torchaudio.datasets¶

All datasets are subclasses of torch.utils.data.Dataset and have __getitem__ and __len__ methods implemented. Hence, they can all be passed to a torch.utils.data.DataLoader which can load multiple samples parallelly using torch.multiprocessing workers. For example:

yesno_data = torchaudio.datasets.YESNO('.', download=True)
data_loader = torch.utils.data.DataLoader(yesno_data,
                                          batch_size=1,
                                          shuffle=True,
                                          num_workers=args.nThreads)

CMUARCTIC¶

class torchaudio.datasets.CMUARCTIC(root: Union[str, pathlib.Path], url: str = 'aew', folder_in_archive: str = 'ARCTIC', download: bool = False)[source]¶

Create a Dataset for CMU ARCTIC [1].

Parameters

root (str or Path) – Path to the directory where the dataset is found or downloaded.
url (str, optional) – The URL to download the dataset from or the type of the dataset to download. (default: "aew") Allowed type values are "aew", "ahw", "aup", "awb", "axb", "bdl", "clb", "eey", "fem", "gka", "jmk", "ksp", "ljm", "lnh", "rms", "rxr", "slp" or "slt".
folder_in_archive (str, optional) – The top-level directory of the dataset. (default: "ARCTIC")
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False).

__getitem__(n: int) → Tuple[torch.Tensor, int, str, str][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform, sample_rate, transcript, utterance_id)
Return type: (Tensor, int, str, str)

CMUDict¶

class torchaudio.datasets.CMUDict(root: Union[str, pathlib.Path], exclude_punctuations: bool = True, *, download: bool = False, url: str = 'http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b', url_symbols: str = 'http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b.symbols')[source]¶

Create a Dataset for CMU Pronouncing Dictionary [2] (CMUDict).

Parameters

root (str or Path) – Path to the directory where the dataset is found or downloaded.
exclude_punctuations (bool, optional) – When enabled, exclude the pronounciation of punctuations, such as !EXCLAMATION-POINT and #HASH-MARK.
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False).
url (str, optional) – The URL to download the dictionary from. (default: "http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b")
url_symbols (str, optional) – The URL to download the list of symbols from. (default: "http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b.symbols")

__getitem__(n: int) → Tuple[str, List[str]][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded.
Returns: The corresponding word and phonemes (word, [phonemes]).
Return type: (str, List[str])

property symbols¶

A list of phonemes symbols, such as AA, AE, AH.

Type: list[str]

COMMONVOICE¶

class torchaudio.datasets.COMMONVOICE(root: Union[str, pathlib.Path], tsv: str = 'train.tsv')[source]¶

Create a Dataset for CommonVoice [3].

Parameters

root (str or Path) – Path to the directory where the dataset is located. (Where the tsv file is present.)
tsv (str, optional) – The name of the tsv file used to construct the metadata, such as "train.tsv", "test.tsv", "dev.tsv", "invalidated.tsv", "validated.tsv" and "other.tsv". (default: "train.tsv")

__getitem__(n: int) → Tuple[torch.Tensor, int, Dict[str, str]][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform, sample_rate, dictionary), where dictionary is built from the TSV file with the following keys: client_id, path, sentence, up_votes, down_votes, age, gender and accent.
Return type: (Tensor, int, Dict[str, str])

GTZAN¶

class torchaudio.datasets.GTZAN(root: Union[str, pathlib.Path], url: str = 'http://opihi.cs.uvic.ca/sound/genres.tar.gz', folder_in_archive: str = 'genres', download: bool = False, subset: Optional[str] = None)[source]¶

Create a Dataset for GTZAN [4].

Note

Please see http://marsyas.info/downloads/datasets.html if you are planning to use this dataset to publish results.

Parameters

root (str or Path) – Path to the directory where the dataset is found or downloaded.
url (str, optional) – The URL to download the dataset from. (default: "http://opihi.cs.uvic.ca/sound/genres.tar.gz")
folder_in_archive (str, optional) – The top-level directory of the dataset.
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False).
subset (str or None, optional) – Which subset of the dataset to use. One of "training", "validation", "testing" or None. If None, the entire dataset is used. (default: None).

__getitem__(n: int) → Tuple[torch.Tensor, int, str][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform, sample_rate, label)
Return type: (Tensor, int, str)

LibriMix¶

class torchaudio.datasets.LibriMix(root: Union[str, pathlib.Path], subset: str = 'train-360', num_speakers: int = 2, sample_rate: int = 8000, task: str = 'sep_clean')[source]¶

Create the LibriMix [5] dataset.

Parameters

root (str or Path) – The path to the directory where the directory Libri2Mix or Libri3Mix is stored.
subset (str, optional) – The subset to use. Options: [train-360, train-100, dev, and test] (Default: train-360).
num_speakers (int, optional) – The number of speakers, which determines the directories to traverse. The Dataset will traverse s1 to sN directories to collect N source audios. (Default: 2)
sample_rate (int, optional) – sample rate of audio files. The sample_rate determines which subdirectory the audio are fetched. If any of the audio has a different sample rate, raises ValueError. Options: [8000, 16000] (Default: 8000)
task (str, optional) – the task of LibriMix. Options: [enh_single, enh_both, sep_clean, sep_noisy] (Default: sep_clean)

Note

The LibriMix dataset needs to be manually generated. Please check https://github.com/JorisCos/LibriMix

__getitem__(key: int) → Tuple[int, torch.Tensor, List[torch.Tensor]][source]¶

Load the n-th sample from the dataset. :param key: The index of the sample to be loaded :type key: int

Returns: (sample_rate, mix_waveform, list_of_source_waveforms)
Return type: (int, Tensor, List[Tensor])

LIBRISPEECH¶

class torchaudio.datasets.LIBRISPEECH(root: Union[str, pathlib.Path], url: str = 'train-clean-100', folder_in_archive: str = 'LibriSpeech', download: bool = False)[source]¶

Create a Dataset for LibriSpeech [6].

Parameters

root (str or Path) – Path to the directory where the dataset is found or downloaded.
url (str, optional) – The URL to download the dataset from, or the type of the dataset to dowload. Allowed type values are "dev-clean", "dev-other", "test-clean", "test-other", "train-clean-100", "train-clean-360" and "train-other-500". (default: "train-clean-100")
folder_in_archive (str, optional) – The top-level directory of the dataset. (default: "LibriSpeech")
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False).

__getitem__(n: int) → Tuple[torch.Tensor, int, str, int, int, int][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform, sample_rate, transcript, speaker_id, chapter_id, utterance_id)
Return type: (Tensor, int, str, int, int, int)

LibriLightLimited¶

class torchaudio.datasets.LibriLightLimited(root: Union[str, pathlib.Path], subset: str = '10min', download: bool = False)[source]¶

Create a Dataset for LibriLightLimited, which is the supervised subset of: LibriLight dataset.

Parameters

root (str or Path) – Path to the directory where the dataset is found or downloaded.
subset (str, optional) – The subset to use. Options: [10min, 1h, 10h] (Default: 10min).
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False).

__getitem__(n: int) → Tuple[torch.Tensor, int, str, int, int, int][source]¶

Load the n-th sample from the dataset. :param n: The index of the sample to be loaded :type n: int

Returns: (waveform, sample_rate, transcript, speaker_id, chapter_id, utterance_id)
Return type: (Tensor, int, str, int, int, int)

LIBRITTS¶

class torchaudio.datasets.LIBRITTS(root: Union[str, pathlib.Path], url: str = 'train-clean-100', folder_in_archive: str = 'LibriTTS', download: bool = False)[source]¶

Create a Dataset for LibriTTS [7].

Parameters

root (str or Path) – Path to the directory where the dataset is found or downloaded.
url (str, optional) – The URL to download the dataset from, or the type of the dataset to dowload. Allowed type values are "dev-clean", "dev-other", "test-clean", "test-other", "train-clean-100", "train-clean-360" and "train-other-500". (default: "train-clean-100")
folder_in_archive (str, optional) – The top-level directory of the dataset. (default: "LibriTTS")
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False).

__getitem__(n: int) → Tuple[torch.Tensor, int, str, str, int, int, str][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform, sample_rate, original_text, normalized_text, speaker_id, chapter_id, utterance_id)
Return type: (Tensor, int, str, str, str, int, int, str)

LJSPEECH¶

class torchaudio.datasets.LJSPEECH(root: Union[str, pathlib.Path], url: str = 'https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2', folder_in_archive: str = 'wavs', download: bool = False)[source]¶

Create a Dataset for LJSpeech-1.1 [8].

Parameters

root (str or Path) – Path to the directory where the dataset is found or downloaded.
url (str, optional) – The URL to download the dataset from. (default: "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2")
folder_in_archive (str, optional) – The top-level directory of the dataset. (default: "wavs")
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False).

__getitem__(n: int) → Tuple[torch.Tensor, int, str, str][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform, sample_rate, transcript, normalized_transcript)
Return type: (Tensor, int, str, str)

SPEECHCOMMANDS¶

class torchaudio.datasets.SPEECHCOMMANDS(root: Union[str, pathlib.Path], url: str = 'speech_commands_v0.02', folder_in_archive: str = 'SpeechCommands', download: bool = False, subset: Optional[str] = None)[source]¶

Create a Dataset for Speech Commands [9].

Parameters

root (str or Path) – Path to the directory where the dataset is found or downloaded.
url (str, optional) – The URL to download the dataset from, or the type of the dataset to dowload. Allowed type values are "speech_commands_v0.01" and "speech_commands_v0.02" (default: "speech_commands_v0.02")
folder_in_archive (str, optional) – The top-level directory of the dataset. (default: "SpeechCommands")
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False).
subset (str or None, optional) – Select a subset of the dataset [None, “training”, “validation”, “testing”]. None means the whole dataset. “validation” and “testing” are defined in “validation_list.txt” and “testing_list.txt”, respectively, and “training” is the rest. Details for the files “validation_list.txt” and “testing_list.txt” are explained in the README of the dataset and in the introduction of Section 7 of the original paper and its reference 12. The original paper can be found here. (Default: None)

__getitem__(n: int) → Tuple[torch.Tensor, int, str, str, int][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform, sample_rate, label, speaker_id, utterance_number)
Return type: (Tensor, int, str, str, int)

TEDLIUM¶

class torchaudio.datasets.TEDLIUM(root: Union[str, pathlib.Path], release: str = 'release1', subset: str = 'train', download: bool = False, audio_ext: str = '.sph')[source]¶

Create a Dataset for Tedlium [10]. It supports releases 1,2 and 3.

Parameters

root (str or Path) – Path to the directory where the dataset is found or downloaded.
release (str, optional) – Release version. Allowed values are "release1", "release2" or "release3". (default: "release1").
subset (str, optional) – The subset of dataset to use. Valid options are "train", "dev", and "test". Defaults to "train".
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False).
audio_ext (str, optional) – extension for audio file (default: ".sph")

__getitem__(n: int) → Tuple[torch.Tensor, int, str, int, int, int][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform, sample_rate, transcript, talk_id, speaker_id, identifier)
Return type: tuple

property phoneme_dict¶

Phonemes. Mapping from word to tuple of phonemes. Note that some words have empty phonemes.

Type: dict[str, tuple[str]]

VCTK_092¶

class torchaudio.datasets.VCTK_092(root: str, mic_id: str = 'mic2', download: bool = False, url: str = 'https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip', audio_ext='.flac')[source]¶

Create VCTK 0.92 [11] Dataset

Parameters

root (str) – Root directory where the dataset’s top level directory is found.
mic_id (str, optional) – Microphone ID. Either "mic1" or "mic2". (default: "mic2")
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False).
url (str, optional) – The URL to download the dataset from. (default: "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip")
audio_ext (str, optional) – Custom audio extension if dataset is converted to non-default audio format.

Note

All the speeches from speaker p315 will be skipped due to the lack of the corresponding text files.
All the speeches from p280 will be skipped for mic_id="mic2" due to the lack of the audio files.
Some of the speeches from speaker p362 will be skipped due to the lack of the audio files.
See Also: https://datashare.is.ed.ac.uk/handle/10283/3443

__getitem__(n: int) → Tuple[torch.Tensor, int, str, str, str][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform, sample_rate, transcript, speaker_id, utterance_id)
Return type: (Tensor, int, str, str, str)

DR_VCTK¶

class torchaudio.datasets.DR_VCTK(root: Union[str, pathlib.Path], subset: str = 'train', *, download: bool = False, url: str = 'https://datashare.ed.ac.uk/bitstream/handle/10283/3038/DR-VCTK.zip')[source]¶

Create a dataset for Device Recorded VCTK (Small subset version) [12].

Parameters

root (str or Path) – Root directory where the dataset’s top level directory is found.
subset (str) – The subset to use. Can be one of "train" and "test". (default: "train").
download (bool) – Whether to download the dataset if it is not found at root path. (default: False).
url (str) – The URL to download the dataset from. (default: "https://datashare.ed.ac.uk/bitstream/handle/10283/3038/DR-VCTK.zip")

__getitem__(n: int) → Tuple[torch.Tensor, int, torch.Tensor, int, str, str, str, int][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform_clean, sample_rate_clean, waveform_noisy, sample_rate_noisy, speaker_id, utterance_id, source, channel_id)
Return type: (Tensor, int, Tensor, int, str, str, str, int)

YESNO¶

class torchaudio.datasets.YESNO(root: Union[str, pathlib.Path], url: str = 'http://www.openslr.org/resources/1/waves_yesno.tar.gz', folder_in_archive: str = 'waves_yesno', download: bool = False)[source]¶

Create a Dataset for YesNo [13].

Parameters

root (str or Path) – Path to the directory where the dataset is found or downloaded.
url (str, optional) – The URL to download the dataset from. (default: "http://www.openslr.org/resources/1/waves_yesno.tar.gz")
folder_in_archive (str, optional) – The top-level directory of the dataset. (default: "waves_yesno")
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False).

Tutorials using YESNO:: Audio Datasets¶

__getitem__(n: int) → Tuple[torch.Tensor, int, List[int]][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform, sample_rate, labels)
Return type: (Tensor, int, List[int])

QUESST14¶

class torchaudio.datasets.QUESST14(root: Union[str, pathlib.Path], subset: str, language: Optional[str] = 'nnenglish', download: bool = False)[source]¶

Create QUESST14 [14] Dataset

Parameters

root (str or Path) – Root directory where the dataset’s top level directory is found
subset (str) – Subset of the dataset to use. Options: ["docs", "dev", "eval"].
language (str or None, optional) – Language to get dataset for. Options: [None, albanian, basque, czech, nnenglish, romanian, slovak]. If None, dataset consists of all languages. (default: "nnenglish")
download (bool, optional) – Whether to download the dataset if it is not found at root path. (default: False)

__getitem__(n: int) → Tuple[torch.Tensor, int, str][source]¶

Load the n-th sample from the dataset.

Parameters: n (int) – The index of the sample to be loaded
Returns: (waveform, sample_rate, file_name)
Return type: (Tensor, int, str)

References¶

1: John Kominek, Alan W Black, and Ver Ver. Cmu arctic databases for speech synthesis. Technical Report, 2003.
2: R.L. Weide. The carnegie mellon pronuncing dictionary. 1998. URL: http://www.speech.cs.cmu.edu/cgi-bin/cmudict.
3: Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, Francis M. Tyers, and Gregor Weber. Common voice: a massively-multilingual speech corpus. 2020. arXiv:1912.06670.
4: George Tzanetakis, Georg Essl, and Perry Cook. Automatic musical genre classification of audio signals. 2001. URL: http://ismir2001.ismir.net/pdf/tzanetakis.pdf.
5: Joris Cosentino, Manuel Pariente, Samuele Cornell, Antoine Deleforge, and Emmanuel Vincent. Librimix: an open-source dataset for generalizable speech separation. 2020. arXiv:2005.11262.
6: Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. Librispeech: an asr corpus based on public domain audio books. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), volume, 5206–5210. 2015. doi:10.1109/ICASSP.2015.7178964.
7: Heiga Zen, Viet-Trung Dang, Robert A. J. Clark, Yu Zhang, Ron J. Weiss, Ye Jia, Z. Chen, and Yonghui Wu. Libritts: a corpus derived from librispeech for text-to-speech. ArXiv, 2019.
8: Keith Ito and Linda Johnson. The lj speech dataset. https://keithito.com/LJ-Speech-Dataset/, 2017.
9: P. Warden. Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition. ArXiv e-prints, April 2018. URL: https://arxiv.org/abs/1804.03209, arXiv:1804.03209.
10: Anthony Rousseau, Paul Deléglise, and Yannick Estève. Ted-lium: an automatic speech recognition dedicated corpus. In Conference on Language Resources and Evaluation (LREC), 125–129. 2012.
11: Junichi Yamagishi, Christophe Veaux, and Kirsten MacDonald. CSTR VCTK Corpus: english multi-speaker corpus for CSTR voice cloning toolkit (version 0.92). 2019. doi:10.7488/ds/2645.
12: Seyyed Saeed Sarfjoo and Junichi Yamagishi. Device recorded vctk (small subset version). 2018.
13: Yesno. URL: http://www.openslr.org/1/.
14: Xavier Anguera Miro, Luis Javier Rodriguez-Fuentes, Andi Buzo, Florian Metze, Igor Szoke, and Mikel Peñagarikano. Quesst2014: evaluating query-by-example speech search in a zero-resource setting with real-life queries. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5833–5837, 2015.

torchaudio.datasets¶

CMUARCTIC¶

CMUDict¶

COMMONVOICE¶

GTZAN¶

LibriMix¶

LIBRISPEECH¶

LibriLightLimited¶

LIBRITTS¶

LJSPEECH¶

SPEECHCOMMANDS¶

TEDLIUM¶

VCTK_092¶

DR_VCTK¶

YESNO¶

QUESST14¶

References¶

Docs

Tutorials

Resources