515
+ "subsidiser": "subsidizer",
1516
+ "subsidisers": "subsidizers",
1517
+ "subsidises": "subsidizes",
1518
+ "subsidising": "subsidizing",
1519
+ "succour": "succor",
1520
+ "succoured": "succored",
1521
+ "succouring": "succoring",
1522
+ "succours": "succors",
1523
+ "sulphate": "sulfate",
1524
+ "sulphates": "sulfates",
1525
+ "sulphide": "sulfide",
1526
+ "sulphides": "sulfides",
1527
+ "sulphur": "sulfur",
1528
+ "sulphurous": "sulfurous",
1529
+ "summarise": "summarize",
1530
+ "summarised": "summarized",
1531
+ "summarises": "summarizes",
1532
+ "summarising": "summarizing",
1533
+ "swivelled": "swiveled",
1534
+ "swivelling": "swiveling",
1535
+ "symbolise": "symbolize",
1536
+ "symbolised": "symbolized",
1537
+ "symbolises": "symbolizes",
1538
+ "symbolising": "symbolizing",
1539
+ "sympathise": "sympathize",
1540
+ "sympathised": "sympathized",
1541
+ "sympathiser": "sympathizer",
1542
+ "sympathisers": "sympathizers",
1543
+ "sympathises": "sympathizes",
1544
+ "sympathising": "sympathizing",
1545
+ "synchronisation": "synchronization",
1546
+ "synchronise": "synchronize",
1547
+ "synchronised": "synchronized",
1548
+ "synchronises": "synchronizes",
1549
+ "synchronising": "synchronizing",
1550
+ "synthesise": "synthesize",
1551
+ "synthesised": "synthesized",
1552
+ "synthesiser": "synthesizer",
1553
+ "synthesisers": "synthesizers",
1554
+ "synthesises": "synthesizes",
1555
+ "synthesising": "synthesizing",
1556
+ "syphon": "siphon",
1557
+ "syphoned": "siphoned",
1558
+ "syphoning": "siphoning",
1559
+ "syphons": "siphons",
1560
+ "systematisation": "systematization",
1561
+ "systematise": "systematize",
1562
+ "systematised": "systematized",
1563
+ "systematises": "systematizes",
1564
+ "systematising": "systematizing",
1565
+ "tantalise": "tantalize",
1566
+ "tantalised": "tantalized",
1567
+ "tantalises": "tantalizes",
1568
+ "tantalising": "tantalizing",
1569
+ "tantalisingly": "tantalizingly",
1570
+ "tasselled": "tasseled",
1571
+ "technicolour": "technicolor",
1572
+ "temporise": "temporize",
1573
+ "temporised": "temporized",
1574
+ "temporises": "temporizes",
1575
+ "temporising": "temporizing",
1576
+ "tenderise": "tenderize",
1577
+ "tenderised": "tenderized",
1578
+ "tenderises": "tenderizes",
1579
+ "tenderising": "tenderizing",
1580
+ "terrorise": "terrorize",
1581
+ "terrorised": "terrorized",
1582
+ "terrorises": "terrorizes",
1583
+ "terrorising": "terrorizing",
1584
+ "theatre": "theater",
1585
+ "theatregoer": "theatergoer",
1586
+ "theatregoers": "theatergoers",
1587
+ "theatres": "theaters",
1588
+ "theorise": "theorize",
1589
+ "theorised": "theorized",
1590
+ "theorises": "theorizes",
1591
+ "theorising": "theorizing",
1592
+ "tonne": "ton",
1593
+ "tonnes": "tons",
1594
+ "towelled": "toweled",
1595
+ "towelling": "toweling",
1596
+ "toxaemia": "toxemia",
1597
+ "tranquillise": "tranquilize",
1598
+ "tranquillised": "tranquilized",
1599
+ "tranquilliser": "tranquilizer",
1600
+ "tranquillisers": "tranquilizers",
1601
+ "tranquillises": "tranquilizes",
1602
+ "tranquillising": "tranquilizing",
1603
+ "tranquillity": "tranquility",
1604
+ "tranquillize": "tranquilize",
1605
+ "tranquillized": "tranquilized",
1606
+ "tranquillizer": "tranquilizer",
1607
+ "tranquillizers": "tranquilizers",
1608
+ "tranquillizes": "tranquilizes",
1609
+ "tranquillizing": "tranquilizing",
1610
+ "tranquilly": "tranquility",
1611
+ "transistorised": "transistorized",
1612
+ "traumatise": "traumatize",
1613
+ "traumatised": "traumatized",
1614
+ "traumatises": "traumatizes",
1615
+ "traumatising": "traumatizing",
1616
+ "travelled": "traveled",
1617
+ "traveller": "traveler",
1618
+ "travellers": "travelers",
1619
+ "travelling": "traveling",
1620
+ "travelog": "travelogue",
1621
+ "travelogs": "travelogues",
1622
+ "trialled": "trialed",
1623
+ "trialling": "trialing",
1624
+ "tricolour": "tricolor",
1625
+ "tricolours": "tricolors",
1626
+ "trivialise": "trivialize",
1627
+ "trivialised": "trivialized",
1628
+ "trivialises": "trivializes",
1629
+ "trivialising": "trivializing",
1630
+ "tumour": "tumor",
1631
+ "tumours": "tumors",
1632
+ "tunnelled": "tunneled",
1633
+ "tunnelling": "tunneling",
1634
+ "tyrannise": "tyrannize",
1635
+ "tyrannised": "tyrannized",
1636
+ "tyrannises": "tyrannizes",
1637
+ "tyrannising": "tyrannizing",
1638
+ "tyre": "tire",
1639
+ "tyres": "tires",
1640
+ "unauthorised": "unauthorized",
1641
+ "uncivilised": "uncivilized",
1642
+ "underutilised": "underutilized",
1643
+ "unequalled": "unequaled",
1644
+ "unfavourable": "unfavorable",
1645
+ "unfavourably": "unfavorably",
1646
+ "unionisation": "unionization",
1647
+ "unionise": "unionize",
1648
+ "unionised": "unionized",
1649
+ "unionises": "unionizes",
1650
+ "unionising": "unionizing",
1651
+ "unorganised": "unorganized",
1652
+ "unravelled": "unraveled",
1653
+ "unravelling": "unraveling",
1654
+ "unrecognisable": "unrecognizable",
1655
+ "unrecognised": "unrecognized",
1656
+ "unrivalled": "unrivaled",
1657
+ "unsavoury": "unsavory",
1658
+ "untrammelled": "untrammeled",
1659
+ "urbanisation": "urbanization",
1660
+ "urbanise": "urbanize",
1661
+ "urbanised": "urbanized",
1662
+ "urbanises": "urbanizes",
1663
+ "urbanising": "urbanizing",
1664
+ "utilisable": "utilizable",
1665
+ "utilisation": "utilization",
1666
+ "utilise": "utilize",
1667
+ "utilised": "utilized",
1668
+ "utilises": "utilizes",
1669
+ "utilising": "utilizing",
1670
+ "valour": "valor",
1671
+ "vandalise": "vandalize",
1672
+ "vandalised": "vandalized",
1673
+ "vandalises": "vandalizes",
1674
+ "vandalising": "vandalizing",
1675
+ "vaporisation": "vaporization",
1676
+ "vaporise": "vaporize",
1677
+ "vaporised": "vaporized",
1678
+ "vaporises": "vaporizes",
1679
+ "vaporising": "vaporizing",
1680
+ "vapour": "vapor",
1681
+ "vapours": "vapors",
1682
+ "verbalise": "verbalize",
1683
+ "verbalised": "verbalized",
1684
+ "verbalises": "verbalizes",
1685
+ "verbalising": "verbalizing",
1686
+ "victimisation": "victimization",
1687
+ "victimise": "victimize",
1688
+ "victimised": "victimized",
1689
+ "victimises": "victimizes",
1690
+ "victimising": "victimizing",
1691
+ "videodisc": "videodisk",
1692
+ "videodiscs": "videodisks",
1693
+ "vigour": "vigor",
1694
+ "visualisation": "visualization",
1695
+ "visualisations": "visualizations",
1696
+ "visualise": "visualize",
1697
+ "visualised": "visualized",
1698
+ "visualises": "visualizes",
1699
+ "visualising": "visualizing",
1700
+ "vocalisation": "vocalization",
1701
+ "vocalisations": "vocalizations",
1702
+ "vocalise": "vocalize",
1703
+ "vocalised": "vocalized",
1704
+ "vocalises": "vocalizes",
1705
+ "vocalising": "vocalizing",
1706
+ "vulcanised": "vulcanized",
1707
+ "vulgarisation": "vulgarization",
1708
+ "vulgarise": "vulgarize",
1709
+ "vulgarised": "vulgarized",
1710
+ "vulgarises": "vulgarizes",
1711
+ "vulgarising": "vulgarizing",
1712
+ "waggon": "wagon",
1713
+ "waggons": "wagons",
1714
+ "watercolour": "watercolor",
1715
+ "watercolours": "watercolors",
1716
+ "weaselled": "weaseled",
1717
+ "weaselling": "weaseling",
1718
+ "westernisation": "westernization",
1719
+ "westernise": "westernize",
1720
+ "westernised": "westernized",
1721
+ "westernises": "westernizes",
1722
+ "westernising": "westernizing",
1723
+ "womanise": "womanize",
1724
+ "womanised": "womanized",
1725
+ "womaniser": "womanizer",
1726
+ "womanisers": "womanizers",
1727
+ "womanises": "womanizes",
1728
+ "womanising": "womanizing",
1729
+ "woollen": "woolen",
1730
+ "woollens": "woolens",
1731
+ "woollies": "woolies",
1732
+ "woolly": "wooly",
1733
+ "worshipped": "worshiped",
1734
+ "worshipper": "worshiper",
1735
+ "worshipping": "worshiping",
1736
+ "yodelled": "yodeled",
1737
+ "yodelling": "yodeling",
1738
+ "yoghourt": "yogurt",
1739
+ "yoghourts": "yogurts",
1740
+ "yoghurt": "yogurt",
1741
+ "yoghurts": "yogurts"
1742
+ }
preprocessor_config.json ADDED
The diff for this file is too large to render. See raw diff
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9a57377207996ba226819d182f420159e5815527efa57a72eafa1b28f7cb1b51
3
+ size 967102601
run.sh ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ python run_speech_recognition_seq2seq_streaming.py \
2
+ --model_name_or_path="openai/whisper-small" \
3
+ --dataset_name="mozilla-foundation/common_voice_11_0" \
4
+ --dataset_config_name="ba" \
5
+ --language="bashkir" \
6
+ --train_split_name="train+validation" \
7
+ --eval_split_name="test" \
8
+ --model_index_name="Whisper Small Bashkir" \
9
+ --max_steps="30000" \
10
+ --output_dir="./" \
11
+ --per_device_train_batch_size="16" \
12
+ --per_device_eval_batch_size="8" \
13
+ --logging_steps="100" \
14
+ --learning_rate="1e-5" \
15
+ --warmup_steps="500" \
16
+ --evaluation_strategy="steps" \
17
+ --eval_steps="2000" \
18
+ --save_strategy="steps" \
19
+ --save_steps="2000" \
20
+ --generation_max_length="225" \
21
+ --length_column_name="input_length" \
22
+ --max_duration_in_seconds="30" \
23
+ --text_column_name="sentence" \
24
+ --freeze_feature_encoder="False" \
25
+ --report_to="tensorboard" \
26
+ --metric_for_best_model="wer" \
27
+ --greater_is_better="False" \
28
+ --load_best_model_at_end \
29
+ --gradient_checkpointing \
30
+ --fp16 \
31
+ --overwrite_output_dir \
32
+ --do_train \
33
+ --do_eval \
34
+ --predict_with_generate \
35
+ --do_normalize_eval \
36
+ --streaming \
37
+ --use_auth_token
special_tokens_map.json ADDED
@@ -0,0 +1,133 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|endoftext|>",
4
+ "<|startoftranscript|>",
5
+ "<|en|>",
6
+ "<|zh|>",
7
+ "<|de|>",
8
+ "<|es|>",
9
+ "<|ru|>",
10
+ "<|ko|>",
11
+ "<|fr|>",
12
+ "<|ja|>",
13
+ "<|pt|>",
14
+ "<|tr|>",
15
+ "<|pl|>",
16
+ "<|ca|>",
17
+ "<|nl|>",
18
+ "<|ar|>",
19
+ "<|sv|>",
20
+ "<|it|>",
21
+ "<|id|>",
22
+ "<|hi|>",
23
+ "<|fi|>",
24
+ "<|vi|>",
25
+ "<|iw|>",
26
+ "<|uk|>",
27
+ "<|el|>",
28
+ "<|ms|>",
29
+ "<|cs|>",
30
+ "<|ro|>",
31
+ "<|da|>",
32
+ "<|hu|>",
33
+ "<|ta|>",
34
+ "<|no|>",
35
+ "<|th|>",
36
+ "<|ur|>",
37
+ "<|hr|>",
38
+ "<|bg|>",
39
+ "<|lt|>",
40
+ "<|la|>",
41
+ "<|mi|>",
42
+ "<|ml|>",
43
+ "<|cy|>",
44
+ "<|sk|>",
45
+ "<|te|>",
46
+ "<|fa|>",
47
+ "<|lv|>",
48
+ "<|bn|>",
49
+ "<|sr|>",
50
+ "<|az|>",
51
+ "<|sl|>",
52
+ "<|kn|>",
53
+ "<|et|>",
54
+ "<|mk|>",
55
+ "<|br|>",
56
+ "<|eu|>",
57
+ "<|is|>",
58
+ "<|hy|>",
59
+ "<|ne|>",
60
+ "<|mn|>",
61
+ "<|bs|>",
62
+ "<|kk|>",
63
+ "<|sq|>",
64
+ "<|sw|>",
65
+ "<|gl|>",
66
+ "<|mr|>",
67
+ "<|pa|>",
68
+ "<|si|>",
69
+ "<|km|>",
70
+ "<|sn|>",
71
+ "<|yo|>",
72
+ "<|so|>",
73
+ "<|af|>",
74
+ "<|oc|>",
75
+ "<|ka|>",
76
+ "<|be|>",
77
+ "<|tg|>",
78
+ "<|sd|>",
79
+ "<|gu|>",
80
+ "<|am|>",
81
+ "<|yi|>",
82
+ "<|lo|>",
83
+ "<|uz|>",
84
+ "<|fo|>",
85
+ "<|ht|>",
86
+ "<|ps|>",
87
+ "<|tk|>",
88
+ "<|nn|>",
89
+ "<|mt|>",
90
+ "<|sa|>",
91
+ "<|lb|>",
92
+ "<|my|>",
93
+ "<|bo|>",
94
+ "<|tl|>",
95
+ "<|mg|>",
96
+ "<|as|>",
97
+ "<|tt|>",
98
+ "<|haw|>",
99
+ "<|ln|>",
100
+ "<|ha|>",
101
+ "<|ba|>",
102
+ "<|jw|>",
103
+ "<|su|>",
104
+ "<|translate|>",
105
+ "<|transcribe|>",
106
+ "<|startoflm|>",
107
+ "<|startofprev|>",
108
+ "<|nocaptions|>",
109
+ "<|notimestamps|>"
110
+ ],
111
+ "bos_token": {
112
+ "content": "<|endoftext|>",
113
+ "lstrip": false,
114
+ "normalized": true,
115
+ "rstrip": false,
116
+ "single_word": false
117
+ },
118
+ "eos_token": {
119
+ "content": "<|endoftext|>",
120
+ "lstrip": false,
121
+ "normalized": true,
122
+ "rstrip": false,
123
+ "single_word": false
124
+ },
125
+ "pad_token": "<|endoftext|>",
126
+ "unk_token": {
127
+ "content": "",
128
+ "lstrip": false,
129
+ "normalized": true,
130
+ "rstrip": false,
131
+ "single_word": false
132
+ }
133
+ }
tokenizer_config.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": false,
3
+ "add_prefix_space": false,
4
+ "bos_token": {
5
+ "__type": "AddedToken",
6
+ "content": "<|endoftext|>",
7
+ "lstrip": false,
8
+ "normalized": true,
9
+ "rstrip": false,
10
+ "single_word": false
11
+ },
12
+ "eos_token": {
13
+ "__type": "AddedToken",
14
+ "content": "<|endoftext|>",
15
+ "lstrip": false,
16
+ "normalized": true,
17
+ "rstrip": false,
18
+ "single_word": false
19
+ },
20
+ "errors": "replace",
21
+ "model_max_length": 1024,
22
+ "name_or_path": "openai/whisper-small",
23
+ "pad_token": null,
24
+ "processor_class": "WhisperProcessor",
25
+ "return_attention_mask": false,
26
+ "special_tokens_map_file": null,
27
+ "tokenizer_class": "WhisperTokenizer",
28
+ "unk_token": {
29
+ "__type": "AddedToken",
30
+ "content": "",
31
+ "lstrip": false,
32
+ "normalized": true,
33
+ "rstrip": false,
34
+ "single_word": false
35
+ }
36
+ }
train_results.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
1
+ {
2
+ "epoch": 16.03,
3
+ "train_loss": 0.07104161073341966,
4
+ "train_runtime": 196903.7961,
5
+ "train_samples_per_second": 2.438,
6
+ "train_steps_per_second": 0.152
7
+ }
trainer_state.json ADDED
@@ -0,0 +1,1960 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": 15.072300680807968,
3
+ "best_model_checkpoint": "./checkpoint-30000",
4
+ "epoch": 16.029333333333334,
5
+ "global_step": 30000,
6
+ "is_hyper_param_search": false,
7
+ "is_local_process_zero": true,
8
+ "is_world_process_zero": true,
9
+ "log_history": [
10
+ {
11
+ "epoch": 0.0,
12
+ "learning_rate": 1.94e-06,
13
+ "loss": 2.2038,
14
+ "step": 100
15
+ },
16
+ {
17
+ "epoch": 0.01,
18
+ "learning_rate": 3.94e-06,
19
+ "loss": 0.7125,
20
+ "step": 200
21
+ },
22
+ {
23
+ "epoch": 0.01,
24
+ "learning_rate": 5.94e-06,
25
+ "loss": 0.4459,
26
+ "step": 300
27
+ },
28
+ {
29
+ "epoch": 0.01,
30
+ "learning_rate": 7.94e-06,
31
+ "loss": 0.3765,
32
+ "step": 400
33
+ },
34
+ {
35
+ "epoch": 0.02,
36
+ "learning_rate": 9.940000000000001e-06,
37
+ "loss": 0.3301,
38
+ "step": 500
39
+ },
40
+ {
41
+ "epoch": 0.02,
42
+ "learning_rate": 9.967118644067797e-06,
43
+ "loss": 0.3847,
44
+ "step": 600
45
+ },
46
+ {
47
+ "epoch": 0.02,
48
+ "learning_rate": 9.933220338983051e-06,
49
+ "loss": 0.3367,
50
+ "step": 700
51
+ },
52
+ {
53
+ "epoch": 0.03,
54
+ "learning_rate": 9.899322033898305e-06,
55
+ "loss": 0.303,
56
+ "step": 800
57
+ },
58
+ {
59
+ "epoch": 0.03,
60
+ "learning_rate": 9.86542372881356e-06,
61
+ "loss": 0.2564,
62
+ "step": 900
63
+ },
64
+ {
65
+ "epoch": 0.03,
66
+ "learning_rate": 9.831525423728815e-06,
67
+ "loss": 0.2271,
68
+ "step": 1000
69
+ },
70
+ {
71
+ "epoch": 0.04,
72
+ "learning_rate": 9.797627118644069e-06,
73
+ "loss": 0.2222,
74
+ "step": 1100
75
+ },
76
+ {
77
+ "epoch": 0.04,
78
+ "learning_rate": 9.763728813559323e-06,
79
+ "loss": 0.2269,
80
+ "step": 1200
81
+ },
82
+ {
83
+ "epoch": 0.04,
84
+ "learning_rate": 9.729830508474576e-06,
85
+ "loss": 0.196,
86
+ "step": 1300
87
+ },
88
+ {
89
+ "epoch": 0.05,
90
+ "learning_rate": 9.695932203389831e-06,
91
+ "loss": 0.2159,
92
+ "step": 1400
93
+ },
94
+ {
95
+ "epoch": 0.05,
96
+ "learning_rate": 9.662033898305085e-06,
97
+ "loss": 0.2502,
98
+ "step": 1500
99
+ },
100
+ {
101
+ "epoch": 0.05,
102
+ "learning_rate": 9.62813559322034e-06,
103
+ "loss": 0.2134,
104
+ "step": 1600
105
+ },
106
+ {
107
+ "epoch": 0.06,
108
+ "learning_rate": 9.594237288135594e-06,
109
+ "loss": 0.1912,
110
+ "step": 1700
111
+ },
112
+ {
113
+ "epoch": 0.06,
114
+ "learning_rate": 9.56033898305085e-06,
115
+ "loss": 0.2222,
116
+ "step": 1800
117
+ },
118
+ {
119
+ "epoch": 1.0,
120
+ "learning_rate": 9.526440677966103e-06,
121
+ "loss": 0.2021,
122
+ "step": 1900
123
+ },
124
+ {
125
+ "epoch": 1.01,
126
+ "learning_rate": 9.492542372881357e-06,
127
+ "loss": 0.1637,
128
+ "step": 2000
129
+ },
130
+ {
131
+ "epoch": 1.01,
132
+ "eval_loss": 0.25550365447998047,
133
+ "eval_runtime": 7151.6815,
134
+ "eval_samples_per_second": 2.033,
135
+ "eval_steps_per_second": 0.254,
136
+ "eval_wer": 26.468238339053617,
137
+ "step": 2000
138
+ },
139
+ {
140
+ "epoch": 1.01,
141
+ "learning_rate": 9.45864406779661e-06,
142
+ "loss": 0.1463,
143
+ "step": 2100
144
+ },
145
+ {
146
+ "epoch": 1.01,
147
+ "learning_rate": 9.424745762711865e-06,
148
+ "loss": 0.1564,
149
+ "step": 2200
150
+ },
151
+ {
152
+ "epoch": 1.02,
153
+ "learning_rate": 9.390847457627119e-06,
154
+ "loss": 0.1364,
155
+ "step": 2300
156
+ },
157
+ {
158
+ "epoch": 1.02,
159
+ "learning_rate": 9.356949152542374e-06,
160
+ "loss": 0.1658,
161
+ "step": 2400
162
+ },
163
+ {
164
+ "epoch": 1.02,
165
+ "learning_rate": 9.323050847457628e-06,
166
+ "loss": 0.1629,
167
+ "step": 2500
168
+ },
169
+ {
170
+ "epoch": 1.03,
171
+ "learning_rate": 9.289152542372882e-06,
172
+ "loss": 0.152,
173
+ "step": 2600
174
+ },
175
+ {
176
+ "epoch": 1.03,
177
+ "learning_rate": 9.255254237288135e-06,
178
+ "loss": 0.1301,
179
+ "step": 2700
180
+ },
181
+ {
182
+ "epoch": 1.03,
183
+ "learning_rate": 9.22135593220339e-06,
184
+ "loss": 0.1091,
185
+ "step": 2800
186
+ },
187
+ {
188
+ "epoch": 1.04,
189
+ "learning_rate": 9.187457627118644e-06,
190
+ "loss": 0.1064,
191
+ "step": 2900
192
+ },
193
+ {
194
+ "epoch": 1.04,
195
+ "learning_rate": 9.1535593220339e-06,
196
+ "loss": 0.1137,
197
+ "step": 3000
198
+ },
199
+ {
200
+ "epoch": 1.04,
201
+ "learning_rate": 9.119661016949153e-06,
202
+ "loss": 0.1096,
203
+ "step": 3100
204
+ },
205
+ {
206
+ "epoch": 1.05,
207
+ "learning_rate": 9.085762711864408e-06,
208
+ "loss": 0.1067,
209
+ "step": 3200
210
+ },
211
+ {
212
+ "epoch": 1.05,
213
+ "learning_rate": 9.051864406779662e-06,
214
+ "loss": 0.1277,
215
+ "step": 3300
216
+ },
217
+ {
218
+ "epoch": 1.05,
219
+ "learning_rate": 9.017966101694916e-06,
220
+ "loss": 0.1131,
221
+ "step": 3400
222
+ },
223
+ {
224
+ "epoch": 1.06,
225
+ "learning_rate": 8.98406779661017e-06,
226
+ "loss": 0.1024,
227
+ "step": 3500
228
+ },
229
+ {
230
+ "epoch": 1.06,
231
+ "learning_rate": 8.950169491525425e-06,
232
+ "loss": 0.107,
233
+ "step": 3600
234
+ },
235
+ {
236
+ "epoch": 2.0,
237
+ "learning_rate": 8.91627118644068e-06,
238
+ "loss": 0.1422,
239
+ "step": 3700
240
+ },
241
+ {
242
+ "epoch": 2.01,
243
+ "learning_rate": 8.882372881355933e-06,
244
+ "loss": 0.1602,
245
+ "step": 3800
246
+ },
247
+ {
248
+ "epoch": 2.01,
249
+ "learning_rate": 8.848474576271187e-06,
250
+ "loss": 0.1187,
251
+ "step": 3900
252
+ },
253
+ {
254
+ "epoch": 2.01,
255
+ "learning_rate": 8.81457627118644e-06,
256
+ "loss": 0.1375,
257
+ "step": 4000
258
+ },
259
+ {
260
+ "epoch": 2.01,
261
+ "eval_loss": 0.22229675948619843,
262
+ "eval_runtime": 7004.2343,
263
+ "eval_samples_per_second": 2.076,
264
+ "eval_steps_per_second": 0.26,
265
+ "eval_wer": 21.539413717436563,
266
+ "step": 4000
267
+ },
268
+ {
269
+ "epoch": 2.02,
270
+ "learning_rate": 8.780677966101696e-06,
271
+ "loss": 0.1246,
272
+ "step": 4100
273
+ },
274
+ {
275
+ "epoch": 2.02,
276
+ "learning_rate": 8.74677966101695e-06,
277
+ "loss": 0.1577,
278
+ "step": 4200
279
+ },
280
+ {
281
+ "epoch": 2.02,
282
+ "learning_rate": 8.712881355932205e-06,
283
+ "loss": 0.1267,
284
+ "step": 4300
285
+ },
286
+ {
287
+ "epoch": 2.03,
288
+ "learning_rate": 8.678983050847459e-06,
289
+ "loss": 0.1178,
290
+ "step": 4400
291
+ },
292
+ {
293
+ "epoch": 2.03,
294
+ "learning_rate": 8.645084745762712e-06,
295
+ "loss": 0.1335,
296
+ "step": 4500
297
+ },
298
+ {
299
+ "epoch": 2.03,
300
+ "learning_rate": 8.611186440677966e-06,
301
+ "loss": 0.1288,
302
+ "step": 4600
303
+ },
304
+ {
305
+ "epoch": 2.04,
306
+ "learning_rate": 8.577288135593221e-06,
307
+ "loss": 0.1592,
308
+ "step": 4700
309
+ },
310
+ {
311
+ "epoch": 2.04,
312
+ "learning_rate": 8.543389830508475e-06,
313
+ "loss": 0.161,
314
+ "step": 4800
315
+ },
316
+ {
317
+ "epoch": 2.04,
318
+ "learning_rate": 8.509830508474578e-06,
319
+ "loss": 0.1658,
320
+ "step": 4900
321
+ },
322
+ {
323
+ "epoch": 2.05,
324
+ "learning_rate": 8.475932203389831e-06,
325
+ "loss": 0.12,
326
+ "step": 5000
327
+ },
328
+ {
329
+ "epoch": 2.05,
330
+ "learning_rate": 8.442033898305085e-06,
331
+ "loss": 0.1518,
332
+ "step": 5100
333
+ },
334
+ {
335
+ "epoch": 2.05,
336
+ "learning_rate": 8.40813559322034e-06,
337
+ "loss": 0.1806,
338
+ "step": 5200
339
+ },
340
+ {
341
+ "epoch": 2.06,
342
+ "learning_rate": 8.374237288135594e-06,
343
+ "loss": 0.1206,
344
+ "step": 5300
345
+ },
346
+ {
347
+ "epoch": 2.06,
348
+ "learning_rate": 8.340338983050849e-06,
349
+ "loss": 0.1254,
350
+ "step": 5400
351
+ },
352
+ {
353
+ "epoch": 3.0,
354
+ "learning_rate": 8.306440677966103e-06,
355
+ "loss": 0.1198,
356
+ "step": 5500
357
+ },
358
+ {
359
+ "epoch": 3.0,
360
+ "learning_rate": 8.272542372881356e-06,
361
+ "loss": 0.0787,
362
+ "step": 5600
363
+ },
364
+ {
365
+ "epoch": 3.01,
366
+ "learning_rate": 8.23864406779661e-06,
367
+ "loss": 0.0666,
368
+ "step": 5700
369
+ },
370
+ {
371
+ "epoch": 3.01,
372
+ "learning_rate": 8.204745762711865e-06,
373
+ "loss": 0.0742,
374
+ "step": 5800
375
+ },
376
+ {
377
+ "epoch": 3.01,
378
+ "learning_rate": 8.170847457627119e-06,
379
+ "loss": 0.0668,
380
+ "step": 5900
381
+ },
382
+ {
383
+ "epoch": 3.02,
384
+ "learning_rate": 8.136949152542374e-06,
385
+ "loss": 0.0851,
386
+ "step": 6000
387
+ },
388
+ {
389
+ "epoch": 3.02,
390
+ "eval_loss": 0.2086436152458191,
391
+ "eval_runtime": 7023.6759,
392
+ "eval_samples_per_second": 2.07,
393
+ "eval_steps_per_second": 0.259,
394
+ "eval_wer": 19.672536994317223,
395
+ "step": 6000
396
+ },
397
+ {
398
+ "epoch": 3.02,
399
+ "learning_rate": 8.103050847457628e-06,
400
+ "loss": 0.0861,
401
+ "step": 6100
402
+ },
403
+ {
404
+ "epoch": 3.02,
405
+ "learning_rate": 8.069152542372881e-06,
406
+ "loss": 0.0815,
407
+ "step": 6200
408
+ },
409
+ {
410
+ "epoch": 3.03,
411
+ "learning_rate": 8.035254237288137e-06,
412
+ "loss": 0.0715,
413
+ "step": 6300
414
+ },
415
+ {
416
+ "epoch": 3.03,
417
+ "learning_rate": 8.00135593220339e-06,
418
+ "loss": 0.0582,
419
+ "step": 6400
420
+ },
421
+ {
422
+ "epoch": 3.03,
423
+ "learning_rate": 7.967457627118644e-06,
424
+ "loss": 0.056,
425
+ "step": 6500
426
+ },
427
+ {
428
+ "epoch": 3.04,
429
+ "learning_rate": 7.9335593220339e-06,
430
+ "loss": 0.0497,
431
+ "step": 6600
432
+ },
433
+ {
434
+ "epoch": 3.04,
435
+ "learning_rate": 7.899661016949153e-06,
436
+ "loss": 0.061,
437
+ "step": 6700
438
+ },
439
+ {
440
+ "epoch": 3.04,
441
+ "learning_rate": 7.865762711864408e-06,
442
+ "loss": 0.0557,
443
+ "step": 6800
444
+ },
445
+ {
446
+ "epoch": 3.05,
447
+ "learning_rate": 7.831864406779662e-06,
448
+ "loss": 0.0545,
449
+ "step": 6900
450
+ },
451
+ {
452
+ "epoch": 3.05,
453
+ "learning_rate": 7.797966101694915e-06,
454
+ "loss": 0.0581,
455
+ "step": 7000
456
+ },
457
+ {
458
+ "epoch": 3.05,
459
+ "learning_rate": 7.765762711864407e-06,
460
+ "loss": 0.4649,
461
+ "step": 7100
462
+ },
463
+ {
464
+ "epoch": 3.06,
465
+ "learning_rate": 7.731864406779661e-06,
466
+ "loss": 1.2438,
467
+ "step": 7200
468
+ },
469
+ {
470
+ "epoch": 4.0,
471
+ "learning_rate": 7.697966101694916e-06,
472
+ "loss": 0.1309,
473
+ "step": 7300
474
+ },
475
+ {
476
+ "epoch": 4.0,
477
+ "learning_rate": 7.66406779661017e-06,
478
+ "loss": 0.0762,
479
+ "step": 7400
480
+ },
481
+ {
482
+ "epoch": 4.01,
483
+ "learning_rate": 7.630169491525424e-06,
484
+ "loss": 0.0604,
485
+ "step": 7500
486
+ },
487
+ {
488
+ "epoch": 4.01,
489
+ "learning_rate": 7.596271186440679e-06,
490
+ "loss": 0.0653,
491
+ "step": 7600
492
+ },
493
+ {
494
+ "epoch": 4.01,
495
+ "learning_rate": 7.5623728813559326e-06,
496
+ "loss": 0.0609,
497
+ "step": 7700
498
+ },
499
+ {
500
+ "epoch": 4.02,
501
+ "learning_rate": 7.528474576271188e-06,
502
+ "loss": 0.0755,
503
+ "step": 7800
504
+ },
505
+ {
506
+ "epoch": 4.02,
507
+ "learning_rate": 7.4945762711864415e-06,
508
+ "loss": 0.0663,
509
+ "step": 7900
510
+ },
511
+ {
512
+ "epoch": 4.02,
513
+ "learning_rate": 7.460677966101696e-06,
514
+ "loss": 0.0573,
515
+ "step": 8000
516
+ },
517
+ {
518
+ "epoch": 4.02,
519
+ "eval_loss": 0.2178211659193039,
520
+ "eval_runtime": 7037.8839,
521
+ "eval_samples_per_second": 2.066,
522
+ "eval_steps_per_second": 0.258,
523
+ "eval_wer": 18.427952512237663,
524
+ "step": 8000
525
+ },
526
+ {
527
+ "epoch": 4.03,
528
+ "learning_rate": 7.4267796610169496e-06,
529
+ "loss": 0.073,
530
+ "step": 8100
531
+ },
532
+ {
533
+ "epoch": 4.03,
534
+ "learning_rate": 7.392881355932204e-06,
535
+ "loss": 0.0633,
536
+ "step": 8200
537
+ },
538
+ {
539
+ "epoch": 4.03,
540
+ "learning_rate": 7.358983050847458e-06,
541
+ "loss": 0.077,
542
+ "step": 8300
543
+ },
544
+ {
545
+ "epoch": 4.04,
546
+ "learning_rate": 7.325084745762713e-06,
547
+ "loss": 0.0701,
548
+ "step": 8400
549
+ },
550
+ {
551
+ "epoch": 4.04,
552
+ "learning_rate": 7.2911864406779666e-06,
553
+ "loss": 0.0867,
554
+ "step": 8500
555
+ },
556
+ {
557
+ "epoch": 4.04,
558
+ "learning_rate": 7.257288135593221e-06,
559
+ "loss": 0.0637,
560
+ "step": 8600
561
+ },
562
+ {
563
+ "epoch": 4.05,
564
+ "learning_rate": 7.223389830508475e-06,
565
+ "loss": 0.0649,
566
+ "step": 8700
567
+ },
568
+ {
569
+ "epoch": 4.05,
570
+ "learning_rate": 7.18949152542373e-06,
571
+ "loss": 0.0945,
572
+ "step": 8800
573
+ },
574
+ {
575
+ "epoch": 4.05,
576
+ "learning_rate": 7.1555932203389836e-06,
577
+ "loss": 0.0719,
578
+ "step": 8900
579
+ },
580
+ {
581
+ "epoch": 4.06,
582
+ "learning_rate": 7.121694915254238e-06,
583
+ "loss": 0.0608,
584
+ "step": 9000
585
+ },
586
+ {
587
+ "epoch": 4.06,
588
+ "learning_rate": 7.087796610169492e-06,
589
+ "loss": 0.0669,
590
+ "step": 9100
591
+ },
592
+ {
593
+ "epoch": 5.0,
594
+ "learning_rate": 7.053898305084746e-06,
595
+ "loss": 0.0429,
596
+ "step": 9200
597
+ },
598
+ {
599
+ "epoch": 5.01,
600
+ "learning_rate": 7.0200000000000006e-06,
601
+ "loss": 0.0346,
602
+ "step": 9300
603
+ },
604
+ {
605
+ "epoch": 5.01,
606
+ "learning_rate": 6.986101694915255e-06,
607
+ "loss": 0.0314,
608
+ "step": 9400
609
+ },
610
+ {
611
+ "epoch": 5.01,
612
+ "learning_rate": 6.952203389830509e-06,
613
+ "loss": 0.0351,
614
+ "step": 9500
615
+ },
616
+ {
617
+ "epoch": 5.02,
618
+ "learning_rate": 6.918305084745763e-06,
619
+ "loss": 0.0372,
620
+ "step": 9600
621
+ },
622
+ {
623
+ "epoch": 5.02,
624
+ "learning_rate": 6.884406779661017e-06,
625
+ "loss": 0.0367,
626
+ "step": 9700
627
+ },
628
+ {
629
+ "epoch": 5.02,
630
+ "learning_rate": 6.850508474576272e-06,
631
+ "loss": 0.0297,
632
+ "step": 9800
633
+ },
634
+ {
635
+ "epoch": 5.03,
636
+ "learning_rate": 6.816610169491526e-06,
637
+ "loss": 0.0372,
638
+ "step": 9900
639
+ },
640
+ {
641
+ "epoch": 5.03,
642
+ "learning_rate": 6.78271186440678e-06,
643
+ "loss": 0.036,
644
+ "step": 10000
645
+ },
646
+ {
647
+ "epoch": 5.03,
648
+ "eval_loss": 0.23119649291038513,
649
+ "eval_runtime": 6929.3454,
650
+ "eval_samples_per_second": 2.099,
651
+ "eval_steps_per_second": 0.262,
652
+ "eval_wer": 17.82479041242334,
653
+ "step": 10000
654
+ },
655
+ {
656
+ "epoch": 5.03,
657
+ "learning_rate": 6.7488135593220345e-06,
658
+ "loss": 0.0407,
659
+ "step": 10100
660
+ },
661
+ {
662
+ "epoch": 5.04,
663
+ "learning_rate": 6.714915254237289e-06,
664
+ "loss": 0.0337,
665
+ "step": 10200
666
+ },
667
+ {
668
+ "epoch": 5.04,
669
+ "learning_rate": 6.6810169491525435e-06,
670
+ "loss": 0.0412,
671
+ "step": 10300
672
+ },
673
+ {
674
+ "epoch": 5.04,
675
+ "learning_rate": 6.647118644067797e-06,
676
+ "loss": 0.035,
677
+ "step": 10400
678
+ },
679
+ {
680
+ "epoch": 5.05,
681
+ "learning_rate": 6.6132203389830515e-06,
682
+ "loss": 0.0355,
683
+ "step": 10500
684
+ },
685
+ {
686
+ "epoch": 5.05,
687
+ "learning_rate": 6.579322033898305e-06,
688
+ "loss": 0.0443,
689
+ "step": 10600
690
+ },
691
+ {
692
+ "epoch": 5.05,
693
+ "learning_rate": 6.5454237288135605e-06,
694
+ "loss": 0.0384,
695
+ "step": 10700
696
+ },
697
+ {
698
+ "epoch": 5.06,
699
+ "learning_rate": 6.511525423728814e-06,
700
+ "loss": 0.0321,
701
+ "step": 10800
702
+ },
703
+ {
704
+ "epoch": 5.06,
705
+ "learning_rate": 6.4776271186440685e-06,
706
+ "loss": 0.034,
707
+ "step": 10900
708
+ },
709
+ {
710
+ "epoch": 6.0,
711
+ "learning_rate": 6.443728813559322e-06,
712
+ "loss": 0.04,
713
+ "step": 11000
714
+ },
715
+ {
716
+ "epoch": 6.01,
717
+ "learning_rate": 6.4098305084745775e-06,
718
+ "loss": 0.0329,
719
+ "step": 11100
720
+ },
721
+ {
722
+ "epoch": 6.01,
723
+ "learning_rate": 6.375932203389831e-06,
724
+ "loss": 0.0292,
725
+ "step": 11200
726
+ },
727
+ {
728
+ "epoch": 6.01,
729
+ "learning_rate": 6.3420338983050855e-06,
730
+ "loss": 0.0359,
731
+ "step": 11300
732
+ },
733
+ {
734
+ "epoch": 6.02,
735
+ "learning_rate": 6.308135593220339e-06,
736
+ "loss": 0.0335,
737
+ "step": 11400
738
+ },
739
+ {
740
+ "epoch": 6.02,
741
+ "learning_rate": 6.274237288135594e-06,
742
+ "loss": 0.0392,
743
+ "step": 11500
744
+ },
745
+ {
746
+ "epoch": 6.02,
747
+ "learning_rate": 6.240338983050847e-06,
748
+ "loss": 0.0374,
749
+ "step": 11600
750
+ },
751
+ {
752
+ "epoch": 6.03,
753
+ "learning_rate": 6.2064406779661025e-06,
754
+ "loss": 0.0387,
755
+ "step": 11700
756
+ },
757
+ {
758
+ "epoch": 6.03,
759
+ "learning_rate": 6.172542372881356e-06,
760
+ "loss": 0.0274,
761
+ "step": 11800
762
+ },
763
+ {
764
+ "epoch": 6.03,
765
+ "learning_rate": 6.138644067796611e-06,
766
+ "loss": 0.0244,
767
+ "step": 11900
768
+ },
769
+ {
770
+ "epoch": 6.04,
771
+ "learning_rate": 6.104745762711864e-06,
772
+ "loss": 0.0238,
773
+ "step": 12000
774
+ },
775
+ {
776
+ "epoch": 6.04,
777
+ "eval_loss": 0.26207804679870605,
778
+ "eval_runtime": 6973.9359,
779
+ "eval_samples_per_second": 2.085,
780
+ "eval_steps_per_second": 0.261,
781
+ "eval_wer": 17.409553817588478,
782
+ "step": 12000
783
+ },
784
+ {
785
+ "epoch": 6.04,
786
+ "learning_rate": 6.0708474576271195e-06,
787
+ "loss": 0.0276,
788
+ "step": 12100
789
+ },
790
+ {
791
+ "epoch": 6.04,
792
+ "learning_rate": 6.036949152542373e-06,
793
+ "loss": 0.0277,
794
+ "step": 12200
795
+ },
796
+ {
797
+ "epoch": 6.05,
798
+ "learning_rate": 6.003050847457628e-06,
799
+ "loss": 0.0253,
800
+ "step": 12300
801
+ },
802
+ {
803
+ "epoch": 6.05,
804
+ "learning_rate": 5.969152542372881e-06,
805
+ "loss": 0.0248,
806
+ "step": 12400
807
+ },
808
+ {
809
+ "epoch": 6.05,
810
+ "learning_rate": 5.935254237288136e-06,
811
+ "loss": 0.0262,
812
+ "step": 12500
813
+ },
814
+ {
815
+ "epoch": 6.06,
816
+ "learning_rate": 5.90135593220339e-06,
817
+ "loss": 0.0324,
818
+ "step": 12600
819
+ },
820
+ {
821
+ "epoch": 6.06,
822
+ "learning_rate": 5.867457627118645e-06,
823
+ "loss": 0.0357,
824
+ "step": 12700
825
+ },
826
+ {
827
+ "epoch": 7.0,
828
+ "learning_rate": 5.833559322033899e-06,
829
+ "loss": 0.0642,
830
+ "step": 12800
831
+ },
832
+ {
833
+ "epoch": 7.01,
834
+ "learning_rate": 5.799661016949153e-06,
835
+ "loss": 0.0973,
836
+ "step": 12900
837
+ },
838
+ {
839
+ "epoch": 7.01,
840
+ "learning_rate": 5.765762711864408e-06,
841
+ "loss": 0.0925,
842
+ "step": 13000
843
+ },
844
+ {
845
+ "epoch": 7.01,
846
+ "learning_rate": 5.731864406779662e-06,
847
+ "loss": 0.1244,
848
+ "step": 13100
849
+ },
850
+ {
851
+ "epoch": 7.02,
852
+ "learning_rate": 5.697966101694916e-06,
853
+ "loss": 0.0937,
854
+ "step": 13200
855
+ },
856
+ {
857
+ "epoch": 7.02,
858
+ "learning_rate": 5.66406779661017e-06,
859
+ "loss": 0.1086,
860
+ "step": 13300
861
+ },
862
+ {
863
+ "epoch": 7.02,
864
+ "learning_rate": 5.630169491525425e-06,
865
+ "loss": 0.0851,
866
+ "step": 13400
867
+ },
868
+ {
869
+ "epoch": 7.03,
870
+ "learning_rate": 5.596271186440679e-06,
871
+ "loss": 0.0796,
872
+ "step": 13500
873
+ },
874
+ {
875
+ "epoch": 7.03,
876
+ "learning_rate": 5.562372881355933e-06,
877
+ "loss": 0.0684,
878
+ "step": 13600
879
+ },
880
+ {
881
+ "epoch": 7.03,
882
+ "learning_rate": 5.528474576271187e-06,
883
+ "loss": 0.0711,
884
+ "step": 13700
885
+ },
886
+ {
887
+ "epoch": 7.04,
888
+ "learning_rate": 5.494576271186441e-06,
889
+ "loss": 0.0679,
890
+ "step": 13800
891
+ },
892
+ {
893
+ "epoch": 7.04,
894
+ "learning_rate": 5.460677966101695e-06,
895
+ "loss": 0.0669,
896
+ "step": 13900
897
+ },
898
+ {
899
+ "epoch": 7.04,
900
+ "learning_rate": 5.42677966101695e-06,
901
+ "loss": 0.0733,
902
+ "step": 14000
903
+ },
904
+ {
905
+ "epoch": 7.04,
906
+ "eval_loss": 0.21199654042720795,
907
+ "eval_runtime": 6989.399,
908
+ "eval_samples_per_second": 2.081,
909
+ "eval_steps_per_second": 0.26,
910
+ "eval_wer": 16.565576998818432,
911
+ "step": 14000
912
+ },
913
+ {
914
+ "epoch": 7.05,
915
+ "learning_rate": 5.392881355932204e-06,
916
+ "loss": 0.073,
917
+ "step": 14100
918
+ },
919
+ {
920
+ "epoch": 7.05,
921
+ "learning_rate": 5.358983050847458e-06,
922
+ "loss": 0.076,
923
+ "step": 14200
924
+ },
925
+ {
926
+ "epoch": 7.05,
927
+ "learning_rate": 5.325084745762712e-06,
928
+ "loss": 0.0898,
929
+ "step": 14300
930
+ },
931
+ {
932
+ "epoch": 7.06,
933
+ "learning_rate": 5.291186440677967e-06,
934
+ "loss": 0.0829,
935
+ "step": 14400
936
+ },
937
+ {
938
+ "epoch": 7.06,
939
+ "learning_rate": 5.257288135593221e-06,
940
+ "loss": 0.0896,
941
+ "step": 14500
942
+ },
943
+ {
944
+ "epoch": 8.0,
945
+ "learning_rate": 5.223389830508475e-06,
946
+ "loss": 0.06,
947
+ "step": 14600
948
+ },
949
+ {
950
+ "epoch": 8.0,
951
+ "learning_rate": 5.189491525423729e-06,
952
+ "loss": 0.0192,
953
+ "step": 14700
954
+ },
955
+ {
956
+ "epoch": 8.01,
957
+ "learning_rate": 5.155593220338983e-06,
958
+ "loss": 0.0161,
959
+ "step": 14800
960
+ },
961
+ {
962
+ "epoch": 8.01,
963
+ "learning_rate": 5.121694915254237e-06,
964
+ "loss": 0.0175,
965
+ "step": 14900
966
+ },
967
+ {
968
+ "epoch": 8.01,
969
+ "learning_rate": 5.087796610169492e-06,
970
+ "loss": 0.0167,
971
+ "step": 15000
972
+ },
973
+ {
974
+ "epoch": 8.02,
975
+ "learning_rate": 5.053898305084746e-06,
976
+ "loss": 0.0186,
977
+ "step": 15100
978
+ },
979
+ {
980
+ "epoch": 8.02,
981
+ "learning_rate": 5.02e-06,
982
+ "loss": 0.0198,
983
+ "step": 15200
984
+ },
985
+ {
986
+ "epoch": 8.02,
987
+ "learning_rate": 4.986101694915255e-06,
988
+ "loss": 0.0181,
989
+ "step": 15300
990
+ },
991
+ {
992
+ "epoch": 8.03,
993
+ "learning_rate": 4.952203389830509e-06,
994
+ "loss": 0.0156,
995
+ "step": 15400
996
+ },
997
+ {
998
+ "epoch": 8.03,
999
+ "learning_rate": 4.918305084745763e-06,
1000
+ "loss": 0.0103,
1001
+ "step": 15500
1002
+ },
1003
+ {
1004
+ "epoch": 8.03,
1005
+ "learning_rate": 4.884406779661017e-06,
1006
+ "loss": 0.0114,
1007
+ "step": 15600
1008
+ },
1009
+ {
1010
+ "epoch": 8.04,
1011
+ "learning_rate": 4.850508474576272e-06,
1012
+ "loss": 0.0109,
1013
+ "step": 15700
1014
+ },
1015
+ {
1016
+ "epoch": 8.04,
1017
+ "learning_rate": 4.816610169491525e-06,
1018
+ "loss": 0.015,
1019
+ "step": 15800
1020
+ },
1021
+ {
1022
+ "epoch": 8.04,
1023
+ "learning_rate": 4.78271186440678e-06,
1024
+ "loss": 0.0114,
1025
+ "step": 15900
1026
+ },
1027
+ {
1028
+ "epoch": 8.05,
1029
+ "learning_rate": 4.748813559322034e-06,
1030
+ "loss": 0.0111,
1031
+ "step": 16000
1032
+ },
1033
+ {
1034
+ "epoch": 8.05,
1035
+ "eval_loss": 0.2681731879711151,
1036
+ "eval_runtime": 6987.2917,
1037
+ "eval_samples_per_second": 2.081,
1038
+ "eval_steps_per_second": 0.26,
1039
+ "eval_wer": 16.22911157373544,
1040
+ "step": 16000
1041
+ },
1042
+ {
1043
+ "epoch": 8.05,
1044
+ "learning_rate": 4.714915254237289e-06,
1045
+ "loss": 0.011,
1046
+ "step": 16100
1047
+ },
1048
+ {
1049
+ "epoch": 8.05,
1050
+ "learning_rate": 4.681016949152542e-06,
1051
+ "loss": 0.0133,
1052
+ "step": 16200
1053
+ },
1054
+ {
1055
+ "epoch": 8.06,
1056
+ "learning_rate": 4.647118644067797e-06,
1057
+ "loss": 0.0162,
1058
+ "step": 16300
1059
+ },
1060
+ {
1061
+ "epoch": 9.0,
1062
+ "learning_rate": 4.613220338983051e-06,
1063
+ "loss": 0.0147,
1064
+ "step": 16400
1065
+ },
1066
+ {
1067
+ "epoch": 9.0,
1068
+ "learning_rate": 4.579322033898305e-06,
1069
+ "loss": 0.0173,
1070
+ "step": 16500
1071
+ },
1072
+ {
1073
+ "epoch": 9.01,
1074
+ "learning_rate": 4.545423728813559e-06,
1075
+ "loss": 0.0135,
1076
+ "step": 16600
1077
+ },
1078
+ {
1079
+ "epoch": 9.01,
1080
+ "learning_rate": 4.511525423728815e-06,
1081
+ "loss": 0.0146,
1082
+ "step": 16700
1083
+ },
1084
+ {
1085
+ "epoch": 9.01,
1086
+ "learning_rate": 4.477627118644068e-06,
1087
+ "loss": 0.0152,
1088
+ "step": 16800
1089
+ },
1090
+ {
1091
+ "epoch": 9.02,
1092
+ "learning_rate": 4.443728813559323e-06,
1093
+ "loss": 0.0166,
1094
+ "step": 16900
1095
+ },
1096
+ {
1097
+ "epoch": 9.02,
1098
+ "learning_rate": 4.409830508474577e-06,
1099
+ "loss": 0.0138,
1100
+ "step": 17000
1101
+ },
1102
+ {
1103
+ "epoch": 9.02,
1104
+ "learning_rate": 4.375932203389831e-06,
1105
+ "loss": 0.0128,
1106
+ "step": 17100
1107
+ },
1108
+ {
1109
+ "epoch": 9.03,
1110
+ "learning_rate": 4.342033898305085e-06,
1111
+ "loss": 0.0195,
1112
+ "step": 17200
1113
+ },
1114
+ {
1115
+ "epoch": 9.03,
1116
+ "learning_rate": 4.30813559322034e-06,
1117
+ "loss": 0.0147,
1118
+ "step": 17300
1119
+ },
1120
+ {
1121
+ "epoch": 9.03,
1122
+ "learning_rate": 4.274237288135593e-06,
1123
+ "loss": 0.0177,
1124
+ "step": 17400
1125
+ },
1126
+ {
1127
+ "epoch": 9.04,
1128
+ "learning_rate": 4.240338983050848e-06,
1129
+ "loss": 0.0155,
1130
+ "step": 17500
1131
+ },
1132
+ {
1133
+ "epoch": 9.04,
1134
+ "learning_rate": 4.206440677966102e-06,
1135
+ "loss": 0.019,
1136
+ "step": 17600
1137
+ },
1138
+ {
1139
+ "epoch": 9.04,
1140
+ "learning_rate": 4.172542372881357e-06,
1141
+ "loss": 0.0157,
1142
+ "step": 17700
1143
+ },
1144
+ {
1145
+ "epoch": 9.05,
1146
+ "learning_rate": 4.13864406779661e-06,
1147
+ "loss": 0.0149,
1148
+ "step": 17800
1149
+ },
1150
+ {
1151
+ "epoch": 9.05,
1152
+ "learning_rate": 4.104745762711865e-06,
1153
+ "loss": 0.0228,
1154
+ "step": 17900
1155
+ },
1156
+ {
1157
+ "epoch": 9.05,
1158
+ "learning_rate": 4.070847457627119e-06,
1159
+ "loss": 0.0155,
1160
+ "step": 18000
1161
+ },
1162
+ {
1163
+ "epoch": 9.05,
1164
+ "eval_loss": 0.2676936686038971,
1165
+ "eval_runtime": 7019.5973,
1166
+ "eval_samples_per_second": 2.072,
1167
+ "eval_steps_per_second": 0.259,
1168
+ "eval_wer": 15.9241546165532,
1169
+ "step": 18000
1170
+ },
1171
+ {
1172
+ "epoch": 9.06,
1173
+ "learning_rate": 4.036949152542373e-06,
1174
+ "loss": 0.0117,
1175
+ "step": 18100
1176
+ },
1177
+ {
1178
+ "epoch": 9.06,
1179
+ "learning_rate": 4.003050847457627e-06,
1180
+ "loss": 0.0131,
1181
+ "step": 18200
1182
+ },
1183
+ {
1184
+ "epoch": 10.0,
1185
+ "learning_rate": 3.969152542372882e-06,
1186
+ "loss": 0.0072,
1187
+ "step": 18300
1188
+ },
1189
+ {
1190
+ "epoch": 10.01,
1191
+ "learning_rate": 3.935254237288136e-06,
1192
+ "loss": 0.0051,
1193
+ "step": 18400
1194
+ },
1195
+ {
1196
+ "epoch": 10.01,
1197
+ "learning_rate": 3.90135593220339e-06,
1198
+ "loss": 0.0055,
1199
+ "step": 18500
1200
+ },
1201
+ {
1202
+ "epoch": 10.01,
1203
+ "learning_rate": 3.867457627118644e-06,
1204
+ "loss": 0.0054,
1205
+ "step": 18600
1206
+ },
1207
+ {
1208
+ "epoch": 10.02,
1209
+ "learning_rate": 3.833559322033899e-06,
1210
+ "loss": 0.0056,
1211
+ "step": 18700
1212
+ },
1213
+ {
1214
+ "epoch": 10.02,
1215
+ "learning_rate": 3.7996610169491528e-06,
1216
+ "loss": 0.0054,
1217
+ "step": 18800
1218
+ },
1219
+ {
1220
+ "epoch": 10.02,
1221
+ "learning_rate": 3.765762711864407e-06,
1222
+ "loss": 0.0031,
1223
+ "step": 18900
1224
+ },
1225
+ {
1226
+ "epoch": 10.03,
1227
+ "learning_rate": 3.7318644067796613e-06,
1228
+ "loss": 0.0061,
1229
+ "step": 19000
1230
+ },
1231
+ {
1232
+ "epoch": 10.03,
1233
+ "learning_rate": 3.6979661016949153e-06,
1234
+ "loss": 0.0059,
1235
+ "step": 19100
1236
+ },
1237
+ {
1238
+ "epoch": 10.03,
1239
+ "learning_rate": 3.6640677966101694e-06,
1240
+ "loss": 0.0053,
1241
+ "step": 19200
1242
+ },
1243
+ {
1244
+ "epoch": 10.04,
1245
+ "learning_rate": 3.630169491525424e-06,
1246
+ "loss": 0.0051,
1247
+ "step": 19300
1248
+ },
1249
+ {
1250
+ "epoch": 10.04,
1251
+ "learning_rate": 3.5962711864406787e-06,
1252
+ "loss": 0.007,
1253
+ "step": 19400
1254
+ },
1255
+ {
1256
+ "epoch": 10.04,
1257
+ "learning_rate": 3.5623728813559327e-06,
1258
+ "loss": 0.0046,
1259
+ "step": 19500
1260
+ },
1261
+ {
1262
+ "epoch": 10.05,
1263
+ "learning_rate": 3.528813559322034e-06,
1264
+ "loss": 0.0057,
1265
+ "step": 19600
1266
+ },
1267
+ {
1268
+ "epoch": 10.05,
1269
+ "learning_rate": 3.494915254237288e-06,
1270
+ "loss": 0.0069,
1271
+ "step": 19700
1272
+ },
1273
+ {
1274
+ "epoch": 10.05,
1275
+ "learning_rate": 3.4610169491525425e-06,
1276
+ "loss": 0.0057,
1277
+ "step": 19800
1278
+ },
1279
+ {
1280
+ "epoch": 10.06,
1281
+ "learning_rate": 3.4271186440677966e-06,
1282
+ "loss": 0.0038,
1283
+ "step": 19900
1284
+ },
1285
+ {
1286
+ "epoch": 10.06,
1287
+ "learning_rate": 3.393220338983051e-06,
1288
+ "loss": 0.0041,
1289
+ "step": 20000
1290
+ },
1291
+ {
1292
+ "epoch": 10.06,
1293
+ "eval_loss": 0.3177700340747833,
1294
+ "eval_runtime": 7017.5255,
1295
+ "eval_samples_per_second": 2.072,
1296
+ "eval_steps_per_second": 0.259,
1297
+ "eval_wer": 15.953412479603893,
1298
+ "step": 20000
1299
+ },
1300
+ {
1301
+ "epoch": 11.0,
1302
+ "learning_rate": 3.359322033898305e-06,
1303
+ "loss": 0.0071,
1304
+ "step": 20100
1305
+ },
1306
+ {
1307
+ "epoch": 11.01,
1308
+ "learning_rate": 3.3254237288135595e-06,
1309
+ "loss": 0.0053,
1310
+ "step": 20200
1311
+ },
1312
+ {
1313
+ "epoch": 11.01,
1314
+ "learning_rate": 3.2915254237288136e-06,
1315
+ "loss": 0.0055,
1316
+ "step": 20300
1317
+ },
1318
+ {
1319
+ "epoch": 11.01,
1320
+ "learning_rate": 3.2576271186440685e-06,
1321
+ "loss": 0.007,
1322
+ "step": 20400
1323
+ },
1324
+ {
1325
+ "epoch": 11.02,
1326
+ "learning_rate": 3.2237288135593225e-06,
1327
+ "loss": 0.0055,
1328
+ "step": 20500
1329
+ },
1330
+ {
1331
+ "epoch": 11.02,
1332
+ "learning_rate": 3.189830508474577e-06,
1333
+ "loss": 0.0078,
1334
+ "step": 20600
1335
+ },
1336
+ {
1337
+ "epoch": 11.02,
1338
+ "learning_rate": 3.155932203389831e-06,
1339
+ "loss": 0.0062,
1340
+ "step": 20700
1341
+ },
1342
+ {
1343
+ "epoch": 11.03,
1344
+ "learning_rate": 3.122033898305085e-06,
1345
+ "loss": 0.0064,
1346
+ "step": 20800
1347
+ },
1348
+ {
1349
+ "epoch": 11.03,
1350
+ "learning_rate": 3.0881355932203395e-06,
1351
+ "loss": 0.0038,
1352
+ "step": 20900
1353
+ },
1354
+ {
1355
+ "epoch": 11.03,
1356
+ "learning_rate": 3.0542372881355935e-06,
1357
+ "loss": 0.0036,
1358
+ "step": 21000
1359
+ },
1360
+ {
1361
+ "epoch": 11.04,
1362
+ "learning_rate": 3.020338983050848e-06,
1363
+ "loss": 0.0041,
1364
+ "step": 21100
1365
+ },
1366
+ {
1367
+ "epoch": 11.04,
1368
+ "learning_rate": 2.986440677966102e-06,
1369
+ "loss": 0.0039,
1370
+ "step": 21200
1371
+ },
1372
+ {
1373
+ "epoch": 11.04,
1374
+ "learning_rate": 2.952542372881356e-06,
1375
+ "loss": 0.0056,
1376
+ "step": 21300
1377
+ },
1378
+ {
1379
+ "epoch": 11.05,
1380
+ "learning_rate": 2.9186440677966105e-06,
1381
+ "loss": 0.0036,
1382
+ "step": 21400
1383
+ },
1384
+ {
1385
+ "epoch": 11.05,
1386
+ "learning_rate": 2.8847457627118646e-06,
1387
+ "loss": 0.0044,
1388
+ "step": 21500
1389
+ },
1390
+ {
1391
+ "epoch": 11.05,
1392
+ "learning_rate": 2.850847457627119e-06,
1393
+ "loss": 0.0036,
1394
+ "step": 21600
1395
+ },
1396
+ {
1397
+ "epoch": 11.06,
1398
+ "learning_rate": 2.816949152542373e-06,
1399
+ "loss": 0.0051,
1400
+ "step": 21700
1401
+ },
1402
+ {
1403
+ "epoch": 11.06,
1404
+ "learning_rate": 2.7830508474576275e-06,
1405
+ "loss": 0.0053,
1406
+ "step": 21800
1407
+ },
1408
+ {
1409
+ "epoch": 12.0,
1410
+ "learning_rate": 2.7491525423728816e-06,
1411
+ "loss": 0.0027,
1412
+ "step": 21900
1413
+ },
1414
+ {
1415
+ "epoch": 12.01,
1416
+ "learning_rate": 2.7152542372881356e-06,
1417
+ "loss": 0.0023,
1418
+ "step": 22000
1419
+ },
1420
+ {
1421
+ "epoch": 12.01,
1422
+ "eval_loss": 0.3218221366405487,
1423
+ "eval_runtime": 7009.1784,
1424
+ "eval_samples_per_second": 2.075,
1425
+ "eval_steps_per_second": 0.259,
1426
+ "eval_wer": 16.053564395431273,
1427
+ "step": 22000
1428
+ },
1429
+ {
1430
+ "epoch": 12.01,
1431
+ "learning_rate": 2.68135593220339e-06,
1432
+ "loss": 0.0017,
1433
+ "step": 22100
1434
+ },
1435
+ {
1436
+ "epoch": 12.01,
1437
+ "learning_rate": 2.647457627118644e-06,
1438
+ "loss": 0.0022,
1439
+ "step": 22200
1440
+ },
1441
+ {
1442
+ "epoch": 12.02,
1443
+ "learning_rate": 2.6135593220338986e-06,
1444
+ "loss": 0.002,
1445
+ "step": 22300
1446
+ },
1447
+ {
1448
+ "epoch": 12.02,
1449
+ "learning_rate": 2.5796610169491526e-06,
1450
+ "loss": 0.0023,
1451
+ "step": 22400
1452
+ },
1453
+ {
1454
+ "epoch": 12.02,
1455
+ "learning_rate": 2.5457627118644066e-06,
1456
+ "loss": 0.0016,
1457
+ "step": 22500
1458
+ },
1459
+ {
1460
+ "epoch": 12.03,
1461
+ "learning_rate": 2.511864406779661e-06,
1462
+ "loss": 0.002,
1463
+ "step": 22600
1464
+ },
1465
+ {
1466
+ "epoch": 12.03,
1467
+ "learning_rate": 2.4779661016949156e-06,
1468
+ "loss": 0.0027,
1469
+ "step": 22700
1470
+ },
1471
+ {
1472
+ "epoch": 12.03,
1473
+ "learning_rate": 2.4440677966101696e-06,
1474
+ "loss": 0.0022,
1475
+ "step": 22800
1476
+ },
1477
+ {
1478
+ "epoch": 12.04,
1479
+ "learning_rate": 2.410169491525424e-06,
1480
+ "loss": 0.0022,
1481
+ "step": 22900
1482
+ },
1483
+ {
1484
+ "epoch": 12.04,
1485
+ "learning_rate": 2.376271186440678e-06,
1486
+ "loss": 0.0023,
1487
+ "step": 23000
1488
+ },
1489
+ {
1490
+ "epoch": 12.04,
1491
+ "learning_rate": 2.3423728813559326e-06,
1492
+ "loss": 0.0026,
1493
+ "step": 23100
1494
+ },
1495
+ {
1496
+ "epoch": 12.05,
1497
+ "learning_rate": 2.3084745762711866e-06,
1498
+ "loss": 0.0018,
1499
+ "step": 23200
1500
+ },
1501
+ {
1502
+ "epoch": 12.05,
1503
+ "learning_rate": 2.2745762711864406e-06,
1504
+ "loss": 0.0028,
1505
+ "step": 23300
1506
+ },
1507
+ {
1508
+ "epoch": 12.05,
1509
+ "learning_rate": 2.240677966101695e-06,
1510
+ "loss": 0.0029,
1511
+ "step": 23400
1512
+ },
1513
+ {
1514
+ "epoch": 12.06,
1515
+ "learning_rate": 2.206779661016949e-06,
1516
+ "loss": 0.0016,
1517
+ "step": 23500
1518
+ },
1519
+ {
1520
+ "epoch": 12.06,
1521
+ "learning_rate": 2.1728813559322036e-06,
1522
+ "loss": 0.0014,
1523
+ "step": 23600
1524
+ },
1525
+ {
1526
+ "epoch": 13.0,
1527
+ "learning_rate": 2.1393220338983053e-06,
1528
+ "loss": 0.0245,
1529
+ "step": 23700
1530
+ },
1531
+ {
1532
+ "epoch": 13.0,
1533
+ "learning_rate": 2.1054237288135594e-06,
1534
+ "loss": 0.0488,
1535
+ "step": 23800
1536
+ },
1537
+ {
1538
+ "epoch": 13.01,
1539
+ "learning_rate": 2.071525423728814e-06,
1540
+ "loss": 0.0468,
1541
+ "step": 23900
1542
+ },
1543
+ {
1544
+ "epoch": 13.01,
1545
+ "learning_rate": 2.0379661016949155e-06,
1546
+ "loss": 0.0621,
1547
+ "step": 24000
1548
+ },
1549
+ {
1550
+ "epoch": 13.01,
1551
+ "eval_loss": 0.23132386803627014,
1552
+ "eval_runtime": 7011.068,
1553
+ "eval_samples_per_second": 2.074,
1554
+ "eval_steps_per_second": 0.259,
1555
+ "eval_wer": 15.616947054520903,
1556
+ "step": 24000
1557
+ },
1558
+ {
1559
+ "epoch": 13.01,
1560
+ "learning_rate": 2.0040677966101696e-06,
1561
+ "loss": 0.0542,
1562
+ "step": 24100
1563
+ },
1564
+ {
1565
+ "epoch": 13.02,
1566
+ "learning_rate": 1.9701694915254236e-06,
1567
+ "loss": 0.0534,
1568
+ "step": 24200
1569
+ },
1570
+ {
1571
+ "epoch": 13.02,
1572
+ "learning_rate": 1.936271186440678e-06,
1573
+ "loss": 0.0409,
1574
+ "step": 24300
1575
+ },
1576
+ {
1577
+ "epoch": 13.02,
1578
+ "learning_rate": 1.9023728813559323e-06,
1579
+ "loss": 0.0431,
1580
+ "step": 24400
1581
+ },
1582
+ {
1583
+ "epoch": 13.03,
1584
+ "learning_rate": 1.8684745762711868e-06,
1585
+ "loss": 0.0341,
1586
+ "step": 24500
1587
+ },
1588
+ {
1589
+ "epoch": 13.03,
1590
+ "learning_rate": 1.834576271186441e-06,
1591
+ "loss": 0.0348,
1592
+ "step": 24600
1593
+ },
1594
+ {
1595
+ "epoch": 13.03,
1596
+ "learning_rate": 1.800677966101695e-06,
1597
+ "loss": 0.0332,
1598
+ "step": 24700
1599
+ },
1600
+ {
1601
+ "epoch": 13.04,
1602
+ "learning_rate": 1.7667796610169493e-06,
1603
+ "loss": 0.0308,
1604
+ "step": 24800
1605
+ },
1606
+ {
1607
+ "epoch": 13.04,
1608
+ "learning_rate": 1.7328813559322036e-06,
1609
+ "loss": 0.0336,
1610
+ "step": 24900
1611
+ },
1612
+ {
1613
+ "epoch": 13.04,
1614
+ "learning_rate": 1.6989830508474578e-06,
1615
+ "loss": 0.0334,
1616
+ "step": 25000
1617
+ },
1618
+ {
1619
+ "epoch": 13.05,
1620
+ "learning_rate": 1.665084745762712e-06,
1621
+ "loss": 0.034,
1622
+ "step": 25100
1623
+ },
1624
+ {
1625
+ "epoch": 13.05,
1626
+ "learning_rate": 1.6311864406779663e-06,
1627
+ "loss": 0.0433,
1628
+ "step": 25200
1629
+ },
1630
+ {
1631
+ "epoch": 13.05,
1632
+ "learning_rate": 1.5972881355932204e-06,
1633
+ "loss": 0.0401,
1634
+ "step": 25300
1635
+ },
1636
+ {
1637
+ "epoch": 13.06,
1638
+ "learning_rate": 1.5633898305084746e-06,
1639
+ "loss": 0.0404,
1640
+ "step": 25400
1641
+ },
1642
+ {
1643
+ "epoch": 14.0,
1644
+ "learning_rate": 1.5294915254237289e-06,
1645
+ "loss": 0.0328,
1646
+ "step": 25500
1647
+ },
1648
+ {
1649
+ "epoch": 14.0,
1650
+ "learning_rate": 1.4955932203389831e-06,
1651
+ "loss": 0.0033,
1652
+ "step": 25600
1653
+ },
1654
+ {
1655
+ "epoch": 14.01,
1656
+ "learning_rate": 1.4616949152542374e-06,
1657
+ "loss": 0.0021,
1658
+ "step": 25700
1659
+ },
1660
+ {
1661
+ "epoch": 14.01,
1662
+ "learning_rate": 1.4277966101694916e-06,
1663
+ "loss": 0.0023,
1664
+ "step": 25800
1665
+ },
1666
+ {
1667
+ "epoch": 14.01,
1668
+ "learning_rate": 1.393898305084746e-06,
1669
+ "loss": 0.0021,
1670
+ "step": 25900
1671
+ },
1672
+ {
1673
+ "epoch": 14.02,
1674
+ "learning_rate": 1.3600000000000001e-06,
1675
+ "loss": 0.0022,
1676
+ "step": 26000
1677
+ },
1678
+ {
1679
+ "epoch": 14.02,
1680
+ "eval_loss": 0.28866246342658997,
1681
+ "eval_runtime": 6982.432,
1682
+ "eval_samples_per_second": 2.083,
1683
+ "eval_steps_per_second": 0.26,
1684
+ "eval_wer": 15.108310358408822,
1685
+ "step": 26000
1686
+ },
1687
+ {
1688
+ "epoch": 14.02,
1689
+ "learning_rate": 1.3261016949152544e-06,
1690
+ "loss": 0.0025,
1691
+ "step": 26100
1692
+ },
1693
+ {
1694
+ "epoch": 14.02,
1695
+ "learning_rate": 1.2922033898305086e-06,
1696
+ "loss": 0.002,
1697
+ "step": 26200
1698
+ },
1699
+ {
1700
+ "epoch": 14.03,
1701
+ "learning_rate": 1.2583050847457629e-06,
1702
+ "loss": 0.0016,
1703
+ "step": 26300
1704
+ },
1705
+ {
1706
+ "epoch": 14.03,
1707
+ "learning_rate": 1.2244067796610171e-06,
1708
+ "loss": 0.001,
1709
+ "step": 26400
1710
+ },
1711
+ {
1712
+ "epoch": 14.03,
1713
+ "learning_rate": 1.1905084745762714e-06,
1714
+ "loss": 0.0014,
1715
+ "step": 26500
1716
+ },
1717
+ {
1718
+ "epoch": 14.04,
1719
+ "learning_rate": 1.1566101694915254e-06,
1720
+ "loss": 0.0013,
1721
+ "step": 26600
1722
+ },
1723
+ {
1724
+ "epoch": 14.04,
1725
+ "learning_rate": 1.1227118644067796e-06,
1726
+ "loss": 0.0015,
1727
+ "step": 26700
1728
+ },
1729
+ {
1730
+ "epoch": 14.04,
1731
+ "learning_rate": 1.0888135593220339e-06,
1732
+ "loss": 0.0014,
1733
+ "step": 26800
1734
+ },
1735
+ {
1736
+ "epoch": 14.05,
1737
+ "learning_rate": 1.0549152542372884e-06,
1738
+ "loss": 0.0014,
1739
+ "step": 26900
1740
+ },
1741
+ {
1742
+ "epoch": 14.05,
1743
+ "learning_rate": 1.0210169491525424e-06,
1744
+ "loss": 0.0013,
1745
+ "step": 27000
1746
+ },
1747
+ {
1748
+ "epoch": 14.05,
1749
+ "learning_rate": 9.871186440677966e-07,
1750
+ "loss": 0.0012,
1751
+ "step": 27100
1752
+ },
1753
+ {
1754
+ "epoch": 14.06,
1755
+ "learning_rate": 9.532203389830509e-07,
1756
+ "loss": 0.0015,
1757
+ "step": 27200
1758
+ },
1759
+ {
1760
+ "epoch": 14.06,
1761
+ "learning_rate": 9.193220338983051e-07,
1762
+ "loss": 0.0012,
1763
+ "step": 27300
1764
+ },
1765
+ {
1766
+ "epoch": 15.0,
1767
+ "learning_rate": 8.854237288135594e-07,
1768
+ "loss": 0.0197,
1769
+ "step": 27400
1770
+ },
1771
+ {
1772
+ "epoch": 15.01,
1773
+ "learning_rate": 8.515254237288137e-07,
1774
+ "loss": 0.0192,
1775
+ "step": 27500
1776
+ },
1777
+ {
1778
+ "epoch": 15.01,
1779
+ "learning_rate": 8.176271186440679e-07,
1780
+ "loss": 0.0242,
1781
+ "step": 27600
1782
+ },
1783
+ {
1784
+ "epoch": 15.01,
1785
+ "learning_rate": 7.837288135593221e-07,
1786
+ "loss": 0.0281,
1787
+ "step": 27700
1788
+ },
1789
+ {
1790
+ "epoch": 15.02,
1791
+ "learning_rate": 7.498305084745763e-07,
1792
+ "loss": 0.0274,
1793
+ "step": 27800
1794
+ },
1795
+ {
1796
+ "epoch": 15.02,
1797
+ "learning_rate": 7.159322033898305e-07,
1798
+ "loss": 0.0212,
1799
+ "step": 27900
1800
+ },
1801
+ {
1802
+ "epoch": 15.02,
1803
+ "learning_rate": 6.820338983050848e-07,
1804
+ "loss": 0.0199,
1805
+ "step": 28000
1806
+ },
1807
+ {
1808
+ "epoch": 15.02,
1809
+ "eval_loss": 0.25532078742980957,
1810
+ "eval_runtime": 7006.3593,
1811
+ "eval_samples_per_second": 2.076,
1812
+ "eval_steps_per_second": 0.259,
1813
+ "eval_wer": 15.18483092331064,
1814
+ "step": 28000
1815
+ },
1816
+ {
1817
+ "epoch": 15.03,
1818
+ "learning_rate": 6.484745762711865e-07,
1819
+ "loss": 0.0197,
1820
+ "step": 28100
1821
+ },
1822
+ {
1823
+ "epoch": 15.03,
1824
+ "learning_rate": 6.145762711864408e-07,
1825
+ "loss": 0.0137,
1826
+ "step": 28200
1827
+ },
1828
+ {
1829
+ "epoch": 15.03,
1830
+ "learning_rate": 5.806779661016949e-07,
1831
+ "loss": 0.0167,
1832
+ "step": 28300
1833
+ },
1834
+ {
1835
+ "epoch": 15.04,
1836
+ "learning_rate": 5.467796610169493e-07,
1837
+ "loss": 0.0165,
1838
+ "step": 28400
1839
+ },
1840
+ {
1841
+ "epoch": 15.04,
1842
+ "learning_rate": 5.128813559322034e-07,
1843
+ "loss": 0.0161,
1844
+ "step": 28500
1845
+ },
1846
+ {
1847
+ "epoch": 15.04,
1848
+ "learning_rate": 4.789830508474576e-07,
1849
+ "loss": 0.0147,
1850
+ "step": 28600
1851
+ },
1852
+ {
1853
+ "epoch": 15.05,
1854
+ "learning_rate": 4.4508474576271195e-07,
1855
+ "loss": 0.0186,
1856
+ "step": 28700
1857
+ },
1858
+ {
1859
+ "epoch": 15.05,
1860
+ "learning_rate": 4.1118644067796615e-07,
1861
+ "loss": 0.0228,
1862
+ "step": 28800
1863
+ },
1864
+ {
1865
+ "epoch": 15.05,
1866
+ "learning_rate": 3.7728813559322034e-07,
1867
+ "loss": 0.0228,
1868
+ "step": 28900
1869
+ },
1870
+ {
1871
+ "epoch": 15.06,
1872
+ "learning_rate": 3.433898305084746e-07,
1873
+ "loss": 0.0195,
1874
+ "step": 29000
1875
+ },
1876
+ {
1877
+ "epoch": 15.06,
1878
+ "learning_rate": 3.0949152542372884e-07,
1879
+ "loss": 0.0233,
1880
+ "step": 29100
1881
+ },
1882
+ {
1883
+ "epoch": 16.0,
1884
+ "learning_rate": 2.755932203389831e-07,
1885
+ "loss": 0.0118,
1886
+ "step": 29200
1887
+ },
1888
+ {
1889
+ "epoch": 16.01,
1890
+ "learning_rate": 2.4169491525423734e-07,
1891
+ "loss": 0.0103,
1892
+ "step": 29300
1893
+ },
1894
+ {
1895
+ "epoch": 16.01,
1896
+ "learning_rate": 2.0779661016949154e-07,
1897
+ "loss": 0.0118,
1898
+ "step": 29400
1899
+ },
1900
+ {
1901
+ "epoch": 16.01,
1902
+ "learning_rate": 1.7389830508474579e-07,
1903
+ "loss": 0.0161,
1904
+ "step": 29500
1905
+ },
1906
+ {
1907
+ "epoch": 16.02,
1908
+ "learning_rate": 1.4e-07,
1909
+ "loss": 0.016,
1910
+ "step": 29600
1911
+ },
1912
+ {
1913
+ "epoch": 16.02,
1914
+ "learning_rate": 1.0610169491525425e-07,
1915
+ "loss": 0.0138,
1916
+ "step": 29700
1917
+ },
1918
+ {
1919
+ "epoch": 16.02,
1920
+ "learning_rate": 7.220338983050848e-08,
1921
+ "loss": 0.0125,
1922
+ "step": 29800
1923
+ },
1924
+ {
1925
+ "epoch": 16.03,
1926
+ "learning_rate": 3.830508474576272e-08,
1927
+ "loss": 0.0129,
1928
+ "step": 29900
1929
+ },
1930
+ {
1931
+ "epoch": 16.03,
1932
+ "learning_rate": 4.406779661016949e-09,
1933
+ "loss": 0.0083,
1934
+ "step": 30000
1935
+ },
1936
+ {
1937
+ "epoch": 16.03,
1938
+ "eval_loss": 0.2589367628097534,
1939
+ "eval_runtime": 7159.0133,
1940
+ "eval_samples_per_second": 2.031,
1941
+ "eval_steps_per_second": 0.254,
1942
+ "eval_wer": 15.072300680807968,
1943
+ "step": 30000
1944
+ },
1945
+ {
1946
+ "epoch": 16.03,
1947
+ "step": 30000,
1948
+ "total_flos": 1.3849328795516928e+20,
1949
+ "train_loss": 0.07104161073341966,
1950
+ "train_runtime": 196903.7961,
1951
+ "train_samples_per_second": 2.438,
1952
+ "train_steps_per_second": 0.152
1953
+ }
1954
+ ],
1955
+ "max_steps": 30000,
1956
+ "num_train_epochs": 9223372036854775807,
1957
+ "total_flos": 1.3849328795516928e+20,
1958
+ "trial_name": null,
1959
+ "trial_params": null
1960
+ }
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e1d5449db756a40a94b01129559c46681b1fafd8eb8478e2eefa5c73c1db1f99
3
+ size 3515
vocab.json ADDED
The diff for this file is too large to render. See raw diff