After I run trainer.train and I try to predict the wer of the model, I always get this output.
How to solve it?
The code is below:
def predict(batch, model):
input_dict = processor(batch["input_values"], sampling_rate=16000, return_tensors='pt',padding=True)

logits = model(input_dict.input_values.to(device)).logits

pred_ids = torch.argmax(logits, dim=-1)[0]

batch['pred_ids'] = processor.decode(pred_ids)

return batch

from transformers import TrainingArguments
from transformers import Wav2Vec2ForCTC
model = Wav2Vec2ForCTC.from_pretrained(
model_name, 

attention_dropout=0.1,

hidden_dropout=0.1,

feat_proj_dropout=0.0,

mask_time_prob=0.05,

layerdrop=0.1,

gradient_checkpointing=True, 

ctc_loss_reduction="mean", 

pad_token_id=processor.tokenizer.pad_token_id,

vocab_size=len(processor.tokenizer)

)
training_args = TrainingArguments(
output_dir="/content/gdrive/MyDrive/wav2vec2-large-xlsr-portuguese-demo/modelo",

output_dir="./wav2vec2-large-xlsr-portuguese-demo",
group_by_length=True,
per_device_train_batch_size=16,
gradient_accumulation_steps=2,
evaluation_strategy=“steps”,
num_train_epochs=5,
fp16=True,
save_steps=400,
eval_steps=400,
logging_steps=400,
learning_rate=3e-4,
warmup_steps=500,
save_total_limit=2,
)
from transformers import Trainer
trainer = Trainer(
model=model,

data_collator=data_collator,

args=training_args,

compute_metrics=compute_metrics,

train_dataset=d_train,

eval_dataset=d_val,

tokenizer=processor.feature_extractor,

)
If you want to acceed to the whole project, it is avaiable at:

  
      
      colab.research.google.com
  
  
    

Google Colaboratory 1