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wav2vec2
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s300_shuff100 / indicwav2vec_MUCS_warmup500_shuff100_2128361.out
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 wandb: Currently logged in as: priyanshi-pal (priyanshipal). Use `wandb login --relogin` to force relogin
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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-20240821_141937-nb85kn9g
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/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(
08/21/2024 14:19:40 - WARNING - __main__ - device: cuda:0, n_gpu: 116-bits training: True
Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 52825 examples [00:01, 50646.81 examples/s] Generating train split: 52825 examples [00:01, 48965.28 examples/s]
Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 3136 examples [00:00, 31324.11 examples/s] Generating train split: 3136 examples [00:00, 30612.58 examples/s]
/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_indicw2v_partdata.py:508: 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=149, 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={
147: AddedToken("[UNK]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False),
148: AddedToken("[PAD]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False),
149: AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
150: 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=151, bias=True)
)
0%| | 0/1000 [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(
/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
return fn(*args, **kwargs)
/scratch/work/palp3/myenv/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]
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[06:28<10:30, 1.27it/s] 20%|█▉ | 198/1000 [06:29<10:30, 1.27it/s] 20%|█▉ | 199/1000 [06:29<10:10, 1.31it/s] 20%|█▉ | 199/1000 [06:29<10:10, 1.31it/s] 20%|██ | 200/1000 [06:32<17:56, 1.35s/it] 20%|██ | 200/1000 [06:32<17:56, 1.35s/it] 20%|██ | 201/1000 [06:40<44:39, 3.35s/it] 20%|██ | 201/1000 [06:40<44:39, 3.35s/it] 20%|██ | 202/1000 [06:44<47:38, 3.58s/it] 20%|██ | 202/1000 [06:44<47:38, 3.58s/it] 20%|██ | 203/1000 [06:48<47:52, 3.60s/it] 20%|██ | 203/1000 [06:48<47:52, 3.60s/it]slurmstepd: error: *** JOB 2128361 ON gpu5 CANCELLED AT 2024-08-21T14:36:31 ***