Training in progress, step 500

indicwav2vec_trainwtagsv2ibs_MUCS_warmup500_s300shuff100_2933139.out

@@ -0,0 +1,185 @@
+wandb: Currently logged in as: priyanshi-pal (priyanshipal). Use `wandb login --relogin` to force relogin
+wandb: - Waiting for wandb.init()...
+wandb:  $ pip install wandb --upgrade
+wandb: Tracking run with wandb version 0.17.6
+wandb: Run data is saved locally in /scratch/elec/t405-puhe/p/palp3/MUCS/wandb/run-20240924_172213-gwlxc9jh
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run retrainwithtagsv2ibs_indicw2v_ad0_3_hd_02_featd_0_3_lr6e-4_warmup500_s300_shuff100
+wandb: ⭐️ View project at https://wandb.ai/priyanshipal/huggingface
+wandb: 🚀 View run at https://wandb.ai/priyanshipal/huggingface/runs/gwlxc9jh
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
+  warnings.warn(
+09/24/2024 17:22:18 - WARNING - __main__ - device: cuda:0, n_gpu: 116-bits training: True
+
+
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/configuration_auto.py:957: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
+  warnings.warn(
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/configuration_utils.py:364: 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(
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/feature_extraction_auto.py:329: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
+  warnings.warn(
+/scratch/elec/puhe/p/palp3/MUCS/finetune_script_wtags_partdata.py:509: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  state_dict = torch.load(f"{model_args.model_name_or_path}/pytorch_model.bin")
+Some weights of the model checkpoint at /m/triton/scratch/elec/puhe/p/palp3/MUCS/indicwav2vec-hindi were not used when initializing Wav2Vec2ForCTC: ['wav2vec2.encoder.pos_conv_embed.conv.weight_g', 'wav2vec2.encoder.pos_conv_embed.conv.weight_v']
+- 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 /m/triton/scratch/elec/puhe/p/palp3/MUCS/indicwav2vec-hindi and are newly initialized: ['lm_head.bias', 'lm_head.weight', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original1']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/accelerate/accelerator.py:488: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
+  self.scaler = torch.cuda.amp.GradScaler(**kwargs)
+max_steps is given, it will override any value given in num_train_epochs
+Wav2Vec2CTCTokenizer(name_or_path='', vocab_size=151, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '[UNK]', 'pad_token': '[PAD]'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={
+	149: AddedToken("[UNK]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False),
+	150: AddedToken("[PAD]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False),
+	151: AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	152: AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+}
+CHECK MODEL PARAMS Wav2Vec2ForCTC(
+  (wav2vec2): Wav2Vec2Model(
+    (feature_extractor): Wav2Vec2FeatureEncoder(
+      (conv_layers): ModuleList(
+        (0): Wav2Vec2LayerNormConvLayer(
+          (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,))
+          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+          (activation): GELUActivation()
+        )
+        (1-4): 4 x Wav2Vec2LayerNormConvLayer(
+          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
+          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+          (activation): GELUActivation()
+        )
+        (5-6): 2 x Wav2Vec2LayerNormConvLayer(
+          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))
+          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+          (activation): GELUActivation()
+        )
+      )
+    )
+    (feature_projection): Wav2Vec2FeatureProjection(
+      (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+      (projection): Linear(in_features=512, out_features=1024, bias=True)
+      (dropout): Dropout(p=0.3, inplace=False)
+    )
+    (encoder): Wav2Vec2EncoderStableLayerNorm(
+      (pos_conv_embed): Wav2Vec2PositionalConvEmbedding(
+        (conv): ParametrizedConv1d(
+          1024, 1024, kernel_size=(128,), stride=(1,), padding=(64,), groups=16
+          (parametrizations): ModuleDict(
+            (weight): ParametrizationList(
+              (0): _WeightNorm()
+            )
+          )
+        )
+        (padding): Wav2Vec2SamePadLayer()
+        (activation): GELUActivation()
+      )
+      (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+      (dropout): Dropout(p=0.2, inplace=False)
+      (layers): ModuleList(
+        (0-23): 24 x Wav2Vec2EncoderLayerStableLayerNorm(
+          (attention): Wav2Vec2SdpaAttention(
+            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
+            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
+            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
+            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
+          )
+          (dropout): Dropout(p=0.2, inplace=False)
+          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+          (feed_forward): Wav2Vec2FeedForward(
+            (intermediate_dropout): Dropout(p=0.0, inplace=False)
+            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)
+            (intermediate_act_fn): GELUActivation()
+            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)
+            (output_dropout): Dropout(p=0.2, inplace=False)
+          )
+          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+        )
+      )
+    )
+  )
+  (dropout): Dropout(p=0.0, inplace=False)
+  (lm_head): Linear(in_features=1024, out_features=153, bias=True)
+)
+
  0%|          | 0/15000 [00:00<?, ?it/s]/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py:157: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.
+  warnings.warn(
+Traceback (most recent call last):
+  File "/scratch/elec/puhe/p/palp3/MUCS/finetune_script_wtags_partdata.py", line 799, in <module>
+    main()
+  File "/scratch/elec/puhe/p/palp3/MUCS/finetune_script_wtags_partdata.py", line 750, in main
+    train_result = trainer.train()
+                   ^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/trainer.py", line 1929, in train
+    return inner_training_loop(
+           ^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/trainer.py", line 2279, in _inner_training_loop
+    tr_loss_step = self.training_step(model, inputs)
+                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/trainer.py", line 3318, in training_step
+    loss = self.compute_loss(model, inputs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/trainer.py", line 3363, in compute_loss
+    outputs = model(**inputs)
+              ^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/accelerate/utils/operations.py", line 819, in forward
+    return model_forward(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/accelerate/utils/operations.py", line 807, in __call__
+    return convert_to_fp32(self.model_forward(*args, **kwargs))
+                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/amp/autocast_mode.py", line 43, in decorate_autocast
+    return func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 2228, in forward
+    outputs = self.wav2vec2(
+              ^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 1809, in forward
+    extract_features = self.feature_extractor(input_values)
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 463, in forward
+    hidden_states = conv_layer(hidden_states)
+                    ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 335, in forward
+    hidden_states = self.layer_norm(hidden_states)
+                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/normalization.py", line 202, in forward
+    return F.layer_norm(
+           ^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/functional.py", line 2576, in layer_norm
+    return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 22.27 GiB. GPU 0 has a total capacity of 31.73 GiB of which 18.62 GiB is free. Including non-PyTorch memory, this process has 13.11 GiB memory in use. Of the allocated memory 12.74 GiB is allocated by PyTorch, and 6.49 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+wandb: - 0.008 MB of 0.008 MB uploaded
+wandb: ⭐️ View project at: https://wandb.ai/priyanshipal/huggingface
+wandb: Synced 6 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)
+wandb: Find logs at: ./wandb/run-20240924_172213-gwlxc9jh/logs
+wandb: WARNING The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require("core")`! See https://wandb.me/wandb-core for more information.

indicwav2vec_trainwtagsv2ibs_MUCS_warmup500_s300shuff100_2933695.out

@@ -0,0 +1,183 @@
+wandb: Currently logged in as: priyanshi-pal (priyanshipal). Use `wandb login --relogin` to force relogin
+wandb: wandb version 0.18.1 is available!  To upgrade, please run:
+wandb:  $ pip install wandb --upgrade
+wandb: Tracking run with wandb version 0.17.6
+wandb: Run data is saved locally in /scratch/elec/t405-puhe/p/palp3/MUCS/wandb/run-20240924_173727-kvrf6uiu
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run retrainwithtagsv2ibs_indicw2v_ad0_3_hd_02_featd_0_3_lr6e-4_warmup500_s300_shuff100
+wandb: ⭐️ View project at https://wandb.ai/priyanshipal/huggingface
+wandb: 🚀 View run at https://wandb.ai/priyanshipal/huggingface/runs/kvrf6uiu
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
+  warnings.warn(
+09/24/2024 17:37:32 - WARNING - __main__ - device: cuda:0, n_gpu: 116-bits training: True
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/configuration_auto.py:957: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
+  warnings.warn(
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/configuration_utils.py:364: 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(
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/feature_extraction_auto.py:329: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
+  warnings.warn(
+/scratch/elec/puhe/p/palp3/MUCS/finetune_script_wtags_partdata.py:509: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  state_dict = torch.load(f"{model_args.model_name_or_path}/pytorch_model.bin")
+Some weights of the model checkpoint at /m/triton/scratch/elec/puhe/p/palp3/MUCS/indicwav2vec-hindi were not used when initializing Wav2Vec2ForCTC: ['wav2vec2.encoder.pos_conv_embed.conv.weight_g', 'wav2vec2.encoder.pos_conv_embed.conv.weight_v']
+- 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 /m/triton/scratch/elec/puhe/p/palp3/MUCS/indicwav2vec-hindi and are newly initialized: ['lm_head.bias', 'lm_head.weight', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original1']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+/scratch/work/palp3/myenv/lib/python3.11/site-packages/accelerate/accelerator.py:488: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
+  self.scaler = torch.cuda.amp.GradScaler(**kwargs)
+max_steps is given, it will override any value given in num_train_epochs
+Wav2Vec2CTCTokenizer(name_or_path='', vocab_size=151, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '[UNK]', 'pad_token': '[PAD]'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={
+	149: AddedToken("[UNK]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False),
+	150: AddedToken("[PAD]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False),
+	151: AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	152: AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+}
+CHECK MODEL PARAMS Wav2Vec2ForCTC(
+  (wav2vec2): Wav2Vec2Model(
+    (feature_extractor): Wav2Vec2FeatureEncoder(
+      (conv_layers): ModuleList(
+        (0): Wav2Vec2LayerNormConvLayer(
+          (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,))
+          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+          (activation): GELUActivation()
+        )
+        (1-4): 4 x Wav2Vec2LayerNormConvLayer(
+          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
+          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+          (activation): GELUActivation()
+        )
+        (5-6): 2 x Wav2Vec2LayerNormConvLayer(
+          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))
+          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+          (activation): GELUActivation()
+        )
+      )
+    )
+    (feature_projection): Wav2Vec2FeatureProjection(
+      (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+      (projection): Linear(in_features=512, out_features=1024, bias=True)
+      (dropout): Dropout(p=0.3, inplace=False)
+    )
+    (encoder): Wav2Vec2EncoderStableLayerNorm(
+      (pos_conv_embed): Wav2Vec2PositionalConvEmbedding(
+        (conv): ParametrizedConv1d(
+          1024, 1024, kernel_size=(128,), stride=(1,), padding=(64,), groups=16
+          (parametrizations): ModuleDict(
+            (weight): ParametrizationList(
+              (0): _WeightNorm()
+            )
+          )
+        )
+        (padding): Wav2Vec2SamePadLayer()
+        (activation): GELUActivation()
+      )
+      (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+      (dropout): Dropout(p=0.2, inplace=False)
+      (layers): ModuleList(
+        (0-23): 24 x Wav2Vec2EncoderLayerStableLayerNorm(
+          (attention): Wav2Vec2SdpaAttention(
+            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
+            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
+            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
+            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
+          )
+          (dropout): Dropout(p=0.2, inplace=False)
+          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+          (feed_forward): Wav2Vec2FeedForward(
+            (intermediate_dropout): Dropout(p=0.0, inplace=False)
+            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)
+            (intermediate_act_fn): GELUActivation()
+            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)
+            (output_dropout): Dropout(p=0.2, inplace=False)
+          )
+          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+        )
+      )
+    )
+  )
+  (dropout): Dropout(p=0.0, inplace=False)
+  (lm_head): Linear(in_features=1024, out_features=153, bias=True)
+)
+
  0%|          | 0/15000 [00:00<?, ?it/s]/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py:157: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.
+  warnings.warn(
+Traceback (most recent call last):
+  File "/scratch/elec/puhe/p/palp3/MUCS/finetune_script_wtags_partdata.py", line 799, in <module>
+    main()
+  File "/scratch/elec/puhe/p/palp3/MUCS/finetune_script_wtags_partdata.py", line 750, in main
+    train_result = trainer.train()
+                   ^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/trainer.py", line 1929, in train
+    return inner_training_loop(
+           ^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/trainer.py", line 2279, in _inner_training_loop
+    tr_loss_step = self.training_step(model, inputs)
+                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/trainer.py", line 3318, in training_step
+    loss = self.compute_loss(model, inputs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/trainer.py", line 3363, in compute_loss
+    outputs = model(**inputs)
+              ^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/accelerate/utils/operations.py", line 819, in forward
+    return model_forward(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/accelerate/utils/operations.py", line 807, in __call__
+    return convert_to_fp32(self.model_forward(*args, **kwargs))
+                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/amp/autocast_mode.py", line 43, in decorate_autocast
+    return func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 2228, in forward
+    outputs = self.wav2vec2(
+              ^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 1809, in forward
+    extract_features = self.feature_extractor(input_values)
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 463, in forward
+    hidden_states = conv_layer(hidden_states)
+                    ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 335, in forward
+    hidden_states = self.layer_norm(hidden_states)
+                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/modules/normalization.py", line 202, in forward
+    return F.layer_norm(
+           ^^^^^^^^^^^^^
+  File "/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/nn/functional.py", line 2576, in layer_norm
+    return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 11.13 GiB. GPU 0 has a total capacity of 31.73 GiB of which 2.14 GiB is free. Including non-PyTorch memory, this process has 29.59 GiB memory in use. Of the allocated memory 29.23 GiB is allocated by PyTorch, and 3.95 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+wandb: - 0.005 MB of 0.005 MB uploaded
+wandb: ⭐️ View project at: https://wandb.ai/priyanshipal/huggingface
+wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
+wandb: Find logs at: ./wandb/run-20240924_173727-kvrf6uiu/logs
+wandb: WARNING The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require("core")`! See https://wandb.me/wandb-core for more information.

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