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48
README.md
48
README.md
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---
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language: en
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datasets:
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- LIUM/tedlium
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tags:
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- speech
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- audio
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- automatic-speech-recognition
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---
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Finetuned from [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self).
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# Installation
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1. PyTorch installation: https://pytorch.org/
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2. Install transformers: https://huggingface.co/docs/transformers/installation
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e.g., installation by conda
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```
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>> conda create -n wav2vec2 python=3.8
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>> conda install pytorch cudatoolkit=11.3 -c pytorch
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>> conda install -c conda-forge transformers
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```
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# Usage
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```python
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# Load the model and processor
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
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import numpy as np
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import torch
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model = Wav2Vec2ForCTC.from_pretrained(r'yongjian/wav2vec2-large-a') # Note: PyTorch Model
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processor = Wav2Vec2Processor.from_pretrained(r'yongjian/wav2vec2-large-a')
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# Load input
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np_wav = np.random.normal(size=(16000)).clip(-1, 1) # change it to your sample
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# Inference
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sample_rate = processor.feature_extractor.sampling_rate
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with torch.no_grad():
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model_inputs = processor(np_wav, sampling_rate=sample_rate, return_tensors="pt", padding=True)
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logits = model(model_inputs.input_values, attention_mask=model_inputs.attention_mask).logits # use .cuda() for GPU acceleration
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pred_ids = torch.argmax(logits, dim=-1).cpu()
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pred_text = processor.batch_decode(pred_ids)
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print('Transcription:', pred_text)
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```
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# Code
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GitHub Repo:
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https://github.com/CassiniHuy/wav2vec2_finetune
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108
config.json
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config.json
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{
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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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"Wav2Vec2ForCTC"
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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": 256,
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"contrastive_logits_temperature": 0.1,
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"conv_bias": true,
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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": "gelu",
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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": 0.1,
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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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"model_type": "wav2vec2",
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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": 0,
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"proj_codevector_dim": 256,
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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": "4.19.4",
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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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{
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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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"processor_class": "Wav2Vec2Processor",
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"return_attention_mask": true,
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"sampling_rate": 16000
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}
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BIN
pytorch_model.bin (Stored with Git LFS)
BIN
pytorch_model.bin (Stored with Git LFS)
Binary file not shown.
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
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{
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"unk_token": "<unk>",
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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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"do_lower_case": false,
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"word_delimiter_token": "|",
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"replace_word_delimiter_char": " ",
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"return_attention_mask": true,
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"do_normalize": true,
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"processor_class": "Wav2Vec2Processor",
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"tokenizer_class": "Wav2Vec2CTCTokenizer"
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}
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34
vocab.json
34
vocab.json
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{
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"<pad>": 0,
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"<s>": 1,
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"</s>": 2,
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"<unk>": 3,
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"|": 4,
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"E": 5,
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"T": 6,
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"A": 7,
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"O": 8,
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"N": 9,
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"I": 10,
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"H": 11,
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"S": 12,
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"R": 13,
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"D": 14,
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"L": 15,
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"U": 16,
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"M": 17,
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"W": 18,
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"C": 19,
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"F": 20,
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"G": 21,
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"Y": 22,
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"P": 23,
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"B": 24,
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"V": 25,
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"K": 26,
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"'": 27,
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"X": 28,
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"J": 29,
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"Q": 30,
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"Z": 31
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}
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