diff --git "a/run.timit.log" "b/run.timit.log" new file mode 100644--- /dev/null +++ "b/run.timit.log" @@ -0,0 +1,8730 @@ +/opt/conda/lib/python3.12/site-packages/transformers/training_args.py:1483: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead + warnings.warn( +05/24/2024 13:33:16 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: True +05/24/2024 13:33:16 - INFO - __main__ - Training/evaluation parameters TrainingArguments( +_n_gpu=1, +accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None, 'use_configured_state': False}, +adafactor=False, +adam_beta1=0.9, +adam_beta2=0.999, +adam_epsilon=1e-08, +auto_find_batch_size=False, +batch_eval_metrics=False, +bf16=False, +bf16_full_eval=False, +data_seed=None, +dataloader_drop_last=False, +dataloader_num_workers=0, +dataloader_persistent_workers=False, +dataloader_pin_memory=True, +dataloader_prefetch_factor=None, +ddp_backend=None, +ddp_broadcast_buffers=None, +ddp_bucket_cap_mb=None, +ddp_find_unused_parameters=None, +ddp_timeout=1800, +debug=[], +deepspeed=None, +disable_tqdm=False, +dispatch_batches=None, +do_eval=True, +do_predict=False, +do_train=True, +eval_accumulation_steps=None, +eval_delay=0, +eval_do_concat_batches=True, +eval_steps=100, +eval_strategy=IntervalStrategy.STEPS, +evaluation_strategy=steps, +fp16=True, +fp16_backend=auto, +fp16_full_eval=False, +fp16_opt_level=O1, +fsdp=[], +fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, +fsdp_min_num_params=0, +fsdp_transformer_layer_cls_to_wrap=None, +full_determinism=False, +gradient_accumulation_steps=1, +gradient_checkpointing=False, +gradient_checkpointing_kwargs=None, +greater_is_better=None, +group_by_length=True, +half_precision_backend=auto, +hub_always_push=False, +hub_model_id=None, +hub_private_repo=False, +hub_strategy=HubStrategy.EVERY_SAVE, +hub_token=, +ignore_data_skip=False, +include_inputs_for_metrics=False, +include_num_input_tokens_seen=False, +include_tokens_per_second=False, +jit_mode_eval=False, +label_names=None, +label_smoothing_factor=0.0, +learning_rate=0.0001, +length_column_name=length, +load_best_model_at_end=False, +local_rank=0, +log_level=passive, +log_level_replica=warning, +log_on_each_node=True, +logging_dir=./wav2vec2-base-timit-fine-tuned/runs/May24_13-33-16_tz579-raptorlake, +logging_first_step=False, +logging_nan_inf_filter=True, +logging_steps=10, +logging_strategy=IntervalStrategy.STEPS, +lr_scheduler_kwargs={}, +lr_scheduler_type=SchedulerType.LINEAR, +max_grad_norm=1.0, +max_steps=-1, +metric_for_best_model=None, +mp_parameters=, +neftune_noise_alpha=None, +no_cuda=False, +num_train_epochs=20.0, +optim=OptimizerNames.ADAMW_TORCH, +optim_args=None, +optim_target_modules=None, +output_dir=./wav2vec2-base-timit-fine-tuned, +overwrite_output_dir=True, +past_index=-1, +per_device_eval_batch_size=1, +per_device_train_batch_size=32, +prediction_loss_only=False, +push_to_hub=True, +push_to_hub_model_id=None, +push_to_hub_organization=None, +push_to_hub_token=, +ray_scope=last, +remove_unused_columns=True, +report_to=['tensorboard'], +restore_callback_states_from_checkpoint=False, +resume_from_checkpoint=None, +run_name=./wav2vec2-base-timit-fine-tuned, +save_on_each_node=False, +save_only_model=False, +save_safetensors=True, +save_steps=400, +save_strategy=IntervalStrategy.STEPS, +save_total_limit=3, +seed=42, +skip_memory_metrics=True, +split_batches=None, +tf32=None, +torch_compile=False, +torch_compile_backend=None, +torch_compile_mode=None, +torchdynamo=None, +tpu_metrics_debug=False, +tpu_num_cores=None, +use_cpu=False, +use_ipex=False, +use_legacy_prediction_loop=False, +use_mps_device=False, +warmup_ratio=0.0, +warmup_steps=1000, +weight_decay=0.005, +) +/opt/conda/lib/python3.12/site-packages/datasets/load.py:1486: FutureWarning: The repository for timit_asr contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/timit_asr +You can avoid this message in future by passing the argument `trust_remote_code=True`. +Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`. + warnings.warn( + Downloading builder script: 0%| | 0.00/7.48k [00:00', 'eos_token': '', 'unk_token': '[UNK]', 'pad_token': '[PAD]'}, clean_up_tokenization_spaces=True), added_tokens_decoder={ + 27: AddedToken("[UNK]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False), + 28: AddedToken("[PAD]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False), + 29: AddedToken("", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), + 30: AddedToken("", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), +} + +{ + "processor_class": "Wav2Vec2Processor" +} + +Using auto 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message. +***** Running training ***** + Num examples = 3,696 + Num Epochs = 20 + Instantaneous batch size per device = 32 + Total train batch size (w. parallel, distributed & accumulation) = 32 + Gradient Accumulation steps = 1 + Total optimization steps = 2,320 + Number of trainable parameters = 90,195,103 + 0%| | 0/2320 [00:00