mindspore.dataset.TedliumDataset

class mindspore.dataset.TedliumDataset(dataset_dir, release, usage=None, extensions=None, num_samples=None, num_parallel_workers=None, shuffle=None, sampler=None, num_shards=None, shard_id=None, cache=None)

Tedlium dataset. The columns of generated dataset depend on the source SPH files and the corresponding STM files.

The generated dataset has six columns [waveform, sample_rate, transcript, talk_id, speaker_id, identifier] .

The data type of column waveform is float32, the data type of column sample_rate is int32, and the data type of columns transcript , talk_id , speaker_id and identifier is string.

Parameters

• dataset_dir (str) – Path to the root directory that contains the dataset.

• release (str) – Release of the dataset, can be 'release1', 'release2', 'release3'.

• usage (str, optional) – Usage of this dataset. For release1 or release2, can be 'train', 'test', 'dev' or 'all'. 'train' will read from train samples, 'test' will read from test samples, 'dev' will read from dev samples, 'all' will read from all samples. For release3, can only be 'all', it will read from data samples. Default: None , all samples.

• extensions (str, optional) – Extensions of the SPH files, only '.sph' is valid. Default: None , set to ".sph".

• num_samples (int, optional) – The number of audio samples to be included in the dataset. Default: None , all samples.

• num_parallel_workers (int, optional) – Number of worker threads to read the data. Default: None , will use global default workers(8), it can be set by mindspore.dataset.config.set_num_parallel_workers() .

• shuffle (bool, optional) – Whether to perform shuffle on the dataset. Default: None , expected order behavior shown in the table below.

• sampler (Sampler, optional) – Object used to choose samples from the dataset. Default: None , expected order behavior shown in the table below.

• num_shards (int, optional) – Number of shards that the dataset will be divided into. Default: None . When this argument is specified, num_samples reflects the maximum sample number of per shard. Used in data parallel training .

• shard_id (int, optional) – The shard ID within num_shards . Default: None . This argument can only be specified when num_shards is also specified.

• cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. More details: Single-Node Data Cache . Default: None , which means no cache is used.

Raises

• RuntimeError – If dataset_dir does not contain stm files.

• RuntimeError – If sampler and shuffle are specified at the same time.

• RuntimeError – If sampler and num_shards/shard_id are specified at the same time.

• RuntimeError – If num_shards is specified but shard_id is None.

• RuntimeError – If shard_id is specified but num_shards is None.

• ValueError – If num_parallel_workers exceeds the max thread numbers.

• ValueError – If shard_id is not in range of [0, num_shards ).

Tutorial Examples:

• Load & Process Data With Dataset Pipeline

Note

• The parameters num_samples , shuffle , num_shards , shard_id can be used to control the sampler used in the dataset, and their effects when combined with parameter sampler are as follows.

Sampler obtained by different combinations of parameters sampler and num_samples , shuffle , num_shards , shard_id

Parameter sampler	Parameter num_shards / shard_id	Parameter shuffle	Parameter num_samples	Sampler Used
mindspore.dataset.Sampler type	None	None	None	sampler
numpy.ndarray,list,tuple,int type	/	/	num_samples	SubsetSampler(indices = sampler , num_samples = num_samples )
iterable type	/	/	num_samples	IterSampler(sampler = sampler , num_samples = num_samples )
None	num_shards / shard_id	None / True	num_samples	DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = True , num_samples = num_samples )
None	num_shards / shard_id	False	num_samples	DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = False , num_samples = num_samples )
None	None	None / True	None	RandomSampler(num_samples = num_samples )
None	None	None / True	num_samples	RandomSampler(replacement = True , num_samples = num_samples )
None	None	False	num_samples	SequentialSampler(num_samples = num_samples )

Examples

>>> import mindspore.dataset as ds
>>> # 1) Get all train samples from TEDLIUM_release1 dataset in sequence.
>>> dataset = ds.TedliumDataset(dataset_dir="/path/to/tedlium1_dataset_directory",
...                             release="release1", shuffle=False)
>>>
>>> # 2) Randomly select 10 samples from TEDLIUM_release2 dataset.
>>> dataset = ds.TedliumDataset(dataset_dir="/path/to/tedlium2_dataset_directory",
...                             release="release2", num_samples=10, shuffle=True)
>>>
>>> # 3) Get samples from TEDLIUM_release-3 dataset for shard 0 in a 2-way distributed training.
>>> dataset = ds.TedliumDataset(dataset_dir="/path/to/tedlium3_dataset_directory",
...                             release="release3", num_shards=2, shard_id=0)
>>>
>>> # In TEDLIUM dataset, each dictionary has keys : waveform, sample_rate, transcript, talk_id,
>>> # speaker_id and identifier.

About TEDLIUM_release1 dataset:

The TED-LIUM corpus is English-language TED talks, with transcriptions, sampled at 16kHz. It contains about 118 hours of speech.

About TEDLIUM_release2 dataset:

This is the TED-LIUM corpus release 2, licensed under Creative Commons BY-NC-ND 3.0. All talks and text are property of TED Conferences LLC. The TED-LIUM corpus was made from audio talks and their transcriptions available on the TED website. We have prepared and filtered these data in order to train acoustic models to participate to the International Workshop on Spoken Language Translation 2011 (the LIUM English/French SLT system reached the first rank in the SLT task).

About TEDLIUM_release-3 dataset:

This is the TED-LIUM corpus release 3, licensed under Creative Commons BY-NC-ND 3.0. All talks and text are property of TED Conferences LLC. This new TED-LIUM release was made through a collaboration between the Ubiqus company and the LIUM (University of Le Mans, France).

You can unzip the dataset files into the following directory structure and read by MindSpore's API.

The structure of TEDLIUM release2 is the same as TEDLIUM release1, only the data is different.

.
└──TEDLIUM_release1
    └── dev
        ├── sph
            ├── AlGore_2009.sph
            ├── BarrySchwartz_2005G.sph
        ├── stm
            ├── AlGore_2009.stm
            ├── BarrySchwartz_2005G.stm
    └── test
        ├── sph
            ├── AimeeMullins_2009P.sph
            ├── BillGates_2010.sph
        ├── stm
            ├── AimeeMullins_2009P.stm
            ├── BillGates_2010.stm
    └── train
        ├── sph
            ├── AaronHuey_2010X.sph
            ├── AdamGrosser_2007.sph
        ├── stm
            ├── AaronHuey_2010X.stm
            ├── AdamGrosser_2007.stm
    └── readme
    └── TEDLIUM.150k.dic

The directory structure of TEDLIUM release3 is slightly different.

.
└──TEDLIUM_release-3
    └── data
        ├── ctl
        ├── sph
            ├── 911Mothers_2010W.sph
            ├── AalaElKhani.sph
        ├── stm
            ├── 911Mothers_2010W.stm
            ├── AalaElKhani.stm
    └── doc
    └── legacy
    └── LM
    └── speaker-adaptation
    └── readme
    └── TEDLIUM.150k.dic

Citation:

@article{
  title={TED-LIUM: an automatic speech recognition dedicated corpus},
  author={A. Rousseau, P. Deléglise, Y. Estève},
  journal={Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)},
  year={May 2012},
  biburl={https://www.openslr.org/7/}
}

@article{
  title={Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks},
  author={A. Rousseau, P. Deléglise, and Y. Estève},
  journal={Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)},
  year={May 2014},
  biburl={https://www.openslr.org/19/}
}

@article{
  title={TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation},
  author={François Hernandez, Vincent Nguyen, Sahar Ghannay, Natalia Tomashenko, and Yannick Estève},
  journal={the 20th International Conference on Speech and Computer (SPECOM 2018)},
  year={September 2018},
  biburl={https://www.openslr.org/51/}
}

Pre-processing Operation

mindspore.dataset.Dataset.apply	Apply a function in this dataset.
mindspore.dataset.Dataset.concat	Concatenate the dataset objects in the input list.
mindspore.dataset.Dataset.filter	Filter dataset by prediction.
mindspore.dataset.Dataset.flat_map	Map func to each row in dataset and flatten the result.
mindspore.dataset.Dataset.map	Apply each operation in operations to this dataset.
mindspore.dataset.Dataset.project	The specified columns will be selected from the dataset and passed into the pipeline with the order specified.
mindspore.dataset.Dataset.rename	Rename the columns in input datasets.
mindspore.dataset.Dataset.repeat	Repeat this dataset count times.
mindspore.dataset.Dataset.reset	Reset the dataset for next epoch.
mindspore.dataset.Dataset.save	Save the dynamic data processed by the dataset pipeline in common dataset format.
mindspore.dataset.Dataset.shuffle	Shuffle the dataset by creating a cache with the size of buffer_size .
mindspore.dataset.Dataset.skip	Skip the first N elements of this dataset.
mindspore.dataset.Dataset.split	Split the dataset into smaller, non-overlapping datasets.
mindspore.dataset.Dataset.take	Take the first specified number of samples from the dataset.
mindspore.dataset.Dataset.zip	Zip the datasets in the sense of input tuple of datasets.

Batch

mindspore.dataset.Dataset.batch	Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first.
mindspore.dataset.Dataset.bucket_batch_by_length	Bucket elements according to their lengths.
mindspore.dataset.Dataset.padded_batch	Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first.

Iterator

mindspore.dataset.Dataset.create_dict_iterator	Create an iterator over the dataset that yields samples of type dict, while the key is the column name and the value is the data.
mindspore.dataset.Dataset.create_tuple_iterator	Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column.

Attribute

mindspore.dataset.Dataset.get_batch_size	Return the size of batch.
mindspore.dataset.Dataset.get_class_indexing	Get the mapping dictionary from category names to category indexes.
mindspore.dataset.Dataset.get_col_names	Return the names of the columns in dataset.
mindspore.dataset.Dataset.get_dataset_size	Return the number of batches in an epoch.
mindspore.dataset.Dataset.get_repeat_count	Get the replication times in RepeatDataset.
mindspore.dataset.Dataset.input_indexs	Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode.
mindspore.dataset.Dataset.num_classes	Get the number of classes in a dataset.
mindspore.dataset.Dataset.output_shapes	Get the shapes of output data.
mindspore.dataset.Dataset.output_types	Get the types of output data.

Apply Sampler

mindspore.dataset.MappableDataset.add_sampler	Add a child sampler for the current dataset.
mindspore.dataset.MappableDataset.use_sampler	Replace the last child sampler of the current dataset, remaining the parent sampler unchanged.

Others

mindspore.dataset.Dataset.sync_update	Release a blocking condition and trigger callback with given data.
mindspore.dataset.Dataset.sync_wait	Add a blocking condition to the input Dataset and a synchronize action will be applied.
mindspore.dataset.Dataset.to_json	Serialize a pipeline into JSON string and dump into file if filename is provided.