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https://github.com/Jakobovski/free-spoken-digit-dataset
A free audio dataset of spoken digits. An audio version of MNIST.
https://github.com/Jakobovski/free-spoken-digit-dataset
audio dataset machine-learning mnist speech-recognition spoken-digits spoken-language
Last synced: about 2 months ago
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A free audio dataset of spoken digits. An audio version of MNIST.
- Host: GitHub
- URL: https://github.com/Jakobovski/free-spoken-digit-dataset
- Owner: Jakobovski
- Created: 2016-06-21T09:46:20.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-05-02T20:55:50.000Z (5 months ago)
- Last Synced: 2024-06-03T10:48:44.433Z (4 months ago)
- Topics: audio, dataset, machine-learning, mnist, speech-recognition, spoken-digits, spoken-language
- Language: Python
- Homepage:
- Size: 29.7 MB
- Stars: 606
- Watchers: 22
- Forks: 249
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-python-audio - Free Spoken Digit Dataset
README
# Free Spoken Digit Dataset (FSDD)
[](https://zenodo.org/badge/latestdoi/61622039)
A simple audio/speech dataset consisting of recordings of spoken digits in `wav` files at 8kHz. The recordings are trimmed so that they have near minimal silence at the beginnings and ends.
FSDD is an open dataset, which means it will grow over time as data is contributed. In order to enable reproducibility and accurate citation the dataset is versioned using Zenodo DOI as well as `git tags`.
### Current status
- 6 speakers
- 3,000 recordings (50 of each digit per speaker)
- English pronunciations
### Organization
Files are named in the following format:
`{digitLabel}_{speakerName}_{index}.wav`
Example: `7_jackson_32.wav`
### How to use with Hub
A simple way of using this dataset is with [Activeloop](https://activeloop.ai)'s python package [Hub](https://github.com/activeloopai/Hub)!
First, run `pip install hub` (or `pip3 install hub`).
```python
import hub
ds = hub.load("hub://activeloop/spoken_mnist")
# check out the first spectrogram, it's label, and who spoke it!
import matplotlib.pyplot as plt
plt.imshow(ds.spectrograms[0].numpy())
plt.title(f"{ds.speakers[0].data()} spoke {ds.labels[0].numpy()}")
plt.show()
# train a model in pytorch
for sample in ds.pytorch():
# ... model code here ...
# train a model in tensorflow
for sample in ds.tensorflow():
# ... model code here ...
```
available tensors can be shown by printing dataset:
```python
print(ds)
# prints: Dataset(path='hub://activeloop/spoken_mnist', tensors=['spectrograms', 'labels', 'audio', 'speakers'])
```
For more information, check out the [hub documentation](https://docs.activeloop.ai/).
### Contributions
Please contribute your homemade recordings. All recordings should be mono 8kHz `wav` files and be trimmed to have minimal silence. Don't forget to update `metadata.py` with the speaker meta-data.
To add your data, follow the recording instructions in `acquire_data/say_numbers_prompt.py`
and then run `split_and_label_numbers.py` to make your files.
### Metadata
`metadata.py` contains meta-data regarding the speakers gender and accents.
### Included utilities
`trimmer.py`
Trims silences at beginning and end of an audio file. Splits an audio file into multiple audio files by periods of silence.
`fsdd.py`
A simple class that provides an easy to use API to access the data.
`spectogramer.py`
Used for creating spectrograms of the audio data. Spectrograms are often a useful pre-processing step.
### Usage
The test set officially consists of the first 10% of the recordings. Recordings numbered `0-4` (inclusive) are in the test and `5-49` are in the training set.
### Made with FSDD
Did you use FSDD in a paper, project or app? Add it here!
* [More than 50+ scholarly articles](https://scholar.google.com/citations?hl=en&user=OAJpVUkAAAAJ)
* https://github.com/Jakobovski/decoupled-multimodal-learning/
* https://adhishthite.github.io/sound-mnist/ by [Adhish Thite](https://adhishthite.github.io/)
* https://github.com/eonu/torch-fsdd/ - A simple PyTorch data loader for the dataset by [Edwin Onuonga](https://github.com/eonu)
* https://proglearn.neurodata.io/ by [NeuroData](https://github.com/neurodata)
* https://neurodata.io/df_dn/ by [NeuroData](https://github.com/neurodata)
### External tools
- **Tensorflow** https://www.tensorflow.org/datasets/catalog/spoken_digit
- **C#/.NET.** The FSDD dataset can be used in .NET applications using the [FreeSpokenDigitsDataset class](http://accord-framework.net/docs/html/T_Accord_DataSets_FreeSpokenDigitsDataset.htm) included withing the Accord.NET Framework. A basic example on how to perform spoken digits classification using audio MFCC features can be found [here](http://accord-framework.net/docs/html/T_Accord_Audition_BagOfAudioWords.htm).
### License
[Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/)