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TIMIT Dataset
Load TIMIT dataset in Python fast with one line of code. Stream TIMIT Dataset while training models in PyTorch & TensorFlow.
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TIMIT dataset

What is TIMIT Dataset?

The TIMIT Acoustic-Phonetic Continuous Speech Corpus dataset is a standard dataset used for the evaluation of automatic speech recognition systems. It contains recordings of 630 speakers. Also, the recordings include eight dialects of American English. Each speaker in the dataset reads 10 phonetically-rich sentences. The TIMIT corpus includes time-aligned orthographic, phonetic, and word transcriptions. It also includes a 16-bit, 16kHz speech waveform file for each phrase said. The TIMIT corpus transcriptions have been hand-verified.

Download TIMIT Dataset in Python

Instead of downloading the TIMIT dataset in Python, you can effortlessly load it in Python via our open-source package Hub with just one line of code.

Load TIMIT Dataset Training Subset in Python

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import hub
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ds = hub.load("hub://activeloop/timit-train")
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Load TIMIT Dataset Testing Subset in Python

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import hub
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ds = hub.load("hub://activeloop/timit-test")
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TIMIT Dataset Structure

TIMIT Data Fields

  • audios: tensor represent audio in wav format.
  • texts: tensor representing the text spoken in audio.
  • dialects: tensor representing the dialect of the speaker.
  • is_sentences: tensor to identify if the audio is a sentence.
  • is_word_files: tensor to identify if the audio is word.
  • is_phoenetics: tensor to identify if the audio is phonetic.
  • speaker_ids: tensor representing the speaker id.

TIMIT Data Splits

How to use TIMIT Dataset with PyTorch and TensorFlow in Python

Train a model on TIMIT dataset with PyTorch in Python

Let's use Hub's built-in PyTorch one-line dataloader to connect the data to the compute:
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dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
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Train a model on TIMIT dataset with TensorFlow in Python

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dataloader = ds.tensorflow()
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Additional Information about TIMIT Dataset

TIMIT Dataset Description

  • Paper: Garofolo, John S., et al. TIMIT Acoustic-Phonetic Continuous Speech Corpus LDC93S1. Web Download. Philadelphia: Linguistic Data Consortium, 1993.

Timit Dataset Curators

John S. Garofolo, Lori F. Lamel, William M. Fisher, Jonathan G. Fiscus, David S. Pallett, Nancy L. Dahlgren, Victor Zue

TIMIT Dataset Licensing Information

Hub users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.
If you're a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

Timit Dataset Citation Information

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@inproceedings,
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title = {TIMIT Acoustic-Phonetic Continuous Speech Corpus},
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author = {John S. Garofolo, Lori F. Lamel, William M. Fisher, Jonathan G. Fiscus, David S. Pallett, Nancy L. Dahlgren, Victor Zue},
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booktitle = {Linguistic Data Consortium, },
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year = {1993}
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}
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TIMIT Dataset FAQs

What is the TIMIT dataset for Python?

The TIMIT dataset is a speech dataset that is often used for the evaluation of automatic speech recognition systems. It was developed by Texas Instruments and MIT with DARPA's (Defense Advanced Research Projects Agency) financial support. It is often used in the domain of speech recognition.
How to download the TIMIT dataset in Python?
You can load TIMIT dataset fast with one line of code using the open-source package Activeloop Hub in Python. See detailed instructions on how to load TIMIT dataset training subset in Python.

How can I use TIMIT dataset in PyTorch or TensorFlow?

You can stream the TIMIT dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Hub in Python. See detailed instructions on how to train a model on TIMIT dataset with PyTorch in Python or train a model on TIMIT with Tensorflow in Python.