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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-20241015_222330-h5k9kszn
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wandb: Syncing run rerun_bestrun_wgas1fp16false_indicw2v_ad0_3_hd_02_featd_0_3_lr6e-4_warmup500_s300_shuff100
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/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of ๐ค Transformers. Use `eval_strategy` instead
warnings.warn(
10/15/2024 22:23:35 - WARNING - __main__ - device: cuda:0, n_gpu: 116-bits training: False
/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/configuration_auto.py:991: 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:302: 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:331: 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: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 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']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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': '', 'eos_token': '', 'unk_token': '[UNK]', 'pad_token': '[PAD]'}, clean_up_tokenization_spaces=False), 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("", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
150: AddedToken("", 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)
)
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