Training in progress, step 1000
Browse files- .gitignore +1 -0
- added_tokens.json +109 -0
- config.json +41 -0
- ds_config.json +50 -0
- merges.txt +0 -0
- normalizer.json +1742 -0
- preprocessor_config.json +0 -0
- pytorch_model.bin +3 -0
- run.log +677 -0
- run.sh +39 -0
- run_speech_recognition_seq2seq_streaming.py +629 -0
- runs/Dec18_08-41-04_fe2747a042f0/1671381730.2013636/events.out.tfevents.1671381730.fe2747a042f0.46148.1 +3 -0
- runs/Dec18_08-41-04_fe2747a042f0/events.out.tfevents.1671381730.fe2747a042f0.46148.0 +3 -0
- special_tokens_map.json +133 -0
- tokenizer_config.json +36 -0
- training_args.bin +3 -0
- vocab.json +0 -0
.gitignore
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
checkpoint-*/
|
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 |
+
}
|
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": "float16",
|
| 38 |
+
"transformers_version": "4.26.0.dev0",
|
| 39 |
+
"use_cache": false,
|
| 40 |
+
"vocab_size": 51865
|
| 41 |
+
}
|
ds_config.json
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"fp16": {
|
| 3 |
+
"enabled": "auto",
|
| 4 |
+
"loss_scale": 0,
|
| 5 |
+
"loss_scale_window": 1000,
|
| 6 |
+
"initial_scale_power": 16,
|
| 7 |
+
"hysteresis": 2,
|
| 8 |
+
"min_loss_scale": 1
|
| 9 |
+
},
|
| 10 |
+
|
| 11 |
+
"optimizer": {
|
| 12 |
+
"type": "AdamW",
|
| 13 |
+
"params": {
|
| 14 |
+
"lr": "auto",
|
| 15 |
+
"betas": "auto",
|
| 16 |
+
"eps": "auto",
|
| 17 |
+
"weight_decay": "auto"
|
| 18 |
+
}
|
| 19 |
+
},
|
| 20 |
+
|
| 21 |
+
"scheduler": {
|
| 22 |
+
"type": "WarmupDecayLR",
|
| 23 |
+
"params": {
|
| 24 |
+
"last_batch_iteration": -1,
|
| 25 |
+
"total_num_steps": "auto",
|
| 26 |
+
"warmup_min_lr": "auto",
|
| 27 |
+
"warmup_max_lr": "auto",
|
| 28 |
+
"warmup_num_steps": "auto"
|
| 29 |
+
}
|
| 30 |
+
},
|
| 31 |
+
|
| 32 |
+
"zero_optimization": {
|
| 33 |
+
"stage": 2,
|
| 34 |
+
"offload_optimizer": {
|
| 35 |
+
"device": "cpu",
|
| 36 |
+
"pin_memory": true
|
| 37 |
+
},
|
| 38 |
+
"allgather_partitions": true,
|
| 39 |
+
"allgather_bucket_size": 2e8,
|
| 40 |
+
"overlap_comm": true,
|
| 41 |
+
"reduce_scatter": true,
|
| 42 |
+
"reduce_bucket_size": 2e8,
|
| 43 |
+
"contiguous_gradients": true
|
| 44 |
+
},
|
| 45 |
+
|
| 46 |
+
"gradient_accumulation_steps": "auto",
|
| 47 |
+
"gradient_clipping": "auto",
|
| 48 |
+
"train_batch_size": "auto",
|
| 49 |
+
"train_micro_batch_size_per_gpu": "auto"
|
| 50 |
+
}
|
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 sha256:154295da2b680e283731469d66fb3552823d07524d02e1453e1606abef5b5318
|
| 3 |
+
size 483536061
|
run.log
ADDED
|
@@ -0,0 +1,677 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[2022-12-18 08:40:52,091] [WARNING] [runner.py:179:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only.
|
| 2 |
+
[2022-12-18 08:40:52,100] [INFO] [runner.py:508:main] cmd = /usr/bin/python -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMF19 --master_addr=127.0.0.1 --master_port=29500 run_speech_recognition_seq2seq_streaming.py --deepspeed=ds_config.json --model_name_or_path=openai/whisper-small --dataset_name=mozilla-foundation/common_voice_11_0 --dataset_config_name=ro --language=romanian --train_split_name=train+validation --eval_split_name=test --model_index_name=Whisper Small Romanian CV11 --max_steps=5000 --output_dir=./ --per_device_train_batch_size=64 --per_device_eval_batch_size=32 --logging_steps=25 --learning_rate=1e-5 --warmup_steps=500 --evaluation_strategy=steps --eval_steps=1000 --save_strategy=steps --save_steps=1000 --generation_max_length=225 --length_column_name=input_length --max_duration_in_seconds=30 --text_column_name=sentence --freeze_feature_encoder=False --report_to=tensorboard --metric_for_best_model=wer --greater_is_better=False --load_best_model_at_end --gradient_checkpointing --fp16 --overwrite_output_dir --do_train --do_eval --predict_with_generate --do_normalize_eval --streaming --use_auth_token --push_to_hub
|
| 3 |
+
[2022-12-18 08:40:55,346] [INFO] [launch.py:135:main] 0 NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.13.4-1+cuda11.7
|
| 4 |
+
[2022-12-18 08:40:55,346] [INFO] [launch.py:135:main] 0 NV_LIBNCCL_DEV_PACKAGE_VERSION=2.13.4-1
|
| 5 |
+
[2022-12-18 08:40:55,346] [INFO] [launch.py:135:main] 0 NCCL_VERSION=2.13.4-1
|
| 6 |
+
[2022-12-18 08:40:55,347] [INFO] [launch.py:135:main] 0 NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
|
| 7 |
+
[2022-12-18 08:40:55,347] [INFO] [launch.py:135:main] 0 NV_LIBNCCL_PACKAGE=libnccl2=2.13.4-1+cuda11.7
|
| 8 |
+
[2022-12-18 08:40:55,347] [INFO] [launch.py:135:main] 0 NV_LIBNCCL_PACKAGE_NAME=libnccl2
|
| 9 |
+
[2022-12-18 08:40:55,347] [INFO] [launch.py:135:main] 0 NV_LIBNCCL_PACKAGE_VERSION=2.13.4-1
|
| 10 |
+
[2022-12-18 08:40:55,347] [INFO] [launch.py:142:main] WORLD INFO DICT: {'localhost': [0]}
|
| 11 |
+
[2022-12-18 08:40:55,347] [INFO] [launch.py:148:main] nnodes=1, num_local_procs=1, node_rank=0
|
| 12 |
+
[2022-12-18 08:40:55,347] [INFO] [launch.py:161:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0]})
|
| 13 |
+
[2022-12-18 08:40:55,347] [INFO] [launch.py:162:main] dist_world_size=1
|
| 14 |
+
[2022-12-18 08:40:55,347] [INFO] [launch.py:164:main] Setting CUDA_VISIBLE_DEVICES=0
|
| 15 |
+
[2022-12-18 08:41:04,141] [INFO] [comm.py:654:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
|
| 16 |
+
12/18/2022 08:41:04 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: True
|
| 17 |
+
12/18/2022 08:41:04 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(
|
| 18 |
+
_n_gpu=1,
|
| 19 |
+
adafactor=False,
|
| 20 |
+
adam_beta1=0.9,
|
| 21 |
+
adam_beta2=0.999,
|
| 22 |
+
adam_epsilon=1e-08,
|
| 23 |
+
auto_find_batch_size=False,
|
| 24 |
+
bf16=False,
|
| 25 |
+
bf16_full_eval=False,
|
| 26 |
+
data_seed=None,
|
| 27 |
+
dataloader_drop_last=False,
|
| 28 |
+
dataloader_num_workers=0,
|
| 29 |
+
dataloader_pin_memory=True,
|
| 30 |
+
ddp_bucket_cap_mb=None,
|
| 31 |
+
ddp_find_unused_parameters=None,
|
| 32 |
+
ddp_timeout=1800,
|
| 33 |
+
debug=[],
|
| 34 |
+
deepspeed=ds_config.json,
|
| 35 |
+
disable_tqdm=False,
|
| 36 |
+
do_eval=True,
|
| 37 |
+
do_predict=False,
|
| 38 |
+
do_train=True,
|
| 39 |
+
eval_accumulation_steps=None,
|
| 40 |
+
eval_delay=0,
|
| 41 |
+
eval_steps=1000,
|
| 42 |
+
evaluation_strategy=steps,
|
| 43 |
+
fp16=True,
|
| 44 |
+
fp16_backend=auto,
|
| 45 |
+
fp16_full_eval=False,
|
| 46 |
+
fp16_opt_level=O1,
|
| 47 |
+
fsdp=[],
|
| 48 |
+
fsdp_min_num_params=0,
|
| 49 |
+
fsdp_transformer_layer_cls_to_wrap=None,
|
| 50 |
+
full_determinism=False,
|
| 51 |
+
generation_max_length=225,
|
| 52 |
+
generation_num_beams=None,
|
| 53 |
+
gradient_accumulation_steps=1,
|
| 54 |
+
gradient_checkpointing=True,
|
| 55 |
+
greater_is_better=False,
|
| 56 |
+
group_by_length=False,
|
| 57 |
+
half_precision_backend=auto,
|
| 58 |
+
hub_model_id=None,
|
| 59 |
+
hub_private_repo=False,
|
| 60 |
+
hub_strategy=every_save,
|
| 61 |
+
hub_token=<HUB_TOKEN>,
|
| 62 |
+
ignore_data_skip=False,
|
| 63 |
+
include_inputs_for_metrics=False,
|
| 64 |
+
jit_mode_eval=False,
|
| 65 |
+
label_names=None,
|
| 66 |
+
label_smoothing_factor=0.0,
|
| 67 |
+
learning_rate=1e-05,
|
| 68 |
+
length_column_name=input_length,
|
| 69 |
+
load_best_model_at_end=True,
|
| 70 |
+
local_rank=0,
|
| 71 |
+
log_level=passive,
|
| 72 |
+
log_level_replica=passive,
|
| 73 |
+
log_on_each_node=True,
|
| 74 |
+
logging_dir=./runs/Dec18_08-41-04_fe2747a042f0,
|
| 75 |
+
logging_first_step=False,
|
| 76 |
+
logging_nan_inf_filter=True,
|
| 77 |
+
logging_steps=25,
|
| 78 |
+
logging_strategy=steps,
|
| 79 |
+
lr_scheduler_type=linear,
|
| 80 |
+
max_grad_norm=1.0,
|
| 81 |
+
max_steps=5000,
|
| 82 |
+
metric_for_best_model=wer,
|
| 83 |
+
mp_parameters=,
|
| 84 |
+
no_cuda=False,
|
| 85 |
+
num_train_epochs=3.0,
|
| 86 |
+
optim=adamw_hf,
|
| 87 |
+
optim_args=None,
|
| 88 |
+
output_dir=./,
|
| 89 |
+
overwrite_output_dir=True,
|
| 90 |
+
past_index=-1,
|
| 91 |
+
per_device_eval_batch_size=32,
|
| 92 |
+
per_device_train_batch_size=64,
|
| 93 |
+
predict_with_generate=True,
|
| 94 |
+
prediction_loss_only=False,
|
| 95 |
+
push_to_hub=True,
|
| 96 |
+
push_to_hub_model_id=None,
|
| 97 |
+
push_to_hub_organization=None,
|
| 98 |
+
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
|
| 99 |
+
ray_scope=last,
|
| 100 |
+
remove_unused_columns=True,
|
| 101 |
+
report_to=['tensorboard'],
|
| 102 |
+
resume_from_checkpoint=None,
|
| 103 |
+
run_name=./,
|
| 104 |
+
save_on_each_node=False,
|
| 105 |
+
save_steps=1000,
|
| 106 |
+
save_strategy=steps,
|
| 107 |
+
save_total_limit=None,
|
| 108 |
+
seed=42,
|
| 109 |
+
sharded_ddp=[],
|
| 110 |
+
skip_memory_metrics=True,
|
| 111 |
+
sortish_sampler=False,
|
| 112 |
+
tf32=None,
|
| 113 |
+
torch_compile=False,
|
| 114 |
+
torch_compile_backend=None,
|
| 115 |
+
torch_compile_mode=None,
|
| 116 |
+
torchdynamo=None,
|
| 117 |
+
tpu_metrics_debug=False,
|
| 118 |
+
tpu_num_cores=None,
|
| 119 |
+
use_ipex=False,
|
| 120 |
+
use_legacy_prediction_loop=False,
|
| 121 |
+
use_mps_device=False,
|
| 122 |
+
warmup_ratio=0.0,
|
| 123 |
+
warmup_steps=500,
|
| 124 |
+
weight_decay=0.0,
|
| 125 |
+
xpu_backend=None,
|
| 126 |
+
)
|
| 127 |
+
12/18/2022 08:41:04 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(
|
| 128 |
+
_n_gpu=1,
|
| 129 |
+
adafactor=False,
|
| 130 |
+
adam_beta1=0.9,
|
| 131 |
+
adam_beta2=0.999,
|
| 132 |
+
adam_epsilon=1e-08,
|
| 133 |
+
auto_find_batch_size=False,
|
| 134 |
+
bf16=False,
|
| 135 |
+
bf16_full_eval=False,
|
| 136 |
+
data_seed=None,
|
| 137 |
+
dataloader_drop_last=False,
|
| 138 |
+
dataloader_num_workers=0,
|
| 139 |
+
dataloader_pin_memory=True,
|
| 140 |
+
ddp_bucket_cap_mb=None,
|
| 141 |
+
ddp_find_unused_parameters=None,
|
| 142 |
+
ddp_timeout=1800,
|
| 143 |
+
debug=[],
|
| 144 |
+
deepspeed=ds_config.json,
|
| 145 |
+
disable_tqdm=False,
|
| 146 |
+
do_eval=True,
|
| 147 |
+
do_predict=False,
|
| 148 |
+
do_train=True,
|
| 149 |
+
eval_accumulation_steps=None,
|
| 150 |
+
eval_delay=0,
|
| 151 |
+
eval_steps=1000,
|
| 152 |
+
evaluation_strategy=steps,
|
| 153 |
+
fp16=True,
|
| 154 |
+
fp16_backend=auto,
|
| 155 |
+
fp16_full_eval=False,
|
| 156 |
+
fp16_opt_level=O1,
|
| 157 |
+
fsdp=[],
|
| 158 |
+
fsdp_min_num_params=0,
|
| 159 |
+
fsdp_transformer_layer_cls_to_wrap=None,
|
| 160 |
+
full_determinism=False,
|
| 161 |
+
generation_max_length=225,
|
| 162 |
+
generation_num_beams=None,
|
| 163 |
+
gradient_accumulation_steps=1,
|
| 164 |
+
gradient_checkpointing=True,
|
| 165 |
+
greater_is_better=False,
|
| 166 |
+
group_by_length=False,
|
| 167 |
+
half_precision_backend=auto,
|
| 168 |
+
hub_model_id=None,
|
| 169 |
+
hub_private_repo=False,
|
| 170 |
+
hub_strategy=every_save,
|
| 171 |
+
hub_token=<HUB_TOKEN>,
|
| 172 |
+
ignore_data_skip=False,
|
| 173 |
+
include_inputs_for_metrics=False,
|
| 174 |
+
jit_mode_eval=False,
|
| 175 |
+
label_names=None,
|
| 176 |
+
label_smoothing_factor=0.0,
|
| 177 |
+
learning_rate=1e-05,
|
| 178 |
+
length_column_name=input_length,
|
| 179 |
+
load_best_model_at_end=True,
|
| 180 |
+
local_rank=0,
|
| 181 |
+
log_level=passive,
|
| 182 |
+
log_level_replica=passive,
|
| 183 |
+
log_on_each_node=True,
|
| 184 |
+
logging_dir=./runs/Dec18_08-41-04_fe2747a042f0,
|
| 185 |
+
logging_first_step=False,
|
| 186 |
+
logging_nan_inf_filter=True,
|
| 187 |
+
logging_steps=25,
|
| 188 |
+
logging_strategy=steps,
|
| 189 |
+
lr_scheduler_type=linear,
|
| 190 |
+
max_grad_norm=1.0,
|
| 191 |
+
max_steps=5000,
|
| 192 |
+
metric_for_best_model=wer,
|
| 193 |
+
mp_parameters=,
|
| 194 |
+
no_cuda=False,
|
| 195 |
+
num_train_epochs=3.0,
|
| 196 |
+
optim=adamw_hf,
|
| 197 |
+
optim_args=None,
|
| 198 |
+
output_dir=./,
|
| 199 |
+
overwrite_output_dir=True,
|
| 200 |
+
past_index=-1,
|
| 201 |
+
per_device_eval_batch_size=32,
|
| 202 |
+
per_device_train_batch_size=64,
|
| 203 |
+
predict_with_generate=True,
|
| 204 |
+
prediction_loss_only=False,
|
| 205 |
+
push_to_hub=True,
|
| 206 |
+
push_to_hub_model_id=None,
|
| 207 |
+
push_to_hub_organization=None,
|
| 208 |
+
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
|
| 209 |
+
ray_scope=last,
|
| 210 |
+
remove_unused_columns=True,
|
| 211 |
+
report_to=['tensorboard'],
|
| 212 |
+
resume_from_checkpoint=None,
|
| 213 |
+
run_name=./,
|
| 214 |
+
save_on_each_node=False,
|
| 215 |
+
save_steps=1000,
|
| 216 |
+
save_strategy=steps,
|
| 217 |
+
save_total_limit=None,
|
| 218 |
+
seed=42,
|
| 219 |
+
sharded_ddp=[],
|
| 220 |
+
skip_memory_metrics=True,
|
| 221 |
+
sortish_sampler=False,
|
| 222 |
+
tf32=None,
|
| 223 |
+
torch_compile=False,
|
| 224 |
+
torch_compile_backend=None,
|
| 225 |
+
torch_compile_mode=None,
|
| 226 |
+
torchdynamo=None,
|
| 227 |
+
tpu_metrics_debug=False,
|
| 228 |
+
tpu_num_cores=None,
|
| 229 |
+
use_ipex=False,
|
| 230 |
+
use_legacy_prediction_loop=False,
|
| 231 |
+
use_mps_device=False,
|
| 232 |
+
warmup_ratio=0.0,
|
| 233 |
+
warmup_steps=500,
|
| 234 |
+
weight_decay=0.0,
|
| 235 |
+
xpu_backend=None,
|
| 236 |
+
)
|
| 237 |
+
12/18/2022 08:41:07 - INFO - datasets.info - Loading Dataset Infos from /root/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f
|
| 238 |
+
12/18/2022 08:41:11 - INFO - datasets.info - Loading Dataset Infos from /root/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f
|
| 239 |
+
12/18/2022 08:41:14 - INFO - datasets.info - Loading Dataset Infos from /root/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f
|
| 240 |
+
12/18/2022 08:41:59 - WARNING - huggingface_hub.repository - /usr/src/app/models/whisper-small-ro-cv11/./ is already a clone of https://huggingface.co/mikr/whisper-small-ro-cv11. Make sure you pull the latest changes with `repo.git_pull()`.
|
| 241 |
+
[2022-12-18 08:42:04,348] [INFO] [logging.py:68:log_dist] [Rank 0] DeepSpeed info: version=0.8.0+a25c31b6, git-hash=a25c31b6, git-branch=master
|
| 242 |
+
[2022-12-18 08:42:04,669] [INFO] [logging.py:68:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False
|
| 243 |
+
Adam Optimizer #0 is created with AVX2 arithmetic capability.
|
| 244 |
+
Config: alpha=0.000010, betas=(0.900000, 0.999000), weight_decay=0.000000, adam_w=1
|
| 245 |
+
[2022-12-18 08:42:07,543] [INFO] [logging.py:68:log_dist] [Rank 0] Using DeepSpeed Optimizer param name adamw as basic optimizer
|
| 246 |
+
[2022-12-18 08:42:07,597] [INFO] [logging.py:68:log_dist] [Rank 0] DeepSpeed Basic Optimizer = DeepSpeedCPUAdam
|
| 247 |
+
[2022-12-18 08:42:07,597] [INFO] [utils.py:52:is_zero_supported_optimizer] Checking ZeRO support for optimizer=DeepSpeedCPUAdam type=<class 'deepspeed.ops.adam.cpu_adam.DeepSpeedCPUAdam'>
|
| 248 |
+
[2022-12-18 08:42:07,598] [INFO] [logging.py:68:log_dist] [Rank 0] Creating fp16 ZeRO stage 2 optimizer
|
| 249 |
+
[2022-12-18 08:42:07,598] [INFO] [stage_1_and_2.py:141:__init__] Reduce bucket size 200000000
|
| 250 |
+
[2022-12-18 08:42:07,598] [INFO] [stage_1_and_2.py:142:__init__] Allgather bucket size 200000000
|
| 251 |
+
[2022-12-18 08:42:07,598] [INFO] [stage_1_and_2.py:143:__init__] CPU Offload: True
|
| 252 |
+
[2022-12-18 08:42:07,598] [INFO] [stage_1_and_2.py:144:__init__] Round robin gradient partitioning: False
|
| 253 |
+
Rank: 0 partition count [1] and sizes[(241734912, False)]
|
| 254 |
+
[2022-12-18 08:42:08,957] [INFO] [utils.py:831:see_memory_usage] Before initializing optimizer states
|
| 255 |
+
[2022-12-18 08:42:08,958] [INFO] [utils.py:832:see_memory_usage] MA 0.53 GB Max_MA 0.53 GB CA 0.53 GB Max_CA 1 GB
|
| 256 |
+
[2022-12-18 08:42:08,958] [INFO] [utils.py:840:see_memory_usage] CPU Virtual Memory: used = 379.95 GB, percent = 75.4%
|
| 257 |
+
[2022-12-18 08:42:10,038] [INFO] [utils.py:831:see_memory_usage] After initializing optimizer states
|
| 258 |
+
[2022-12-18 08:42:10,039] [INFO] [utils.py:832:see_memory_usage] MA 0.53 GB Max_MA 0.53 GB CA 0.53 GB Max_CA 1 GB
|
| 259 |
+
[2022-12-18 08:42:10,039] [INFO] [utils.py:840:see_memory_usage] CPU Virtual Memory: used = 382.79 GB, percent = 76.0%
|
| 260 |
+
[2022-12-18 08:42:10,039] [INFO] [stage_1_and_2.py:527:__init__] optimizer state initialized
|
| 261 |
+
[2022-12-18 08:42:10,147] [INFO] [utils.py:831:see_memory_usage] After initializing ZeRO optimizer
|
| 262 |
+
[2022-12-18 08:42:10,148] [INFO] [utils.py:832:see_memory_usage] MA 0.53 GB Max_MA 0.53 GB CA 0.53 GB Max_CA 1 GB
|
| 263 |
+
[2022-12-18 08:42:10,148] [INFO] [utils.py:840:see_memory_usage] CPU Virtual Memory: used = 382.83 GB, percent = 76.0%
|
| 264 |
+
[2022-12-18 08:42:10,170] [INFO] [logging.py:68:log_dist] [Rank 0] DeepSpeed Final Optimizer = adamw
|
| 265 |
+
[2022-12-18 08:42:10,170] [INFO] [logging.py:68:log_dist] [Rank 0] DeepSpeed using configured LR scheduler = WarmupDecayLR
|
| 266 |
+
[2022-12-18 08:42:10,170] [INFO] [logging.py:68:log_dist] [Rank 0] DeepSpeed LR Scheduler = <deepspeed.runtime.lr_schedules.WarmupDecayLR object at 0x7ff2cab02f10>
|
| 267 |
+
[2022-12-18 08:42:10,170] [INFO] [logging.py:68:log_dist] [Rank 0] step=0, skipped=0, lr=[1e-05], mom=[[0.9, 0.999]]
|
| 268 |
+
[2022-12-18 08:42:10,172] [INFO] [config.py:1008:print] DeepSpeedEngine configuration:
|
| 269 |
+
[2022-12-18 08:42:10,172] [INFO] [config.py:1012:print] activation_checkpointing_config {
|
| 270 |
+
"partition_activations": false,
|
| 271 |
+
"contiguous_memory_optimization": false,
|
| 272 |
+
"cpu_checkpointing": false,
|
| 273 |
+
"number_checkpoints": null,
|
| 274 |
+
"synchronize_checkpoint_boundary": false,
|
| 275 |
+
"profile": false
|
| 276 |
+
}
|
| 277 |
+
[2022-12-18 08:42:10,172] [INFO] [config.py:1012:print] aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True}
|
| 278 |
+
[2022-12-18 08:42:10,173] [INFO] [config.py:1012:print] amp_enabled .................. False
|
| 279 |
+
[2022-12-18 08:42:10,173] [INFO] [config.py:1012:print] amp_params ................... False
|
| 280 |
+
[2022-12-18 08:42:10,173] [INFO] [config.py:1012:print] autotuning_config ............ {
|
| 281 |
+
"enabled": false,
|
| 282 |
+
"start_step": null,
|
| 283 |
+
"end_step": null,
|
| 284 |
+
"metric_path": null,
|
| 285 |
+
"arg_mappings": null,
|
| 286 |
+
"metric": "throughput",
|
| 287 |
+
"model_info": null,
|
| 288 |
+
"results_dir": "autotuning_results",
|
| 289 |
+
"exps_dir": "autotuning_exps",
|
| 290 |
+
"overwrite": true,
|
| 291 |
+
"fast": true,
|
| 292 |
+
"start_profile_step": 3,
|
| 293 |
+
"end_profile_step": 5,
|
| 294 |
+
"tuner_type": "gridsearch",
|
| 295 |
+
"tuner_early_stopping": 5,
|
| 296 |
+
"tuner_num_trials": 50,
|
| 297 |
+
"model_info_path": null,
|
| 298 |
+
"mp_size": 1,
|
| 299 |
+
"max_train_batch_size": null,
|
| 300 |
+
"min_train_batch_size": 1,
|
| 301 |
+
"max_train_micro_batch_size_per_gpu": 1.024000e+03,
|
| 302 |
+
"min_train_micro_batch_size_per_gpu": 1,
|
| 303 |
+
"num_tuning_micro_batch_sizes": 3
|
| 304 |
+
}
|
| 305 |
+
[2022-12-18 08:42:10,173] [INFO] [config.py:1012:print] bfloat16_enabled ............. False
|
| 306 |
+
[2022-12-18 08:42:10,173] [INFO] [config.py:1012:print] checkpoint_parallel_write_pipeline False
|
| 307 |
+
[2022-12-18 08:42:10,173] [INFO] [config.py:1012:print] checkpoint_tag_validation_enabled True
|
| 308 |
+
[2022-12-18 08:42:10,173] [INFO] [config.py:1012:print] checkpoint_tag_validation_fail False
|
| 309 |
+
[2022-12-18 08:42:10,173] [INFO] [config.py:1012:print] comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x7ff2cde355b0>
|
| 310 |
+
[2022-12-18 08:42:10,173] [INFO] [config.py:1012:print] communication_data_type ...... None
|
| 311 |
+
[2022-12-18 08:42:10,173] [INFO] [config.py:1012:print] compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}}
|
| 312 |
+
[2022-12-18 08:42:10,173] [INFO] [config.py:1012:print] curriculum_enabled_legacy .... False
|
| 313 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] curriculum_params_legacy ..... False
|
| 314 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'curriculum_learning': {'enabled': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}}
|
| 315 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] data_efficiency_enabled ...... False
|
| 316 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] dataloader_drop_last ......... False
|
| 317 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] disable_allgather ............ False
|
| 318 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] dump_state ................... False
|
| 319 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] dynamic_loss_scale_args ...... {'init_scale': 65536, 'scale_window': 1000, 'delayed_shift': 2, 'min_scale': 1}
|
| 320 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] eigenvalue_enabled ........... False
|
| 321 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] eigenvalue_gas_boundary_resolution 1
|
| 322 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] eigenvalue_layer_name ........ bert.encoder.layer
|
| 323 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] eigenvalue_layer_num ......... 0
|
| 324 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] eigenvalue_max_iter .......... 100
|
| 325 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] eigenvalue_stability ......... 1e-06
|
| 326 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] eigenvalue_tol ............... 0.01
|
| 327 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] eigenvalue_verbose ........... False
|
| 328 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] elasticity_enabled ........... False
|
| 329 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] flops_profiler_config ........ {
|
| 330 |
+
"enabled": false,
|
| 331 |
+
"profile_step": 1,
|
| 332 |
+
"module_depth": -1,
|
| 333 |
+
"top_modules": 1,
|
| 334 |
+
"detailed": true,
|
| 335 |
+
"output_file": null
|
| 336 |
+
}
|
| 337 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] fp16_auto_cast ............... False
|
| 338 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] fp16_enabled ................. True
|
| 339 |
+
[2022-12-18 08:42:10,174] [INFO] [config.py:1012:print] fp16_master_weights_and_gradients False
|
| 340 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] global_rank .................. 0
|
| 341 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] grad_accum_dtype ............. None
|
| 342 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] gradient_accumulation_steps .. 1
|
| 343 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] gradient_clipping ............ 1.0
|
| 344 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] gradient_predivide_factor .... 1.0
|
| 345 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] initial_dynamic_scale ........ 65536
|
| 346 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] load_universal_checkpoint .... False
|
| 347 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] loss_scale ................... 0
|
| 348 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] memory_breakdown ............. False
|
| 349 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] monitor_config ............... <deepspeed.monitor.config.DeepSpeedMonitorConfig object at 0x7ff2cde359d0>
|
| 350 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] nebula_config ................ {
|
| 351 |
+
"enabled": false,
|
| 352 |
+
"persistent_storage_path": null,
|
| 353 |
+
"persistent_time_interval": 100,
|
| 354 |
+
"num_of_version_in_retention": 2,
|
| 355 |
+
"enable_nebula_load": true,
|
| 356 |
+
"load_path": null
|
| 357 |
+
}
|
| 358 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] optimizer_legacy_fusion ...... False
|
| 359 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] optimizer_name ............... adamw
|
| 360 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] optimizer_params ............. {'lr': 1e-05, 'betas': [0.9, 0.999], 'eps': 1e-08, 'weight_decay': 0.0}
|
| 361 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0}
|
| 362 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] pld_enabled .................. False
|
| 363 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] pld_params ................... False
|
| 364 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] prescale_gradients ........... False
|
| 365 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] scheduler_name ............... WarmupDecayLR
|
| 366 |
+
[2022-12-18 08:42:10,175] [INFO] [config.py:1012:print] scheduler_params ............. {'last_batch_iteration': -1, 'total_num_steps': 5000, 'warmup_min_lr': 0, 'warmup_max_lr': 1e-05, 'warmup_num_steps': 500}
|
| 367 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] sparse_attention ............. None
|
| 368 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] sparse_gradients_enabled ..... False
|
| 369 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] steps_per_print .............. 10
|
| 370 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] train_batch_size ............. 64
|
| 371 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] train_micro_batch_size_per_gpu 64
|
| 372 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] use_node_local_storage ....... False
|
| 373 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] wall_clock_breakdown ......... False
|
| 374 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] world_size ................... 1
|
| 375 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] zero_allow_untested_optimizer False
|
| 376 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] zero_config .................. stage=2 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=200000000 allgather_partitions=True allgather_bucket_size=200000000 overlap_comm=True load_from_fp32_weights=True elastic_checkpoint=False offload_param=None offload_optimizer=DeepSpeedZeroOffloadOptimizerConfig(device='cpu', nvme_path=None, buffer_count=4, pin_memory=True, pipeline=False, pipeline_read=False, pipeline_write=False, fast_init=False) sub_group_size=1,000,000,000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=50,000,000 param_persistence_threshold=100,000 model_persistence_threshold=sys.maxsize max_live_parameters=1,000,000,000 max_reuse_distance=1,000,000,000 gather_16bit_weights_on_model_save=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False
|
| 377 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] zero_enabled ................. True
|
| 378 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:1012:print] zero_optimization_stage ...... 2
|
| 379 |
+
[2022-12-18 08:42:10,176] [INFO] [config.py:997:print_user_config] json = {
|
| 380 |
+
"fp16": {
|
| 381 |
+
"enabled": true,
|
| 382 |
+
"loss_scale": 0,
|
| 383 |
+
"loss_scale_window": 1000,
|
| 384 |
+
"initial_scale_power": 16,
|
| 385 |
+
"hysteresis": 2,
|
| 386 |
+
"min_loss_scale": 1
|
| 387 |
+
},
|
| 388 |
+
"optimizer": {
|
| 389 |
+
"type": "AdamW",
|
| 390 |
+
"params": {
|
| 391 |
+
"lr": 1e-05,
|
| 392 |
+
"betas": [0.9, 0.999],
|
| 393 |
+
"eps": 1e-08,
|
| 394 |
+
"weight_decay": 0.0
|
| 395 |
+
}
|
| 396 |
+
},
|
| 397 |
+
"scheduler": {
|
| 398 |
+
"type": "WarmupDecayLR",
|
| 399 |
+
"params": {
|
| 400 |
+
"last_batch_iteration": -1,
|
| 401 |
+
"total_num_steps": 5.000000e+03,
|
| 402 |
+
"warmup_min_lr": 0,
|
| 403 |
+
"warmup_max_lr": 1e-05,
|
| 404 |
+
"warmup_num_steps": 500
|
| 405 |
+
}
|
| 406 |
+
},
|
| 407 |
+
"zero_optimization": {
|
| 408 |
+
"stage": 2,
|
| 409 |
+
"offload_optimizer": {
|
| 410 |
+
"device": "cpu",
|
| 411 |
+
"pin_memory": true
|
| 412 |
+
},
|
| 413 |
+
"allgather_partitions": true,
|
| 414 |
+
"allgather_bucket_size": 2.000000e+08,
|
| 415 |
+
"overlap_comm": true,
|
| 416 |
+
"reduce_scatter": true,
|
| 417 |
+
"reduce_bucket_size": 2.000000e+08,
|
| 418 |
+
"contiguous_gradients": true
|
| 419 |
+
},
|
| 420 |
+
"gradient_accumulation_steps": 1,
|
| 421 |
+
"gradient_clipping": 1.0,
|
| 422 |
+
"train_batch_size": 64,
|
| 423 |
+
"train_micro_batch_size_per_gpu": 64
|
| 424 |
+
}
|
| 425 |
+
[2022-12-18 08:44:27,389] [INFO] [stage_1_and_2.py:1767:step] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 65536, reducing to 65536
|
| 426 |
+
[2022-12-18 08:44:43,482] [INFO] [stage_1_and_2.py:1767:step] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 65536, reducing to 32768.0
|
| 427 |
+
[2022-12-18 08:45:01,180] [INFO] [stage_1_and_2.py:1767:step] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 32768.0, reducing to 16384.0
|
| 428 |
+
[2022-12-18 08:45:17,756] [INFO] [stage_1_and_2.py:1767:step] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 16384.0, reducing to 8192.0
|
| 429 |
+
[2022-12-18 08:47:03,107] [INFO] [logging.py:68:log_dist] [Rank 0] step=10, skipped=4, lr=[2.883141528559073e-06], mom=[[0.9, 0.999]]
|
| 430 |
+
[2022-12-18 08:47:03,108] [INFO] [timer.py:196:stop] epoch=0/micro_step=10/global_step=10, RunningAvgSamplesPerSec=17.81398816266197, CurrSamplesPerSec=17.536521056985904, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 431 |
+
[2022-12-18 08:49:58,788] [INFO] [logging.py:68:log_dist] [Rank 0] step=20, skipped=4, lr=[4.461405575910259e-06], mom=[[0.9, 0.999]]
|
| 432 |
+
[2022-12-18 08:49:58,790] [INFO] [timer.py:196:stop] epoch=0/micro_step=20/global_step=20, RunningAvgSamplesPerSec=17.707913433080698, CurrSamplesPerSec=17.67266334257317, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 433 |
+
{'loss': 0.817, 'learning_rate': 4.898977360288234e-06, 'epoch': 0.01}
|
| 434 |
+
[2022-12-18 08:52:59,924] [INFO] [logging.py:68:log_dist] [Rank 0] step=30, skipped=4, lr=[5.242641991936178e-06], mom=[[0.9, 0.999]]
|
| 435 |
+
[2022-12-18 08:52:59,926] [INFO] [timer.py:196:stop] epoch=0/micro_step=30/global_step=30, RunningAvgSamplesPerSec=17.624193801450975, CurrSamplesPerSec=17.42146956646065, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 436 |
+
[2022-12-18 08:56:04,927] [INFO] [logging.py:68:log_dist] [Rank 0] step=40, skipped=4, lr=[5.766283057118146e-06], mom=[[0.9, 0.999]]
|
| 437 |
+
[2022-12-18 08:56:04,928] [INFO] [timer.py:196:stop] epoch=0/micro_step=40/global_step=40, RunningAvgSamplesPerSec=17.611825644201268, CurrSamplesPerSec=17.199435261553543, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 438 |
+
[2022-12-18 08:59:02,160] [INFO] [logging.py:68:log_dist] [Rank 0] step=50, skipped=4, lr=[6.160712527409633e-06], mom=[[0.9, 0.999]]
|
| 439 |
+
[2022-12-18 08:59:02,163] [INFO] [timer.py:196:stop] epoch=0/micro_step=50/global_step=50, RunningAvgSamplesPerSec=17.57224681838786, CurrSamplesPerSec=17.471532518337153, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 440 |
+
{'loss': 0.3452, 'learning_rate': 6.160712527409633e-06, 'epoch': 0.01}
|
| 441 |
+
[2022-12-18 09:01:57,588] [INFO] [logging.py:68:log_dist] [Rank 0] step=60, skipped=4, lr=[6.4772414076394205e-06], mom=[[0.9, 0.999]]
|
| 442 |
+
[2022-12-18 09:01:57,590] [INFO] [timer.py:196:stop] epoch=0/micro_step=60/global_step=60, RunningAvgSamplesPerSec=17.556437081890596, CurrSamplesPerSec=17.58886056667178, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 443 |
+
[2022-12-18 09:04:51,502] [INFO] [logging.py:68:log_dist] [Rank 0] step=70, skipped=4, lr=[6.741623406776245e-06], mom=[[0.9, 0.999]]
|
| 444 |
+
[2022-12-18 09:04:51,503] [INFO] [timer.py:196:stop] epoch=0/micro_step=70/global_step=70, RunningAvgSamplesPerSec=17.550034157580424, CurrSamplesPerSec=17.404004920974277, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 445 |
+
{'loss': 0.3043, 'learning_rate': 6.85912902234906e-06, 'epoch': 0.01}
|
| 446 |
+
[2022-12-18 09:07:42,313] [INFO] [logging.py:68:log_dist] [Rank 0] step=80, skipped=4, lr=[6.968634661590082e-06], mom=[[0.9, 0.999]]
|
| 447 |
+
[2022-12-18 09:07:42,314] [INFO] [timer.py:196:stop] epoch=0/micro_step=80/global_step=80, RunningAvgSamplesPerSec=17.54554576845597, CurrSamplesPerSec=17.300649307375817, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 448 |
+
[2022-12-18 09:10:37,574] [INFO] [logging.py:68:log_dist] [Rank 0] step=90, skipped=4, lr=[7.1675433522258775e-06], mom=[[0.9, 0.999]]
|
| 449 |
+
[2022-12-18 09:10:37,575] [INFO] [timer.py:196:stop] epoch=0/micro_step=90/global_step=90, RunningAvgSamplesPerSec=17.54364083829183, CurrSamplesPerSec=17.543068524409126, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 450 |
+
[2022-12-18 09:13:33,056] [INFO] [logging.py:68:log_dist] [Rank 0] step=100, skipped=4, lr=[7.344547104469332e-06], mom=[[0.9, 0.999]]
|
| 451 |
+
[2022-12-18 09:13:33,058] [INFO] [timer.py:196:stop] epoch=0/micro_step=100/global_step=100, RunningAvgSamplesPerSec=17.544745558767882, CurrSamplesPerSec=17.311613610419286, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 452 |
+
{'loss': 0.2484, 'learning_rate': 7.344547104469332e-06, 'epoch': 0.02}
|
| 453 |
+
[2022-12-18 09:16:00,166] [INFO] [logging.py:68:log_dist] [Rank 0] step=110, skipped=4, lr=[7.503995457567235e-06], mom=[[0.9, 0.999]]
|
| 454 |
+
[2022-12-18 09:16:00,167] [INFO] [timer.py:196:stop] epoch=0/micro_step=110/global_step=110, RunningAvgSamplesPerSec=17.544294890083517, CurrSamplesPerSec=17.83244775214949, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 455 |
+
[2022-12-18 09:19:12,626] [INFO] [logging.py:68:log_dist] [Rank 0] step=120, skipped=4, lr=[7.649058662787184e-06], mom=[[0.9, 0.999]]
|
| 456 |
+
[2022-12-18 09:19:12,627] [INFO] [timer.py:196:stop] epoch=0/micro_step=120/global_step=120, RunningAvgSamplesPerSec=17.573285029996587, CurrSamplesPerSec=17.949146033725896, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 457 |
+
{'loss': 0.1897, 'learning_rate': 7.716963756434345e-06, 'epoch': 1.0}
|
| 458 |
+
[2022-12-18 09:22:01,411] [INFO] [logging.py:68:log_dist] [Rank 0] step=130, skipped=4, lr=[7.782118888847307e-06], mom=[[0.9, 0.999]]
|
| 459 |
+
[2022-12-18 09:22:01,412] [INFO] [timer.py:196:stop] epoch=0/micro_step=130/global_step=130, RunningAvgSamplesPerSec=17.55456949498131, CurrSamplesPerSec=17.13766063397266, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 460 |
+
[2022-12-18 09:24:51,224] [INFO] [logging.py:68:log_dist] [Rank 0] step=140, skipped=4, lr=[7.905011559752758e-06], mom=[[0.9, 0.999]]
|
| 461 |
+
[2022-12-18 09:24:51,225] [INFO] [timer.py:196:stop] epoch=0/micro_step=140/global_step=140, RunningAvgSamplesPerSec=17.55073845541743, CurrSamplesPerSec=17.72275019960219, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 462 |
+
[2022-12-18 09:27:38,350] [INFO] [logging.py:68:log_dist] [Rank 0] step=150, skipped=4, lr=[8.019180844200955e-06], mom=[[0.9, 0.999]]
|
| 463 |
+
[2022-12-18 09:27:38,352] [INFO] [timer.py:196:stop] epoch=0/micro_step=150/global_step=150, RunningAvgSamplesPerSec=17.54153626249399, CurrSamplesPerSec=17.28127515360052, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 464 |
+
{'loss': 0.1751, 'learning_rate': 8.019180844200955e-06, 'epoch': 1.01}
|
| 465 |
+
[2022-12-18 09:30:26,932] [INFO] [logging.py:68:log_dist] [Rank 0] step=160, skipped=4, lr=[8.125783520495252e-06], mom=[[0.9, 0.999]]
|
| 466 |
+
[2022-12-18 09:30:26,933] [INFO] [timer.py:196:stop] epoch=0/micro_step=160/global_step=160, RunningAvgSamplesPerSec=17.54237608474746, CurrSamplesPerSec=17.425431147803835, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 467 |
+
[2022-12-18 09:33:14,947] [INFO] [logging.py:68:log_dist] [Rank 0] step=170, skipped=4, lr=[8.225760510392298e-06], mom=[[0.9, 0.999]]
|
| 468 |
+
[2022-12-18 09:33:14,948] [INFO] [timer.py:196:stop] epoch=0/micro_step=170/global_step=170, RunningAvgSamplesPerSec=17.5394079030006, CurrSamplesPerSec=17.734454226542, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 469 |
+
{'loss': 0.1499, 'learning_rate': 8.27351214279797e-06, 'epoch': 1.01}
|
| 470 |
+
[2022-12-18 09:36:02,185] [INFO] [logging.py:68:log_dist] [Rank 0] step=180, skipped=4, lr=[8.31988745412743e-06], mom=[[0.9, 0.999]]
|
| 471 |
+
[2022-12-18 09:36:02,186] [INFO] [timer.py:196:stop] epoch=0/micro_step=180/global_step=180, RunningAvgSamplesPerSec=17.537096713384305, CurrSamplesPerSec=17.369822224845038, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 472 |
+
[2022-12-18 09:38:51,989] [INFO] [logging.py:68:log_dist] [Rank 0] step=190, skipped=4, lr=[8.408811289387583e-06], mom=[[0.9, 0.999]]
|
| 473 |
+
[2022-12-18 09:38:51,991] [INFO] [timer.py:196:stop] epoch=0/micro_step=190/global_step=190, RunningAvgSamplesPerSec=17.532972451853638, CurrSamplesPerSec=17.369981828636313, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 474 |
+
[2022-12-18 09:41:41,517] [INFO] [logging.py:68:log_dist] [Rank 0] step=200, skipped=4, lr=[8.49307723936858e-06], mom=[[0.9, 0.999]]
|
| 475 |
+
[2022-12-18 09:41:41,519] [INFO] [timer.py:196:stop] epoch=0/micro_step=200/global_step=200, RunningAvgSamplesPerSec=17.521639611761227, CurrSamplesPerSec=17.05773687963696, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 476 |
+
{'loss': 0.145, 'learning_rate': 8.49307723936858e-06, 'epoch': 1.02}
|
| 477 |
+
[2022-12-18 09:44:30,225] [INFO] [logging.py:68:log_dist] [Rank 0] step=210, skipped=4, lr=[8.573149077803088e-06], mom=[[0.9, 0.999]]
|
| 478 |
+
[2022-12-18 09:44:30,227] [INFO] [timer.py:196:stop] epoch=0/micro_step=210/global_step=210, RunningAvgSamplesPerSec=17.518351230897235, CurrSamplesPerSec=17.491189976865027, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 479 |
+
[2022-12-18 09:47:20,015] [INFO] [logging.py:68:log_dist] [Rank 0] step=220, skipped=4, lr=[8.64942458567722e-06], mom=[[0.9, 0.999]]
|
| 480 |
+
[2022-12-18 09:47:20,016] [INFO] [timer.py:196:stop] epoch=0/micro_step=220/global_step=220, RunningAvgSamplesPerSec=17.52044478044767, CurrSamplesPerSec=17.551188301729674, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 481 |
+
{'loss': 0.1039, 'learning_rate': 8.686247975778677e-06, 'epoch': 1.02}
|
| 482 |
+
[2022-12-18 09:48:47,089] [INFO] [logging.py:68:log_dist] [Rank 0] step=230, skipped=4, lr=[8.722247506883805e-06], mom=[[0.9, 0.999]]
|
| 483 |
+
[2022-12-18 09:48:47,090] [INFO] [timer.py:196:stop] epoch=0/micro_step=230/global_step=230, RunningAvgSamplesPerSec=17.52583657733623, CurrSamplesPerSec=17.795484760153986, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 484 |
+
[2022-12-18 09:52:53,020] [INFO] [logging.py:68:log_dist] [Rank 0] step=240, skipped=4, lr=[8.79191691333329e-06], mom=[[0.9, 0.999]]
|
| 485 |
+
[2022-12-18 09:52:53,022] [INFO] [timer.py:196:stop] epoch=0/micro_step=240/global_step=240, RunningAvgSamplesPerSec=17.540278044164523, CurrSamplesPerSec=17.340279812152833, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 486 |
+
[2022-12-18 09:55:42,935] [INFO] [logging.py:68:log_dist] [Rank 0] step=250, skipped=4, lr=[8.858694625217149e-06], mom=[[0.9, 0.999]]
|
| 487 |
+
[2022-12-18 09:55:42,936] [INFO] [timer.py:196:stop] epoch=0/micro_step=250/global_step=250, RunningAvgSamplesPerSec=17.528756039040783, CurrSamplesPerSec=17.753428194487324, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 488 |
+
{'loss': 0.0958, 'learning_rate': 8.858694625217149e-06, 'epoch': 2.0}
|
| 489 |
+
[2022-12-18 09:58:31,035] [INFO] [logging.py:68:log_dist] [Rank 0] step=260, skipped=4, lr=[8.922811151820517e-06], mom=[[0.9, 0.999]]
|
| 490 |
+
[2022-12-18 09:58:31,036] [INFO] [timer.py:196:stop] epoch=0/micro_step=260/global_step=260, RunningAvgSamplesPerSec=17.51445195778724, CurrSamplesPerSec=17.76546084196235, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 491 |
+
[2022-12-18 10:01:19,374] [INFO] [logging.py:68:log_dist] [Rank 0] step=270, skipped=4, lr=[8.984470493319244e-06], mom=[[0.9, 0.999]]
|
| 492 |
+
[2022-12-18 10:01:19,375] [INFO] [timer.py:196:stop] epoch=0/micro_step=270/global_step=270, RunningAvgSamplesPerSec=17.514860492931785, CurrSamplesPerSec=17.65513873928837, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 493 |
+
{'loss': 0.086, 'learning_rate': 9.014436199608479e-06, 'epoch': 2.01}
|
| 494 |
+
[2022-12-18 10:04:09,796] [INFO] [logging.py:68:log_dist] [Rank 0] step=280, skipped=4, lr=[9.043854055968706e-06], mom=[[0.9, 0.999]]
|
| 495 |
+
[2022-12-18 10:04:09,797] [INFO] [timer.py:196:stop] epoch=0/micro_step=280/global_step=280, RunningAvgSamplesPerSec=17.507487376679528, CurrSamplesPerSec=17.419261679194896, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 496 |
+
[2022-12-18 10:06:58,165] [INFO] [logging.py:68:log_dist] [Rank 0] step=290, skipped=4, lr=[9.10112387015335e-06], mom=[[0.9, 0.999]]
|
| 497 |
+
[2022-12-18 10:06:58,167] [INFO] [timer.py:196:stop] epoch=0/micro_step=290/global_step=290, RunningAvgSamplesPerSec=17.506782737249797, CurrSamplesPerSec=17.55021064009011, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 498 |
+
[2022-12-18 10:09:48,667] [INFO] [logging.py:68:log_dist] [Rank 0] step=300, skipped=4, lr=[9.156425255148058e-06], mom=[[0.9, 0.999]]
|
| 499 |
+
[2022-12-18 10:09:48,669] [INFO] [timer.py:196:stop] epoch=0/micro_step=300/global_step=300, RunningAvgSamplesPerSec=17.505229793083824, CurrSamplesPerSec=17.57551569533738, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 500 |
+
{'loss': 0.0686, 'learning_rate': 9.156425255148058e-06, 'epoch': 2.01}
|
| 501 |
+
[2022-12-18 10:12:41,600] [INFO] [logging.py:68:log_dist] [Rank 0] step=310, skipped=4, lr=[9.209889040960644e-06], mom=[[0.9, 0.999]]
|
| 502 |
+
[2022-12-18 10:12:41,601] [INFO] [timer.py:196:stop] epoch=0/micro_step=310/global_step=310, RunningAvgSamplesPerSec=17.506193858975763, CurrSamplesPerSec=17.60106809421937, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 503 |
+
[2022-12-18 10:15:34,448] [INFO] [logging.py:68:log_dist] [Rank 0] step=320, skipped=4, lr=[9.261633432763397e-06], mom=[[0.9, 0.999]]
|
| 504 |
+
[2022-12-18 10:15:34,449] [INFO] [timer.py:196:stop] epoch=0/micro_step=320/global_step=320, RunningAvgSamplesPerSec=17.502672734107193, CurrSamplesPerSec=17.71995293610675, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 505 |
+
{'loss': 0.0684, 'learning_rate': 9.28689473531776e-06, 'epoch': 2.02}
|
| 506 |
+
[2022-12-18 10:18:25,400] [INFO] [logging.py:68:log_dist] [Rank 0] step=330, skipped=4, lr=[9.311765584761373e-06], mom=[[0.9, 0.999]]
|
| 507 |
+
[2022-12-18 10:18:25,402] [INFO] [timer.py:196:stop] epoch=0/micro_step=330/global_step=330, RunningAvgSamplesPerSec=17.502310309234087, CurrSamplesPerSec=17.587128551283918, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 508 |
+
[2022-12-18 10:21:18,016] [INFO] [logging.py:68:log_dist] [Rank 0] step=340, skipped=4, lr=[9.360382936198493e-06], mom=[[0.9, 0.999]]
|
| 509 |
+
[2022-12-18 10:21:18,018] [INFO] [timer.py:196:stop] epoch=0/micro_step=340/global_step=340, RunningAvgSamplesPerSec=17.500643734444402, CurrSamplesPerSec=17.005072277531735, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 510 |
+
[2022-12-18 10:24:04,872] [INFO] [logging.py:68:log_dist] [Rank 0] step=350, skipped=4, lr=[9.407574351377137e-06], mom=[[0.9, 0.999]]
|
| 511 |
+
[2022-12-18 10:24:04,874] [INFO] [timer.py:196:stop] epoch=0/micro_step=350/global_step=350, RunningAvgSamplesPerSec=17.51464932561927, CurrSamplesPerSec=17.630059089676152, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 512 |
+
{'loss': 0.0482, 'learning_rate': 9.407574351377137e-06, 'epoch': 3.0}
|
| 513 |
+
[2022-12-18 10:26:57,641] [INFO] [logging.py:68:log_dist] [Rank 0] step=360, skipped=4, lr=[9.45342109721062e-06], mom=[[0.9, 0.999]]
|
| 514 |
+
[2022-12-18 10:26:57,643] [INFO] [timer.py:196:stop] epoch=0/micro_step=360/global_step=360, RunningAvgSamplesPerSec=17.505842284973642, CurrSamplesPerSec=17.025209457256768, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 515 |
+
[2022-12-18 10:29:51,036] [INFO] [logging.py:68:log_dist] [Rank 0] step=370, skipped=4, lr=[9.497997685324628e-06], mom=[[0.9, 0.999]]
|
| 516 |
+
[2022-12-18 10:29:51,038] [INFO] [timer.py:196:stop] epoch=0/micro_step=370/global_step=370, RunningAvgSamplesPerSec=17.505074655498845, CurrSamplesPerSec=17.17602944826478, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 517 |
+
{'loss': 0.0504, 'learning_rate': 9.519831289296397e-06, 'epoch': 3.01}
|
| 518 |
+
[2022-12-18 10:32:41,738] [INFO] [logging.py:68:log_dist] [Rank 0] step=380, skipped=4, lr=[9.541372600623587e-06], mom=[[0.9, 0.999]]
|
| 519 |
+
[2022-12-18 10:32:41,739] [INFO] [timer.py:196:stop] epoch=0/micro_step=380/global_step=380, RunningAvgSamplesPerSec=17.50077202769039, CurrSamplesPerSec=17.66240728131972, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 520 |
+
[2022-12-18 10:35:32,596] [INFO] [logging.py:68:log_dist] [Rank 0] step=390, skipped=4, lr=[9.583608934209288e-06], mom=[[0.9, 0.999]]
|
| 521 |
+
[2022-12-18 10:35:32,597] [INFO] [timer.py:196:stop] epoch=0/micro_step=390/global_step=390, RunningAvgSamplesPerSec=17.502615176175013, CurrSamplesPerSec=17.671288199732384, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 522 |
+
[2022-12-18 10:38:24,713] [INFO] [logging.py:68:log_dist] [Rank 0] step=400, skipped=4, lr=[9.624764935335318e-06], mom=[[0.9, 0.999]]
|
| 523 |
+
[2022-12-18 10:38:24,714] [INFO] [timer.py:196:stop] epoch=0/micro_step=400/global_step=400, RunningAvgSamplesPerSec=17.501141402908466, CurrSamplesPerSec=17.413306679702867, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 524 |
+
{'loss': 0.0446, 'learning_rate': 9.624764935335318e-06, 'epoch': 3.01}
|
| 525 |
+
[2022-12-18 10:41:14,089] [INFO] [logging.py:68:log_dist] [Rank 0] step=410, skipped=4, lr=[9.664894494516345e-06], mom=[[0.9, 0.999]]
|
| 526 |
+
[2022-12-18 10:41:14,090] [INFO] [timer.py:196:stop] epoch=0/micro_step=410/global_step=410, RunningAvgSamplesPerSec=17.50299955137452, CurrSamplesPerSec=16.856797996893206, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 527 |
+
[2022-12-18 10:44:03,121] [INFO] [logging.py:68:log_dist] [Rank 0] step=420, skipped=4, lr=[9.704047567846437e-06], mom=[[0.9, 0.999]]
|
| 528 |
+
[2022-12-18 10:44:03,123] [INFO] [timer.py:196:stop] epoch=0/micro_step=420/global_step=420, RunningAvgSamplesPerSec=17.49955598397826, CurrSamplesPerSec=17.39210969006482, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 529 |
+
{'loss': 0.0396, 'learning_rate': 9.723272550712454e-06, 'epoch': 3.02}
|
| 530 |
+
[2022-12-18 10:46:53,231] [INFO] [logging.py:68:log_dist] [Rank 0] step=430, skipped=4, lr=[9.742270550908135e-06], mom=[[0.9, 0.999]]
|
| 531 |
+
[2022-12-18 10:46:53,233] [INFO] [timer.py:196:stop] epoch=0/micro_step=430/global_step=430, RunningAvgSamplesPerSec=17.502169263783284, CurrSamplesPerSec=17.82420181661996, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 532 |
+
[2022-12-18 10:49:44,733] [INFO] [logging.py:68:log_dist] [Rank 0] step=440, skipped=4, lr=[9.779606609292176e-06], mom=[[0.9, 0.999]]
|
| 533 |
+
[2022-12-18 10:49:44,734] [INFO] [timer.py:196:stop] epoch=0/micro_step=440/global_step=440, RunningAvgSamplesPerSec=17.502468898132452, CurrSamplesPerSec=17.01432443514231, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 534 |
+
[2022-12-18 10:52:34,347] [INFO] [logging.py:68:log_dist] [Rank 0] step=450, skipped=4, lr=[9.816095971633122e-06], mom=[[0.9, 0.999]]
|
| 535 |
+
[2022-12-18 10:52:34,348] [INFO] [timer.py:196:stop] epoch=0/micro_step=450/global_step=450, RunningAvgSamplesPerSec=17.50661530301726, CurrSamplesPerSec=17.68532190940408, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 536 |
+
{'loss': 0.0309, 'learning_rate': 9.816095971633122e-06, 'epoch': 3.02}
|
| 537 |
+
[2022-12-18 10:54:31,089] [INFO] [logging.py:68:log_dist] [Rank 0] step=460, skipped=4, lr=[9.851776190149156e-06], mom=[[0.9, 0.999]]
|
| 538 |
+
[2022-12-18 10:54:31,091] [INFO] [timer.py:196:stop] epoch=0/micro_step=460/global_step=460, RunningAvgSamplesPerSec=17.505343508354425, CurrSamplesPerSec=17.622945563320123, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 539 |
+
[2022-12-18 10:58:09,980] [INFO] [logging.py:68:log_dist] [Rank 0] step=470, skipped=4, lr=[9.886682372916766e-06], mom=[[0.9, 0.999]]
|
| 540 |
+
[2022-12-18 10:58:09,982] [INFO] [timer.py:196:stop] epoch=0/micro_step=470/global_step=470, RunningAvgSamplesPerSec=17.51432072204074, CurrSamplesPerSec=17.560018580808034, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 541 |
+
{'loss': 0.0269, 'learning_rate': 9.90385555539545e-06, 'epoch': 4.0}
|
| 542 |
+
[2022-12-18 11:01:02,577] [INFO] [logging.py:68:log_dist] [Rank 0] step=480, skipped=4, lr=[9.92084739148192e-06], mom=[[0.9, 0.999]]
|
| 543 |
+
[2022-12-18 11:01:02,579] [INFO] [timer.py:196:stop] epoch=0/micro_step=480/global_step=480, RunningAvgSamplesPerSec=17.51471041450659, CurrSamplesPerSec=17.770770916559375, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 544 |
+
[2022-12-18 11:03:53,720] [INFO] [logging.py:68:log_dist] [Rank 0] step=490, skipped=4, lr=[9.954302066885107e-06], mom=[[0.9, 0.999]]
|
| 545 |
+
[2022-12-18 11:03:53,722] [INFO] [timer.py:196:stop] epoch=0/micro_step=490/global_step=490, RunningAvgSamplesPerSec=17.514707806076828, CurrSamplesPerSec=17.474965145825077, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 546 |
+
[2022-12-18 11:06:40,711] [INFO] [logging.py:68:log_dist] [Rank 0] step=500, skipped=4, lr=[9.987075336738768e-06], mom=[[0.9, 0.999]]
|
| 547 |
+
[2022-12-18 11:06:40,713] [INFO] [timer.py:196:stop] epoch=0/micro_step=500/global_step=500, RunningAvgSamplesPerSec=17.51673358493395, CurrSamplesPerSec=17.728490220205128, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 548 |
+
{'loss': 0.03, 'learning_rate': 9.987075336738768e-06, 'epoch': 4.01}
|
| 549 |
+
[2022-12-18 11:09:32,253] [INFO] [logging.py:68:log_dist] [Rank 0] step=510, skipped=4, lr=[9.98888888888889e-06], mom=[[0.9, 0.999]]
|
| 550 |
+
[2022-12-18 11:09:32,254] [INFO] [timer.py:196:stop] epoch=0/micro_step=510/global_step=510, RunningAvgSamplesPerSec=17.515181954535795, CurrSamplesPerSec=17.44658815742413, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 551 |
+
[2022-12-18 11:12:24,243] [INFO] [logging.py:68:log_dist] [Rank 0] step=520, skipped=4, lr=[9.966666666666667e-06], mom=[[0.9, 0.999]]
|
| 552 |
+
[2022-12-18 11:12:24,244] [INFO] [timer.py:196:stop] epoch=0/micro_step=520/global_step=520, RunningAvgSamplesPerSec=17.516449396915945, CurrSamplesPerSec=17.71918211824117, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 553 |
+
{'loss': 0.025, 'learning_rate': 9.955555555555556e-06, 'epoch': 4.01}
|
| 554 |
+
[2022-12-18 11:15:10,174] [INFO] [logging.py:68:log_dist] [Rank 0] step=530, skipped=4, lr=[9.944444444444445e-06], mom=[[0.9, 0.999]]
|
| 555 |
+
[2022-12-18 11:15:10,175] [INFO] [timer.py:196:stop] epoch=0/micro_step=530/global_step=530, RunningAvgSamplesPerSec=17.515811116445363, CurrSamplesPerSec=17.44589309419759, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 556 |
+
[2022-12-18 11:17:59,140] [INFO] [logging.py:68:log_dist] [Rank 0] step=540, skipped=4, lr=[9.922222222222222e-06], mom=[[0.9, 0.999]]
|
| 557 |
+
[2022-12-18 11:17:59,141] [INFO] [timer.py:196:stop] epoch=0/micro_step=540/global_step=540, RunningAvgSamplesPerSec=17.51307013237252, CurrSamplesPerSec=17.709312456169634, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 558 |
+
[2022-12-18 11:20:51,468] [INFO] [logging.py:68:log_dist] [Rank 0] step=550, skipped=4, lr=[9.9e-06], mom=[[0.9, 0.999]]
|
| 559 |
+
[2022-12-18 11:20:51,469] [INFO] [timer.py:196:stop] epoch=0/micro_step=550/global_step=550, RunningAvgSamplesPerSec=17.51360962534701, CurrSamplesPerSec=17.514523814355172, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 560 |
+
{'loss': 0.0214, 'learning_rate': 9.9e-06, 'epoch': 4.02}
|
| 561 |
+
[2022-12-18 11:23:41,540] [INFO] [logging.py:68:log_dist] [Rank 0] step=560, skipped=4, lr=[9.877777777777778e-06], mom=[[0.9, 0.999]]
|
| 562 |
+
[2022-12-18 11:23:41,542] [INFO] [timer.py:196:stop] epoch=0/micro_step=560/global_step=560, RunningAvgSamplesPerSec=17.515173467375842, CurrSamplesPerSec=17.700679243083353, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 563 |
+
[2022-12-18 11:26:28,412] [INFO] [logging.py:68:log_dist] [Rank 0] step=570, skipped=4, lr=[9.855555555555555e-06], mom=[[0.9, 0.999]]
|
| 564 |
+
[2022-12-18 11:26:28,413] [INFO] [timer.py:196:stop] epoch=0/micro_step=570/global_step=570, RunningAvgSamplesPerSec=17.515792660700566, CurrSamplesPerSec=17.636638364608388, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 565 |
+
{'loss': 0.0176, 'learning_rate': 9.844444444444446e-06, 'epoch': 4.02}
|
| 566 |
+
[2022-12-18 11:27:30,542] [INFO] [logging.py:68:log_dist] [Rank 0] step=580, skipped=4, lr=[9.833333333333333e-06], mom=[[0.9, 0.999]]
|
| 567 |
+
[2022-12-18 11:27:30,544] [INFO] [timer.py:196:stop] epoch=0/micro_step=580/global_step=580, RunningAvgSamplesPerSec=17.525312485669467, CurrSamplesPerSec=22.15033486404057, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 568 |
+
[2022-12-18 11:31:57,731] [INFO] [logging.py:68:log_dist] [Rank 0] step=590, skipped=4, lr=[9.811111111111112e-06], mom=[[0.9, 0.999]]
|
| 569 |
+
[2022-12-18 11:31:57,733] [INFO] [timer.py:196:stop] epoch=0/micro_step=590/global_step=590, RunningAvgSamplesPerSec=17.52578604906633, CurrSamplesPerSec=17.542104377157184, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 570 |
+
[2022-12-18 11:34:45,506] [INFO] [logging.py:68:log_dist] [Rank 0] step=600, skipped=4, lr=[9.78888888888889e-06], mom=[[0.9, 0.999]]
|
| 571 |
+
[2022-12-18 11:34:45,507] [INFO] [timer.py:196:stop] epoch=0/micro_step=600/global_step=600, RunningAvgSamplesPerSec=17.525721346081685, CurrSamplesPerSec=17.728407089792164, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 572 |
+
{'loss': 0.0186, 'learning_rate': 9.78888888888889e-06, 'epoch': 5.0}
|
| 573 |
+
[2022-12-18 11:37:32,907] [INFO] [logging.py:68:log_dist] [Rank 0] step=610, skipped=4, lr=[9.766666666666667e-06], mom=[[0.9, 0.999]]
|
| 574 |
+
[2022-12-18 11:37:32,909] [INFO] [timer.py:196:stop] epoch=0/micro_step=610/global_step=610, RunningAvgSamplesPerSec=17.52620539215051, CurrSamplesPerSec=17.665249180375554, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 575 |
+
[2022-12-18 11:40:23,836] [INFO] [logging.py:68:log_dist] [Rank 0] step=620, skipped=4, lr=[9.744444444444445e-06], mom=[[0.9, 0.999]]
|
| 576 |
+
[2022-12-18 11:40:23,837] [INFO] [timer.py:196:stop] epoch=0/micro_step=620/global_step=620, RunningAvgSamplesPerSec=17.524553828947862, CurrSamplesPerSec=17.647862254407272, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 577 |
+
{'loss': 0.0179, 'learning_rate': 9.733333333333334e-06, 'epoch': 5.01}
|
| 578 |
+
[2022-12-18 11:43:10,955] [INFO] [logging.py:68:log_dist] [Rank 0] step=630, skipped=4, lr=[9.722222222222223e-06], mom=[[0.9, 0.999]]
|
| 579 |
+
[2022-12-18 11:43:10,957] [INFO] [timer.py:196:stop] epoch=0/micro_step=630/global_step=630, RunningAvgSamplesPerSec=17.524283076579064, CurrSamplesPerSec=17.71436339529198, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 580 |
+
[2022-12-18 11:45:59,599] [INFO] [logging.py:68:log_dist] [Rank 0] step=640, skipped=4, lr=[9.7e-06], mom=[[0.9, 0.999]]
|
| 581 |
+
[2022-12-18 11:45:59,600] [INFO] [timer.py:196:stop] epoch=0/micro_step=640/global_step=640, RunningAvgSamplesPerSec=17.5239493520232, CurrSamplesPerSec=17.29191637538188, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 582 |
+
[2022-12-18 11:48:51,158] [INFO] [logging.py:68:log_dist] [Rank 0] step=650, skipped=4, lr=[9.677777777777778e-06], mom=[[0.9, 0.999]]
|
| 583 |
+
[2022-12-18 11:48:51,159] [INFO] [timer.py:196:stop] epoch=0/micro_step=650/global_step=650, RunningAvgSamplesPerSec=17.520107118857844, CurrSamplesPerSec=17.428385128642994, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 584 |
+
{'loss': 0.0141, 'learning_rate': 9.677777777777778e-06, 'epoch': 5.01}
|
| 585 |
+
[2022-12-18 11:51:44,941] [INFO] [logging.py:68:log_dist] [Rank 0] step=660, skipped=4, lr=[9.655555555555556e-06], mom=[[0.9, 0.999]]
|
| 586 |
+
[2022-12-18 11:51:44,942] [INFO] [timer.py:196:stop] epoch=0/micro_step=660/global_step=660, RunningAvgSamplesPerSec=17.519444727080156, CurrSamplesPerSec=16.994613216593315, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 587 |
+
[2022-12-18 11:54:37,926] [INFO] [logging.py:68:log_dist] [Rank 0] step=670, skipped=4, lr=[9.633333333333335e-06], mom=[[0.9, 0.999]]
|
| 588 |
+
[2022-12-18 11:54:37,928] [INFO] [timer.py:196:stop] epoch=0/micro_step=670/global_step=670, RunningAvgSamplesPerSec=17.519984376324164, CurrSamplesPerSec=17.66502830473203, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 589 |
+
{'loss': 0.0131, 'learning_rate': 9.622222222222222e-06, 'epoch': 5.02}
|
| 590 |
+
[2022-12-18 11:57:26,593] [INFO] [logging.py:68:log_dist] [Rank 0] step=680, skipped=4, lr=[9.611111111111112e-06], mom=[[0.9, 0.999]]
|
| 591 |
+
[2022-12-18 11:57:26,595] [INFO] [timer.py:196:stop] epoch=0/micro_step=680/global_step=680, RunningAvgSamplesPerSec=17.51917469369641, CurrSamplesPerSec=17.81082610549716, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 592 |
+
[2022-12-18 11:59:46,615] [INFO] [logging.py:68:log_dist] [Rank 0] step=690, skipped=4, lr=[9.58888888888889e-06], mom=[[0.9, 0.999]]
|
| 593 |
+
[2022-12-18 11:59:46,616] [INFO] [timer.py:196:stop] epoch=0/micro_step=690/global_step=690, RunningAvgSamplesPerSec=17.520953040279323, CurrSamplesPerSec=17.893703708073588, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 594 |
+
[2022-12-18 12:03:02,532] [INFO] [logging.py:68:log_dist] [Rank 0] step=700, skipped=4, lr=[9.566666666666668e-06], mom=[[0.9, 0.999]]
|
| 595 |
+
[2022-12-18 12:03:02,534] [INFO] [timer.py:196:stop] epoch=0/micro_step=700/global_step=700, RunningAvgSamplesPerSec=17.526881403928282, CurrSamplesPerSec=17.811006916180688, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 596 |
+
{'loss': 0.0111, 'learning_rate': 9.566666666666668e-06, 'epoch': 6.0}
|
| 597 |
+
[2022-12-18 12:05:50,396] [INFO] [logging.py:68:log_dist] [Rank 0] step=710, skipped=4, lr=[9.544444444444445e-06], mom=[[0.9, 0.999]]
|
| 598 |
+
[2022-12-18 12:05:50,397] [INFO] [timer.py:196:stop] epoch=0/micro_step=710/global_step=710, RunningAvgSamplesPerSec=17.525663669476064, CurrSamplesPerSec=17.627963556175473, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 599 |
+
[2022-12-18 12:08:40,501] [INFO] [logging.py:68:log_dist] [Rank 0] step=720, skipped=4, lr=[9.522222222222223e-06], mom=[[0.9, 0.999]]
|
| 600 |
+
[2022-12-18 12:08:40,503] [INFO] [timer.py:196:stop] epoch=0/micro_step=720/global_step=720, RunningAvgSamplesPerSec=17.522315207058163, CurrSamplesPerSec=17.08808021934021, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 601 |
+
{'loss': 0.0115, 'learning_rate': 9.511111111111112e-06, 'epoch': 6.01}
|
| 602 |
+
[2022-12-18 12:11:29,479] [INFO] [logging.py:68:log_dist] [Rank 0] step=730, skipped=4, lr=[9.5e-06], mom=[[0.9, 0.999]]
|
| 603 |
+
[2022-12-18 12:11:29,481] [INFO] [timer.py:196:stop] epoch=0/micro_step=730/global_step=730, RunningAvgSamplesPerSec=17.522397647878112, CurrSamplesPerSec=17.69114963575808, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 604 |
+
[2022-12-18 12:14:18,585] [INFO] [logging.py:68:log_dist] [Rank 0] step=740, skipped=4, lr=[9.47777777777778e-06], mom=[[0.9, 0.999]]
|
| 605 |
+
[2022-12-18 12:14:18,587] [INFO] [timer.py:196:stop] epoch=0/micro_step=740/global_step=740, RunningAvgSamplesPerSec=17.522899649689435, CurrSamplesPerSec=17.835842357826117, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 606 |
+
[2022-12-18 12:17:07,842] [INFO] [logging.py:68:log_dist] [Rank 0] step=750, skipped=4, lr=[9.455555555555557e-06], mom=[[0.9, 0.999]]
|
| 607 |
+
[2022-12-18 12:17:07,843] [INFO] [timer.py:196:stop] epoch=0/micro_step=750/global_step=750, RunningAvgSamplesPerSec=17.52216964269384, CurrSamplesPerSec=17.715532463583024, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 608 |
+
{'loss': 0.0097, 'learning_rate': 9.455555555555557e-06, 'epoch': 6.01}
|
| 609 |
+
[2022-12-18 12:19:56,259] [INFO] [logging.py:68:log_dist] [Rank 0] step=760, skipped=4, lr=[9.433333333333335e-06], mom=[[0.9, 0.999]]
|
| 610 |
+
[2022-12-18 12:19:56,260] [INFO] [timer.py:196:stop] epoch=0/micro_step=760/global_step=760, RunningAvgSamplesPerSec=17.52350085830269, CurrSamplesPerSec=17.825603229647047, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 611 |
+
[2022-12-18 12:22:43,814] [INFO] [logging.py:68:log_dist] [Rank 0] step=770, skipped=4, lr=[9.411111111111113e-06], mom=[[0.9, 0.999]]
|
| 612 |
+
[2022-12-18 12:22:43,816] [INFO] [timer.py:196:stop] epoch=0/micro_step=770/global_step=770, RunningAvgSamplesPerSec=17.523872671951036, CurrSamplesPerSec=17.464620175491046, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 613 |
+
{'loss': 0.0091, 'learning_rate': 9.4e-06, 'epoch': 6.02}
|
| 614 |
+
[2022-12-18 12:25:34,399] [INFO] [logging.py:68:log_dist] [Rank 0] step=780, skipped=4, lr=[9.38888888888889e-06], mom=[[0.9, 0.999]]
|
| 615 |
+
[2022-12-18 12:25:34,401] [INFO] [timer.py:196:stop] epoch=0/micro_step=780/global_step=780, RunningAvgSamplesPerSec=17.523888473835193, CurrSamplesPerSec=17.62105645548852, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 616 |
+
[2022-12-18 12:28:22,069] [INFO] [logging.py:68:log_dist] [Rank 0] step=790, skipped=4, lr=[9.366666666666668e-06], mom=[[0.9, 0.999]]
|
| 617 |
+
[2022-12-18 12:28:22,070] [INFO] [timer.py:196:stop] epoch=0/micro_step=790/global_step=790, RunningAvgSamplesPerSec=17.52396646475176, CurrSamplesPerSec=17.7942001353745, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 618 |
+
[2022-12-18 12:31:10,611] [INFO] [logging.py:68:log_dist] [Rank 0] step=800, skipped=4, lr=[9.344444444444446e-06], mom=[[0.9, 0.999]]
|
| 619 |
+
[2022-12-18 12:31:10,613] [INFO] [timer.py:196:stop] epoch=0/micro_step=800/global_step=800, RunningAvgSamplesPerSec=17.52362785335887, CurrSamplesPerSec=17.645739287742696, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 620 |
+
{'loss': 0.0099, 'learning_rate': 9.344444444444446e-06, 'epoch': 6.02}
|
| 621 |
+
[2022-12-18 12:32:39,816] [INFO] [logging.py:68:log_dist] [Rank 0] step=810, skipped=4, lr=[9.322222222222223e-06], mom=[[0.9, 0.999]]
|
| 622 |
+
[2022-12-18 12:32:39,818] [INFO] [timer.py:196:stop] epoch=0/micro_step=810/global_step=810, RunningAvgSamplesPerSec=17.525217387924933, CurrSamplesPerSec=17.6307387978646, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 623 |
+
[2022-12-18 12:36:44,201] [INFO] [logging.py:68:log_dist] [Rank 0] step=820, skipped=4, lr=[9.3e-06], mom=[[0.9, 0.999]]
|
| 624 |
+
[2022-12-18 12:36:44,203] [INFO] [timer.py:196:stop] epoch=0/micro_step=820/global_step=820, RunningAvgSamplesPerSec=17.527549173776915, CurrSamplesPerSec=17.24311195262599, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 625 |
+
{'loss': 0.0073, 'learning_rate': 9.28888888888889e-06, 'epoch': 7.0}
|
| 626 |
+
[2022-12-18 12:39:35,915] [INFO] [logging.py:68:log_dist] [Rank 0] step=830, skipped=4, lr=[9.277777777777778e-06], mom=[[0.9, 0.999]]
|
| 627 |
+
[2022-12-18 12:39:35,917] [INFO] [timer.py:196:stop] epoch=0/micro_step=830/global_step=830, RunningAvgSamplesPerSec=17.527139118776905, CurrSamplesPerSec=17.824774664911605, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 628 |
+
[2022-12-18 12:42:25,214] [INFO] [logging.py:68:log_dist] [Rank 0] step=840, skipped=4, lr=[9.255555555555556e-06], mom=[[0.9, 0.999]]
|
| 629 |
+
[2022-12-18 12:42:25,215] [INFO] [timer.py:196:stop] epoch=0/micro_step=840/global_step=840, RunningAvgSamplesPerSec=17.529876938383726, CurrSamplesPerSec=17.677469879502205, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 630 |
+
[2022-12-18 12:45:16,974] [INFO] [logging.py:68:log_dist] [Rank 0] step=850, skipped=4, lr=[9.233333333333334e-06], mom=[[0.9, 0.999]]
|
| 631 |
+
[2022-12-18 12:45:16,976] [INFO] [timer.py:196:stop] epoch=0/micro_step=850/global_step=850, RunningAvgSamplesPerSec=17.533049828024673, CurrSamplesPerSec=17.934881195101724, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 632 |
+
{'loss': 0.0062, 'learning_rate': 9.233333333333334e-06, 'epoch': 7.01}
|
| 633 |
+
[2022-12-18 12:48:07,233] [INFO] [logging.py:68:log_dist] [Rank 0] step=860, skipped=4, lr=[9.211111111111111e-06], mom=[[0.9, 0.999]]
|
| 634 |
+
[2022-12-18 12:48:07,234] [INFO] [timer.py:196:stop] epoch=0/micro_step=860/global_step=860, RunningAvgSamplesPerSec=17.536686576379214, CurrSamplesPerSec=17.72774792834313, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 635 |
+
[2022-12-18 12:50:56,254] [INFO] [logging.py:68:log_dist] [Rank 0] step=870, skipped=4, lr=[9.188888888888889e-06], mom=[[0.9, 0.999]]
|
| 636 |
+
[2022-12-18 12:50:56,255] [INFO] [timer.py:196:stop] epoch=0/micro_step=870/global_step=870, RunningAvgSamplesPerSec=17.53939897716571, CurrSamplesPerSec=17.863981354170473, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 637 |
+
{'loss': 0.006, 'learning_rate': 9.17777777777778e-06, 'epoch': 7.01}
|
| 638 |
+
[2022-12-18 12:53:49,512] [INFO] [logging.py:68:log_dist] [Rank 0] step=880, skipped=4, lr=[9.166666666666666e-06], mom=[[0.9, 0.999]]
|
| 639 |
+
[2022-12-18 12:53:49,514] [INFO] [timer.py:196:stop] epoch=0/micro_step=880/global_step=880, RunningAvgSamplesPerSec=17.54251129449292, CurrSamplesPerSec=17.672755259073455, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 640 |
+
[2022-12-18 12:56:39,481] [INFO] [logging.py:68:log_dist] [Rank 0] step=890, skipped=4, lr=[9.144444444444444e-06], mom=[[0.9, 0.999]]
|
| 641 |
+
[2022-12-18 12:56:39,482] [INFO] [timer.py:196:stop] epoch=0/micro_step=890/global_step=890, RunningAvgSamplesPerSec=17.545431248601155, CurrSamplesPerSec=17.820363263008623, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 642 |
+
[2022-12-18 12:59:28,307] [INFO] [logging.py:68:log_dist] [Rank 0] step=900, skipped=4, lr=[9.122222222222223e-06], mom=[[0.9, 0.999]]
|
| 643 |
+
[2022-12-18 12:59:28,309] [INFO] [timer.py:196:stop] epoch=0/micro_step=900/global_step=900, RunningAvgSamplesPerSec=17.5474477902634, CurrSamplesPerSec=17.84197843798558, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 644 |
+
{'loss': 0.0046, 'learning_rate': 9.122222222222223e-06, 'epoch': 7.02}
|
| 645 |
+
[2022-12-18 13:02:23,366] [INFO] [logging.py:68:log_dist] [Rank 0] step=910, skipped=4, lr=[9.100000000000001e-06], mom=[[0.9, 0.999]]
|
| 646 |
+
[2022-12-18 13:02:23,368] [INFO] [timer.py:196:stop] epoch=0/micro_step=910/global_step=910, RunningAvgSamplesPerSec=17.54704328541452, CurrSamplesPerSec=17.599693685140846, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 647 |
+
[2022-12-18 13:05:10,345] [INFO] [logging.py:68:log_dist] [Rank 0] step=920, skipped=4, lr=[9.077777777777779e-06], mom=[[0.9, 0.999]]
|
| 648 |
+
[2022-12-18 13:05:10,346] [INFO] [timer.py:196:stop] epoch=0/micro_step=920/global_step=920, RunningAvgSamplesPerSec=17.548757345088557, CurrSamplesPerSec=17.80023677023281, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 649 |
+
{'loss': 0.0045, 'learning_rate': 9.066666666666667e-06, 'epoch': 7.02}
|
| 650 |
+
[2022-12-18 13:07:52,456] [INFO] [logging.py:68:log_dist] [Rank 0] step=930, skipped=4, lr=[9.055555555555556e-06], mom=[[0.9, 0.999]]
|
| 651 |
+
[2022-12-18 13:07:52,457] [INFO] [timer.py:196:stop] epoch=0/micro_step=930/global_step=930, RunningAvgSamplesPerSec=17.555835577865636, CurrSamplesPerSec=16.947261645741815, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 652 |
+
[2022-12-18 13:10:39,445] [INFO] [logging.py:68:log_dist] [Rank 0] step=940, skipped=4, lr=[9.033333333333334e-06], mom=[[0.9, 0.999]]
|
| 653 |
+
[2022-12-18 13:10:39,447] [INFO] [timer.py:196:stop] epoch=0/micro_step=940/global_step=940, RunningAvgSamplesPerSec=17.558626670023333, CurrSamplesPerSec=17.837192265183017, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 654 |
+
[2022-12-18 13:13:28,825] [INFO] [logging.py:68:log_dist] [Rank 0] step=950, skipped=4, lr=[9.011111111111111e-06], mom=[[0.9, 0.999]]
|
| 655 |
+
[2022-12-18 13:13:28,827] [INFO] [timer.py:196:stop] epoch=0/micro_step=950/global_step=950, RunningAvgSamplesPerSec=17.561116636037408, CurrSamplesPerSec=17.875913197667035, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 656 |
+
{'loss': 0.0053, 'learning_rate': 9.011111111111111e-06, 'epoch': 8.0}
|
| 657 |
+
[2022-12-18 13:16:15,728] [INFO] [logging.py:68:log_dist] [Rank 0] step=960, skipped=4, lr=[8.988888888888889e-06], mom=[[0.9, 0.999]]
|
| 658 |
+
[2022-12-18 13:16:15,729] [INFO] [timer.py:196:stop] epoch=0/micro_step=960/global_step=960, RunningAvgSamplesPerSec=17.56308806750036, CurrSamplesPerSec=17.781870571307575, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 659 |
+
[2022-12-18 13:19:02,146] [INFO] [logging.py:68:log_dist] [Rank 0] step=970, skipped=4, lr=[8.966666666666667e-06], mom=[[0.9, 0.999]]
|
| 660 |
+
[2022-12-18 13:19:02,147] [INFO] [timer.py:196:stop] epoch=0/micro_step=970/global_step=970, RunningAvgSamplesPerSec=17.564578542499007, CurrSamplesPerSec=17.74904968508855, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 661 |
+
{'loss': 0.0039, 'learning_rate': 8.955555555555555e-06, 'epoch': 8.01}
|
| 662 |
+
[2022-12-18 13:21:50,887] [INFO] [logging.py:68:log_dist] [Rank 0] step=980, skipped=4, lr=[8.944444444444446e-06], mom=[[0.9, 0.999]]
|
| 663 |
+
[2022-12-18 13:21:50,889] [INFO] [timer.py:196:stop] epoch=0/micro_step=980/global_step=980, RunningAvgSamplesPerSec=17.566081411767524, CurrSamplesPerSec=18.11684822516133, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 664 |
+
[2022-12-18 13:24:37,286] [INFO] [logging.py:68:log_dist] [Rank 0] step=990, skipped=4, lr=[8.922222222222224e-06], mom=[[0.9, 0.999]]
|
| 665 |
+
[2022-12-18 13:24:37,287] [INFO] [timer.py:196:stop] epoch=0/micro_step=990/global_step=990, RunningAvgSamplesPerSec=17.56737818663697, CurrSamplesPerSec=17.898017842980305, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 666 |
+
[2022-12-18 13:27:24,217] [INFO] [logging.py:68:log_dist] [Rank 0] step=1000, skipped=4, lr=[8.900000000000001e-06], mom=[[0.9, 0.999]]
|
| 667 |
+
[2022-12-18 13:27:24,219] [INFO] [timer.py:196:stop] epoch=0/micro_step=1000/global_step=1000, RunningAvgSamplesPerSec=17.569257091709765, CurrSamplesPerSec=17.374920879792757, MemAllocated=0.53GB, MaxMemAllocated=17.47GB
|
| 668 |
+
{'loss': 0.0046, 'learning_rate': 8.900000000000001e-06, 'epoch': 8.01}
|
| 669 |
+
{'eval_loss': 0.28076171875, 'eval_wer': 17.571297148114077, 'eval_runtime': 1237.4696, 'eval_samples_per_second': 3.118, 'eval_steps_per_second': 0.098, 'epoch': 8.01}
|
| 670 |
+
[2022-12-18 13:48:02,674] [INFO] [logging.py:68:log_dist] [Rank 0] [Torch] Checkpoint global_step1000 is begin to save!
|
| 671 |
+
[2022-12-18 13:48:02,684] [INFO] [logging.py:68:log_dist] [Rank 0] Saving model checkpoint: ./checkpoint-1000/global_step1000/mp_rank_00_model_states.pt
|
| 672 |
+
[2022-12-18 13:48:02,684] [INFO] [torch_checkpoint_engine.py:15:save] [Torch] Saving ./checkpoint-1000/global_step1000/mp_rank_00_model_states.pt...
|
| 673 |
+
[2022-12-18 13:48:03,680] [INFO] [torch_checkpoint_engine.py:17:save] [Torch] Saved ./checkpoint-1000/global_step1000/mp_rank_00_model_states.pt.
|
| 674 |
+
[2022-12-18 13:48:03,682] [INFO] [torch_checkpoint_engine.py:15:save] [Torch] Saving ./checkpoint-1000/global_step1000/zero_pp_rank_0_mp_rank_00_optim_states.pt...
|
| 675 |
+
[2022-12-18 13:48:08,206] [INFO] [torch_checkpoint_engine.py:17:save] [Torch] Saved ./checkpoint-1000/global_step1000/zero_pp_rank_0_mp_rank_00_optim_states.pt.
|
| 676 |
+
[2022-12-18 13:48:08,208] [INFO] [engine.py:3394:_save_zero_checkpoint] zero checkpoint saved ./checkpoint-1000/global_step1000/zero_pp_rank_0_mp_rank_00_optim_states.pt
|
| 677 |
+
[2022-12-18 13:48:08,208] [INFO] [torch_checkpoint_engine.py:27:commit] [Torch] Checkpoint global_step1000 is ready now!
|
run.sh
ADDED
|
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
deepspeed run_speech_recognition_seq2seq_streaming.py \
|
| 2 |
+
--deepspeed="ds_config.json" \
|
| 3 |
+
--model_name_or_path="openai/whisper-small" \
|
| 4 |
+
--dataset_name="mozilla-foundation/common_voice_11_0" \
|
| 5 |
+
--dataset_config_name="ro" \
|
| 6 |
+
--language="romanian" \
|
| 7 |
+
--train_split_name="train+validation" \
|
| 8 |
+
--eval_split_name="test" \
|
| 9 |
+
--model_index_name="Whisper Small Romanian CV11" \
|
| 10 |
+
--max_steps="5000" \
|
| 11 |
+
--output_dir="./" \
|
| 12 |
+
--per_device_train_batch_size="64" \
|
| 13 |
+
--per_device_eval_batch_size="32" \
|
| 14 |
+
--logging_steps="25" \
|
| 15 |
+
--learning_rate="1e-5" \
|
| 16 |
+
--warmup_steps="500" \
|
| 17 |
+
--evaluation_strategy="steps" \
|
| 18 |
+
--eval_steps="1000" \
|
| 19 |
+
--save_strategy="steps" \
|
| 20 |
+
--save_steps="1000" \
|
| 21 |
+
--generation_max_length="225" \
|
| 22 |
+
--length_column_name="input_length" \
|
| 23 |
+
--max_duration_in_seconds="30" \
|
| 24 |
+
--text_column_name="sentence" \
|
| 25 |
+
--freeze_feature_encoder="False" \
|
| 26 |
+
--report_to="tensorboard" \
|
| 27 |
+
--metric_for_best_model="wer" \
|
| 28 |
+
--greater_is_better="False" \
|
| 29 |
+
--load_best_model_at_end \
|
| 30 |
+
--gradient_checkpointing \
|
| 31 |
+
--fp16 \
|
| 32 |
+
--overwrite_output_dir \
|
| 33 |
+
--do_train \
|
| 34 |
+
--do_eval \
|
| 35 |
+
--predict_with_generate \
|
| 36 |
+
--do_normalize_eval \
|
| 37 |
+
--streaming \
|
| 38 |
+
--use_auth_token \
|
| 39 |
+
--push_to_hub
|
run_speech_recognition_seq2seq_streaming.py
ADDED
|
@@ -0,0 +1,629 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
# coding=utf-8
|
| 3 |
+
# Copyright 2022 The HuggingFace Team. All rights reserved.
|
| 4 |
+
#
|
| 5 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 6 |
+
# you may not use this file except in compliance with the License.
|
| 7 |
+
# You may obtain a copy of the License at
|
| 8 |
+
#
|
| 9 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 10 |
+
#
|
| 11 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 12 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 13 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 14 |
+
# See the License for the specific language governing permissions and
|
| 15 |
+
# limitations under the License.
|
| 16 |
+
"""
|
| 17 |
+
Fine-tuning the library models for sequence to sequence speech recognition
|
| 18 |
+
with 🤗 Datasets' streaming mode.
|
| 19 |
+
"""
|
| 20 |
+
# You can also adapt this script for your own sequence to sequence speech
|
| 21 |
+
# recognition task. Pointers for this are left as comments.
|
| 22 |
+
|
| 23 |
+
import logging
|
| 24 |
+
import os
|
| 25 |
+
import sys
|
| 26 |
+
from dataclasses import dataclass, field
|
| 27 |
+
from typing import Any, Dict, List, Optional, Union
|
| 28 |
+
|
| 29 |
+
import datasets
|
| 30 |
+
import torch
|
| 31 |
+
from datasets import DatasetDict, IterableDatasetDict, interleave_datasets, load_dataset
|
| 32 |
+
from torch.utils.data import IterableDataset
|
| 33 |
+
|
| 34 |
+
import evaluate
|
| 35 |
+
import transformers
|
| 36 |
+
from transformers import (
|
| 37 |
+
AutoConfig,
|
| 38 |
+
AutoFeatureExtractor,
|
| 39 |
+
AutoModelForSpeechSeq2Seq,
|
| 40 |
+
AutoProcessor,
|
| 41 |
+
AutoTokenizer,
|
| 42 |
+
HfArgumentParser,
|
| 43 |
+
Seq2SeqTrainer,
|
| 44 |
+
Seq2SeqTrainingArguments,
|
| 45 |
+
TrainerCallback,
|
| 46 |
+
set_seed,
|
| 47 |
+
)
|
| 48 |
+
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
|
| 49 |
+
from transformers.trainer_pt_utils import IterableDatasetShard
|
| 50 |
+
from transformers.trainer_utils import get_last_checkpoint, is_main_process
|
| 51 |
+
from transformers.utils import check_min_version, send_example_telemetry
|
| 52 |
+
from transformers.utils.versions import require_version
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
|
| 56 |
+
check_min_version("4.25.0.dev0")
|
| 57 |
+
|
| 58 |
+
require_version("datasets>=1.18.2", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
|
| 59 |
+
|
| 60 |
+
logger = logging.getLogger(__name__)
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
@dataclass
|
| 64 |
+
class ModelArguments:
|
| 65 |
+
"""
|
| 66 |
+
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
|
| 67 |
+
"""
|
| 68 |
+
|
| 69 |
+
model_name_or_path: str = field(
|
| 70 |
+
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
|
| 71 |
+
)
|
| 72 |
+
config_name: Optional[str] = field(
|
| 73 |
+
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
| 74 |
+
)
|
| 75 |
+
tokenizer_name: Optional[str] = field(
|
| 76 |
+
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
| 77 |
+
)
|
| 78 |
+
feature_extractor_name: Optional[str] = field(
|
| 79 |
+
default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
|
| 80 |
+
)
|
| 81 |
+
cache_dir: Optional[str] = field(
|
| 82 |
+
default=None,
|
| 83 |
+
metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
|
| 84 |
+
)
|
| 85 |
+
use_fast_tokenizer: bool = field(
|
| 86 |
+
default=True,
|
| 87 |
+
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
|
| 88 |
+
)
|
| 89 |
+
model_revision: str = field(
|
| 90 |
+
default="main",
|
| 91 |
+
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
|
| 92 |
+
)
|
| 93 |
+
use_auth_token: bool = field(
|
| 94 |
+
default=False,
|
| 95 |
+
metadata={
|
| 96 |
+
"help": (
|
| 97 |
+
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
|
| 98 |
+
"with private models)."
|
| 99 |
+
)
|
| 100 |
+
},
|
| 101 |
+
)
|
| 102 |
+
freeze_feature_encoder: bool = field(
|
| 103 |
+
default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
|
| 104 |
+
)
|
| 105 |
+
freeze_encoder: bool = field(
|
| 106 |
+
default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}
|
| 107 |
+
)
|
| 108 |
+
forced_decoder_ids: List[List[int]] = field(
|
| 109 |
+
default=None,
|
| 110 |
+
metadata={
|
| 111 |
+
"help": (
|
| 112 |
+
"A list of pairs of integers which indicates a mapping from generation indices to token indices "
|
| 113 |
+
"that will be forced before sampling. For example, [[0, 123]] means the first generated token "
|
| 114 |
+
"will always be a token of index 123."
|
| 115 |
+
)
|
| 116 |
+
},
|
| 117 |
+
)
|
| 118 |
+
suppress_tokens: List[int] = field(
|
| 119 |
+
default=None, metadata={"help": "A list of tokens that will be suppressed at generation."}
|
| 120 |
+
)
|
| 121 |
+
model_index_name: str = field(default=None, metadata={"help": "Pretty name for the model card."})
|
| 122 |
+
|
| 123 |
+
|
| 124 |
+
@dataclass
|
| 125 |
+
class DataTrainingArguments:
|
| 126 |
+
"""
|
| 127 |
+
Arguments pertaining to what data we are going to input our model for training and eval.
|
| 128 |
+
"""
|
| 129 |
+
|
| 130 |
+
dataset_name: str = field(
|
| 131 |
+
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
| 132 |
+
)
|
| 133 |
+
dataset_config_name: Optional[str] = field(
|
| 134 |
+
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
| 135 |
+
)
|
| 136 |
+
text_column: Optional[str] = field(
|
| 137 |
+
default=None,
|
| 138 |
+
metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
|
| 139 |
+
)
|
| 140 |
+
max_train_samples: Optional[int] = field(
|
| 141 |
+
default=None,
|
| 142 |
+
metadata={
|
| 143 |
+
"help": (
|
| 144 |
+
"For debugging purposes or quicker training, truncate the number of training examples to this "
|
| 145 |
+
"value if set."
|
| 146 |
+
)
|
| 147 |
+
},
|
| 148 |
+
)
|
| 149 |
+
max_eval_samples: Optional[int] = field(
|
| 150 |
+
default=None,
|
| 151 |
+
metadata={
|
| 152 |
+
"help": (
|
| 153 |
+
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
|
| 154 |
+
"value if set."
|
| 155 |
+
)
|
| 156 |
+
},
|
| 157 |
+
)
|
| 158 |
+
audio_column_name: str = field(
|
| 159 |
+
default="audio",
|
| 160 |
+
metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
|
| 161 |
+
)
|
| 162 |
+
text_column_name: str = field(
|
| 163 |
+
default="text",
|
| 164 |
+
metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
|
| 165 |
+
)
|
| 166 |
+
max_duration_in_seconds: float = field(
|
| 167 |
+
default=20.0,
|
| 168 |
+
metadata={
|
| 169 |
+
"help": (
|
| 170 |
+
"Truncate audio files that are longer than `max_duration_in_seconds` seconds to"
|
| 171 |
+
" 'max_duration_in_seconds`"
|
| 172 |
+
)
|
| 173 |
+
},
|
| 174 |
+
)
|
| 175 |
+
min_duration_in_seconds: float = field(
|
| 176 |
+
default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
|
| 177 |
+
)
|
| 178 |
+
train_split_name: str = field(
|
| 179 |
+
default="train",
|
| 180 |
+
metadata={
|
| 181 |
+
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
|
| 182 |
+
},
|
| 183 |
+
)
|
| 184 |
+
eval_split_name: str = field(
|
| 185 |
+
default="test",
|
| 186 |
+
metadata={
|
| 187 |
+
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
|
| 188 |
+
},
|
| 189 |
+
)
|
| 190 |
+
do_lower_case: bool = field(
|
| 191 |
+
default=False,
|
| 192 |
+
metadata={"help": "Whether the target text should be lower cased."},
|
| 193 |
+
)
|
| 194 |
+
do_remove_punctuation: bool = field(
|
| 195 |
+
default=False,
|
| 196 |
+
metadata={"help": "Whether the target text should be striped of punctuation."},
|
| 197 |
+
)
|
| 198 |
+
do_normalize_eval: bool = field(
|
| 199 |
+
default=True,
|
| 200 |
+
metadata={"help": "Whether to normalise the references and predictions in the eval WER calculation."},
|
| 201 |
+
)
|
| 202 |
+
language: str = field(
|
| 203 |
+
default=None,
|
| 204 |
+
metadata={
|
| 205 |
+
"help": (
|
| 206 |
+
"Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
|
| 207 |
+
"only. For English speech recognition, it should be set to `None`."
|
| 208 |
+
)
|
| 209 |
+
},
|
| 210 |
+
)
|
| 211 |
+
task: str = field(
|
| 212 |
+
default="transcribe",
|
| 213 |
+
metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."},
|
| 214 |
+
)
|
| 215 |
+
shuffle_buffer_size: Optional[int] = field(
|
| 216 |
+
default=500,
|
| 217 |
+
metadata={
|
| 218 |
+
"help": (
|
| 219 |
+
"The number of streamed examples to download before shuffling them. The large the buffer, "
|
| 220 |
+
"the closer it is to real offline shuffling."
|
| 221 |
+
)
|
| 222 |
+
},
|
| 223 |
+
)
|
| 224 |
+
streaming: bool = field(
|
| 225 |
+
default=True,
|
| 226 |
+
metadata={"help": "Whether to use streaming mode to load and pre-process the data."},
|
| 227 |
+
)
|
| 228 |
+
|
| 229 |
+
|
| 230 |
+
@dataclass
|
| 231 |
+
class DataCollatorSpeechSeq2SeqWithPadding:
|
| 232 |
+
"""
|
| 233 |
+
Data collator that will dynamically pad the inputs received.
|
| 234 |
+
Args:
|
| 235 |
+
processor ([`WhisperProcessor`])
|
| 236 |
+
The processor used for processing the data.
|
| 237 |
+
decoder_start_token_id (`int`)
|
| 238 |
+
The begin-of-sentence of the decoder.
|
| 239 |
+
"""
|
| 240 |
+
|
| 241 |
+
processor: Any
|
| 242 |
+
decoder_start_token_id: int
|
| 243 |
+
|
| 244 |
+
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
|
| 245 |
+
# split inputs and labels since they have to be of different lengths and need
|
| 246 |
+
# different padding methods
|
| 247 |
+
model_input_name = self.processor.model_input_names[0]
|
| 248 |
+
input_features = [{model_input_name: feature[model_input_name]} for feature in features]
|
| 249 |
+
label_features = [{"input_ids": feature["labels"]} for feature in features]
|
| 250 |
+
|
| 251 |
+
batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
|
| 252 |
+
|
| 253 |
+
labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
|
| 254 |
+
|
| 255 |
+
# replace padding with -100 to ignore loss correctly
|
| 256 |
+
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
|
| 257 |
+
|
| 258 |
+
# if bos token is appended in previous tokenization step,
|
| 259 |
+
# cut bos token here as it's append later anyways
|
| 260 |
+
if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
|
| 261 |
+
labels = labels[:, 1:]
|
| 262 |
+
|
| 263 |
+
batch["labels"] = labels
|
| 264 |
+
|
| 265 |
+
return batch
|
| 266 |
+
|
| 267 |
+
|
| 268 |
+
def load_maybe_streaming_dataset(dataset_name, dataset_config_name, split="train", streaming=True, **kwargs):
|
| 269 |
+
"""
|
| 270 |
+
Utility function to load a dataset in streaming mode. For datasets with multiple splits,
|
| 271 |
+
each split is loaded individually and then splits combined by taking alternating examples from
|
| 272 |
+
each (interleaving).
|
| 273 |
+
"""
|
| 274 |
+
if "+" in split:
|
| 275 |
+
# load multiple splits separated by the `+` symbol with streaming mode
|
| 276 |
+
dataset_splits = [
|
| 277 |
+
load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=streaming, **kwargs)
|
| 278 |
+
for split_name in split.split("+")
|
| 279 |
+
]
|
| 280 |
+
# interleave multiple splits to form one dataset
|
| 281 |
+
interleaved_dataset = interleave_datasets(dataset_splits)
|
| 282 |
+
return interleaved_dataset
|
| 283 |
+
else:
|
| 284 |
+
# load a single split *with* streaming mode
|
| 285 |
+
dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=streaming, **kwargs)
|
| 286 |
+
return dataset
|
| 287 |
+
|
| 288 |
+
|
| 289 |
+
def main():
|
| 290 |
+
# 1. Parse input arguments
|
| 291 |
+
# See all possible arguments in src/transformers/training_args.py
|
| 292 |
+
# or by passing the --help flag to this script.
|
| 293 |
+
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
| 294 |
+
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
|
| 295 |
+
|
| 296 |
+
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
| 297 |
+
# If we pass only one argument to the script and it's the path to a json file,
|
| 298 |
+
# let's parse it to get our arguments.
|
| 299 |
+
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
| 300 |
+
else:
|
| 301 |
+
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
| 302 |
+
|
| 303 |
+
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
|
| 304 |
+
# information sent is the one passed as arguments along with your Python/PyTorch versions.
|
| 305 |
+
send_example_telemetry("run_speech_recognition_seq2seq_streaming", model_args, data_args)
|
| 306 |
+
|
| 307 |
+
# 2. Setup logging
|
| 308 |
+
logging.basicConfig(
|
| 309 |
+
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
| 310 |
+
datefmt="%m/%d/%Y %H:%M:%S",
|
| 311 |
+
handlers=[logging.StreamHandler(sys.stdout)],
|
| 312 |
+
)
|
| 313 |
+
log_level = training_args.get_process_log_level()
|
| 314 |
+
logger.setLevel(log_level)
|
| 315 |
+
datasets.utils.logging.set_verbosity(log_level)
|
| 316 |
+
transformers.utils.logging.set_verbosity(log_level)
|
| 317 |
+
transformers.utils.logging.enable_default_handler()
|
| 318 |
+
transformers.utils.logging.enable_explicit_format()
|
| 319 |
+
|
| 320 |
+
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
|
| 321 |
+
|
| 322 |
+
# Log on each process the small summary:
|
| 323 |
+
logger.warning(
|
| 324 |
+
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
|
| 325 |
+
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
|
| 326 |
+
)
|
| 327 |
+
logger.info(f"Training/evaluation parameters {training_args}")
|
| 328 |
+
|
| 329 |
+
# Set the verbosity to info of the Transformers logger (on main process only):
|
| 330 |
+
if is_main_process(training_args.local_rank):
|
| 331 |
+
transformers.utils.logging.set_verbosity_info()
|
| 332 |
+
logger.info("Training/evaluation parameters %s", training_args)
|
| 333 |
+
|
| 334 |
+
# 3. Detecting last checkpoint and eventually continue from last checkpoint
|
| 335 |
+
last_checkpoint = None
|
| 336 |
+
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
|
| 337 |
+
last_checkpoint = get_last_checkpoint(training_args.output_dir)
|
| 338 |
+
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
|
| 339 |
+
raise ValueError(
|
| 340 |
+
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
|
| 341 |
+
"Use --overwrite_output_dir to overcome."
|
| 342 |
+
)
|
| 343 |
+
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
|
| 344 |
+
logger.info(
|
| 345 |
+
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
|
| 346 |
+
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
|
| 347 |
+
)
|
| 348 |
+
|
| 349 |
+
# Set seed before initializing model.
|
| 350 |
+
set_seed(training_args.seed)
|
| 351 |
+
|
| 352 |
+
# 4. Load dataset
|
| 353 |
+
raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
|
| 354 |
+
|
| 355 |
+
if training_args.do_train:
|
| 356 |
+
raw_datasets["train"] = load_maybe_streaming_dataset(
|
| 357 |
+
data_args.dataset_name,
|
| 358 |
+
data_args.dataset_config_name,
|
| 359 |
+
split=data_args.train_split_name,
|
| 360 |
+
use_auth_token=True if model_args.use_auth_token else None,
|
| 361 |
+
streaming=data_args.streaming,
|
| 362 |
+
)
|
| 363 |
+
|
| 364 |
+
if training_args.do_eval:
|
| 365 |
+
raw_datasets["eval"] = load_maybe_streaming_dataset(
|
| 366 |
+
data_args.dataset_name,
|
| 367 |
+
data_args.dataset_config_name,
|
| 368 |
+
split=data_args.eval_split_name,
|
| 369 |
+
use_auth_token=True if model_args.use_auth_token else None,
|
| 370 |
+
streaming=data_args.streaming,
|
| 371 |
+
)
|
| 372 |
+
|
| 373 |
+
raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
|
| 374 |
+
|
| 375 |
+
if data_args.audio_column_name not in raw_datasets_features:
|
| 376 |
+
raise ValueError(
|
| 377 |
+
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
|
| 378 |
+
"Make sure to set `--audio_column_name` to the correct audio column - one of "
|
| 379 |
+
f"{', '.join(raw_datasets_features)}."
|
| 380 |
+
)
|
| 381 |
+
|
| 382 |
+
if data_args.text_column_name not in raw_datasets_features:
|
| 383 |
+
raise ValueError(
|
| 384 |
+
f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
|
| 385 |
+
"Make sure to set `--text_column_name` to the correct text column - one of "
|
| 386 |
+
f"{', '.join(raw_datasets_features)}."
|
| 387 |
+
)
|
| 388 |
+
|
| 389 |
+
# 5. Load pretrained model, tokenizer, and feature extractor
|
| 390 |
+
#
|
| 391 |
+
# Distributed training:
|
| 392 |
+
# The .from_pretrained methods guarantee that only one local process can concurrently
|
| 393 |
+
config = AutoConfig.from_pretrained(
|
| 394 |
+
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
|
| 395 |
+
cache_dir=model_args.cache_dir,
|
| 396 |
+
revision=model_args.model_revision,
|
| 397 |
+
use_auth_token=True if model_args.use_auth_token else None,
|
| 398 |
+
)
|
| 399 |
+
|
| 400 |
+
config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
|
| 401 |
+
|
| 402 |
+
if training_args.gradient_checkpointing:
|
| 403 |
+
config.update({"use_cache": False})
|
| 404 |
+
|
| 405 |
+
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
| 406 |
+
model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
|
| 407 |
+
cache_dir=model_args.cache_dir,
|
| 408 |
+
revision=model_args.model_revision,
|
| 409 |
+
use_auth_token=True if model_args.use_auth_token else None,
|
| 410 |
+
)
|
| 411 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
| 412 |
+
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
|
| 413 |
+
cache_dir=model_args.cache_dir,
|
| 414 |
+
use_fast=model_args.use_fast_tokenizer,
|
| 415 |
+
revision=model_args.model_revision,
|
| 416 |
+
use_auth_token=True if model_args.use_auth_token else None,
|
| 417 |
+
)
|
| 418 |
+
model = AutoModelForSpeechSeq2Seq.from_pretrained(
|
| 419 |
+
model_args.model_name_or_path,
|
| 420 |
+
config=config,
|
| 421 |
+
cache_dir=model_args.cache_dir,
|
| 422 |
+
revision=model_args.model_revision,
|
| 423 |
+
use_auth_token=True if model_args.use_auth_token else None,
|
| 424 |
+
)
|
| 425 |
+
|
| 426 |
+
if model.config.decoder_start_token_id is None:
|
| 427 |
+
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
|
| 428 |
+
|
| 429 |
+
if model_args.freeze_feature_encoder:
|
| 430 |
+
model.freeze_feature_encoder()
|
| 431 |
+
|
| 432 |
+
if model_args.freeze_encoder:
|
| 433 |
+
model.freeze_encoder()
|
| 434 |
+
|
| 435 |
+
if data_args.language is not None:
|
| 436 |
+
# We only need to set the task id when the language is specified (i.e. in a multilingual setting)
|
| 437 |
+
tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
|
| 438 |
+
|
| 439 |
+
# 6. Resample speech dataset if necessary
|
| 440 |
+
dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
|
| 441 |
+
if dataset_sampling_rate != feature_extractor.sampling_rate:
|
| 442 |
+
raw_datasets = raw_datasets.cast_column(
|
| 443 |
+
data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
|
| 444 |
+
)
|
| 445 |
+
|
| 446 |
+
# 7. Preprocessing the datasets.
|
| 447 |
+
# We need to read the audio files as arrays and tokenize the targets.
|
| 448 |
+
max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
|
| 449 |
+
min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
|
| 450 |
+
audio_column_name = data_args.audio_column_name
|
| 451 |
+
text_column_name = data_args.text_column_name
|
| 452 |
+
model_input_name = feature_extractor.model_input_names[0]
|
| 453 |
+
do_lower_case = data_args.do_lower_case
|
| 454 |
+
do_remove_punctuation = data_args.do_remove_punctuation
|
| 455 |
+
normalizer = BasicTextNormalizer() # 'official' text normalizer from OpenAI
|
| 456 |
+
|
| 457 |
+
if data_args.max_train_samples is not None:
|
| 458 |
+
raw_datasets["train"] = (
|
| 459 |
+
raw_datasets["train"].take(data_args.max_train_samples)
|
| 460 |
+
if data_args.streaming
|
| 461 |
+
else raw_datasets["train"].select(range(data_args.max_train_samples))
|
| 462 |
+
)
|
| 463 |
+
|
| 464 |
+
if data_args.max_eval_samples is not None:
|
| 465 |
+
raw_datasets["eval"] = (
|
| 466 |
+
raw_datasets["eval"].take(data_args.max_eval_samples)
|
| 467 |
+
if data_args.streaming
|
| 468 |
+
else raw_datasets["eval"].select(range(data_args.max_eval_samples))
|
| 469 |
+
)
|
| 470 |
+
|
| 471 |
+
def prepare_dataset(batch):
|
| 472 |
+
# process audio
|
| 473 |
+
sample = batch[audio_column_name]
|
| 474 |
+
inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
|
| 475 |
+
# process audio length
|
| 476 |
+
batch[model_input_name] = inputs.get(model_input_name)[0]
|
| 477 |
+
batch["input_length"] = len(sample["array"])
|
| 478 |
+
|
| 479 |
+
# process targets
|
| 480 |
+
input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name]
|
| 481 |
+
if do_remove_punctuation:
|
| 482 |
+
input_str = normalizer(input_str).strip()
|
| 483 |
+
batch["labels"] = tokenizer(input_str).input_ids
|
| 484 |
+
return batch
|
| 485 |
+
|
| 486 |
+
with training_args.main_process_first(desc="dataset map pre-processing"):
|
| 487 |
+
vectorized_datasets = raw_datasets.map(
|
| 488 |
+
prepare_dataset,
|
| 489 |
+
remove_columns=raw_datasets_features,
|
| 490 |
+
).with_format("torch")
|
| 491 |
+
|
| 492 |
+
if training_args.do_train and data_args.streaming:
|
| 493 |
+
# manually shuffle if streaming (done by the trainer for non-streaming)
|
| 494 |
+
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(
|
| 495 |
+
buffer_size=data_args.shuffle_buffer_size,
|
| 496 |
+
seed=training_args.seed,
|
| 497 |
+
)
|
| 498 |
+
|
| 499 |
+
# filter training data that is shorter than min_input_length or longer than
|
| 500 |
+
# max_input_length
|
| 501 |
+
def is_audio_in_length_range(length):
|
| 502 |
+
return min_input_length < length < max_input_length
|
| 503 |
+
|
| 504 |
+
if training_args.do_train:
|
| 505 |
+
vectorized_datasets["train"] = vectorized_datasets["train"].filter(
|
| 506 |
+
is_audio_in_length_range,
|
| 507 |
+
input_columns=["input_length"],
|
| 508 |
+
)
|
| 509 |
+
|
| 510 |
+
# 8. Load Metric
|
| 511 |
+
metric = evaluate.load("wer")
|
| 512 |
+
do_normalize_eval = data_args.do_normalize_eval
|
| 513 |
+
|
| 514 |
+
def compute_metrics(pred):
|
| 515 |
+
pred_ids = pred.predictions
|
| 516 |
+
|
| 517 |
+
pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
|
| 518 |
+
|
| 519 |
+
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
|
| 520 |
+
# we do not want to group tokens when computing the metrics
|
| 521 |
+
label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
|
| 522 |
+
|
| 523 |
+
if do_normalize_eval:
|
| 524 |
+
pred_str = [normalizer(pred) for pred in pred_str]
|
| 525 |
+
label_str = [normalizer(label) for label in label_str]
|
| 526 |
+
# filtering step to only evaluate the samples that correspond to non-zero references:
|
| 527 |
+
pred_str = [pred_str[i] for i in range(len(pred_str)) if len(label_str[i]) > 0]
|
| 528 |
+
label_str = [label_str[i] for i in range(len(label_str)) if len(label_str[i]) > 0]
|
| 529 |
+
|
| 530 |
+
wer = 100 * metric.compute(predictions=pred_str, references=label_str)
|
| 531 |
+
|
| 532 |
+
return {"wer": wer}
|
| 533 |
+
|
| 534 |
+
# 9. Create a single speech processor
|
| 535 |
+
if is_main_process(training_args.local_rank):
|
| 536 |
+
# save feature extractor, tokenizer and config
|
| 537 |
+
feature_extractor.save_pretrained(training_args.output_dir)
|
| 538 |
+
tokenizer.save_pretrained(training_args.output_dir)
|
| 539 |
+
config.save_pretrained(training_args.output_dir)
|
| 540 |
+
|
| 541 |
+
processor = AutoProcessor.from_pretrained(training_args.output_dir)
|
| 542 |
+
|
| 543 |
+
# 10. Define data collator
|
| 544 |
+
data_collator = DataCollatorSpeechSeq2SeqWithPadding(
|
| 545 |
+
processor=processor,
|
| 546 |
+
decoder_start_token_id=model.config.decoder_start_token_id,
|
| 547 |
+
)
|
| 548 |
+
|
| 549 |
+
# 11. Configure Trainer
|
| 550 |
+
# Trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch
|
| 551 |
+
# Only required for streaming: Trainer automatically shuffles non-streaming datasets
|
| 552 |
+
class ShuffleCallback(TrainerCallback):
|
| 553 |
+
def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):
|
| 554 |
+
if isinstance(train_dataloader.dataset, IterableDatasetShard):
|
| 555 |
+
pass # set_epoch() is handled by the Trainer
|
| 556 |
+
elif isinstance(train_dataloader.dataset, IterableDataset):
|
| 557 |
+
train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)
|
| 558 |
+
|
| 559 |
+
# Initialize Trainer
|
| 560 |
+
trainer = Seq2SeqTrainer(
|
| 561 |
+
model=model,
|
| 562 |
+
args=training_args,
|
| 563 |
+
train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
|
| 564 |
+
eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
|
| 565 |
+
tokenizer=feature_extractor,
|
| 566 |
+
data_collator=data_collator,
|
| 567 |
+
compute_metrics=compute_metrics if training_args.predict_with_generate else None,
|
| 568 |
+
callbacks=[ShuffleCallback()] if data_args.streaming else None,
|
| 569 |
+
)
|
| 570 |
+
|
| 571 |
+
# 12. Training
|
| 572 |
+
if training_args.do_train:
|
| 573 |
+
checkpoint = None
|
| 574 |
+
if training_args.resume_from_checkpoint is not None:
|
| 575 |
+
checkpoint = training_args.resume_from_checkpoint
|
| 576 |
+
elif last_checkpoint is not None:
|
| 577 |
+
checkpoint = last_checkpoint
|
| 578 |
+
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
| 579 |
+
trainer.save_model() # Saves the feature extractor too for easy upload
|
| 580 |
+
|
| 581 |
+
metrics = train_result.metrics
|
| 582 |
+
if data_args.max_train_samples:
|
| 583 |
+
metrics["train_samples"] = data_args.max_train_samples
|
| 584 |
+
trainer.log_metrics("train", metrics)
|
| 585 |
+
trainer.save_metrics("train", metrics)
|
| 586 |
+
trainer.save_state()
|
| 587 |
+
|
| 588 |
+
# 13. Evaluation
|
| 589 |
+
results = {}
|
| 590 |
+
if training_args.do_eval:
|
| 591 |
+
logger.info("*** Evaluate ***")
|
| 592 |
+
metrics = trainer.evaluate(
|
| 593 |
+
metric_key_prefix="eval",
|
| 594 |
+
max_length=training_args.generation_max_length,
|
| 595 |
+
num_beams=training_args.generation_num_beams,
|
| 596 |
+
)
|
| 597 |
+
if data_args.max_eval_samples:
|
| 598 |
+
metrics["eval_samples"] = data_args.max_eval_samples
|
| 599 |
+
|
| 600 |
+
trainer.log_metrics("eval", metrics)
|
| 601 |
+
trainer.save_metrics("eval", metrics)
|
| 602 |
+
|
| 603 |
+
# 14. Write Training Stats
|
| 604 |
+
kwargs = {
|
| 605 |
+
"finetuned_from": model_args.model_name_or_path,
|
| 606 |
+
"tasks": "automatic-speech-recognition",
|
| 607 |
+
"tags": "whisper-event",
|
| 608 |
+
}
|
| 609 |
+
if data_args.dataset_name is not None:
|
| 610 |
+
kwargs["dataset_tags"] = data_args.dataset_name
|
| 611 |
+
if data_args.dataset_config_name is not None:
|
| 612 |
+
kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
|
| 613 |
+
else:
|
| 614 |
+
kwargs["dataset"] = data_args.dataset_name
|
| 615 |
+
if "common_voice" in data_args.dataset_name:
|
| 616 |
+
kwargs["language"] = data_args.dataset_config_name[:2]
|
| 617 |
+
if model_args.model_index_name is not None:
|
| 618 |
+
kwargs["model_name"] = model_args.model_index_name
|
| 619 |
+
|
| 620 |
+
if training_args.push_to_hub:
|
| 621 |
+
trainer.push_to_hub(**kwargs)
|
| 622 |
+
else:
|
| 623 |
+
trainer.create_model_card(**kwargs)
|
| 624 |
+
|
| 625 |
+
return results
|
| 626 |
+
|
| 627 |
+
|
| 628 |
+
if __name__ == "__main__":
|
| 629 |
+
main()
|
runs/Dec18_08-41-04_fe2747a042f0/1671381730.2013636/events.out.tfevents.1671381730.fe2747a042f0.46148.1
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3968d16c222675f4f8d35641fe6eb4fe3d4394ff49e9518e75090b9347953dd8
|
| 3 |
+
size 5878
|
runs/Dec18_08-41-04_fe2747a042f0/events.out.tfevents.1671381730.fe2747a042f0.46148.0
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:648235cf38bdc7d1daa38bae30699b9ba2dfe43a5b53cc2bc710c8ed357c6f54
|
| 3 |
+
size 10859
|
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 |
+
}
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:60005fd531d7ed5584665e9b5f48c71e53c0c0f68bb1e517baa8da557dd1433a
|
| 3 |
+
size 4731
|
vocab.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|