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02/02/2022 18:04:15 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True
02/02/2022 18:04:15 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
bf16=False,
bf16_full_eval=False,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=True,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_steps=500,
evaluation_strategy=IntervalStrategy.STEPS,
fp16=True,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
gradient_accumulation_steps=4,
gradient_checkpointing=True,
greater_is_better=None,
group_by_length=True,
half_precision_backend=auto,
hub_model_id=None,
hub_strategy=HubStrategy.EVERY_SAVE,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=7.5e-05,
length_column_name=input_length,
load_best_model_at_end=False,
local_rank=-1,
log_level=-1,
log_level_replica=-1,
log_on_each_node=True,
logging_dir=./runs/Feb02_18-04-15_job-86e1d453-0156-4b77-a98d-7d457c737175,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=100,
logging_strategy=IntervalStrategy.STEPS,
lr_scheduler_type=SchedulerType.LINEAR,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=50.0,
optim=OptimizerNames.ADAMW_HF,
output_dir=./,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=8,
prediction_loss_only=False,
push_to_hub=True,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=./,
save_on_each_node=False,
save_steps=500,
save_strategy=IntervalStrategy.STEPS,
save_total_limit=3,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
tf32=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_legacy_prediction_loop=False,
warmup_ratio=0.0,
warmup_steps=2000,
weight_decay=0.0,
xpu_backend=None,
)
02/02/2022 18:04:18 - WARNING - datasets.builder - Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/ur/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)
02/02/2022 18:04:20 - WARNING - datasets.builder - Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/ur/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)
02/02/2022 18:04:20 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/ur/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8/cache-eefb1dcecdbc6361.arrow
02/02/2022 18:04:20 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/ur/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8/cache-bebf53ae59038f0e.arrow
loading configuration file https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/config.json from cache at /workspace/.cache/huggingface/transformers/dabc27df63e37bd2a7a221c7774e35f36a280fbdf917cf54cadfc7df8c786f6f.a3e4c3c967d9985881e0ae550a5f6f668f897db5ab2e0802f9b97973b15970e6
Model config Wav2Vec2Config {
"_name_or_path": "facebook/wav2vec2-xls-r-300m",
"activation_dropout": 0.0,
"adapter_kernel_size": 3,
"adapter_stride": 2,
"add_adapter": false,
"apply_spec_augment": true,
"architectures": [
"Wav2Vec2ForPreTraining"
],
"attention_dropout": 0.1,
"bos_token_id": 1,
"classifier_proj_size": 256,
"codevector_dim": 768,
"contrastive_logits_temperature": 0.1,
"conv_bias": true,
"conv_dim": [
512,
512,
512,
512,
512,
512,
512
],
"conv_kernel": [
10,
3,
3,
3,
3,
2,
2
],
"conv_stride": [
5,
2,
2,
2,
2,
2,
2
],
"ctc_loss_reduction": "sum",
"ctc_zero_infinity": false,
"diversity_loss_weight": 0.1,
"do_stable_layer_norm": true,
"eos_token_id": 2,
"feat_extract_activation": "gelu",
"feat_extract_dropout": 0.0,
"feat_extract_norm": "layer",
"feat_proj_dropout": 0.1,
"feat_quantizer_dropout": 0.0,
"final_dropout": 0.0,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"layerdrop": 0.1,
"mask_feature_length": 10,
"mask_feature_min_masks": 0,
"mask_feature_prob": 0.0,
"mask_time_length": 10,
"mask_time_min_masks": 2,
"mask_time_prob": 0.075,
"model_type": "wav2vec2",
"num_adapter_layers": 3,
"num_attention_heads": 16,
"num_codevector_groups": 2,
"num_codevectors_per_group": 320,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
"num_feat_extract_layers": 7,
"num_hidden_layers": 24,
"num_negatives": 100,
"output_hidden_size": 1024,
"pad_token_id": 0,
"proj_codevector_dim": 768,
"tdnn_dilation": [
1,
2,
3,
1,
1
],
"tdnn_dim": [
512,
512,
512,
512,
1500
],
"tdnn_kernel": [
5,
3,
3,
1,
1
],
"torch_dtype": "float32",
"transformers_version": "4.17.0.dev0",
"use_weighted_layer_sum": false,
"vocab_size": 32,
"xvector_output_dim": 512
}
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Didn't find file ./tokenizer.json. We won't load it.
loading file ./vocab.json
loading file ./tokenizer_config.json
loading file ./added_tokens.json
loading file ./special_tokens_map.json
loading file None
Adding <s> to the vocabulary
Adding </s> to the vocabulary
loading configuration file https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/config.json from cache at /workspace/.cache/huggingface/transformers/dabc27df63e37bd2a7a221c7774e35f36a280fbdf917cf54cadfc7df8c786f6f.a3e4c3c967d9985881e0ae550a5f6f668f897db5ab2e0802f9b97973b15970e6
Model config Wav2Vec2Config {
"_name_or_path": "facebook/wav2vec2-xls-r-300m",
"activation_dropout": 0.0,
"adapter_kernel_size": 3,
"adapter_stride": 2,
"add_adapter": false,
"apply_spec_augment": true,
"architectures": [
"Wav2Vec2ForPreTraining"
],
"attention_dropout": 0.1,
"bos_token_id": 1,
"classifier_proj_size": 256,
"codevector_dim": 768,
"contrastive_logits_temperature": 0.1,
"conv_bias": true,
"conv_dim": [
512,
512,
512,
512,
512,
512,
512
],
"conv_kernel": [
10,
3,
3,
3,
3,
2,
2
],
"conv_stride": [
5,
2,
2,
2,
2,
2,
2
],
"ctc_loss_reduction": "sum",
"ctc_zero_infinity": false,
"diversity_loss_weight": 0.1,
"do_stable_layer_norm": true,
"eos_token_id": 2,
"feat_extract_activation": "gelu",
"feat_extract_dropout": 0.0,
"feat_extract_norm": "layer",
"feat_proj_dropout": 0.1,
"feat_quantizer_dropout": 0.0,
"final_dropout": 0.0,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"layerdrop": 0.1,
"mask_feature_length": 10,
"mask_feature_min_masks": 0,
"mask_feature_prob": 0.0,
"mask_time_length": 10,
"mask_time_min_masks": 2,
"mask_time_prob": 0.075,
"model_type": "wav2vec2",
"num_adapter_layers": 3,
"num_attention_heads": 16,
"num_codevector_groups": 2,
"num_codevectors_per_group": 320,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
"num_feat_extract_layers": 7,
"num_hidden_layers": 24,
"num_negatives": 100,
"output_hidden_size": 1024,
"pad_token_id": 0,
"proj_codevector_dim": 768,
"tdnn_dilation": [
1,
2,
3,
1,
1
],
"tdnn_dim": [
512,
512,
512,
512,
1500
],
"tdnn_kernel": [
5,
3,
3,
1,
1
],
"torch_dtype": "float32",
"transformers_version": "4.17.0.dev0",
"use_weighted_layer_sum": false,
"vocab_size": 32,
"xvector_output_dim": 512
}
loading feature extractor configuration file https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/preprocessor_config.json from cache at /workspace/.cache/huggingface/transformers/6fb028b95b394059e7d3b367bbca2382b576c66aebe896f04d2cd34e1b575f5b.d4484dc1c81456a2461485e7168b04347a7b9a4e3b1ef3aba723323b33e12326
Feature extractor Wav2Vec2FeatureExtractor {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0,
"return_attention_mask": true,
"sampling_rate": 16000
}
loading weights file https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/pytorch_model.bin from cache at /workspace/.cache/huggingface/transformers/1e6a6507f3b689035cd4b247e2a37c154e27f39143f31357a49b4e38baeccc36.1edb32803799e27ed554eb7dd935f6745b1a0b17b0ea256442fe24db6eb546cd
Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['quantizer.weight_proj.weight', 'quantizer.codevectors', 'quantizer.weight_proj.bias', 'project_q.bias', 'project_q.weight', 'project_hid.bias', 'project_hid.weight']
- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.weight', 'lm_head.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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Configuration saved in ./preprocessor_config.json
tokenizer config file saved in ./tokenizer_config.json
Special tokens file saved in ./special_tokens_map.json
added tokens file saved in ./added_tokens.json
Configuration saved in ./config.json
loading feature extractor configuration file ./preprocessor_config.json
loading configuration file ./config.json
Model config Wav2Vec2Config {
"_name_or_path": "./",
"activation_dropout": 0.1,
"adapter_kernel_size": 3,
"adapter_stride": 2,
"add_adapter": false,
"apply_spec_augment": true,
"architectures": [
"Wav2Vec2ForPreTraining"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"classifier_proj_size": 256,
"codevector_dim": 768,
"contrastive_logits_temperature": 0.1,
"conv_bias": true,
"conv_dim": [
512,
512,
512,
512,
512,
512,
512
],
"conv_kernel": [
10,
3,
3,
3,
3,
2,
2
],
"conv_stride": [
5,
2,
2,
2,
2,
2,
2
],
"ctc_loss_reduction": "mean",
"ctc_zero_infinity": false,
"diversity_loss_weight": 0.1,
"do_stable_layer_norm": true,
"eos_token_id": 2,
"feat_extract_activation": "gelu",
"feat_extract_dropout": 0.0,
"feat_extract_norm": "layer",
"feat_proj_dropout": 0.0,
"feat_quantizer_dropout": 0.0,
"final_dropout": 0.0,
"hidden_act": "gelu",
"hidden_dropout": 0.0,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"layerdrop": 0.0,
"mask_feature_length": 64,
"mask_feature_min_masks": 0,
"mask_feature_prob": 0.25,
"mask_time_length": 10,
"mask_time_min_masks": 2,
"mask_time_prob": 0.75,
"model_type": "wav2vec2",
"num_adapter_layers": 3,
"num_attention_heads": 16,
"num_codevector_groups": 2,
"num_codevectors_per_group": 320,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
"num_feat_extract_layers": 7,
"num_hidden_layers": 24,
"num_negatives": 100,
"output_hidden_size": 1024,
"pad_token_id": 58,
"proj_codevector_dim": 768,
"tdnn_dilation": [
1,
2,
3,
1,
1
],
"tdnn_dim": [
512,
512,
512,
512,
1500
],
"tdnn_kernel": [
5,
3,
3,
1,
1
],
"torch_dtype": "float32",
"transformers_version": "4.17.0.dev0",
"use_weighted_layer_sum": false,
"vocab_size": 61,
"xvector_output_dim": 512
}
loading feature extractor configuration file ./preprocessor_config.json
Feature extractor Wav2Vec2FeatureExtractor {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0,
"return_attention_mask": true,
"sampling_rate": 16000
}
Didn't find file ./tokenizer.json. We won't load it.
loading file ./vocab.json
loading file ./tokenizer_config.json
loading file ./added_tokens.json
loading file ./special_tokens_map.json
loading file None
Adding <s> to the vocabulary
Adding </s> to the vocabulary
/workspace/xls-r-300m-ur/./ is already a clone of https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur. Make sure you pull the latest changes with `repo.git_pull()`.
02/02/2022 18:04:42 - WARNING - huggingface_hub.repository - /workspace/xls-r-300m-ur/./ is already a clone of https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur. Make sure you pull the latest changes with `repo.git_pull()`.
Using amp half precision backend
The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.
/opt/conda/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
warnings.warn(
***** Running training *****
Num examples = 810
Num Epochs = 50
Instantaneous batch size per device = 8
Total train batch size (w. parallel, distributed & accumulation) = 32
Gradient Accumulation steps = 4
Total optimization steps = 1250
0%| | 0/1250 [00:00<?, ?it/s]
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1%| | 12/1250 [00:18<27:37, 1.34s/it]
1%| | 13/1250 [00:21<34:42, 1.68s/it]
1%| | 14/1250 [00:23<34:49, 1.69s/it]
1%| | 15/1250 [00:24<34:04, 1.66s/it]
1%|β | 16/1250 [00:26<32:22, 1.57s/it]
1%|β | 17/1250 [00:27<29:57, 1.46s/it]
1%|β | 18/1250 [00:28<26:57, 1.31s/it]
2%|β | 19/1250 [00:30<32:14, 1.57s/it]
2%|β | 20/1250 [00:32<32:52, 1.60s/it]
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2%|β | 23/1250 [00:36<28:38, 1.40s/it]
2%|β | 24/1250 [00:37<26:12, 1.28s/it]
2%|β | 25/1250 [00:38<28:56, 1.42s/it]
2%|β | 26/1250 [00:41<38:28, 1.89s/it]
2%|β | 27/1250 [00:43<37:43, 1.85s/it]
2%|β | 28/1250 [00:45<35:59, 1.77s/it]
2%|β | 29/1250 [00:46<33:21, 1.64s/it]
2%|β | 30/1250 [00:47<30:47, 1.51s/it]
2%|β | 31/1250 [00:48<27:31, 1.36s/it]
3%|β | 32/1250 [00:51<33:43, 1.66s/it]
3%|β | 33/1250 [00:52<34:25, 1.70s/it]
3%|β | 34/1250 [00:54<33:34, 1.66s/it]
3%|β | 35/1250 [00:55<31:55, 1.58s/it]
3%|β | 36/1250 [00:56<29:40, 1.47s/it]
3%|β | 37/1250 [00:58<26:54, 1.33s/it]
3%|β | 38/1250 [01:00<33:02, 1.64s/it]
3%|β | 39/1250 [01:02<33:43, 1.67s/it]
3%|β | 40/1250 [01:03<32:32, 1.61s/it]
3%|β | 41/1250 [01:04<30:50, 1.53s/it]
3%|β | 42/1250 [01:06<28:27, 1.41s/it]
3%|β | 43/1250 [01:07<25:45, 1.28s/it]
4%|β | 44/1250 [01:09<31:31, 1.57s/it]
4%|β | 45/1250 [01:10<32:20, 1.61s/it]
4%|β | 46/1250 [01:12<31:58, 1.59s/it]
4%|β | 47/1250 [01:13<30:42, 1.53s/it]
4%|β | 48/1250 [01:15<28:46, 1.44s/it]
4%|β | 49/1250 [01:16<26:22, 1.32s/it]
4%|β | 50/1250 [01:17<29:12, 1.46s/it]
4%|β | 51/1250 [01:20<37:20, 1.87s/it]
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4%|β | 53/1250 [01:24<35:02, 1.76s/it]
4%|β | 54/1250 [01:25<32:55, 1.65s/it]
4%|β | 55/1250 [01:26<30:10, 1.51s/it]
4%|β | 56/1250 [01:27<26:59, 1.36s/it]
5%|β | 57/1250 [01:29<32:08, 1.62s/it]
5%|β | 58/1250 [01:31<32:44, 1.65s/it]
5%|β | 59/1250 [01:33<31:55, 1.61s/it]
5%|β | 60/1250 [01:34<30:25, 1.53s/it]
5%|β | 61/1250 [01:35<28:26, 1.44s/it]
5%|β | 62/1250 [01:36<26:14, 1.33s/it]
5%|β | 63/1250 [01:39<32:19, 1.63s/it]
5%|β | 64/1250 [01:40<33:12, 1.68s/it]
5%|β | 65/1250 [01:42<32:10, 1.63s/it]
5%|β | 66/1250 [01:43<30:43, 1.56s/it]
5%|β | 67/1250 [01:45<28:35, 1.45s/it]
5%|β | 68/1250 [01:46<25:57, 1.32s/it]
6%|β | 69/1250 [01:48<33:24, 1.70s/it]
6%|β | 70/1250 [01:50<34:10, 1.74s/it]
6%|β | 71/1250 [01:51<32:41, 1.66s/it]
6%|β | 72/1250 [01:53<30:43, 1.57s/it]
6%|β | 73/1250 [01:54<28:32, 1.46s/it]
6%|β | 74/1250 [01:55<25:40, 1.31s/it]
6%|β | 75/1250 [01:57<28:23, 1.45s/it]
6%|β | 76/1250 [02:00<37:02, 1.89s/it]
6%|β | 77/1250 [02:01<36:23, 1.86s/it]
6%|β | 78/1250 [02:03<34:41, 1.78s/it]
6%|β | 79/1250 [02:04<32:21, 1.66s/it]
6%|β | 80/1250 [02:06<29:42, 1.52s/it]
6%|β | 81/1250 [02:07<26:30, 1.36s/it]
7%|β | 82/1250 [02:09<32:23, 1.66s/it]
7%|β | 83/1250 [02:11<32:49, 1.69s/it]
7%|β | 84/1250 [02:12<31:56, 1.64s/it]
7%|β | 85/1250 [02:14<30:09, 1.55s/it]
7%|β | 86/1250 [02:15<27:51, 1.44s/it]
7%|β | 87/1250 [02:16<25:16, 1.30s/it]
7%|β | 88/1250 [02:18<32:07, 1.66s/it]
7%|β | 89/1250 [02:20<32:48, 1.70s/it]
7%|β | 90/1250 [02:22<31:43, 1.64s/it]
7%|β | 91/1250 [02:23<30:21, 1.57s/it]
7%|β | 92/1250 [02:24<28:26, 1.47s/it]
7%|β | 93/1250 [02:25<26:29, 1.37s/it]
8%|β | 94/1250 [02:28<31:23, 1.63s/it]
8%|β | 95/1250 [02:29<31:51, 1.66s/it]
8%|β | 96/1250 [02:31<30:50, 1.60s/it]
8%|β | 97/1250 [02:32<29:30, 1.54s/it]
8%|β | 98/1250 [02:33<27:25, 1.43s/it]
8%|β | 99/1250 [02:34<24:44, 1.29s/it]
8%|β | 100/1250 [02:36<26:59, 1.41s/it]
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8%|β | 104/1250 [02:43<30:05, 1.58s/it]
8%|β | 105/1250 [02:44<27:37, 1.45s/it]
8%|β | 106/1250 [02:45<24:44, 1.30s/it]
9%|β | 107/1250 [02:48<30:38, 1.61s/it]
9%|β | 108/1250 [02:49<31:24, 1.65s/it]
9%|β | 109/1250 [02:51<30:45, 1.62s/it]
9%|β | 110/1250 [02:52<29:22, 1.55s/it]
9%|β | 111/1250 [02:54<27:20, 1.44s/it]
9%|β | 112/1250 [02:55<24:41, 1.30s/it]
9%|β | 113/1250 [02:57<29:34, 1.56s/it]
9%|β | 114/1250 [02:58<30:11, 1.59s/it]
9%|β | 115/1250 [03:00<29:50, 1.58s/it]
9%|β | 116/1250 [03:01<28:49, 1.53s/it]
9%|β | 117/1250 [03:03<26:52, 1.42s/it]
9%|β | 118/1250 [03:04<24:25, 1.29s/it]
10%|β | 119/1250 [03:06<30:27, 1.62s/it]
10%|β | 120/1250 [03:08<31:40, 1.68s/it]
10%|β | 121/1250 [03:09<30:52, 1.64s/it]
10%|β | 122/1250 [03:11<29:20, 1.56s/it]
10%|β | 123/1250 [03:12<27:11, 1.45s/it]
10%|β | 124/1250 [03:13<24:31, 1.31s/it]
10%|β | 125/1250 [03:15<28:39, 1.53s/it]
10%|β | 126/1250 [03:18<36:45, 1.96s/it]
10%|β | 127/1250 [03:20<35:48, 1.91s/it]
10%|β | 128/1250 [03:21<33:48, 1.81s/it]
10%|β | 129/1250 [03:23<31:32, 1.69s/it]
10%|β | 130/1250 [03:24<28:58, 1.55s/it]
10%|β | 131/1250 [03:25<25:53, 1.39s/it]
11%|β | 132/1250 [03:27<31:02, 1.67s/it]
11%|β | 133/1250 [03:29<31:42, 1.70s/it]
11%|β | 134/1250 [03:30<30:44, 1.65s/it]
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22%|βββ | 281/1250 [07:19<21:57, 1.36s/it]
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23%|βββ | 289/1250 [07:33<26:49, 1.68s/it]
23%|βββ | 290/1250 [07:34<26:15, 1.64s/it]
23%|βββ | 291/1250 [07:36<24:52, 1.56s/it]
23%|βββ | 292/1250 [07:37<22:56, 1.44s/it]
23%|βββ | 293/1250 [07:38<20:36, 1.29s/it]
24%|βββ | 294/1250 [07:40<25:20, 1.59s/it]
24%|βββ | 295/1250 [07:42<25:45, 1.62s/it]
24%|βββ | 296/1250 [07:43<24:59, 1.57s/it]
24%|βββ | 297/1250 [07:44<23:42, 1.49s/it]
24%|βββ | 298/1250 [07:46<22:08, 1.40s/it]
24%|βββ | 299/1250 [07:47<20:04, 1.27s/it]
24%|βββ | 300/1250 [07:48<22:58, 1.45s/it]
24%|βββ | 300/1250 [07:48<22:58, 1.45s/it]
24%|βββ | 301/1250 [07:51<29:17, 1.85s/it]
24%|βββ | 302/1250 [07:53<28:36, 1.81s/it]
24%|βββ | 303/1250 [07:55<27:16, 1.73s/it]
24%|βββ | 304/1250 [07:56<25:46, 1.63s/it]
24%|βββ | 305/1250 [07:57<23:38, 1.50s/it]
24%|βββ | 306/1250 [07:58<21:16, 1.35s/it]
25%|βββ | 307/1250 [08:00<25:45, 1.64s/it]
25%|βββ | 308/1250 [08:02<26:24, 1.68s/it]
25%|βββ | 309/1250 [08:04<25:52, 1.65s/it]
25%|βββ | 310/1250 [08:05<24:30, 1.56s/it]
25%|βββ | 311/1250 [08:06<22:52, 1.46s/it]
25%|βββ | 312/1250 [08:07<20:47, 1.33s/it]
25%|βββ | 313/1250 [08:10<26:16, 1.68s/it]
25%|βββ | 314/1250 [08:12<26:45, 1.72s/it]
25%|βββ | 315/1250 [08:13<25:53, 1.66s/it]
25%|βββ | 316/1250 [08:15<24:24, 1.57s/it]
25%|βββ | 317/1250 [08:16<22:36, 1.45s/it]
25%|βββ | 318/1250 [08:17<20:24, 1.31s/it]
26%|βββ | 319/1250 [08:19<25:10, 1.62s/it]
26%|βββ | 320/1250 [08:21<25:30, 1.65s/it]
26%|βββ | 321/1250 [08:22<24:42, 1.60s/it]
26%|βββ | 322/1250 [08:24<23:36, 1.53s/it]
26%|βββ | 323/1250 [08:25<21:59, 1.42s/it]
26%|βββ | 324/1250 [08:26<19:52, 1.29s/it]
26%|βββ | 325/1250 [08:28<22:51, 1.48s/it]
26%|βββ | 326/1250 [08:31<28:59, 1.88s/it]
26%|βββ | 327/1250 [08:32<28:15, 1.84s/it]
26%|βββ | 328/1250 [08:34<26:38, 1.73s/it]
26%|βββ | 329/1250 [08:35<24:51, 1.62s/it]
26%|βββ | 330/1250 [08:36<22:40, 1.48s/it]
26%|βββ | 331/1250 [08:37<20:21, 1.33s/it]
27%|βββ | 332/1250 [08:40<24:40, 1.61s/it]
27%|βββ | 333/1250 [08:41<25:06, 1.64s/it]
27%|βββ | 334/1250 [08:43<24:38, 1.61s/it]
27%|βββ | 335/1250 [08:44<23:33, 1.54s/it]
27%|βββ | 336/1250 [08:45<21:48, 1.43s/it]
27%|βββ | 337/1250 [08:46<19:37, 1.29s/it]
27%|βββ | 338/1250 [08:49<24:31, 1.61s/it]
27%|βββ | 339/1250 [08:50<25:13, 1.66s/it]
27%|βββ | 340/1250 [08:52<24:29, 1.61s/it]
27%|βββ | 341/1250 [08:53<23:28, 1.55s/it]
27%|βββ | 342/1250 [08:55<21:50, 1.44s/it]
27%|βββ | 343/1250 [08:56<20:01, 1.32s/it]
28%|βββ | 344/1250 [08:58<24:53, 1.65s/it]
28%|βββ | 345/1250 [09:00<25:11, 1.67s/it]
28%|βββ | 346/1250 [09:01<24:32, 1.63s/it]
28%|βββ | 347/1250 [09:03<23:18, 1.55s/it]
28%|βββ | 348/1250 [09:04<21:27, 1.43s/it]
28%|βββ | 349/1250 [09:05<19:20, 1.29s/it]
28%|βββ | 350/1250 [09:07<22:39, 1.51s/it]
28%|βββ | 351/1250 [09:10<29:06, 1.94s/it]
28%|βββ | 352/1250 [09:12<28:24, 1.90s/it]
28%|βββ | 353/1250 [09:13<26:48, 1.79s/it]
28%|βββ | 354/1250 [09:14<24:54, 1.67s/it]
28%|βββ | 355/1250 [09:16<22:40, 1.52s/it]
28%|βββ | 356/1250 [09:17<20:29, 1.38s/it]
29%|βββ | 357/1250 [09:19<24:22, 1.64s/it]
29%|βββ | 358/1250 [09:21<24:35, 1.65s/it]
29%|βββ | 359/1250 [09:22<23:54, 1.61s/it]
29%|βββ | 360/1250 [09:23<22:44, 1.53s/it]
29%|βββ | 361/1250 [09:25<21:14, 1.43s/it]
29%|βββ | 362/1250 [09:26<19:13, 1.30s/it]
29%|βββ | 363/1250 [09:28<23:56, 1.62s/it]
29%|βββ | 364/1250 [09:30<24:15, 1.64s/it]
29%|βββ | 365/1250 [09:31<23:39, 1.60s/it]
29%|βββ | 366/1250 [09:33<22:30, 1.53s/it]
29%|βββ | 367/1250 [09:34<21:11, 1.44s/it]
29%|βββ | 368/1250 [09:35<19:05, 1.30s/it]
30%|βββ | 369/1250 [09:37<24:00, 1.63s/it]
30%|βββ | 370/1250 [09:39<24:30, 1.67s/it]
30%|βββ | 371/1250 [09:40<23:53, 1.63s/it]
30%|βββ | 372/1250 [09:42<22:41, 1.55s/it]
30%|βββ | 373/1250 [09:43<20:58, 1.44s/it]
30%|βββ | 374/1250 [09:44<18:54, 1.30s/it]
30%|βββ | 375/1250 [09:46<20:17, 1.39s/it]
30%|βββ | 376/1250 [09:48<26:28, 1.82s/it]
30%|βββ | 377/1250 [09:50<25:49, 1.78s/it]
30%|βββ | 378/1250 [09:52<24:25, 1.68s/it]
30%|βββ | 379/1250 [09:53<22:51, 1.57s/it]
30%|βββ | 380/1250 [09:54<21:04, 1.45s/it]
30%|βββ | 381/1250 [09:55<18:55, 1.31s/it]
31%|βββ | 382/1250 [09:57<23:55, 1.65s/it]
31%|βββ | 383/1250 [09:59<24:26, 1.69s/it]
31%|βββ | 384/1250 [10:01<23:44, 1.64s/it]
31%|βββ | 385/1250 [10:02<22:36, 1.57s/it]
31%|βββ | 386/1250 [10:03<21:00, 1.46s/it]
31%|βββ | 387/1250 [10:04<19:05, 1.33s/it]
31%|βββ | 388/1250 [10:07<22:40, 1.58s/it]
31%|βββ | 389/1250 [10:08<23:05, 1.61s/it]
31%|βββ | 390/1250 [10:10<22:56, 1.60s/it]
31%|ββββ | 391/1250 [10:11<22:10, 1.55s/it]
31%|ββββ | 392/1250 [10:12<20:33, 1.44s/it]
31%|ββββ | 393/1250 [10:13<18:37, 1.30s/it]
32%|ββββ | 394/1250 [10:16<23:25, 1.64s/it]
32%|ββββ | 395/1250 [10:18<23:45, 1.67s/it]
32%|ββββ | 396/1250 [10:19<23:08, 1.63s/it]
32%|ββββ | 397/1250 [10:20<21:55, 1.54s/it]
32%|ββββ | 398/1250 [10:22<20:16, 1.43s/it]
32%|ββββ | 399/1250 [10:23<18:21, 1.29s/it]
32%|ββββ | 400/1250 [10:24<20:34, 1.45s/it]
32%|ββββ | 400/1250 [10:24<20:34, 1.45s/it]
32%|ββββ | 401/1250 [10:27<26:02, 1.84s/it]
32%|ββββ | 402/1250 [10:29<25:43, 1.82s/it]
32%|ββββ | 403/1250 [10:31<24:36, 1.74s/it]
32%|ββββ | 404/1250 [10:32<22:58, 1.63s/it]
32%|ββββ | 405/1250 [10:33<21:12, 1.51s/it]
32%|ββββ | 406/1250 [10:34<19:08, 1.36s/it]
33%|ββββ | 407/1250 [10:37<23:55, 1.70s/it]
33%|ββββ | 408/1250 [10:38<24:11, 1.72s/it]
33%|ββββ | 409/1250 [10:40<23:27, 1.67s/it]
33%|ββββ | 410/1250 [10:41<22:04, 1.58s/it]
33%|ββββ | 411/1250 [10:42<20:25, 1.46s/it]
33%|ββββ | 412/1250 [10:43<18:26, 1.32s/it]
33%|ββββ | 413/1250 [10:46<22:59, 1.65s/it]
33%|ββββ | 414/1250 [10:48<23:20, 1.68s/it]
33%|ββββ | 415/1250 [10:49<22:33, 1.62s/it]
33%|ββββ | 416/1250 [10:50<21:15, 1.53s/it]
33%|ββββ | 417/1250 [10:52<19:25, 1.40s/it]
33%|ββββ | 418/1250 [10:52<17:26, 1.26s/it]
34%|ββββ | 419/1250 [10:55<21:48, 1.57s/it]
34%|ββββ | 420/1250 [10:56<22:18, 1.61s/it]
34%|ββββ | 421/1250 [10:58<21:51, 1.58s/it]
34%|ββββ | 422/1250 [10:59<21:12, 1.54s/it]
34%|ββββ | 423/1250 [11:01<19:59, 1.45s/it]
34%|ββββ | 424/1250 [11:02<18:08, 1.32s/it]
34%|ββββ | 425/1250 [11:03<19:17, 1.40s/it]
34%|ββββ | 426/1250 [11:06<25:20, 1.85s/it]
34%|ββββ | 427/1250 [11:08<24:54, 1.82s/it]
34%|ββββ | 428/1250 [11:09<23:45, 1.73s/it]
34%|ββββ | 429/1250 [11:11<22:22, 1.64s/it]
34%|ββββ | 430/1250 [11:12<20:29, 1.50s/it]
34%|ββββ | 431/1250 [11:13<18:27, 1.35s/it]
35%|ββββ | 432/1250 [11:15<21:54, 1.61s/it]
35%|ββββ | 433/1250 [11:17<22:24, 1.65s/it]
35%|ββββ | 434/1250 [11:18<21:43, 1.60s/it]
35%|ββββ | 435/1250 [11:20<20:37, 1.52s/it]
35%|ββββ | 436/1250 [11:21<19:27, 1.43s/it]
35%|ββββ | 437/1250 [11:22<17:33, 1.30s/it]
35%|ββββ | 438/1250 [11:24<21:45, 1.61s/it]
35%|ββββ | 439/1250 [11:26<22:22, 1.65s/it]
35%|ββββ | 440/1250 [11:28<21:56, 1.62s/it]
35%|ββββ | 441/1250 [11:29<21:01, 1.56s/it]
35%|ββββ | 442/1250 [11:30<19:33, 1.45s/it]
35%|ββββ | 443/1250 [11:31<17:42, 1.32s/it]
36%|ββββ | 444/1250 [11:34<22:11, 1.65s/it]
36%|ββββ | 445/1250 [11:35<22:31, 1.68s/it]
36%|ββββ | 446/1250 [11:37<22:06, 1.65s/it]
36%|ββββ | 447/1250 [11:38<20:52, 1.56s/it]
36%|ββββ | 448/1250 [11:40<19:07, 1.43s/it]
36%|ββββ | 449/1250 [11:40<17:11, 1.29s/it]
36%|ββββ | 450/1250 [11:42<18:49, 1.41s/it]
36%|ββββ | 451/1250 [11:45<24:40, 1.85s/it]
36%|ββββ | 452/1250 [11:47<24:24, 1.84s/it]
36%|ββββ | 453/1250 [11:48<23:15, 1.75s/it]
36%|ββββ | 454/1250 [11:50<21:50, 1.65s/it]
36%|ββββ | 455/1250 [11:51<20:05, 1.52s/it]
36%|ββββ | 456/1250 [11:52<17:55, 1.35s/it]
37%|ββββ | 457/1250 [11:54<22:22, 1.69s/it]
37%|ββββ | 458/1250 [11:56<22:42, 1.72s/it]
37%|ββββ | 459/1250 [11:58<21:46, 1.65s/it]
37%|ββββ | 460/1250 [11:59<20:25, 1.55s/it]
37%|ββββ | 461/1250 [12:00<18:57, 1.44s/it]
37%|ββββ | 462/1250 [12:01<17:05, 1.30s/it]
37%|ββββ | 463/1250 [12:03<20:37, 1.57s/it]
37%|ββββ | 464/1250 [12:05<21:05, 1.61s/it]
37%|ββββ | 465/1250 [12:07<20:35, 1.57s/it]
37%|ββββ | 466/1250 [12:08<19:39, 1.50s/it]
37%|ββββ | 467/1250 [12:09<18:22, 1.41s/it]
37%|ββββ | 468/1250 [12:10<16:57, 1.30s/it]
38%|ββββ | 469/1250 [12:12<20:46, 1.60s/it]
38%|ββββ | 470/1250 [12:14<21:07, 1.62s/it]
38%|ββββ | 471/1250 [12:16<20:47, 1.60s/it]
38%|ββββ | 472/1250 [12:17<19:43, 1.52s/it]
38%|ββββ | 473/1250 [12:18<18:18, 1.41s/it]
38%|ββββ | 474/1250 [12:19<16:37, 1.29s/it]
38%|ββββ | 475/1250 [12:21<18:49, 1.46s/it]
38%|ββββ | 476/1250 [12:24<24:27, 1.90s/it]
38%|ββββ | 477/1250 [12:26<24:00, 1.86s/it]
38%|ββββ | 478/1250 [12:27<22:39, 1.76s/it]
38%|ββββ | 479/1250 [12:29<21:01, 1.64s/it]
38%|ββββ | 480/1250 [12:30<18:58, 1.48s/it]
38%|ββββ | 481/1250 [12:31<16:44, 1.31s/it]
39%|ββββ | 482/1250 [12:33<20:30, 1.60s/it]
39%|ββββ | 483/1250 [12:35<20:39, 1.62s/it]
39%|ββββ | 484/1250 [12:36<20:30, 1.61s/it]
39%|ββββ | 485/1250 [12:38<19:36, 1.54s/it]
39%|ββββ | 486/1250 [12:39<18:15, 1.43s/it]
39%|ββββ | 487/1250 [12:40<16:37, 1.31s/it]
39%|ββββ | 488/1250 [12:42<20:55, 1.65s/it]
39%|ββββ | 489/1250 [12:44<21:17, 1.68s/it]
39%|ββββ | 490/1250 [12:45<20:34, 1.62s/it]
39%|ββββ | 491/1250 [12:47<19:28, 1.54s/it]
39%|ββββ | 492/1250 [12:48<18:28, 1.46s/it]
39%|ββββ | 493/1250 [12:49<16:39, 1.32s/it]
40%|ββββ | 494/1250 [12:51<20:23, 1.62s/it]
40%|ββββ | 495/1250 [12:53<20:39, 1.64s/it]
40%|ββββ | 496/1250 [12:55<20:11, 1.61s/it]
40%|ββββ | 497/1250 [12:56<19:09, 1.53s/it]
40%|ββββ | 498/1250 [12:57<17:44, 1.42s/it]
40%|ββββ | 499/1250 [12:58<15:57, 1.28s/it]
40%|ββββ | 500/1250 [13:00<17:17, 1.38s/it]
40%|ββββ | 500/1250 [13:00<17:17, 1.38s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.
***** Running Evaluation *****
Num examples = 341
Batch size = 8
{'loss': 20.0794, 'learning_rate': 3.675e-06, 'epoch': 3.98}
{'loss': 10.5776, 'learning_rate': 7.425e-06, 'epoch': 7.98}
{'loss': 6.6033, 'learning_rate': 1.1174999999999999e-05, 'epoch': 11.98}
{'loss': 5.3857, 'learning_rate': 1.4925e-05, 'epoch': 15.98}
{'loss': 4.4431, 'learning_rate': 1.8675e-05, 'epoch': 19.98}
0%| | 0/43 [00:00<?, ?it/s][A
5%|β | 2/43 [00:00<00:04, 9.99it/s][A
7%|β | 3/43 [00:00<00:07, 5.48it/s][A
9%|β | 4/43 [00:00<00:09, 4.26it/s][A
12%|ββ | 5/43 [00:01<00:09, 4.09it/s][A
14%|ββ | 6/43 [00:01<00:09, 3.90it/s][A
16%|ββ | 7/43 [00:01<00:09, 3.85it/s][A
19%|ββ | 8/43 [00:01<00:09, 3.77it/s][A
21%|ββ | 9/43 [00:02<00:09, 3.47it/s][A
23%|βββ | 10/43 [00:02<00:08, 3.74it/s][A
26%|βββ | 11/43 [00:02<00:09, 3.54it/s][A
28%|βββ | 12/43 [00:03<00:08, 3.77it/s][A
30%|βββ | 13/43 [00:03<00:07, 3.86it/s][A
33%|ββββ | 14/43 [00:03<00:08, 3.48it/s][A
35%|ββββ | 15/43 [00:04<00:09, 2.99it/s][A
37%|ββββ | 16/43 [00:04<00:08, 3.05it/s][A
40%|ββββ | 17/43 [00:04<00:08, 3.14it/s][A
42%|βββββ | 18/43 [00:05<00:08, 3.05it/s][A
44%|βββββ | 19/43 [00:05<00:08, 2.85it/s][A
47%|βββββ | 20/43 [00:05<00:09, 2.54it/s][A
49%|βββββ | 21/43 [00:06<00:08, 2.66it/s][A
51%|βββββ | 22/43 [00:06<00:07, 2.70it/s][A
53%|ββββββ | 23/43 [00:06<00:07, 2.82it/s][A
56%|ββββββ | 24/43 [00:07<00:06, 3.09it/s][A
58%|ββββββ | 25/43 [00:07<00:05, 3.22it/s][A
60%|ββββββ | 26/43 [00:07<00:05, 3.21it/s][A
63%|βββββββ | 27/43 [00:07<00:04, 3.59it/s][A
65%|βββββββ | 28/43 [00:08<00:04, 3.65it/s][A
67%|βββββββ | 29/43 [00:08<00:03, 3.76it/s][A
70%|βββββββ | 30/43 [00:08<00:03, 3.83it/s][A
72%|ββββββββ | 31/43 [00:08<00:03, 3.79it/s][A
74%|ββββββββ | 32/43 [00:09<00:02, 3.85it/s][A
77%|ββββββββ | 33/43 [00:09<00:02, 3.54it/s][A
79%|ββββββββ | 34/43 [00:09<00:02, 3.51it/s][A
81%|βββββββββ | 35/43 [00:10<00:02, 3.51it/s][A
84%|βββββββββ | 36/43 [00:10<00:02, 3.42it/s][A
86%|βββββββββ | 37/43 [00:10<00:01, 3.56it/s][A
88%|βββββββββ | 38/43 [00:11<00:01, 3.32it/s][A
91%|βββββββββ | 39/43 [00:11<00:01, 3.38it/s][A
93%|ββββββββββ| 40/43 [00:11<00:00, 3.27it/s][A
95%|ββββββββββ| 41/43 [00:11<00:00, 3.37it/s][A
98%|ββββββββββ| 42/43 [00:12<00:00, 3.19it/s][A
100%|ββββββββββ| 43/43 [00:12<00:00, 3.83it/s][A
[A
40%|ββββ | 500/1250 [13:13<17:17, 1.38s/it]
100%|ββββββββββ| 43/43 [00:12<00:00, 3.83it/s][A
[ASaving model checkpoint to ./checkpoint-500
Configuration saved in ./checkpoint-500/config.json
Model weights saved in ./checkpoint-500/pytorch_model.bin
Configuration saved in ./checkpoint-500/preprocessor_config.json
Configuration saved in ./preprocessor_config.json
40%|ββββ | 501/1250 [13:45<3:02:23, 14.61s/it]
40%|ββββ | 502/1250 [13:47<2:13:45, 10.73s/it]
40%|ββββ | 503/1250 [13:48<1:39:10, 7.97s/it]
40%|ββββ | 504/1250 [13:50<1:14:34, 6.00s/it]
40%|ββββ | 505/1250 [13:51<56:37, 4.56s/it]
40%|ββββ | 506/1250 [13:52<43:34, 3.51s/it]
41%|ββββ | 507/1250 [13:54<39:07, 3.16s/it]
41%|ββββ | 508/1250 [13:56<34:15, 2.77s/it]
41%|ββββ | 509/1250 [13:58<29:45, 2.41s/it]
41%|ββββ | 510/1250 [13:59<25:49, 2.09s/it]
41%|ββββ | 511/1250 [14:00<22:25, 1.82s/it]
41%|ββββ | 512/1250 [14:01<19:15, 1.57s/it]
41%|ββββ | 513/1250 [14:04<22:15, 1.81s/it]
41%|ββββ | 514/1250 [14:05<21:55, 1.79s/it]
41%|ββββ | 515/1250 [14:07<20:56, 1.71s/it]
41%|βββββ | 516/1250 [14:08<19:31, 1.60s/it]
41%|βββββ | 517/1250 [14:09<17:54, 1.47s/it]
41%|βββββ | 518/1250 [14:10<16:11, 1.33s/it]
42%|βββββ | 519/1250 [14:13<19:56, 1.64s/it]
42%|βββββ | 520/1250 [14:14<20:11, 1.66s/it]
42%|βββββ | 521/1250 [14:16<19:21, 1.59s/it]
42%|βββββ | 522/1250 [14:17<18:22, 1.51s/it]
42%|βββββ | 523/1250 [14:18<16:55, 1.40s/it]
42%|βββββ | 524/1250 [14:19<15:18, 1.27s/it]
42%|βββββ | 525/1250 [14:21<17:22, 1.44s/it]
42%|βββββ | 526/1250 [14:24<22:06, 1.83s/it]
42%|βββββ | 527/1250 [14:26<21:32, 1.79s/it]
42%|βββββ | 528/1250 [14:27<20:13, 1.68s/it]
42%|βββββ | 529/1250 [14:28<18:59, 1.58s/it]
42%|βββββ | 530/1250 [14:30<17:33, 1.46s/it]
42%|βββββ | 531/1250 [14:31<15:50, 1.32s/it]
43%|βββββ | 532/1250 [14:33<20:04, 1.68s/it]
43%|βββββ | 533/1250 [14:35<20:10, 1.69s/it]
43%|βββββ | 534/1250 [14:36<19:25, 1.63s/it]
43%|βββββ | 535/1250 [14:38<18:30, 1.55s/it]
43%|βββββ | 536/1250 [14:39<17:11, 1.44s/it]
43%|βββββ | 537/1250 [14:40<15:32, 1.31s/it]
43%|βββββ | 538/1250 [14:42<19:16, 1.62s/it]
43%|βββββ | 539/1250 [14:44<19:51, 1.68s/it]
43%|βββββ | 540/1250 [14:46<19:32, 1.65s/it]
43%|βββββ | 541/1250 [14:47<18:35, 1.57s/it]
43%|βββββ | 542/1250 [14:48<17:14, 1.46s/it]
43%|βββββ | 543/1250 [14:49<15:31, 1.32s/it]
44%|βββββ | 544/1250 [14:52<19:16, 1.64s/it]
44%|βββββ | 545/1250 [14:53<19:33, 1.66s/it]
44%|βββββ | 546/1250 [14:55<18:47, 1.60s/it]
44%|βββββ | 547/1250 [14:56<17:40, 1.51s/it]
44%|βββββ | 548/1250 [14:57<16:17, 1.39s/it]
44%|βββββ | 549/1250 [14:58<14:43, 1.26s/it]
44%|βββββ | 550/1250 [15:00<16:04, 1.38s/it]
44%|βββββ | 551/1250 [15:03<21:04, 1.81s/it]
44%|βββββ | 552/1250 [15:04<20:40, 1.78s/it]
44%|βββββ | 553/1250 [15:06<19:33, 1.68s/it]
44%|βββββ | 554/1250 [15:07<18:17, 1.58s/it]
44%|βββββ | 555/1250 [15:08<16:56, 1.46s/it]
44%|βββββ | 556/1250 [15:09<15:27, 1.34s/it]
45%|βββββ | 557/1250 [15:12<19:05, 1.65s/it]
45%|βββββ | 558/1250 [15:14<19:34, 1.70s/it]
45%|βββββ | 559/1250 [15:15<18:49, 1.63s/it]
45%|βββββ | 560/1250 [15:16<17:42, 1.54s/it]
45%|βββββ | 561/1250 [15:17<16:21, 1.42s/it]
45%|βββββ | 562/1250 [15:18<14:47, 1.29s/it]
45%|βββββ | 563/1250 [15:21<18:25, 1.61s/it]
45%|βββββ | 564/1250 [15:23<18:52, 1.65s/it]
45%|βββββ | 565/1250 [15:24<18:19, 1.61s/it]
45%|βββββ | 566/1250 [15:25<17:22, 1.52s/it]
45%|βββββ | 567/1250 [15:27<16:11, 1.42s/it]
45%|βββββ | 568/1250 [15:28<14:37, 1.29s/it]
46%|βββββ | 569/1250 [15:30<17:56, 1.58s/it]
46%|βββββ | 570/1250 [15:31<18:17, 1.61s/it]
46%|βββββ | 571/1250 [15:33<18:00, 1.59s/it]
46%|βββββ | 572/1250 [15:34<17:18, 1.53s/it]
46%|βββββ | 573/1250 [15:36<16:09, 1.43s/it]
46%|βββββ | 574/1250 [15:37<14:39, 1.30s/it]
46%|βββββ | 575/1250 [15:38<16:34, 1.47s/it]
46%|βββββ | 576/1250 [15:41<20:49, 1.85s/it]
46%|βββββ | 577/1250 [15:43<20:21, 1.82s/it]
46%|βββββ | 578/1250 [15:44<19:21, 1.73s/it]
46%|βββββ | 579/1250 [15:46<18:02, 1.61s/it]
46%|βββββ | 580/1250 [15:47<16:36, 1.49s/it]
46%|βββββ | 581/1250 [15:48<14:48, 1.33s/it]
47%|βββββ | 582/1250 [15:50<18:08, 1.63s/it]
47%|βββββ | 583/1250 [15:52<18:36, 1.67s/it]
47%|βββββ | 584/1250 [15:54<18:13, 1.64s/it]
47%|βββββ | 585/1250 [15:55<17:20, 1.56s/it]
47%|βββββ | 586/1250 [15:56<16:06, 1.46s/it]
47%|βββββ | 587/1250 [15:57<14:39, 1.33s/it]
47%|βββββ | 588/1250 [15:59<17:36, 1.60s/it]
47%|βββββ | 589/1250 [16:01<17:51, 1.62s/it]
47%|βββββ | 590/1250 [16:03<17:15, 1.57s/it]
47%|βββββ | 591/1250 [16:04<16:15, 1.48s/it]
47%|βββββ | 592/1250 [16:05<15:05, 1.38s/it]
47%|βββββ | 593/1250 [16:06<13:38, 1.25s/it]
48%|βββββ | 594/1250 [16:08<17:27, 1.60s/it]
48%|βββββ | 595/1250 [16:10<17:50, 1.63s/it]
48%|βββββ | 596/1250 [16:12<17:17, 1.59s/it]
48%|βββββ | 597/1250 [16:13<16:26, 1.51s/it]
48%|βββββ | 598/1250 [16:14<15:18, 1.41s/it]
48%|βββββ | 599/1250 [16:15<13:53, 1.28s/it]
48%|βββββ | 600/1250 [16:17<15:59, 1.48s/it]
48%|βββββ | 600/1250 [16:17<15:59, 1.48s/it]
48%|βββββ | 601/1250 [16:20<20:30, 1.90s/it]
48%|βββββ | 602/1250 [16:22<20:08, 1.86s/it]
48%|βββββ | 603/1250 [16:23<19:03, 1.77s/it]
48%|βββββ | 604/1250 [16:25<17:37, 1.64s/it]
48%|βββββ | 605/1250 [16:26<16:12, 1.51s/it]
48%|βββββ | 606/1250 [16:27<14:25, 1.34s/it]
49%|βββββ | 607/1250 [16:29<17:47, 1.66s/it]
49%|βββββ | 608/1250 [16:31<17:50, 1.67s/it]
49%|βββββ | 609/1250 [16:32<17:20, 1.62s/it]
49%|βββββ | 610/1250 [16:34<16:29, 1.55s/it]
49%|βββββ | 611/1250 [16:35<15:16, 1.43s/it]
49%|βββββ | 612/1250 [16:36<13:48, 1.30s/it]
49%|βββββ | 613/1250 [16:38<16:51, 1.59s/it]
49%|βββββ | 614/1250 [16:40<17:25, 1.64s/it]
49%|βββββ | 615/1250 [16:41<17:00, 1.61s/it]
49%|βββββ | 616/1250 [16:43<16:15, 1.54s/it]
49%|βββββ | 617/1250 [16:44<15:09, 1.44s/it]
49%|βββββ | 618/1250 [16:45<13:43, 1.30s/it]
50%|βββββ | 619/1250 [16:47<17:00, 1.62s/it]
50%|βββββ | 620/1250 [16:49<17:25, 1.66s/it]
50%|βββββ | 621/1250 [16:51<16:57, 1.62s/it]
50%|βββββ | 622/1250 [16:52<16:05, 1.54s/it]
50%|βββββ | 623/1250 [16:53<14:49, 1.42s/it]
50%|βββββ | 624/1250 [16:54<13:25, 1.29s/it]
50%|βββββ | 625/1250 [16:56<14:38, 1.41s/it]
50%|βββββ | 626/1250 [16:59<19:06, 1.84s/it]
50%|βββββ | 627/1250 [17:00<18:41, 1.80s/it]
50%|βββββ | 628/1250 [17:02<17:47, 1.72s/it]
50%|βββββ | 629/1250 [17:03<16:47, 1.62s/it]
50%|βββββ | 630/1250 [17:04<15:26, 1.49s/it]
50%|βββββ | 631/1250 [17:05<13:57, 1.35s/it]
51%|βββββ | 632/1250 [17:08<16:53, 1.64s/it]
51%|βββββ | 633/1250 [17:09<17:05, 1.66s/it]
51%|βββββ | 634/1250 [17:11<16:41, 1.63s/it]
51%|βββββ | 635/1250 [17:12<15:50, 1.55s/it]
51%|βββββ | 636/1250 [17:14<14:41, 1.44s/it]
51%|βββββ | 637/1250 [17:15<13:16, 1.30s/it]
51%|βββββ | 638/1250 [17:17<16:26, 1.61s/it]
51%|βββββ | 639/1250 [17:19<16:42, 1.64s/it]
51%|βββββ | 640/1250 [17:20<16:08, 1.59s/it]
51%|ββββββ | 641/1250 [17:21<15:12, 1.50s/it]
51%|ββββββ | 642/1250 [17:22<14:06, 1.39s/it]
51%|ββββββ | 643/1250 [17:23<12:49, 1.27s/it]
52%|ββββββ | 644/1250 [17:26<16:01, 1.59s/it]
52%|ββββββ | 645/1250 [17:28<16:27, 1.63s/it]
52%|ββββββ | 646/1250 [17:29<16:05, 1.60s/it]
52%|ββββββ | 647/1250 [17:30<15:29, 1.54s/it]
52%|ββββββ | 648/1250 [17:32<14:30, 1.45s/it]
52%|ββββββ | 649/1250 [17:33<13:11, 1.32s/it]
52%|ββββββ | 650/1250 [17:34<14:13, 1.42s/it]
52%|ββββββ | 651/1250 [17:37<18:25, 1.85s/it]
52%|ββββββ | 652/1250 [17:39<18:01, 1.81s/it]
52%|ββββββ | 653/1250 [17:40<17:07, 1.72s/it]
52%|ββββββ | 654/1250 [17:42<15:52, 1.60s/it]
52%|ββββββ | 655/1250 [17:43<14:29, 1.46s/it]
52%|ββββββ | 656/1250 [17:44<13:01, 1.32s/it]
53%|ββββββ | 657/1250 [17:46<16:01, 1.62s/it]
53%|ββββββ | 658/1250 [17:48<16:22, 1.66s/it]
53%|ββββββ | 659/1250 [17:49<15:59, 1.62s/it]
53%|ββββββ | 660/1250 [17:51<15:17, 1.55s/it]
53%|ββββββ | 661/1250 [17:52<14:06, 1.44s/it]
53%|ββββββ | 662/1250 [17:53<12:48, 1.31s/it]
53%|ββββββ | 663/1250 [17:55<15:51, 1.62s/it]
53%|ββββββ | 664/1250 [17:57<16:12, 1.66s/it]
53%|ββββββ | 665/1250 [17:59<15:57, 1.64s/it]
53%|ββββββ | 666/1250 [18:00<15:16, 1.57s/it]
53%|ββββββ | 667/1250 [18:01<14:03, 1.45s/it]
53%|ββββββ | 668/1250 [18:02<12:47, 1.32s/it]
54%|ββββββ | 669/1250 [18:05<15:36, 1.61s/it]
54%|ββββββ | 670/1250 [18:06<15:47, 1.63s/it]
54%|ββββββ | 671/1250 [18:08<15:23, 1.60s/it]
54%|ββββββ | 672/1250 [18:09<14:38, 1.52s/it]
54%|ββββββ | 673/1250 [18:10<13:32, 1.41s/it]
54%|ββββββ | 674/1250 [18:11<12:19, 1.28s/it]
54%|ββββββ | 675/1250 [18:13<13:59, 1.46s/it]
54%|ββββββ | 676/1250 [18:16<17:40, 1.85s/it]
54%|ββββββ | 677/1250 [18:18<17:05, 1.79s/it]
54%|ββββββ | 678/1250 [18:19<16:09, 1.69s/it]
54%|ββββββ | 679/1250 [18:20<14:52, 1.56s/it]
54%|ββββββ | 680/1250 [18:21<13:35, 1.43s/it]
54%|ββββββ | 681/1250 [18:22<11:55, 1.26s/it]
55%|ββββββ | 682/1250 [18:25<14:46, 1.56s/it]
55%|ββββββ | 683/1250 [18:26<15:15, 1.62s/it]
55%|ββββββ | 684/1250 [18:28<14:52, 1.58s/it]
55%|ββββββ | 685/1250 [18:29<14:09, 1.50s/it]
55%|ββββββ | 686/1250 [18:30<13:09, 1.40s/it]
55%|ββββββ | 687/1250 [18:31<12:00, 1.28s/it]
55%|ββββββ | 688/1250 [18:34<15:21, 1.64s/it]
55%|ββββββ | 689/1250 [18:36<15:46, 1.69s/it]
55%|ββββββ | 690/1250 [18:37<15:26, 1.65s/it]
55%|ββββββ | 691/1250 [18:39<14:41, 1.58s/it]
55%|ββββββ | 692/1250 [18:40<13:45, 1.48s/it]
55%|ββββββ | 693/1250 [18:41<12:26, 1.34s/it]
56%|ββββββ | 694/1250 [18:43<15:15, 1.65s/it]
56%|ββββββ | 695/1250 [18:45<15:35, 1.69s/it]
56%|ββββββ | 696/1250 [18:46<15:05, 1.63s/it]
56%|ββββββ | 697/1250 [18:48<14:16, 1.55s/it]
56%|ββββββ | 698/1250 [18:49<13:13, 1.44s/it]
56%|ββββββ | 699/1250 [18:50<11:53, 1.29s/it]
56%|ββββββ | 700/1250 [18:52<12:52, 1.41s/it]
56%|ββββββ | 700/1250 [18:52<12:52, 1.41s/it]
56%|ββββββ | 701/1250 [18:55<17:02, 1.86s/it]
56%|ββββββ | 702/1250 [18:56<16:45, 1.83s/it]
56%|ββββββ | 703/1250 [18:58<15:53, 1.74s/it]
56%|ββββββ | 704/1250 [18:59<14:53, 1.64s/it]
56%|ββββββ | 705/1250 [19:00<13:30, 1.49s/it]
56%|ββββββ | 706/1250 [19:01<12:05, 1.33s/it]
57%|ββββββ | 707/1250 [19:03<14:22, 1.59s/it]
57%|ββββββ | 708/1250 [19:05<14:43, 1.63s/it]
57%|ββββββ | 709/1250 [19:07<14:21, 1.59s/it]
57%|ββββββ | 710/1250 [19:08<13:40, 1.52s/it]
57%|ββββββ | 711/1250 [19:09<12:45, 1.42s/it]
57%|ββββββ | 712/1250 [19:10<11:41, 1.30s/it]
57%|ββββββ | 713/1250 [19:13<14:27, 1.62s/it]
57%|ββββββ | 714/1250 [19:14<14:40, 1.64s/it]
57%|ββββββ | 715/1250 [19:16<14:14, 1.60s/it]
57%|ββββββ | 716/1250 [19:17<13:36, 1.53s/it]
57%|ββββββ | 717/1250 [19:18<12:39, 1.43s/it]
57%|ββββββ | 718/1250 [19:19<11:22, 1.28s/it]
58%|ββββββ | 719/1250 [19:22<14:16, 1.61s/it]
58%|ββββββ | 720/1250 [19:23<14:39, 1.66s/it]
58%|ββββββ | 721/1250 [19:25<14:19, 1.62s/it]
58%|ββββββ | 722/1250 [19:26<13:41, 1.56s/it]
58%|ββββββ | 723/1250 [19:28<12:43, 1.45s/it]
58%|ββββββ | 724/1250 [19:29<11:25, 1.30s/it]
58%|ββββββ | 725/1250 [19:30<12:36, 1.44s/it]
58%|ββββββ | 726/1250 [19:33<16:06, 1.84s/it]
58%|ββββββ | 727/1250 [19:35<15:46, 1.81s/it]
58%|ββββββ | 728/1250 [19:36<14:53, 1.71s/it]
58%|ββββββ | 729/1250 [19:38<13:55, 1.60s/it]
58%|ββββββ | 730/1250 [19:39<12:51, 1.48s/it]
58%|ββββββ | 731/1250 [19:40<11:45, 1.36s/it]
59%|ββββββ | 732/1250 [19:42<14:18, 1.66s/it]
59%|ββββββ | 733/1250 [19:44<14:28, 1.68s/it]
59%|ββββββ | 734/1250 [19:46<14:05, 1.64s/it]
59%|ββββββ | 735/1250 [19:47<13:24, 1.56s/it]
59%|ββββββ | 736/1250 [19:48<12:20, 1.44s/it]
59%|ββββββ | 737/1250 [19:49<10:57, 1.28s/it]
59%|ββββββ | 738/1250 [19:51<13:46, 1.61s/it]
59%|ββββββ | 739/1250 [19:53<13:58, 1.64s/it]
59%|ββββββ | 740/1250 [19:55<13:35, 1.60s/it]
59%|ββββββ | 741/1250 [19:56<12:54, 1.52s/it]
59%|ββββββ | 742/1250 [19:57<11:55, 1.41s/it]
59%|ββββββ | 743/1250 [19:58<10:50, 1.28s/it]
60%|ββββββ | 744/1250 [20:00<13:23, 1.59s/it]
60%|ββββββ | 745/1250 [20:02<13:51, 1.65s/it]
60%|ββββββ | 746/1250 [20:04<13:20, 1.59s/it]
60%|ββββββ | 747/1250 [20:05<12:29, 1.49s/it]
60%|ββββββ | 748/1250 [20:06<11:35, 1.38s/it]
60%|ββββββ | 749/1250 [20:07<10:31, 1.26s/it]
60%|ββββββ | 750/1250 [20:09<11:41, 1.40s/it]
60%|ββββββ | 751/1250 [20:12<15:16, 1.84s/it]
60%|ββββββ | 752/1250 [20:13<15:11, 1.83s/it]
60%|ββββββ | 753/1250 [20:15<14:30, 1.75s/it]
60%|ββββββ | 754/1250 [20:16<13:36, 1.65s/it]
60%|ββββββ | 755/1250 [20:18<12:29, 1.51s/it]
60%|ββββββ | 756/1250 [20:19<11:07, 1.35s/it]
61%|ββββββ | 757/1250 [20:21<13:17, 1.62s/it]
61%|ββββββ | 758/1250 [20:23<13:31, 1.65s/it]
61%|ββββββ | 759/1250 [20:24<13:10, 1.61s/it]
61%|ββββββ | 760/1250 [20:25<12:28, 1.53s/it]
61%|ββββββ | 761/1250 [20:27<11:34, 1.42s/it]
61%|ββββββ | 762/1250 [20:28<10:28, 1.29s/it]
61%|ββββββ | 763/1250 [20:30<12:59, 1.60s/it]
61%|ββββββ | 764/1250 [20:32<13:13, 1.63s/it]
61%|ββββββ | 765/1250 [20:33<12:52, 1.59s/it]
61%|βββββββ | 766/1250 [20:34<12:05, 1.50s/it]
61%|βββββββ | 767/1250 [20:36<11:13, 1.39s/it]
61%|βββββββ | 768/1250 [20:36<10:11, 1.27s/it]
62%|βββββββ | 769/1250 [20:39<12:54, 1.61s/it]
62%|βββββββ | 770/1250 [20:41<13:05, 1.64s/it]
62%|βββββββ | 771/1250 [20:42<12:44, 1.60s/it]
62%|βββββββ | 772/1250 [20:43<12:12, 1.53s/it]
62%|βββββββ | 773/1250 [20:45<11:18, 1.42s/it]
62%|βββββββ | 774/1250 [20:46<10:16, 1.29s/it]
62%|βββββββ | 775/1250 [20:47<11:19, 1.43s/it]
62%|βββββββ | 776/1250 [20:50<14:32, 1.84s/it]
62%|βββββββ | 777/1250 [20:52<14:10, 1.80s/it]
62%|βββββββ | 778/1250 [20:53<13:17, 1.69s/it]
62%|βββββββ | 779/1250 [20:55<12:15, 1.56s/it]
62%|βββββββ | 780/1250 [20:56<11:16, 1.44s/it]
62%|βββββββ | 781/1250 [20:57<10:16, 1.31s/it]
63%|βββββββ | 782/1250 [20:59<12:23, 1.59s/it]
63%|βββββββ | 783/1250 [21:01<12:25, 1.60s/it]
63%|βββββββ | 784/1250 [21:02<12:05, 1.56s/it]
63%|βββββββ | 785/1250 [21:03<11:45, 1.52s/it]
63%|βββββββ | 786/1250 [21:05<11:03, 1.43s/it]
63%|βββββββ | 787/1250 [21:06<10:03, 1.30s/it]
63%|βββββββ | 788/1250 [21:08<12:27, 1.62s/it]
63%|βββββββ | 789/1250 [21:10<12:40, 1.65s/it]
63%|βββββββ | 790/1250 [21:11<12:29, 1.63s/it]
63%|βββββββ | 791/1250 [21:13<11:53, 1.55s/it]
63%|βββββββ | 792/1250 [21:14<10:56, 1.43s/it]
63%|βββββββ | 793/1250 [21:15<09:50, 1.29s/it]
64%|βββββββ | 794/1250 [21:17<12:23, 1.63s/it]
64%|βββββββ | 795/1250 [21:19<12:37, 1.66s/it]
64%|βββββββ | 796/1250 [21:21<12:18, 1.63s/it]
64%|βββββββ | 797/1250 [21:22<11:42, 1.55s/it]
64%|βββββββ | 798/1250 [21:23<10:55, 1.45s/it]
64%|βββββββ | 799/1250 [21:24<09:56, 1.32s/it]
64%|βββββββ | 800/1250 [21:26<10:45, 1.44s/it]
64%|βββββββ | 800/1250 [21:26<10:45, 1.44s/it]
64%|βββββββ | 801/1250 [21:29<13:45, 1.84s/it]
64%|βββββββ | 802/1250 [21:30<13:32, 1.81s/it]
64%|βββββββ | 803/1250 [21:32<12:51, 1.73s/it]
64%|βββββββ | 804/1250 [21:33<12:01, 1.62s/it]
64%|βββββββ | 805/1250 [21:35<11:02, 1.49s/it]
64%|βββββββ | 806/1250 [21:36<09:58, 1.35s/it]
65%|βββββββ | 807/1250 [21:38<12:15, 1.66s/it]
65%|βββββββ | 808/1250 [21:40<12:28, 1.69s/it]
65%|βββββββ | 809/1250 [21:41<12:08, 1.65s/it]
65%|βββββββ | 810/1250 [21:43<11:33, 1.58s/it]
65%|βββββββ | 811/1250 [21:44<10:42, 1.46s/it]
65%|βββββββ | 812/1250 [21:45<09:37, 1.32s/it]
65%|βββββββ | 813/1250 [21:47<12:07, 1.67s/it]
65%|βββββββ | 814/1250 [21:49<12:17, 1.69s/it]
65%|βββββββ | 815/1250 [21:51<11:49, 1.63s/it]
65%|βββββββ | 816/1250 [21:52<11:08, 1.54s/it]
65%|βββββββ | 817/1250 [21:53<10:16, 1.42s/it]
65%|βββββββ | 818/1250 [21:54<09:19, 1.29s/it]
66%|βββββββ | 819/1250 [21:56<11:27, 1.59s/it]
66%|βββββββ | 820/1250 [21:58<11:46, 1.64s/it]
66%|βββββββ | 821/1250 [22:00<11:23, 1.59s/it]
66%|βββββββ | 822/1250 [22:01<10:43, 1.50s/it]
66%|βββββββ | 823/1250 [22:02<09:59, 1.40s/it]
66%|βββββββ | 824/1250 [22:03<09:13, 1.30s/it]
66%|βββββββ | 825/1250 [22:05<09:56, 1.40s/it]
66%|βββββββ | 826/1250 [22:07<12:44, 1.80s/it]
66%|βββββββ | 827/1250 [22:09<12:37, 1.79s/it]
66%|βββββββ | 828/1250 [22:11<12:05, 1.72s/it]
66%|βββββββ | 829/1250 [22:12<11:15, 1.60s/it]
66%|βββββββ | 830/1250 [22:13<10:13, 1.46s/it]
66%|βββββββ | 831/1250 [22:14<09:13, 1.32s/it]
67%|βββββββ | 832/1250 [22:17<11:23, 1.64s/it]
67%|βββββββ | 833/1250 [22:18<11:33, 1.66s/it]
67%|βββββββ | 834/1250 [22:20<11:12, 1.62s/it]
67%|βββββββ | 835/1250 [22:21<10:40, 1.54s/it]
67%|βββββββ | 836/1250 [22:22<09:52, 1.43s/it]
67%|βββββββ | 837/1250 [22:23<09:00, 1.31s/it]
67%|βββββββ | 838/1250 [22:26<11:13, 1.63s/it]
67%|βββββββ | 839/1250 [22:28<11:32, 1.68s/it]
67%|βββββββ | 840/1250 [22:29<11:13, 1.64s/it]
67%|βββββββ | 841/1250 [22:31<10:43, 1.57s/it]
67%|βββββββ | 842/1250 [22:32<09:58, 1.47s/it]
67%|βββββββ | 843/1250 [22:33<08:54, 1.31s/it]
68%|βββββββ | 844/1250 [22:35<11:11, 1.65s/it]
68%|βββββββ | 845/1250 [22:37<11:19, 1.68s/it]
68%|βββββββ | 846/1250 [22:38<10:59, 1.63s/it]
68%|βββββββ | 847/1250 [22:40<10:24, 1.55s/it]
68%|βββββββ | 848/1250 [22:41<09:36, 1.43s/it]
68%|βββββββ | 849/1250 [22:42<08:41, 1.30s/it]
68%|βββββββ | 850/1250 [22:44<09:16, 1.39s/it]
68%|βββββββ | 851/1250 [22:46<12:23, 1.86s/it]
68%|βββββββ | 852/1250 [22:48<12:16, 1.85s/it]
68%|βββββββ | 853/1250 [22:50<11:34, 1.75s/it]
68%|βββββββ | 854/1250 [22:51<10:46, 1.63s/it]
68%|βββββββ | 855/1250 [22:52<09:45, 1.48s/it]
68%|βββββββ | 856/1250 [22:53<08:41, 1.32s/it]
69%|βββββββ | 857/1250 [22:55<10:23, 1.59s/it]
69%|βββββββ | 858/1250 [22:57<10:29, 1.61s/it]
69%|βββββββ | 859/1250 [22:59<10:18, 1.58s/it]
69%|βββββββ | 860/1250 [23:00<09:50, 1.51s/it]
69%|βββββββ | 861/1250 [23:01<09:11, 1.42s/it]
69%|βββββββ | 862/1250 [23:02<08:22, 1.29s/it]
69%|βββββββ | 863/1250 [23:05<10:28, 1.62s/it]
69%|βββββββ | 864/1250 [23:06<10:41, 1.66s/it]
69%|βββββββ | 865/1250 [23:08<10:21, 1.61s/it]
69%|βββββββ | 866/1250 [23:09<09:53, 1.54s/it]
69%|βββββββ | 867/1250 [23:10<09:10, 1.44s/it]
69%|βββββββ | 868/1250 [23:11<08:17, 1.30s/it]
70%|βββββββ | 869/1250 [23:14<10:13, 1.61s/it]
70%|βββββββ | 870/1250 [23:15<10:26, 1.65s/it]
70%|βββββββ | 871/1250 [23:17<10:12, 1.62s/it]
70%|βββββββ | 872/1250 [23:18<09:40, 1.54s/it]
70%|βββββββ | 873/1250 [23:20<09:00, 1.43s/it]
70%|βββββββ | 874/1250 [23:21<08:15, 1.32s/it]
70%|βββββββ | 875/1250 [23:22<09:07, 1.46s/it]
70%|βββββββ | 876/1250 [23:25<11:37, 1.86s/it]
70%|βββββββ | 877/1250 [23:27<11:25, 1.84s/it]
70%|βββββββ | 878/1250 [23:28<10:44, 1.73s/it]
70%|βββββββ | 879/1250 [23:30<09:57, 1.61s/it]
70%|βββββββ | 880/1250 [23:31<09:08, 1.48s/it]
70%|βββββββ | 881/1250 [23:32<08:10, 1.33s/it]
71%|βββββββ | 882/1250 [23:34<10:07, 1.65s/it]
71%|βββββββ | 883/1250 [23:36<10:10, 1.66s/it]
71%|βββββββ | 884/1250 [23:38<09:50, 1.61s/it]
71%|βββββββ | 885/1250 [23:39<09:23, 1.54s/it]
71%|βββββββ | 886/1250 [23:40<08:41, 1.43s/it]
71%|βββββββ | 887/1250 [23:41<07:57, 1.32s/it]
71%|βββββββ | 888/1250 [23:43<09:50, 1.63s/it]
71%|βββββββ | 889/1250 [23:45<10:01, 1.67s/it]
71%|βββββββ | 890/1250 [23:47<09:45, 1.63s/it]
71%|ββββββββ | 891/1250 [23:48<09:09, 1.53s/it]
71%|ββββββββ | 892/1250 [23:49<08:20, 1.40s/it]
71%|ββββββββ | 893/1250 [23:50<07:22, 1.24s/it]
72%|ββββββββ | 894/1250 [23:52<09:15, 1.56s/it]
72%|ββββββββ | 895/1250 [23:54<09:33, 1.61s/it]
72%|ββββββββ | 896/1250 [23:56<09:23, 1.59s/it]
72%|ββββββββ | 897/1250 [23:57<08:57, 1.52s/it]
72%|ββββββββ | 898/1250 [23:58<08:19, 1.42s/it]
72%|ββββββββ | 899/1250 [23:59<07:28, 1.28s/it]
72%|ββββββββ | 900/1250 [24:01<08:27, 1.45s/it]
72%|ββββββββ | 900/1250 [24:01<08:27, 1.45s/it]
72%|ββββββββ | 901/1250 [24:04<10:51, 1.87s/it]
72%|ββββββββ | 902/1250 [24:06<10:39, 1.84s/it]
72%|ββββββββ | 903/1250 [24:07<10:00, 1.73s/it]
72%|ββββββββ | 904/1250 [24:08<09:08, 1.59s/it]
72%|ββββββββ | 905/1250 [24:09<08:14, 1.43s/it]
72%|ββββββββ | 906/1250 [24:10<07:16, 1.27s/it]
73%|ββββββββ | 907/1250 [24:13<08:59, 1.57s/it]
73%|ββββββββ | 908/1250 [24:14<09:14, 1.62s/it]
73%|ββββββββ | 909/1250 [24:16<09:03, 1.59s/it]
73%|ββββββββ | 910/1250 [24:17<08:40, 1.53s/it]
73%|ββββββββ | 911/1250 [24:18<08:05, 1.43s/it]
73%|ββββββββ | 912/1250 [24:19<07:20, 1.30s/it]
73%|ββββββββ | 913/1250 [24:22<09:00, 1.60s/it]
73%|ββββββββ | 914/1250 [24:23<09:06, 1.63s/it]
73%|ββββββββ | 915/1250 [24:25<08:50, 1.58s/it]
73%|ββββββββ | 916/1250 [24:26<08:20, 1.50s/it]
73%|ββββββββ | 917/1250 [24:27<07:42, 1.39s/it]
73%|ββββββββ | 918/1250 [24:28<07:01, 1.27s/it]
74%|ββββββββ | 919/1250 [24:31<08:52, 1.61s/it]
74%|ββββββββ | 920/1250 [24:32<09:04, 1.65s/it]
74%|ββββββββ | 921/1250 [24:34<08:46, 1.60s/it]
74%|ββββββββ | 922/1250 [24:35<08:25, 1.54s/it]
74%|ββββββββ | 923/1250 [24:37<07:54, 1.45s/it]
74%|ββββββββ | 924/1250 [24:38<07:06, 1.31s/it]
74%|ββββββββ | 925/1250 [24:39<07:43, 1.42s/it]
74%|ββββββββ | 926/1250 [24:42<09:52, 1.83s/it]
74%|ββββββββ | 927/1250 [24:44<09:41, 1.80s/it]
74%|ββββββββ | 928/1250 [24:45<09:17, 1.73s/it]
74%|ββββββββ | 929/1250 [24:47<08:40, 1.62s/it]
74%|ββββββββ | 930/1250 [24:48<07:59, 1.50s/it]
74%|ββββββββ | 931/1250 [24:49<07:10, 1.35s/it]
75%|ββββββββ | 932/1250 [24:51<08:30, 1.61s/it]
75%|ββββββββ | 933/1250 [24:53<08:39, 1.64s/it]
75%|ββββββββ | 934/1250 [24:54<08:23, 1.59s/it]
75%|ββββββββ | 935/1250 [24:56<07:57, 1.51s/it]
75%|ββββββββ | 936/1250 [24:57<07:22, 1.41s/it]
75%|ββββββββ | 937/1250 [24:58<06:44, 1.29s/it]
75%|ββββββββ | 938/1250 [25:00<08:28, 1.63s/it]
75%|ββββββββ | 939/1250 [25:02<08:36, 1.66s/it]
75%|ββββββββ | 940/1250 [25:03<08:20, 1.61s/it]
75%|ββββββββ | 941/1250 [25:05<07:51, 1.53s/it]
75%|ββββββββ | 942/1250 [25:06<07:16, 1.42s/it]
75%|ββββββββ | 943/1250 [25:07<06:32, 1.28s/it]
76%|ββββββββ | 944/1250 [25:09<08:08, 1.59s/it]
76%|ββββββββ | 945/1250 [25:11<08:16, 1.63s/it]
76%|ββββββββ | 946/1250 [25:12<07:57, 1.57s/it]
76%|ββββββββ | 947/1250 [25:14<07:34, 1.50s/it]
76%|ββββββββ | 948/1250 [25:15<07:06, 1.41s/it]
76%|ββββββββ | 949/1250 [25:16<06:29, 1.29s/it]
76%|ββββββββ | 950/1250 [25:18<07:01, 1.41s/it]
76%|ββββββββ | 951/1250 [25:20<09:10, 1.84s/it]
76%|ββββββββ | 952/1250 [25:22<09:00, 1.81s/it]
76%|ββββββββ | 953/1250 [25:24<08:36, 1.74s/it]
76%|ββββββββ | 954/1250 [25:25<07:59, 1.62s/it]
76%|ββββββββ | 955/1250 [25:26<07:22, 1.50s/it]
76%|ββββββββ | 956/1250 [25:27<06:36, 1.35s/it]
77%|ββββββββ | 957/1250 [25:30<08:12, 1.68s/it]
77%|ββββββββ | 958/1250 [25:32<08:19, 1.71s/it]
77%|ββββββββ | 959/1250 [25:33<08:03, 1.66s/it]
77%|ββββββββ | 960/1250 [25:35<07:39, 1.58s/it]
77%|ββββββββ | 961/1250 [25:36<07:00, 1.45s/it]
77%|ββββββββ | 962/1250 [25:37<06:18, 1.31s/it]
77%|ββββββββ | 963/1250 [25:39<07:38, 1.60s/it]
77%|ββββββββ | 964/1250 [25:41<07:43, 1.62s/it]
77%|ββββββββ | 965/1250 [25:42<07:26, 1.57s/it]
77%|ββββββββ | 966/1250 [25:43<07:02, 1.49s/it]
77%|ββββββββ | 967/1250 [25:45<06:34, 1.39s/it]
77%|ββββββββ | 968/1250 [25:46<06:05, 1.30s/it]
78%|ββββββββ | 969/1250 [25:48<07:25, 1.59s/it]
78%|ββββββββ | 970/1250 [25:50<07:40, 1.65s/it]
78%|ββββββββ | 971/1250 [25:51<07:26, 1.60s/it]
78%|ββββββββ | 972/1250 [25:52<07:03, 1.52s/it]
78%|ββββββββ | 973/1250 [25:54<06:33, 1.42s/it]
78%|ββββββββ | 974/1250 [25:55<05:52, 1.28s/it]
78%|ββββββββ | 975/1250 [25:56<06:38, 1.45s/it]
78%|ββββββββ | 976/1250 [25:59<08:36, 1.88s/it]
78%|ββββββββ | 977/1250 [26:01<08:26, 1.86s/it]
78%|ββββββββ | 978/1250 [26:03<08:00, 1.77s/it]
78%|ββββββββ | 979/1250 [26:04<07:26, 1.65s/it]
78%|ββββββββ | 980/1250 [26:05<06:47, 1.51s/it]
78%|ββββββββ | 981/1250 [26:06<06:06, 1.36s/it]
79%|ββββββββ | 982/1250 [26:09<07:27, 1.67s/it]
79%|ββββββββ | 983/1250 [26:10<07:29, 1.68s/it]
79%|ββββββββ | 984/1250 [26:12<07:11, 1.62s/it]
79%|ββββββββ | 985/1250 [26:13<06:47, 1.54s/it]
79%|ββββββββ | 986/1250 [26:14<06:14, 1.42s/it]
79%|ββββββββ | 987/1250 [26:15<05:37, 1.28s/it]
79%|ββββββββ | 988/1250 [26:18<06:56, 1.59s/it]
79%|ββββββββ | 989/1250 [26:19<07:03, 1.62s/it]
79%|ββββββββ | 990/1250 [26:21<06:53, 1.59s/it]
79%|ββββββββ | 991/1250 [26:22<06:31, 1.51s/it]
79%|ββββββββ | 992/1250 [26:23<06:07, 1.43s/it]
79%|ββββββββ | 993/1250 [26:24<05:32, 1.29s/it]
80%|ββββββββ | 994/1250 [26:27<06:43, 1.57s/it]
80%|ββββββββ | 995/1250 [26:28<06:52, 1.62s/it]
80%|ββββββββ | 996/1250 [26:30<06:43, 1.59s/it]
80%|ββββββββ | 997/1250 [26:31<06:26, 1.53s/it]
80%|ββββββββ | 998/1250 [26:32<05:57, 1.42s/it]
80%|ββββββββ | 999/1250 [26:33<05:22, 1.29s/it]
80%|ββββββββ | 1000/1250 [26:35<06:04, 1.46s/it]
80%|ββββββββ | 1000/1250 [26:35<06:04, 1.46s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.
***** Running Evaluation *****
Num examples = 341
Batch size = 8
{'eval_loss': 4.144110202789307, 'eval_wer': 1.0, 'eval_runtime': 12.9526, 'eval_samples_per_second': 26.327, 'eval_steps_per_second': 3.32, 'epoch': 19.98}
{'loss': 3.7383, 'learning_rate': 2.2424999999999996e-05, 'epoch': 23.98}
{'loss': 3.361, 'learning_rate': 2.6174999999999996e-05, 'epoch': 27.98}
{'loss': 3.2219, 'learning_rate': 2.9925e-05, 'epoch': 31.98}
{'loss': 3.104, 'learning_rate': 3.3675e-05, 'epoch': 35.98}
{'loss': 3.0399, 'learning_rate': 3.7424999999999995e-05, 'epoch': 39.98}
0%| | 0/43 [00:00<?, ?it/s][A
5%|β | 2/43 [00:00<00:04, 10.00it/s][A
9%|β | 4/43 [00:00<00:08, 4.72it/s][A
12%|ββ | 5/43 [00:01<00:08, 4.42it/s][A
14%|ββ | 6/43 [00:01<00:08, 4.12it/s][A
16%|ββ | 7/43 [00:01<00:09, 3.92it/s][A
19%|ββ | 8/43 [00:01<00:09, 3.73it/s][A
21%|ββ | 9/43 [00:02<00:09, 3.49it/s][A
23%|βββ | 10/43 [00:02<00:08, 3.74it/s][A
26%|βββ | 11/43 [00:02<00:08, 3.75it/s][A
28%|βββ | 12/43 [00:02<00:08, 3.87it/s][A
30%|βββ | 13/43 [00:03<00:07, 3.91it/s][A
33%|ββββ | 14/43 [00:03<00:08, 3.51it/s][A
35%|ββββ | 15/43 [00:03<00:09, 3.08it/s][A
37%|ββββ | 16/43 [00:04<00:08, 3.13it/s][A
40%|ββββ | 17/43 [00:04<00:08, 3.20it/s][A
42%|βββββ | 18/43 [00:04<00:08, 3.10it/s][A
44%|βββββ | 19/43 [00:05<00:08, 2.87it/s][A
47%|βββββ | 20/43 [00:05<00:08, 2.61it/s][A
49%|βββββ | 21/43 [00:06<00:08, 2.71it/s][A
51%|βββββ | 22/43 [00:06<00:07, 2.72it/s][A
53%|ββββββ | 23/43 [00:06<00:07, 2.83it/s][A
56%|ββββββ | 24/43 [00:07<00:06, 3.11it/s][A
58%|ββββββ | 25/43 [00:07<00:05, 3.22it/s][A
60%|ββββββ | 26/43 [00:07<00:05, 3.27it/s][A
63%|βββββββ | 27/43 [00:07<00:04, 3.65it/s][A
65%|βββββββ | 28/43 [00:08<00:04, 3.74it/s][A
67%|βββββββ | 29/43 [00:08<00:03, 3.75it/s][A
70%|βββββββ | 30/43 [00:08<00:03, 3.85it/s][A
72%|ββββββββ | 31/43 [00:08<00:03, 3.83it/s][A
74%|ββββββββ | 32/43 [00:09<00:02, 3.87it/s][A
77%|ββββββββ | 33/43 [00:09<00:02, 3.56it/s][A
79%|ββββββββ | 34/43 [00:09<00:02, 3.56it/s][A
81%|βββββββββ | 35/43 [00:10<00:02, 3.56it/s][A
84%|βββββββββ | 36/43 [00:10<00:02, 3.38it/s][A
86%|βββββββββ | 37/43 [00:10<00:01, 3.48it/s][A
88%|βββββββββ | 38/43 [00:10<00:01, 3.22it/s][A
91%|βββββββββ | 39/43 [00:11<00:01, 3.30it/s][A
93%|ββββββββββ| 40/43 [00:11<00:00, 3.17it/s][A
95%|ββββββββββ| 41/43 [00:11<00:00, 3.30it/s][A
98%|ββββββββββ| 42/43 [00:12<00:00, 3.18it/s][A
100%|ββββββββββ| 43/43 [00:12<00:00, 3.82it/s][A
[A
80%|ββββββββ | 1000/1250 [26:48<06:04, 1.46s/it]
100%|ββββββββββ| 43/43 [00:12<00:00, 3.82it/s][A
[ASaving model checkpoint to ./checkpoint-1000
Configuration saved in ./checkpoint-1000/config.json
Model weights saved in ./checkpoint-1000/pytorch_model.bin
Configuration saved in ./checkpoint-1000/preprocessor_config.json
Configuration saved in ./preprocessor_config.json
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80%|ββββββββ | 1006/1250 [28:26<26:09, 6.43s/it]
81%|ββββββββ | 1007/1250 [28:28<20:55, 5.16s/it]
81%|ββββββββ | 1008/1250 [28:30<16:52, 4.18s/it]
81%|ββββββββ | 1009/1250 [28:31<13:32, 3.37s/it]
81%|ββββββββ | 1010/1250 [28:33<10:59, 2.75s/it]
81%|ββββββββ | 1011/1250 [28:34<09:02, 2.27s/it]
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82%|βββββββββ | 1026/1250 [28:58<07:11, 1.92s/it]
82%|βββββββββ | 1027/1250 [29:00<06:58, 1.88s/it]
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86%|βββββββββ | 1080/1250 [30:21<04:15, 1.50s/it]
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90%|βββββββββ | 1121/1250 [31:25<03:29, 1.63s/it]
90%|βββββββββ | 1122/1250 [31:26<03:19, 1.56s/it]
90%|βββββββββ | 1123/1250 [31:27<03:04, 1.46s/it]
90%|βββββββββ | 1124/1250 [31:28<02:45, 1.31s/it]
90%|βββββββββ | 1125/1250 [31:30<02:56, 1.41s/it]
90%|βββββββββ | 1126/1250 [31:33<03:50, 1.86s/it]
90%|βββββββββ | 1127/1250 [31:35<03:42, 1.81s/it]
90%|βββββββββ | 1128/1250 [31:36<03:29, 1.72s/it]
90%|βββββββββ | 1129/1250 [31:38<03:15, 1.61s/it]
90%|βββββββββ | 1130/1250 [31:39<02:57, 1.48s/it]
90%|βββββββββ | 1131/1250 [31:40<02:37, 1.32s/it]
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91%|βββββββββ | 1140/1250 [31:54<02:58, 1.62s/it]
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92%|ββββββββββ| 1144/1250 [32:00<02:50, 1.61s/it]
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Training completed. Do not forget to share your model on huggingface.co/models =)
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Saving model checkpoint to ./
Configuration saved in ./config.json
Model weights saved in ./pytorch_model.bin
Configuration saved in ./preprocessor_config.json
Saving model checkpoint to ./
Configuration saved in ./config.json
Model weights saved in ./pytorch_model.bin
Configuration saved in ./preprocessor_config.json
{'eval_loss': 2.9768528938293457, 'eval_wer': 0.9988929889298893, 'eval_runtime': 12.8843, 'eval_samples_per_second': 26.466, 'eval_steps_per_second': 3.337, 'epoch': 39.98}
{'loss': 2.9893, 'learning_rate': 4.1175e-05, 'epoch': 43.98}
{'loss': 2.953, 'learning_rate': 4.4924999999999994e-05, 'epoch': 47.98}
{'train_runtime': 2084.0288, 'train_samples_per_second': 19.434, 'train_steps_per_second': 0.6, 'train_loss': 5.6772947265625, 'epoch': 49.98}
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Upload file pytorch_model.bin: 98%|ββββββββββ| 1.15G/1.18G [00:42<00:00, 30.5MB/s]To https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur
afc8494..4e1557d main -> main
02/02/2022 18:41:36 - WARNING - huggingface_hub.repository - To https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur
afc8494..4e1557d main -> main
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Upload file pytorch_model.bin: 100%|ββββββββββ| 1.18G/1.18G [00:43<00:00, 29.0MB/s]
Dropping the following result as it does not have all the necessary fields:
{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'ur'}}
To https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur
4e1557d..ab3e230 main -> main
02/02/2022 18:41:43 - WARNING - huggingface_hub.repository - To https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur
4e1557d..ab3e230 main -> main
***** train metrics *****
epoch = 49.98
train_loss = 5.6773
train_runtime = 0:34:44.02
train_samples = 810
train_samples_per_second = 19.434
train_steps_per_second = 0.6
02/02/2022 18:41:45 - INFO - __main__ - *** Evaluate ***
The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.
***** Running Evaluation *****
Num examples = 341
Batch size = 8
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Saving model checkpoint to ./
Configuration saved in ./config.json
Model weights saved in ./pytorch_model.bin
Configuration saved in ./preprocessor_config.json
To https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur
ab3e230..248315d main -> main
***** eval metrics *****
epoch = 49.98
eval_loss = 2.8935
eval_runtime = 0:00:13.07
eval_samples = 341
eval_samples_per_second = 26.076
eval_steps_per_second = 3.288
eval_wer = 0.9875
02/02/2022 18:42:23 - WARNING - huggingface_hub.repository - To https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur
ab3e230..248315d main -> main
Dropping the following result as it does not have all the necessary fields:
{'dataset': {'name': 'MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UR', 'type': 'common_voice', 'args': 'Config: ur, Training split: train+validation, Eval split: test'}}
To https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur
248315d..b0472b7 main -> main
02/02/2022 18:42:29 - WARNING - huggingface_hub.repository - To https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur
248315d..b0472b7 main -> main
|