ylacombe/google-tamil

Dataset Card for Tamil Speech

Dataset Summary

This dataset consists of 7 hours of transcribed high-quality audio of Tamil sentences recorded by 50 volunteers. The dataset is intended for speech technologies.

The data archives were restructured from the original ones from OpenSLR to make it easier to stream.

Supported Tasks

• text-to-speech, text-to-audio: The dataset can be used to train a model for Text-To-Speech (TTS).
• automatic-speech-recognition, speaker-identification: The dataset can also be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).

How to use

The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.

For example, to download the female config, simply specify the corresponding language config name (i.e., "female" for female speakers):

from datasets import load_dataset

dataset =load_dataset("ylacombe/google-tamil", "female", split="train")

Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.

from datasets import load_dataset

dataset =load_dataset("ylacombe/google-tamil", "female", split="train", streaming=True)

print(next(iter(dataset)))

Bonus

You can create a PyTorch dataloader directly with your own datasets (local/streamed).

Local:

from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler

dataset =load_dataset("ylacombe/google-tamil", "female", split="train")
batch_sampler = BatchSampler(RandomSampler(dataset), batch_size=32, drop_last=False)
dataloader = DataLoader(dataset, batch_sampler=batch_sampler)

Streaming:

from datasets import load_dataset
from torch.utils.data import DataLoader

dataset =load_dataset("ylacombe/google-tamil", "female", split="train", streaming=True)
dataloader = DataLoader(dataset, batch_size=32)

To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.

Dataset Structure

Data Instances

A typical data point comprises the path to the audio file called audio and its transcription, called text. Some additional information about the speaker and the passage which contains the transcription is provided.

{'audio': {'path': 'taf_02345_00348037167.wav', 'array': array([-9.15527344e-05, -9.15527344e-05, -1.22070312e-04, ...,
       -3.05175781e-05,  0.00000000e+00,  3.05175781e-05]), 'sampling_rate': 48000}, 'text': 'ஆஸ்த்ரேலியப் பெண்ணுக்கு முப்பத்தி மூன்று ஆண்டுகளுக்குப் பின்னர் இந்தியா இழப்பீடு வழங்கியது', 'speaker_id': 2345}

Data Fields

• audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0].

• text: the transcription of the audio file.

• speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.

Data Statistics

	Total duration (h)	Average duration (s)	# speakers	# sentences	# total words	# unique words	# total syllables	# unique syllables	# total phonemes	# unique phonemes
Female	4.01	6.18	25	2,335	15,880	6,620	56,607	1,696	126,659	37
Male	3.07	5.66	25	1,956	13,545	6,159	48,049	1,642	107,570	37

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

License: (CC BY-SA 4.0 DEED)

Citation Information

@inproceedings{he-etal-2020-open,
    title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems}},
    author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot},
    booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)},
    month = may,
    year = {2020},
    address = {Marseille, France},
    publisher = {European Language Resources Association (ELRA)},
    pages = {6494--6503},
    url = {https://www.aclweb.org/anthology/2020.lrec-1.800},
    ISBN = "{979-10-95546-34-4},
  }

Contributions

Thanks to @ylacombe for adding this dataset.