wandb: Currently logged in as: sanchit-gandhi (use `wandb login --relogin` to force relogin)
wandb: wandb version 0.12.17 is available!  To upgrade, please run:
wandb:  $ pip install wandb --upgrade
wandb: Tracking run with wandb version 0.12.15
wandb: Run data is saved locally in /home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-feature-encoder/wandb/run-20220530_122038-277l6opb
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run flax-wav2vec2-2-bart-large-ls-960h-feature-encoder
wandb: ⭐️ View project at https://wandb.ai/sanchit-gandhi/librispeech_960h
wandb: 🚀 View run at https://wandb.ai/sanchit-gandhi/librispeech_960h/runs/277l6opb
05/30/2022 12:20:39 - INFO - __main__ - Training/evaluation parameters FlaxSeq2SeqTrainingArguments(
_n_gpu=-1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
bf16=False,
bf16_full_eval=False,
data_seed=None,
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=None,
do_eval=True,
do_predict=True,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=10000,
evaluation_strategy=no,
final_generation_max_length=200,
final_generation_num_beams=5,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
generation_length_penalty=1.2,
generation_max_length=40,
generation_num_beams=1,
gradient_accumulation_steps=1,
gradient_checkpointing=True,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_model_id=None,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=0.0001,
length_column_name=input_length,
load_best_model_at_end=False,
local_rank=-1,
log_level=passive,
log_level_replica=passive,
log_on_each_node=True,
logging_dir=None,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=25,
logging_strategy=steps,
lr_scheduler_type=linear,
matmul_precision=default,
max_grad_norm=1.0,
max_steps=50000,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_hf,
output_dir=./,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=4,
per_device_train_batch_size=8,
precision=full,
predict_with_generate=True,
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=None,
resume_from_checkpoint=None,
run_name=None,
save_on_each_node=False,
save_steps=10000,
save_strategy=steps,
save_total_limit=1,
seed=42,
sharded_ddp=,
skip_memory_metrics=True,
sortish_sampler=False,
tf32=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_legacy_prediction_loop=False,
warmup_ratio=0.0,
warmup_steps=500,
weight_decay=0.0,
xpu_backend=None,
)
05/30/2022 12:20:39 - INFO - __main__ - JAX devices: 8, matmul precision: default

Downloading data files:   0% 0/7 [00:00<?, ?it/s]Downloading and preparing dataset librispeech_asr/all (download: 57.14 GiB, generated: 59.44 GiB, post-processed: Unknown size, total: 116.59 GiB) to /home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb...

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Generating validation.clean split:   0% 0/2703 [00:00<?, ? examples/s]
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Generating validation.clean split:  98% 2661/2703 [00:01<00:00, 1389.41 examples/s]
                                                                                   

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Generating test.clean split:   0% 0/2620 [00:00<?, ? examples/s]
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Generating test.clean split:  98% 2560/2620 [00:01<00:00, 1641.58 examples/s]
                                                                             

Generating test.other split:   0% 0/2939 [00:00<?, ? examples/s]
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Generating test.other split:  96% 2816/2939 [00:01<00:00, 1843.49 examples/s]
                                                                             Dataset librispeech_asr downloaded and prepared to /home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb. Subsequent calls will reuse this data.
05/30/2022 12:29:57 - WARNING - datasets.builder - Reusing dataset librispeech_asr (/home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb)
05/30/2022 12:29:57 - WARNING - datasets.builder - Reusing dataset librispeech_asr (/home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb)
05/30/2022 12:29:57 - WARNING - datasets.builder - Reusing dataset librispeech_asr (/home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb)
05/30/2022 12:29:58 - WARNING - datasets.builder - Reusing dataset librispeech_asr (/home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb)

loading configuration file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/config.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/e6d3af8a2b6624d8adf8fc289717c121400164223b3e51d49b639aa34d1d3048.c9a58c9120361b7b034a0136cc74d5dce009e745c4cc111c255d5f3d0a9e2fd9
/home/sanchitgandhi/transformers/src/transformers/configuration_utils.py:358: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
  warnings.warn(
Model config SpeechEncoderDecoderConfig {
  "_name_or_path": "sanchit-gandhi/flax-wav2vec2-2-bart-large-scan",
  "architectures": [
    "SpeechEncoderDecoderModel"
  ],
  "decoder": {
    "_name_or_path": "",
    "activation_dropout": 0.1,
    "activation_function": "gelu",
    "add_bias_logits": false,
    "add_cross_attention": true,
    "add_final_layer_norm": false,
    "architectures": [
      "BartModel"
    ],
    "attention_dropout": 0.1,
    "bad_words_ids": null,
    "bos_token_id": 0,
    "chunk_size_feed_forward": 0,
    "classif_dropout": 0.1,
    "classifier_dropout": 0.0,
    "cross_attention_hidden_size": null,
    "d_model": 1024,
    "decoder_attention_heads": 16,
    "decoder_ffn_dim": 4096,
    "decoder_layerdrop": 0.0,
    "decoder_layers": 12,
    "decoder_start_token_id": 2,
    "diversity_penalty": 0.0,
    "do_sample": false,
    "dropout": 0.1,
    "early_stopping": true,
    "encoder_attention_heads": 16,
    "encoder_ffn_dim": 4096,
    "encoder_layerdrop": 0.0,
    "encoder_layers": 12,
    "encoder_no_repeat_ngram_size": 0,
    "eos_token_id": 2,
    "exponential_decay_length_penalty": null,
    "finetuning_task": null,
    "forced_bos_token_id": 0,
    "forced_eos_token_id": 2,
    "fuse_matmuls": false,
    "gradient_checkpointing": true,
    "id2label": {
      "0": "LABEL_0",
      "1": "LABEL_1",
      "2": "LABEL_2"
    },
    "init_std": 0.02,
    "is_decoder": true,
    "is_encoder_decoder": false,
    "label2id": {
      "LABEL_0": 0,
      "LABEL_1": 1,
      "LABEL_2": 2
    },
    "length_penalty": 1.0,
    "max_length": 20,
    "max_position_embeddings": 1024,
    "min_length": 0,
    "model_type": "bart",
    "no_repeat_ngram_size": 3,
    "normalize_before": false,
    "num_beam_groups": 1,
    "num_beams": 4,
    "num_hidden_layers": 12,
    "num_return_sequences": 1,
    "output_attentions": false,
    "output_hidden_states": false,
    "output_scores": false,
    "pad_token_id": 1,
    "prefix": null,
    "problem_type": null,
    "pruned_heads": {},
    "remove_invalid_values": false,
    "repetition_penalty": 1.0,
    "return_dict": true,
    "return_dict_in_generate": false,
    "scale_embedding": false,
    "sep_token_id": null,
    "task_specific_params": {
      "summarization": {
        "length_penalty": 1.0,
        "max_length": 128,
        "min_length": 12,
        "num_beams": 4
      },
      "summarization_cnn": {
        "length_penalty": 2.0,
        "max_length": 142,
        "min_length": 56,
        "num_beams": 4
      },
      "summarization_xsum": {
        "length_penalty": 1.0,
        "max_length": 62,
        "min_length": 11,
        "num_beams": 6
      }
    },
    "temperature": 1.0,
    "tie_encoder_decoder": false,
    "tie_word_embeddings": true,
    "tokenizer_class": null,
    "top_k": 50,
    "top_p": 1.0,
    "torch_dtype": "float32",
    "torchscript": false,
    "transformers_version": "4.18.0.dev0",
    "typical_p": 1.0,
    "use_bfloat16": false,
    "use_cache": true,
    "use_scan": true,
    "vocab_size": 50265
  },
  "decoder_start_token_id": 0,
  "encoder": {
    "_name_or_path": "",
    "activation_dropout": 0.1,
    "adapter_kernel_size": 3,
    "adapter_stride": 2,
    "add_adapter": true,
    "add_cross_attention": false,
    "apply_spec_augment": true,
    "architectures": [
      "Wav2Vec2ForPreTraining"
    ],
    "attention_dropout": 0.1,
    "bad_words_ids": null,
    "bos_token_id": 1,
    "chunk_size_feed_forward": 0,
    "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
    ],
    "cross_attention_hidden_size": null,
    "ctc_loss_reduction": "sum",
    "ctc_zero_infinity": false,
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loading feature extractor configuration file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/preprocessor_config.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/bc2232c616201c7d3d66ba3f6a7d1186306134838dfb19786149f0e16122787d.bbc1eb890a39c82e710a893223b8452ac5b78e8b57083b2f893aa7dc59d4ed69
Feature extractor Wav2Vec2FeatureExtractor {
  "do_normalize": true,
  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
  "feature_size": 1,
  "padding_side": "right",
  "padding_value": 0.0,
  "return_attention_mask": true,
  "sampling_rate": 16000
}

loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/vocab.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/86c0de13925d1534934e540ff4c9dd778f49761b4eaf59dae3335a4f6690a814.bfdcc444ff249bca1a95ca170ec350b442f81804d7df3a95a2252217574121d7
loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/merges.txt from cache at /home/sanchitgandhi/.cache/huggingface/transformers/7cf4fc91891684e1177d1c519689e4c310ebdec965e00d6e45134bb9227ab01b.f5b91da9e34259b8f4d88dbc97c740667a0e8430b96314460cdb04e86d4fc435
loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/tokenizer.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/c02f3f3009bfacaa24cfead1d0f7fbf4fc2fb5f8092f68703449f02aa3a28e03.393fa6a095aa312a3cce4d5263e471bd94ec0215e6c63448a6464d59ff900814
loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/added_tokens.json from cache at None
loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/special_tokens_map.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/505d61b8f6e05764b5aec1483bfdd13a310681a5af54957263604323be3bbabf.a11ebb04664c067c8fe5ef8f8068b0f721263414a26058692f7b2e4ba2a1b342
loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/tokenizer_config.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/ff79c23164eac352d7f9651f3c3774a962ce80f81460d9e17d689235fa34ee80.0e8b2b497f91e23302894a5c1f19ced6334b0abd450a7bce75a67bf0f9ee5c54
loading weights file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/flax_model.msgpack from cache at /home/sanchitgandhi/.cache/huggingface/transformers/1279dc21f7dd9ed546f166e7e445e068b2672ddfa5386b2e3a3a973b8d668365.8e03496bb6919447aeb468483249e7b65dfb59c42989be9787af0aa6aa9b3f50
tcmalloc: large alloc 2353618944 bytes == 0xb3076000 @  0x7f761e047680 0x7f761e068824 0x5f8a01 0x648cf1 0x5c4676 0x4f290e 0x64f718 0x5048b3 0x56b1da 0x56939a 0x50aaa0 0x56c28c 0x56939a 0x5f6a13 0x56b0ae 0x56939a 0x68d047 0x67e351 0x67e3cf 0x67e471 0x67e817 0x6b6fe2 0x6b736d 0x7f761de5a0b3 0x5fa5ce
/home/sanchitgandhi/hf/lib/python3.8/site-packages/jax/_src/tree_util.py:188: FutureWarning: jax.tree_util.tree_multimap() is deprecated. Please use jax.tree_util.tree_map() instead as a drop-in replacement.
  warnings.warn('jax.tree_util.tree_multimap() is deprecated. Please use jax.tree_util.tree_map() '
All model checkpoint weights were used when initializing FlaxSpeechEncoderDecoderModel.

All the weights of FlaxSpeechEncoderDecoderModel were initialized from the model checkpoint at sanchit-gandhi/flax-wav2vec2-2-bart-large-scan.
If your task is similar to the task the model of the checkpoint was trained on, you can already use FlaxSpeechEncoderDecoderModel for predictions without further training.

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preprocess train dataset: 100% 2703/2703 [00:11<00:00, 231.58ex/s]

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preprocess train dataset: 100% 2864/2864 [00:11<00:00, 254.66ex/s]

preprocess train dataset:   0% 0/2620 [00:00<?, ?ex/s]
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preprocess train dataset: 100% 2620/2620 [00:11<00:00, 223.29ex/s]

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Feature extractor saved in ./preprocessor_config.json
tokenizer config file saved in ./tokenizer_config.json
Special tokens file saved in ./special_tokens_map.json
Configuration saved in ./config.json
loading feature extractor configuration file ./preprocessor_config.json
loading configuration file ./config.json
/home/sanchitgandhi/transformers/src/transformers/configuration_utils.py:358: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
  warnings.warn(
Model config SpeechEncoderDecoderConfig {
  "_name_or_path": "./",
  "architectures": [
    "SpeechEncoderDecoderModel"
  ],
  "decoder": {
    "_name_or_path": "",
    "activation_dropout": 0.1,
    "activation_function": "gelu",
    "add_bias_logits": false,
    "add_cross_attention": true,
    "add_final_layer_norm": false,
    "architectures": [
      "BartModel"
    ],
    "attention_dropout": 0.1,
    "bad_words_ids": null,
    "bos_token_id": 0,
    "chunk_size_feed_forward": 0,
    "classif_dropout": 0.1,
    "classifier_dropout": 0.0,
    "cross_attention_hidden_size": null,
    "d_model": 1024,
    "decoder_attention_heads": 16,
    "decoder_ffn_dim": 4096,
    "decoder_layerdrop": 0.0,
    "decoder_layers": 12,
    "decoder_start_token_id": 2,
    "diversity_penalty": 0.0,
    "do_sample": false,
    "dropout": 0.1,
    "early_stopping": true,
    "encoder_attention_heads": 16,
    "encoder_ffn_dim": 4096,
    "encoder_layerdrop": 0.0,
    "encoder_layers": 12,
    "encoder_no_repeat_ngram_size": 0,
    "eos_token_id": 2,
    "exponential_decay_length_penalty": null,
    "finetuning_task": null,
    "forced_bos_token_id": 0,
    "forced_eos_token_id": 2,
    "fuse_matmuls": false,
    "gradient_checkpointing": true,
    "id2label": {
      "0": "LABEL_0",
      "1": "LABEL_1",
      "2": "LABEL_2"
    },
    "init_std": 0.02,
    "is_decoder": true,
    "is_encoder_decoder": false,
    "label2id": {
      "LABEL_0": 0,
      "LABEL_1": 1,
      "LABEL_2": 2
    },
    "length_penalty": 1.0,
    "max_length": 20,
    "max_position_embeddings": 1024,
    "min_length": 0,
    "model_type": "bart",
    "no_repeat_ngram_size": 3,
    "normalize_before": false,
    "num_beam_groups": 1,
    "num_beams": 4,
    "num_hidden_layers": 12,
    "num_return_sequences": 1,
    "output_attentions": false,
    "output_hidden_states": false,
    "output_scores": false,
    "pad_token_id": 1,
    "prefix": null,
    "problem_type": null,
    "pruned_heads": {},
    "remove_invalid_values": false,
    "repetition_penalty": 1.0,
    "return_dict": true,
    "return_dict_in_generate": false,
    "scale_embedding": false,
    "sep_token_id": null,
    "task_specific_params": {
      "summarization": {
        "length_penalty": 1.0,
        "max_length": 128,
        "min_length": 12,
        "num_beams": 4
      },
      "summarization_cnn": {
        "length_penalty": 2.0,
        "max_length": 142,
        "min_length": 56,
        "num_beams": 4
      },
      "summarization_xsum": {
        "length_penalty": 1.0,
        "max_length": 62,
        "min_length": 11,
        "num_beams": 6
      }
    },
    "temperature": 1.0,
    "tie_encoder_decoder": false,
    "tie_word_embeddings": true,
    "tokenizer_class": null,
    "top_k": 50,
    "top_p": 1.0,
    "torch_dtype": "float32",
    "torchscript": false,
    "transformers_version": "4.18.0.dev0",
    "typical_p": 1.0,
    "use_bfloat16": false,
    "use_cache": true,
    "use_scan": true,
    "vocab_size": 50265
  },
  "decoder_start_token_id": 0,
  "encoder": {
    "_name_or_path": "",
    "activation_dropout": 0.1,
    "adapter_kernel_size": 3,
    "adapter_stride": 2,
    "add_adapter": true,
    "add_cross_attention": false,
    "apply_spec_augment": true,
    "architectures": [
      "Wav2Vec2ForPreTraining"
    ],
    "attention_dropout": 0.1,
    "bad_words_ids": null,
    "bos_token_id": 1,
    "chunk_size_feed_forward": 0,
    "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
    ],
    "cross_attention_hidden_size": null,
    "ctc_loss_reduction": "sum",
    "ctc_zero_infinity": false,
    "decoder_start_token_id": null,
    "diversity_loss_weight": 0.1,
    "diversity_penalty": 0.0,
    "do_sample": false,
    "do_stable_layer_norm": true,
    "early_stopping": false,
    "encoder_no_repeat_ngram_size": 0,
    "eos_token_id": 2,
    "exponential_decay_length_penalty": null,
    "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,
    "finetuning_task": null,
    "forced_bos_token_id": null,
    "forced_eos_token_id": null,
    "fuse_matmuls": false,
    "gradient_checkpointing": true,
    "hidden_act": "gelu",
    "hidden_dropout": 0.1,
    "hidden_dropout_prob": 0.1,
    "hidden_size": 1024,
    "id2label": {
      "0": "LABEL_0",
      "1": "LABEL_1"
    },
    "initializer_range": 0.02,
    "intermediate_size": 4096,
    "is_decoder": false,
    "is_encoder_decoder": false,
    "label2id": {
      "LABEL_0": 0,
      "LABEL_1": 1
    },
    "layer_norm_eps": 1e-05,
    "layerdrop": 0.0,
    "length_penalty": 1.0,
    "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": 0.1,
    "max_length": 20,
    "min_length": 0,
    "model_type": "wav2vec2",
    "no_repeat_ngram_size": 0,
    "num_adapter_layers": 3,
    "num_attention_heads": 16,
    "num_beam_groups": 1,
    "num_beams": 1,
    "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,
    "num_return_sequences": 1,
    "output_attentions": false,
    "output_hidden_size": 1024,
    "output_hidden_states": false,
    "output_scores": false,
    "pad_token_id": 0,
    "prefix": null,
    "problem_type": null,
    "proj_codevector_dim": 768,
    "pruned_heads": {},
    "remove_invalid_values": false,
    "repetition_penalty": 1.0,
    "return_dict": true,
    "return_dict_in_generate": false,
    "sep_token_id": null,
    "task_specific_params": null,
    "tdnn_dilation": [
      1,
      2,
      3,
      1,
      1
    ],
    "tdnn_dim": [
      512,
      512,
      512,
      512,
      1500
    ],
    "tdnn_kernel": [
      5,
      3,
      3,
      1,
      1
    ],
    "temperature": 1.0,
    "tie_encoder_decoder": false,
    "tie_word_embeddings": true,
    "tokenizer_class": null,
    "top_k": 50,
    "top_p": 1.0,
    "torch_dtype": null,
    "torchscript": false,
    "transformers_version": "4.18.0.dev0",
    "typical_p": 1.0,
    "use_bfloat16": false,
    "use_scan": true,
    "use_weighted_layer_sum": false,
    "vocab_size": 32,
    "xvector_output_dim": 512
  },
  "eos_token_id": 2,
  "is_encoder_decoder": true,
  "max_length": 40,
  "model_type": "speech-encoder-decoder",
  "pad_token_id": 1,
  "processor_class": "Wav2Vec2Processor",
  "tie_word_embeddings": false,
  "transformers_version": null,
  "use_cache": false
}

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": 0.0,
  "return_attention_mask": true,
  "sampling_rate": 16000
}

Didn't find file ./added_tokens.json. We won't load it.
loading file ./vocab.json
loading file ./merges.txt
loading file ./tokenizer.json
loading file None
loading file ./special_tokens_map.json
loading file ./tokenizer_config.json
05/30/2022 13:07:01 - WARNING - __main__ - Unable to display metrics through TensorBoard because the package is not installed: Please run `pip install tensorboard` to enable.
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-feature-encoder/./ is already a clone of https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-ls-960h-feature-encoder. Make sure you pull the latest changes with `repo.git_pull()`.
05/30/2022 13:07:05 - WARNING - huggingface_hub.repository - /home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-feature-encoder/./ is already a clone of https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-ls-960h-feature-encoder. Make sure you pull the latest changes with `repo.git_pull()`.
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
05/30/2022 13:07:07 - INFO - __main__ - ***** Running training *****
05/30/2022 13:07:07 - INFO - __main__ -   Num examples = 281186
05/30/2022 13:07:07 - INFO - __main__ -   Num Epochs = 12
05/30/2022 13:07:07 - INFO - __main__ -   Instantaneous batch size per device = 8
05/30/2022 13:07:07 - INFO - __main__ -   Num gradient accumulation steps = 1
05/30/2022 13:07:07 - INFO - __main__ -   Total train batch size (w. parallel & distributed) = 64
05/30/2022 13:07:07 - INFO - __main__ -   Total optimization steps = 50000
05/30/2022 13:07:07 - INFO - __main__ -   Gradient checkpointing: True
05/30/2022 13:07:07 - INFO - __main__ -   Use scan: True
05/30/2022 13:07:07 - INFO - __main__ -   Fuse matmuls: False

Epoch ... (1/12):   0% 0/12 [00:00<?, ?it/s]

Training...:   0% 0/4393 [00:00<?, ?it/s]/home/sanchitgandhi/hf/lib/python3.8/site-packages/jax/_src/tree_util.py:188: FutureWarning: jax.tree_util.tree_multimap() is deprecated. Please use jax.tree_util.tree_map() instead as a drop-in replacement.
  warnings.warn('jax.tree_util.tree_multimap() is deprecated. Please use jax.tree_util.tree_map() '


Training...:   0% 1/4393 [01:06<81:31:30, 66.82s/it]

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Training...:   0% 5/4393 [02:28<22:43:22, 18.64s/it]

Training...:   0% 6/4393 [02:35<17:36:05, 14.44s/it]

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Training...:   1% 22/4393 [05:03<8:29:20,  6.99s/it]

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Training...:   1% 24/4393 [05:14<7:38:44,  6.30s/it]
                                            


                                                    

Epoch ... (1/12):   0% 0/12 [05:28<?, ?it/s]

Training...:   1% 24/4393 [05:21<7:38:44,  6.30s/it]

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Training...:   1% 44/4393 [11:30<35:31:22, 29.41s/it]

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Training...:   1% 49/4393 [13:32<28:46:07, 23.84s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [14:34<?, ?it/s]

Training...:   1% 49/4393 [14:27<28:46:07, 23.84s/it]

Training...:   1% 50/4393 [14:27<39:56:55, 33.11s/it]

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Training...:   2% 74/4393 [16:49<6:34:34,  5.48s/it]
                                            


                                                    

Epoch ... (1/12):   0% 0/12 [17:03<?, ?it/s]

Training...:   2% 74/4393 [16:56<6:34:34,  5.48s/it]

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Training...:   2% 99/4393 [18:26<2:06:24,  1.77s/it]
                                            


                                                    

Epoch ... (1/12):   0% 0/12 [18:35<?, ?it/s]

Training...:   2% 99/4393 [18:27<2:06:24,  1.77s/it]

Training...:   2% 100/4393 [18:27<1:56:20,  1.63s/it]

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Training...:   3% 124/4393 [20:48<6:31:22,  5.50s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [21:02<?, ?it/s]

Training...:   3% 124/4393 [20:55<6:31:22,  5.50s/it]

Training...:   3% 125/4393 [20:55<6:50:50,  5.78s/it]

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Training...:   3% 134/4393 [21:40<5:48:40,  4.91s/it]

Training...:   3% 135/4393 [21:45<5:40:59,  4.80s/it]

Training...:   3% 136/4393 [21:49<5:34:04,  4.71s/it]

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Training...:   3% 148/4393 [22:26<2:32:42,  2.16s/it]

Training...:   3% 149/4393 [22:27<2:12:39,  1.88s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [22:36<?, ?it/s]

Training...:   3% 149/4393 [22:29<2:12:39,  1.88s/it]

Training...:   3% 150/4393 [22:29<2:07:07,  1.80s/it]

Training...:   3% 151/4393 [23:34<24:27:36, 20.76s/it]

Training...:   3% 152/4393 [23:40<19:31:31, 16.57s/it]

Training...:   3% 153/4393 [23:47<15:50:42, 13.45s/it]

Training...:   4% 154/4393 [23:53<13:16:10, 11.27s/it]

Training...:   4% 155/4393 [23:59<11:27:57,  9.74s/it]

Training...:   4% 156/4393 [24:05<10:11:11,  8.66s/it]

Training...:   4% 157/4393 [24:11<9:19:25,  7.92s/it] 

Training...:   4% 158/4393 [24:17<8:43:26,  7.42s/it]

Training...:   4% 159/4393 [24:23<8:13:28,  6.99s/it]

Training...:   4% 160/4393 [24:30<7:52:54,  6.70s/it]

Training...:   4% 161/4393 [24:35<7:37:09,  6.48s/it]

Training...:   4% 162/4393 [24:41<7:25:18,  6.31s/it]

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Training...:   4% 174/4393 [25:49<6:25:47,  5.49s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [26:03<?, ?it/s]

Training...:   4% 174/4393 [25:56<6:25:47,  5.49s/it]

Training...:   4% 175/4393 [25:56<6:41:53,  5.72s/it]

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Training...:   4% 182/4393 [26:31<5:58:33,  5.11s/it]

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Training...:   4% 184/4393 [26:41<5:46:54,  4.95s/it]

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Training...:   4% 186/4393 [26:50<5:33:05,  4.75s/it]

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Training...:   4% 189/4393 [27:03<5:01:54,  4.31s/it]

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Training...:   5% 199/4393 [27:27<2:12:53,  1.90s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [27:36<?, ?it/s]

Training...:   5% 199/4393 [27:29<2:12:53,  1.90s/it]

Training...:   5% 200/4393 [27:29<2:01:01,  1.73s/it]

Training...:   5% 201/4393 [28:34<24:08:22, 20.73s/it]

Training...:   5% 202/4393 [28:40<19:13:27, 16.51s/it]

Training...:   5% 203/4393 [28:47<15:40:56, 13.47s/it]

Training...:   5% 204/4393 [28:53<13:08:25, 11.29s/it]

Training...:   5% 205/4393 [28:59<11:25:08,  9.82s/it]

Training...:   5% 206/4393 [29:06<10:09:11,  8.73s/it]

Training...:   5% 207/4393 [29:12<9:13:05,  7.93s/it] 

Training...:   5% 208/4393 [29:18<8:33:29,  7.36s/it]

Training...:   5% 209/4393 [29:24<8:04:28,  6.95s/it]

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Training...:   5% 224/4393 [30:49<6:21:04,  5.48s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [31:03<?, ?it/s]

Training...:   5% 224/4393 [30:55<6:21:04,  5.48s/it]

Training...:   5% 225/4393 [30:55<6:37:12,  5.72s/it]

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Training...:   5% 228/4393 [31:11<6:17:34,  5.44s/it]

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Training...:   5% 239/4393 [32:02<4:52:49,  4.23s/it]

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Training...:   6% 242/4393 [32:11<4:07:54,  3.58s/it]

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Training...:   6% 245/4393 [32:19<3:13:50,  2.80s/it]

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Training...:   6% 249/4393 [32:25<2:01:55,  1.77s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [32:34<?, ?it/s]

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Epoch ... (1/12):   0% 0/12 [35:03<?, ?it/s]

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Epoch ... (1/12):   0% 0/12 [36:37<?, ?it/s]

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Epoch ... (1/12):   0% 0/12 [39:06<?, ?it/s]

Training...:   7% 324/4393 [38:58<6:17:12,  5.56s/it]

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Epoch ... (1/12):   0% 0/12 [40:38<?, ?it/s]

Training...:   8% 349/4393 [40:31<2:01:29,  1.80s/it]

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Training...:   9% 374/4393 [42:53<6:12:07,  5.56s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [43:07<?, ?it/s]

Training...:   9% 374/4393 [43:00<6:12:07,  5.56s/it]

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Training...:   9% 399/4393 [44:31<1:58:42,  1.78s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [44:40<?, ?it/s]

Training...:   9% 399/4393 [44:33<1:58:42,  1.78s/it]

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Training...:  10% 424/4393 [46:55<6:09:49,  5.59s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [47:09<?, ?it/s]

Training...:  10% 424/4393 [47:01<6:09:49,  5.59s/it]

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Training...:  10% 449/4393 [48:36<2:08:57,  1.96s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [48:45<?, ?it/s]

Training...:  10% 449/4393 [48:38<2:08:57,  1.96s/it]

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Training...:  11% 474/4393 [50:59<5:59:47,  5.51s/it]
                                            


                                                     

Epoch ... (1/12):   0% 0/12 [51:13<?, ?it/s]

Training...:  11% 474/4393 [51:05<5:59:47,  5.51s/it]

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Training...:  13% 550/4393 [56:36<1:46:24,  1.66s/it]2022-05-30 14:03:57.813326: E external/org_tensorflow/tensorflow/compiler/xla/pjrt/pjrt_stream_executor_client.cc:2141] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Attempting to reserve 6.05G at the bottom of memory. That was not possible. There are 7.00G free, 0B reserved, and 5.97G reservable.

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Step... (25 | Loss: 6.368825435638428, Learning Rate: 4.800000169780105e-06, Gradient Norm: 17.596677780151367)
Step... (50 | Loss: 5.564333438873291, Learning Rate: 9.800001862458885e-06, Gradient Norm: 163.74798583984375)
Step... (75 | Loss: 4.981838703155518, Learning Rate: 1.479999627918005e-05, Gradient Norm: 15.386592864990234)
Step... (100 | Loss: 5.144994735717773, Learning Rate: 1.979999797185883e-05, Gradient Norm: 16.634170532226562)
Step... (125 | Loss: 4.6256585121154785, Learning Rate: 2.479999966453761e-05, Gradient Norm: 3.0031583309173584)
Step... (150 | Loss: 4.9728264808654785, Learning Rate: 2.9799994081258774e-05, Gradient Norm: 9.713094711303711)
Step... (175 | Loss: 4.88003396987915, Learning Rate: 3.480000304989517e-05, Gradient Norm: 3.014592170715332)
Step... (200 | Loss: 4.77579402923584, Learning Rate: 3.979999746661633e-05, Gradient Norm: 9.722399711608887)
Step... (225 | Loss: 4.645107746124268, Learning Rate: 4.479999915929511e-05, Gradient Norm: 2.5606939792633057)
Step... (250 | Loss: 4.607709884643555, Learning Rate: 4.980000085197389e-05, Gradient Norm: 8.956076622009277)
Step... (275 | Loss: 4.512480735778809, Learning Rate: 5.480000254465267e-05, Gradient Norm: 2.337062120437622)
Step... (300 | Loss: 4.501528739929199, Learning Rate: 5.980000423733145e-05, Gradient Norm: 9.948266983032227)
Step... (325 | Loss: 4.623092174530029, Learning Rate: 6.479999865405262e-05, Gradient Norm: 19.436397552490234)
Step... (350 | Loss: 4.251543998718262, Learning Rate: 6.98000003467314e-05, Gradient Norm: 5.931419849395752)
Step... (375 | Loss: 4.60753870010376, Learning Rate: 7.480000203941017e-05, Gradient Norm: 2.3052282333374023)
Step... (400 | Loss: 4.407252788543701, Learning Rate: 7.980000373208895e-05, Gradient Norm: 5.140809059143066)
Step... (425 | Loss: 4.492900848388672, Learning Rate: 8.480000542476773e-05, Gradient Norm: 2.3711187839508057)
Step... (450 | Loss: 4.52168083190918, Learning Rate: 8.97999998414889e-05, Gradient Norm: 5.420287132263184)
Step... (475 | Loss: 4.3769707679748535, Learning Rate: 9.480000881012529e-05, Gradient Norm: 2.192904233932495)
Step... (500 | Loss: 4.038209915161133, Learning Rate: 9.980000322684646e-05, Gradient Norm: 9.690165519714355)
Step... (525 | Loss: 4.530711650848389, Learning Rate: 9.995151776820421e-05, Gradient Norm: 2.2029404640197754)
Step... (550 | Loss: 3.933246612548828, Learning Rate: 9.990100807044655e-05, Gradient Norm: 5.914059162139893)
Traceback (most recent call last):
  File "run_flax_speech_recognition_seq2seq.py", line 1507, in <module>
    main()
  File "run_flax_speech_recognition_seq2seq.py", line 1400, in main
    state, train_metric = p_train_step(state, batch)
ValueError: RESOURCE_EXHAUSTED: Attempting to reserve 6.05G at the bottom of memory. That was not possible. There are 7.00G free, 0B reserved, and 5.97G reservable.: while running replica 0 and partition 0 of a replicated computation (other replicas may have failed as well).
wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing.
wandb: - 0.002 MB of 0.002 MB uploaded (0.000 MB deduped)
wandb: \ 0.002 MB of 0.002 MB uploaded (0.000 MB deduped)
wandb: | 0.002 MB of 0.038 MB uploaded (0.000 MB deduped)
wandb: / 0.002 MB of 0.045 MB uploaded (0.000 MB deduped)
wandb: - 0.045 MB of 0.045 MB uploaded (0.000 MB deduped)
wandb: \ 0.045 MB of 0.045 MB uploaded (0.000 MB deduped)
wandb: | 0.045 MB of 0.045 MB uploaded (0.000 MB deduped)
wandb: / 0.045 MB of 0.045 MB uploaded (0.000 MB deduped)
wandb: - 0.045 MB of 0.045 MB uploaded (0.000 MB deduped)
wandb: \ 0.045 MB of 0.045 MB uploaded (0.000 MB deduped)
wandb: | 0.045 MB of 0.045 MB uploaded (0.000 MB deduped)
wandb: / 0.045 MB of 0.045 MB uploaded (0.000 MB deduped)
wandb:                                                                                
wandb: 
wandb: Run history:
wandb:  train/decoder_grad_norm ▂█▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb: train/decoder_param_norm ██▇▇▆▅▅▄▄▃▃▂▂▂▁▁▁▁▁▁▁▁
wandb:  train/encoder_grad_norm ▂█▂▂▁▁▁▁▁▁▁▂▂▁▁▁▁▁▁▂▁▁
wandb: train/encoder_param_norm ▁▁▁▁▁▁▁▂▂▂▂▂▃▃▄▄▅▅▆▆▇█
wandb:          train/grad_norm ▂█▂▂▁▁▁▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁
wandb:      train/learning_rate ▁▁▂▂▂▃▃▄▄▄▅▅▅▆▆▇▇▇████
wandb:               train/loss █▆▄▄▃▄▄▃▃▃▃▃▃▂▃▂▃▃▂▁▃▁
wandb:         train/param_norm ██▇▆▆▅▄▃▃▂▁▁▁▁▁▂▂▂▃▃▄▅
wandb: 
wandb: Run summary:
wandb:  train/decoder_grad_norm 4.9307
wandb: train/decoder_param_norm 1056.73962
wandb:  train/encoder_grad_norm 3.26562
wandb: train/encoder_param_norm 2309.60815
wandb:          train/grad_norm 5.91406
wandb:      train/learning_rate 0.0001
wandb:               train/loss 3.93325
wandb:         train/param_norm 2539.87988
wandb: 
wandb: Synced flax-wav2vec2-2-bart-large-ls-960h-feature-encoder: https://wandb.ai/sanchit-gandhi/librispeech_960h/runs/277l6opb
wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20220530_122038-277l6opb/logs