Update README.md

README.md

@@ -23,10 +23,10 @@ dataset_info:
     num_bytes: 11127461686.356
     num_examples: 18719
   - name: dev
-    num_bytes: 356048249.0
+    num_bytes: 356048249
     num_examples: 853
   - name: test
-    num_bytes: 367796887.0
+    num_bytes: 367796887
     num_examples: 814
   download_size: 8114633186
   dataset_size: 11851306822.356
@@ -39,7 +39,232 @@ configs:
     path: polish/dev-*
   - split: test
     path: polish/test-*
+license: cc-by-4.0
+task_categories:
+- text-to-speech
+- text-to-audio
+language:
+- nl
+- fr
+- de
+- it
+- pl
+- pt
+- es
+pretty_name: CML-TTS
+size_categories:
+- 1M<n<10M
 ---
 # Dataset Card for "cml-tts"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks)
+  - [Languages](#languages)
+  - [How to use](#how-to-use)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+  - [Data Statistics](#data-statistics)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [MultiLingual LibriSpeech ASR corpus](https://www.openslr.org/146/)
+- **Repository:** [CML-TTS-Dataset](https://github.com/freds0/CML-TTS-Dataset)
+- **Paper:** [CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages](https://arxiv.org/abs/2306.10097)
+
+### Dataset Summary
+
+CML-TTS is a recursive acronym for CML-Multi-Lingual-TTS, a Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG).
+CML-TTS is a dataset comprising audiobooks sourced from the public domain books of Project Gutenberg, read by volunteers from the LibriVox project. The dataset includes recordings in Dutch, German, French, Italian, Polish, Portuguese, and Spanish, all at a sampling rate of 24kHz.
+
+The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/146) to make it easier to stream.
+
+
+### Supported Tasks
+
+- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
+
+### Languages
+
+The dataset includes recordings in Dutch, German, French, Italian, Polish, Portuguese, and Spanish, all at a sampling rate of 24kHz.
+
+### 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 German config, simply specify the corresponding language config name (i.e., "german" for German):
+```python
+from datasets import load_dataset
+
+mls = load_dataset("ylacombe/cml-tts", "german", 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.
+```python
+from datasets import load_dataset
+
+mls = load_dataset("ylacombe/cml-tts", "german", split="train", streaming=True)
+
+print(next(iter(mls)))
+```
+
+#### *Bonus*
+You can create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
+
+**Local:**
+
+```python
+from datasets import load_dataset
+from torch.utils.data.sampler import BatchSampler, RandomSampler
+
+mls = load_dataset("ylacombe/cml-tts", "german", split="train")
+batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
+dataloader = DataLoader(mls, batch_sampler=batch_sampler)
+```
+
+**Streaming:**
+
+```python
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+
+mls = load_dataset("ylacombe/cml-tts", "german", split="train", streaming=True)
+dataloader = DataLoader(mls, batch_size=32)
+```
+
+To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
+
+```
+{'audio': {'path': '6892_8912_000729.wav', 'array': array([-1.52587891e-...7344e-05]), 'sampling_rate': 24000}, 'wav_filesize': 601964, 'text': 'Proszę pana, tu pano... zdziwiony', 'transcript_wav2vec': 'proszę pana tu panow... zdziwiony', 'levenshtein': 0.96045197740113, 'duration': 13.648979591836737, 'num_words': 29, 'speaker_id': 6892}
+```
+
+### 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.
+
+- transcript_wav2vec: the transcription of the audio file using the wav2vec model. Has been used to curate the dataset.
+
+- wav_filesize: The size of the audio waveform file. Has been used to curate the dataset.
+
+- levenshtein: The [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) between the wav2vec transcription and the original transcription. Has been used to curate the dataset.
+
+- duration: The duration of the audio in seconds.
+
+- num_words: The number of words of the transcription.
+
+### Data Splits
+
+TODO
+
+### Data Statistics
+
+| Language   | Duration (Train) | Duration (Test) | Duration (Dev) | Speakers (Train) | Speakers (Test) | Speakers (Dev) |
+|------------|-------------------|------------------|----------------|------------------|-----------------|----------------|
+|            | M       | F       | M      | F      | M      | F      | M     | F     | M    | F    | M    | F    |
+| Dutch      | 482.82  | 162.17  | 2.46   | 1.29   | 2.24   | 1.67   | 8     | 27    | 3    | 3    | 2    | 4    |
+| French     | 260.08  | 24.04   | 2.48   | 3.55   | 3.31   | 2.72   | 25    | 20    | 8    | 9    | 10   | 8    |
+| German     | 1128.96 | 436.64  | 3.75   | 5.27   | 4.31   | 5.03   | 78    | 90    | 13   | 17   | 13   | 15   |
+| Italian    | 73.78   | 57.51   | 1.47   | 0.85   | 0.40   | 1.52   | 23    | 38    | 5    | 5    | 4    | 6    |
+| Polish     | 30.61   | 8.32    | 0.70   | 0.90   | 0.56   | 0.80   | 4     | 4     | 2    | 2    | 2    | 2    |
+| Portuguese | 23.14   | 44.81   | 0.28   | 0.24   | 0.68   | 0.20   | 20    | 10    | 5    | 4    | 6    | 3    |
+| Spanish    | 279.15  | 164.08  | 2.77   | 2.06   | 3.40   | 2.34   | 35    | 42    | 10   | 8    | 11   | 9    |
+| Total      | 3,176.13|         | 28.11  |         | 29.19  |         | 424   |       | 94   |      | 95   |      |
+
+
+## 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
+
+Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
+
+### Citation Information
+
+```
+@misc{oliveira2023cmltts,
+      title={CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages}, 
+      author={Frederico S. Oliveira and Edresson Casanova and Arnaldo Cândido Júnior and Anderson S. Soares and Arlindo R. Galvão Filho},
+      year={2023},
+      eprint={2306.10097},
+      archivePrefix={arXiv},
+      primaryClass={eess.AS}
+}
+```
+
+### Contributions
+
+Thanks to [@ylacombe](https://github.com/ylacombe) for adding this dataset.