README.md

metadata

dataset_info:
  - config_name: female
    features:
      - name: audio
        dtype: audio
      - name: text
        dtype: string
      - name: speaker_id
        dtype: int64
    splits:
      - name: train
        num_bytes: 974926631.856
        num_examples: 1738
    download_size: 762982190
    dataset_size: 974926631.856
  - config_name: male
    features:
      - name: audio
        dtype: audio
      - name: text
        dtype: string
      - name: speaker_id
        dtype: int64
    splits:
      - name: train
        num_bytes: 1472568181.048
        num_examples: 2636
    download_size: 1133624286
    dataset_size: 1472568181.048
configs:
  - config_name: female
    data_files:
      - split: train
        path: female/train-*
  - config_name: male
    data_files:
      - split: train
        path: male/train-*
task_categories:
  - text-to-speech
  - text-to-audio
language:
  - es
pretty_name: Chilean Spanish Speech
license: cc-by-sa-4.0

Dataset Card for Tamil Speech

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • How to use
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Statistics
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: Crowdsourced high-quality Chilean Spanish speech data set.
• Repository: Google Language Resources and Tools
• Paper: Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech

Dataset Summary

This dataset consists of 7 hours of transcribed high-quality audio of Chilean Spanish sentences recorded by 31 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-chilean-spanish", "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-chilean-spanish", "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-chilean-spanish", "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-chilean-spanish", "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': 'clf_09334_01278378087.wav', 'array': array([-9.15527344e-05, -4.57763672e-04, -4.88281250e-04, ...,
        1.86157227e-03,  2.10571289e-03,  2.31933594e-03]), 'sampling_rate': 48000}, 'text': 'La vigencia de tu tarjeta es de ocho meses', 'speaker_id': 9334}

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)	# speakers	# sentences	# total words	# unique words
Female	2.84	13	1738	16591	3279
Male	4.31	18	2636	25168	4171

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{guevara-rukoz-etal-2020-crowdsourcing,
    title = {{Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech}},
    author = {Guevara-Rukoz, Adriana and Demirsahin, Isin and He, Fei and Chu, Shan-Hui Cathy and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Gutkin, Alexander and Butryna, Alena and Kjartansson, Oddur},
    booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)},
    year = {2020},
    month = may,
    address = {Marseille, France},
    publisher = {European Language Resources Association (ELRA)},
    url = {https://www.aclweb.org/anthology/2020.lrec-1.801},
    pages = {6504--6513},
    ISBN = {979-10-95546-34-4},
  }

Contributions

Thanks to @ylacombe for adding this dataset.