Datasets:
filename stringlengths 16 21 | label stringclasses 2
values | audio audioduration (s) 14.5 20 |
|---|---|---|
clean_speech\82.wav | speech | |
noise_only\553.wav | noisy | |
clean_speech\626.wav | speech | |
noise_only\438.wav | noisy | |
clean_speech\426.wav | speech | |
clean_speech\890.wav | speech | |
clean_speech\501.wav | speech | |
noise_only\500.wav | noisy | |
noise_only\922.wav | noisy | |
clean_speech\696.wav | speech | |
clean_speech\370.wav | speech | |
noisy_speech\78.wav | noisy | |
noise_only\691.wav | noisy | |
noise_only\379.wav | noisy | |
clean_speech\93.wav | speech | |
noise_only\330.wav | noisy | |
clean_speech\811.wav | speech | |
clean_speech\58.wav | speech | |
clean_speech\334.wav | speech | |
noisy_speech\793.wav | noisy | |
noisy_speech\109.wav | noisy | |
noisy_speech\914.wav | noisy | |
noisy_speech\1094.wav | noisy | |
clean_speech\386.wav | speech | |
noisy_speech\722.wav | noisy | |
clean_speech\704.wav | speech | |
clean_speech\221.wav | speech | |
noisy_speech\74.wav | noisy | |
clean_speech\703.wav | speech | |
noise_only\564.wav | noisy | |
clean_speech\454.wav | speech | |
clean_speech\619.wav | speech | |
noisy_speech\1092.wav | noisy | |
clean_speech\655.wav | speech | |
noisy_speech\111.wav | noisy | |
clean_speech\1024.wav | speech | |
noise_only\366.wav | noisy | |
noisy_speech\572.wav | noisy | |
noisy_speech\464.wav | noisy | |
noise_only\801.wav | noisy | |
clean_speech\683.wav | speech | |
noise_only\937.wav | noisy | |
noise_only\316.wav | noisy | |
clean_speech\1118.wav | speech | |
noisy_speech\897.wav | noisy | |
clean_speech\788.wav | speech | |
noise_only\935.wav | noisy | |
noise_only\145.wav | noisy | |
noise_only\414.wav | noisy | |
noisy_speech\505.wav | noisy | |
clean_speech\1059.wav | speech | |
noisy_speech\385.wav | noisy | |
noisy_speech\1018.wav | noisy | |
noise_only\532.wav | noisy | |
clean_speech\1166.wav | speech | |
noisy_speech\73.wav | noisy | |
noisy_speech\863.wav | noisy | |
clean_speech\693.wav | speech | |
clean_speech\1094.wav | speech | |
noise_only\741.wav | noisy | |
noise_only\839.wav | noisy | |
clean_speech\216.wav | speech | |
noisy_speech\992.wav | noisy | |
noise_only\83.wav | noisy | |
clean_speech\1207.wav | speech | |
noisy_speech\412.wav | noisy | |
noisy_speech\1.wav | noisy | |
noisy_speech\125.wav | noisy | |
clean_speech\210.wav | speech | |
noise_only\391.wav | noisy | |
noisy_speech\632.wav | noisy | |
noisy_speech\1108.wav | noisy | |
noisy_speech\742.wav | noisy | |
clean_speech\551.wav | speech | |
noisy_speech\710.wav | noisy | |
noisy_speech\759.wav | noisy | |
noisy_speech\404.wav | noisy | |
noise_only\213.wav | noisy | |
noisy_speech\72.wav | noisy | |
noisy_speech\470.wav | noisy | |
noisy_speech\238.wav | noisy | |
noisy_speech\983.wav | noisy | |
noise_only\721.wav | noisy | |
noise_only\288.wav | noisy | |
clean_speech\325.wav | speech | |
noisy_speech\657.wav | noisy | |
noise_only\613.wav | noisy | |
noisy_speech\534.wav | noisy | |
clean_speech\1155.wav | speech | |
noisy_speech\1039.wav | noisy | |
clean_speech\980.wav | speech | |
clean_speech\796.wav | speech | |
clean_speech\797.wav | speech | |
noisy_speech\409.wav | noisy | |
clean_speech\466.wav | speech | |
clean_speech\1109.wav | speech | |
noise_only\388.wav | noisy | |
clean_speech\853.wav | speech | |
noisy_speech\1107.wav | noisy | |
clean_speech\7.wav | speech |
End of preview. Expand in Data Studio
Noisy Speech Dataset
Binary classification dataset for detecting noisy audio in speech.
Dataset Description
This dataset is derived from haydarkadioglu/speech-noise-dataset with remapped labels:
- speech: Clean speech audio (originally
clean_speech) - noisy: Noisy audio including both noisy speech and noise-only samples (originally
noisy_speech+noise_only)
Dataset Statistics
| Split | Samples |
|---|---|
| Train | 2873 |
| Test | 508 |
Label Distribution
- speech: 1217 samples
- noisy: 2164 samples
Usage
from datasets import load_dataset
# Load dataset
dataset = load_dataset("Aynursusuz/noisy-speech-dataset")
# Access train/test splits
train_data = dataset['train']
test_data = dataset['test']
# Example
print(train_data[0])
Model Training
from transformers import AutoModelForAudioClassification, TrainingArguments, Trainer
model = AutoModelForAudioClassification.from_pretrained(
"MIT/ast-finetuned-audioset-10-10-0.4593",
num_labels=2,
label2id={"speech": 0, "noisy": 1},
id2label={0: "speech", 1: "noisy"}
)
# Train your model...
Citation
Original dataset: haydarkadioglu/speech-noise-dataset
License
Apache 2.0
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