# Multilabel Audio Classification Training size mismatch

**URL:** <https://discuss.huggingface.co/t/multilabel-audio-classification-training-size-mismatch/74306>\
**Category:** 🤗Transformers\
**Created:** [February 22, 2024, 9:09am UTC](https://discuss.huggingface.co/t/multilabel-audio-classification-training-size-mismatch/74306 "2024-02-22T09:09:37Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![DanMan259](https://avatars.discourse-cdn.com/v4/letter/d/e36b37/32.png) [@DanMan259](https://discuss.huggingface.co/u/DanMan259)\
**Post date:** [February 22, 2024, 9:09am UTC](https://discuss.huggingface.co/t/multilabel-audio-classification-training-size-mismatch/74306/1 "2024-02-22T09:09:37Z")

</div>

After running the train function I keep getting a size mismatch

> trainer.train()  
> `ValueError: Expected input batch_size (4) to match target batch_size (20).`

Here is my setup

My Dataset loader class

```auto
class AudioDataset(torch.utils.data.Dataset):
    def __init__ (self, file_paths, labels, feature_extractor, max_audio_length_seconds=30):
        self.file_paths = file_paths
        self.labels = labels
        self.feature_extractor = feature_extractor
        self.audio_sample_rate = feature_extractor.sampling_rate
        self.max_audio_length_seconds = max_audio_length_seconds
    
    def __len__ (self):
        return len(self.file_paths)
    
    def __getitem__ (self, idx):
        # Load your audio file
        waveform, original_sr = librosa.load(self.file_paths[idx], sr=None)
        waveform = librosa.resample(waveform, orig_sr=original_sr, target_sr=self.audio_sample_rate)
        
        
        # Encode the audio waveform with the feature extractor
        inputs = self.feature_extractor(
            waveform, 
            sampling_rate=self.audio_sample_rate, 
            max_length=self.max_audio_length_seconds*self.audio_sample_rate, 
            truncation=True, 
            padding="max_length",
            return_tensors="pt",
        )
        
        input_values = inputs.input_values.squeeze() # Remove batch dimension added by return_tensors
        labels = torch.tensor(self.labels[idx], dtype=torch.float)

        return {"input_values": input_values, "labels": labels}

feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_TYPE)
torch.cuda.empty_cache() # PyTorch thing
dataset = AudioDataset(
    file_paths=files, 
    labels=labels, 
    feature_extractor=feature_extractor
)

train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])

```

My model init

```auto
model = AutoModelForAudioClassification.from_pretrained(
    MODEL_TYPE, 
    num_labels = len(label_columns), 
    problem_type="multi_label_classification", 
    id2label={idx:label for idx, label in enumerate(label_columns)}, 
    label2id={label:idx for idx, label in enumerate(label_columns)}
)

```

Trainer Args:

```auto
training_args = TrainingArguments(
    output_dir=os.path.join(temp, 'output'),
    overwrite_output_dir = True,
    num_train_epochs=NUM_EPOCHS,
    per_device_train_batch_size=4,
    per_device_eval_batch_size=8,
    gradient_accumulation_steps=2,
    evaluation_strategy="epoch",
    logging_strategy="epoch",
    save_strategy="epoch",
    save_total_limit=5,
    logging_dir=os.path.join('temp', 'logs'),
    learning_rate=LEARNING_RATE,
    load_best_model_at_end=True,
    metric_for_best_model='auc_score_macro',
    disable_tqdm=False,
    optim="adamw_torch",
    fp16=True,
    run_name=MODEL_NAME
)
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    tokenizer=feature_extractor,
    compute_metrics=compute_metrics,
    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
)

```

I am not sure where the mismatch is I have some additional testing, but it seems an issue with just the trainer class.

---

<div class="post-metadata">

**Author:** ![DanMan259](https://avatars.discourse-cdn.com/v4/letter/d/e36b37/32.png) [@DanMan259](https://discuss.huggingface.co/u/DanMan259)\
**Post date:** [February 22, 2024, 9:15am UTC](https://discuss.huggingface.co/t/multilabel-audio-classification-training-size-mismatch/74306/2 "2024-02-22T09:15:16Z")

</div>

`MODEL_TYPE` = “facebook/wav2vec2-base”  
Number of output classes = 5

I tried this as well to make sure

```auto
from torch.utils.data import DataLoader

# Assuming train_dataset is already defined
train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)

# Load one batch and inspect
for batch in train_loader:
    print(f"Input Values Shape: {batch['input_values'].shape}") # Output was [4, 480000]
    print(f"Labels Shape: {batch['labels'].shape}") # Output was [4, 5] for multi-label classification
    break

```

---

<div class="post-metadata">

**Author:** ![nielsr](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/nielsr/32/39617_2.png) [@nielsr](https://discuss.huggingface.co/u/nielsr)\
**Post date:** [February 22, 2024, 10:50am UTC](https://discuss.huggingface.co/t/multilabel-audio-classification-training-size-mismatch/74306/3 "2024-02-22T10:50:49Z")

</div>

Hi,

The Trainer API creates batches for you using the default data collator, see here: [transformers/src/transformers/data/data\_collator.py at v4.38.1 · huggingface/transformers · GitHub](https://github.com/huggingface/transformers/blob/v4.38.1/src/transformers/data/data_collator.py#L127).

You might check whether this one is appropriate for your data. Also note that your labels need to be of type “float” in order to work with the `BCEWithLogitsLoss` (they should not be of type “long”).

---

<div class="post-metadata">

**Author:** ![DanMan259](https://avatars.discourse-cdn.com/v4/letter/d/e36b37/32.png) [@DanMan259](https://discuss.huggingface.co/u/DanMan259)\
**Post date:** [February 22, 2024, 7:12pm UTC](https://discuss.huggingface.co/t/multilabel-audio-classification-training-size-mismatch/74306/4 "2024-02-22T19:12:23Z")

</div>

I just ran the function and here is to verify with a sample of data

```auto
# Take sample
samples = [train_dataset[i] for i in range(4)] # Adjust the range as necessary

# Collate the sample batch
collated_batch = torch_default_data_collator(samples)

# Inspect the batched data
print(f"Batched Input Values Shape: {collated_batch['input_values'].shape}") # Expected: [4, seq_length]
print(f"Batched Labels Shape: {collated_batch['labels'].shape}") # Expected: [4, 5] for multi-label classification
print(f"The datatype of the data: {collated_batch['labels'][0][0].dtype}")

```

> Batched Input Values Shape: torch.Size([4, 480000])  
> Batched Labels Shape: torch.Size([4, 5])  
> The datatype of the data: torch.float32

Seems like everything is in order?