codenamewei
commited on
Commit
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b7f88d8
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Parent(s):
54d4d9a
initial commit
Browse files- config.json +114 -0
- preprocessor_config.json +9 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +6 -0
- tokenizer_config.json +10 -0
- training_args.bin +3 -0
- training_args_tracker.txt +25 -0
- vocab.json +32 -0
config.json
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@@ -0,0 +1,114 @@
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{
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"_name_or_path": "facebook/wav2vec2-conformer-rope-large-960h-ft",
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"activation_dropout": 0.1,
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"adapter_kernel_size": 3,
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"adapter_stride": 2,
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"add_adapter": false,
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"apply_spec_augment": true,
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"architectures": [
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"Wav2Vec2ConformerForCTC"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 1,
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"classifier_proj_size": 256,
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"codevector_dim": 768,
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"conformer_conv_dropout": 0.1,
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"contrastive_logits_temperature": 0.1,
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"conv_bias": true,
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"conv_depthwise_kernel_size": 31,
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"conv_dim": [
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512,
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512,
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512,
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512,
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512,
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512,
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512
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],
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"conv_kernel": [
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10,
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3,
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3,
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3,
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3,
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2,
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2
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],
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"conv_stride": [
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5,
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2,
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2,
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2,
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2,
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2,
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2
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],
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"ctc_loss_reduction": "sum",
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"ctc_zero_infinity": false,
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"diversity_loss_weight": 0.1,
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"do_stable_layer_norm": true,
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"eos_token_id": 2,
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"feat_extract_activation": "gelu",
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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "layer",
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"feat_proj_dropout": 0.1,
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.1,
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"gradient_checkpointing": false,
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"hidden_act": "swish",
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"hidden_dropout": 0.1,
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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"layerdrop": 0.0,
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"mask_feature_length": 10,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.05,
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"max_source_positions": 5000,
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"model_type": "wav2vec2-conformer",
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"num_adapter_layers": 3,
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"num_attention_heads": 16,
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"num_codevector_groups": 2,
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"num_codevectors_per_group": 320,
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"num_conv_pos_embedding_groups": 16,
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"num_conv_pos_embeddings": 128,
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"num_feat_extract_layers": 7,
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"num_hidden_layers": 24,
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"num_negatives": 100,
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"output_hidden_size": 1024,
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"pad_token_id": 29,
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"position_embeddings_type": "rotary",
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"proj_codevector_dim": 768,
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"rotary_embedding_base": 10000,
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"tdnn_dilation": [
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1,
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2,
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3,
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1,
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1
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],
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"tdnn_dim": [
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512,
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512,
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512,
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512,
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1500
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],
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"tdnn_kernel": [
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5,
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3,
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3,
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1,
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1
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],
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"torch_dtype": "float32",
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"transformers_version": "4.20.1",
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"use_weighted_layer_sum": false,
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"vocab_size": 32,
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"xvector_output_dim": 512
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}
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preprocessor_config.json
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{
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"do_normalize": true,
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"feature_extractor_type": "Wav2Vec2FeatureExtractor",
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0.0,
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"return_attention_mask": false,
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"sampling_rate": 16000
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:580594ee9975f1c251b2470cd3d6e0193765b714cccdf25f97f6419bf6eef333
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size 2374006415
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special_tokens_map.json
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{
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"bos_token": "<s>",
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"eos_token": "</s>",
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"pad_token": "[PAD]",
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"unk_token": "[UNK]"
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}
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tokenizer_config.json
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{
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"bos_token": "<s>",
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"do_lower_case": false,
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"eos_token": "</s>",
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"pad_token": "[PAD]",
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"replace_word_delimiter_char": " ",
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"tokenizer_class": "Wav2Vec2CTCTokenizer",
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"unk_token": "[UNK]",
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"word_delimiter_token": "|"
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:2d16b5f9a88e3a9364cee3741aa6358951112bdc4da47d076a86bd0b9536279f
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size 3439
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training_args_tracker.txt
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from transformers import TrainingArguments
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steps = 200
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training_args = TrainingArguments(
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output_dir=outmodelpath,
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overwrite_output_dir=True, #Set to true to overwrite output directory
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group_by_length=True, # group data with same length to save memory (only for dynamic padding)
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per_device_train_batch_size=8, #16
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#save_strategy="steps",# The checkpoint save strategy to adopt during training.
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evaluation_strategy="steps", # alternative: None, epoch
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num_train_epochs=5, #50
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#max_gradient_norm=0.8, #Maximum gradient norm (for gradient clipping). Default: 1.0
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fp16=True, # Available only on GPU, when set true save memory
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gradient_checkpointing=True, # save memory at the expense of slower backward pass
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save_steps=steps,
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eval_steps=steps,
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logging_steps=steps,
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learning_rate=1e-4, # default: 5e-5
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weight_decay=0.005, # prevent overfitting, generalize better
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warmup_ratio=0.1, # Ratio of total training steps used for a linear warmup from 0 to learning_rate. default 0.0
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max_grad_norm=0.8,
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save_total_limit=2, # number of checkpoint to save
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load_best_model_at_end=True #load best model to save the best model in trainer.save_model
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)
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vocab.json
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{
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"'": 7,
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"[PAD]": 29,
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"[UNK]": 28,
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"a": 20,
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"b": 22,
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"c": 18,
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"d": 21,
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"e": 3,
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"f": 17,
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"g": 5,
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"h": 2,
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"i": 25,
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"j": 1,
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"k": 0,
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"l": 12,
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"m": 9,
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"n": 8,
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"o": 26,
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"p": 13,
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"q": 16,
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"r": 14,
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"s": 11,
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"t": 6,
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"u": 27,
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"v": 10,
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"w": 15,
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"x": 19,
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"y": 4,
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"z": 24,
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"|": 23
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}
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