Datasets:
Dataset Viewer
filename
stringlengths 16
21
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stringclasses 2
values | audio
audioduration (s) 14.5
20
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|---|---|---|
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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