Pulls and pre-processes major Open Source (non-commercial mostly) datasets for spoken audio
Pulls and pre-processes major Open Source datasets for spoken audio
- Supported Datasets:
- This is intended for use on Linux servers and it is expected that you will be using the library to feed a machine learning system (not necessary, but that’s sort of the point of collecting these datasets)
- MIT license for the software, but please note that the datasets themselves are generally for non-commercial use only
- Downloads common Open Source datasets and performs basic preprocessing on them
- Provides iterables that produce Numpy arrays from the audio data in common formats
- Uses sphfile to directly accesses sph files instead of needing to convert to wav first
- Uses a single shared location for the datasets intended to be used by multiple projects
You need to create the download directory and make it writable by the running user. Preferably you will do that via group-based permissions to allow sharing, but we will here show creation of a user-specific ownership:
$ mkdir -p /var/datasets $ chown user:group /var/datasets $ chmod g+rw /var/datasets
if /var/datasets doesn’t exist, or isn’t writable, the downloader will instead populate ~/.config/datasets with the data. You may wish to link that directory to /var/datasets so that you can use default instantiations of the corpora:
$ ln -s /var/datasets ~/.config/datasets
Note that the downloader expects that you have the following available, this may not yet be the case in a docker or minimal OS installation:
Now you can download the datasets.
The datasets are big (100+GB)!
If you are paying for data or are working on a slow connection you will likely want to arrange to do this step during a low-rated period or on a separate data connection.
From a command prompt:
$ pip install audiodatasets # this will download 100+GB and then unpack it on disk, it will take a while... $ audiodatasets-download
Creating MFCC data-files:
# this will generate Multi-frequency Cepestral Coefficient (MFCC) summaries for the # audio datasets (and download them if that hasn't been done). This isn't necessary # if you are doing only raw-audio processing $ audiodatasets-preprocess
Playing some audio:
# this will iterate through playing every utterance that includes 'moon' in the transcript $ audiodatasets-search 'moon'
Once setup, you likely want to iterate over the data-sets using, for instance, a partition to separate out test/train/validate data. To iterate over the raw audio:
from audiodatasets.corpora import build_corpora, partition import random def train_valid_test(): """Create training, validation and tests datasets returns three iterators yielding (array[10:512],transcript) batches """ utterances =  for corpus in build_corpora(): utterances.extend( corpus.iter_utterances()) random.shuffle(utterances) train, test,valid = partition( utterances, (3,1,1) ) def generation( utterances ): while True: offset = random.randint(0,511) for name,transcript,audio_file in utterances: for batch in t.iter_batches( audio_file, batch_size=10, input=512, offset=offset ): yield batch,transcript return generation(train),generation(test),generation(valid)
To iterate over the 10ms MFCC preprocessed data, which yields 20 frequency batches per processing window (10ms):
from audiodatasets.corpora import build_corpora, partition import random def train_valid_test(): """Create training, validation and tests datasets Note: the batches vary in *time* at highest frequency, while the frequency bins are the second-highest frequency. See: `LibRosa MFCC <https://librosa.github.io/librosa/generated/librosa.feature.mfcc.html>`_ returns three iterators yielding (array[10:20:63],transcript) batches """ utterances =  for corpus in build_corpora(): utterances.extend( corpus.mfcc_utterances()) random.shuffle(utterances) train, test,valid = partition( utterances, (3,1,1) ) def generation( utterances ): while True: offset = random.randint(0,62) for name,transcript,audio_file in utterances: for batch in t.iter_batches( audio_file, batch_size=10, input=63, offset=offset ): yield batch,transcript return generation(train),generation(test),generation(valid)
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|Filename, size & hash||File type||Python version||Upload date|
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