Upload README.md

README.md

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 ---
-license: apache-2.0
+annotations_creators:
+- expert-generated
+- crowdsourced
+- machine-generated
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- afr
+- amh
+- ara
+- asm
+- ast
+- azj
+- bel
+- ben
+- bos
+- cat
+- ceb
+- cmn
+- ces
+- cym
+- dan
+- deu
+- ell
+- eng
+- spa
+- est
+- fas
+- ful
+- fin
+- tgl
+- fra
+- gle
+- glg
+- guj
+- hau
+- heb
+- hin
+- hrv
+- hun
+- hye
+- ind
+- ibo
+- isl
+- ita
+- jpn
+- jav
+- kat
+- kam
+- kea
+- kaz
+- khm
+- kan
+- kor
+- ckb
+- kir
+- ltz
+- lug
+- lin
+- lao
+- lit
+- luo
+- lav
+- mri
+- mkd
+- mal
+- mon
+- mar
+- msa
+- mlt
+- mya
+- nob
+- npi
+- nld
+- nso
+- nya
+- oci
+- orm
+- ory
+- pan
+- pol
+- pus
+- por
+- ron
+- rus
+- bul
+- snd
+- slk
+- slv
+- sna
+- som
+- srp
+- swe
+- swh
+- tam
+- tel
+- tgk
+- tha
+- tur
+- ukr
+- umb
+- urd
+- uzb
+- vie
+- wol
+- xho
+- yor
+- yue
+- zul
+license:
+- cc-by-4.0
+multilinguality:
+- multilingual
+size_categories:
+- 10K<n<100K
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+pretty_name: 'The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech
+  (XTREME-S) benchmark is a benchmark designed to evaluate speech representations
+  across languages, tasks, domains and data regimes. It covers 102 languages from
+  10+ language families, 3 different domains and 4 task families: speech recognition,
+  translation, classification and retrieval.'
+tags:
+- speech-recognition
 ---
+
+# FLEURS
+
+## Dataset Description
+
+- **Fine-Tuning script:** [pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition)
+- **Paper:** [FLEURS: Few-shot Learning Evaluation of
+Universal Representations of Speech](https://arxiv.org/abs/2205.12446)
+- **Total amount of disk used:** ca. 350 GB
+
+Fleurs is the speech version of the [FLoRes machine translation benchmark](https://arxiv.org/abs/2106.03193). 
+We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages. 
+
+Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is
+used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven geographical areas: 
+
+- **Western Europe**: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh* 
+- **Eastern Europe**: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian*
+- **Central-Asia/Middle-East/North-Africa**: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek*
+- **Sub-Saharan Africa**: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu*
+- **South-Asia**: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu*
+- **South-East Asia**: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese*
+- **CJK languages**: *Cantonese and Mandarin Chinese, Japanese, Korean*
+
+
+## How to use & Supported Tasks
+
+### 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 Hindi config, simply specify the corresponding language config name (i.e., "hi_in" for Hindi):
+```python
+from datasets import load_dataset
+fleurs = load_dataset("google/fleurs", "hi_in", 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
+fleurs = load_dataset("google/fleurs", "hi_in", split="train", streaming=True)
+print(next(iter(fleurs)))
+```
+
+*Bonus*: 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
+fleurs = load_dataset("google/fleurs", "hi_in", split="train")
+batch_sampler = BatchSampler(RandomSampler(fleurs), batch_size=32, drop_last=False)
+dataloader = DataLoader(fleurs, batch_sampler=batch_sampler)
+```
+
+Streaming:
+
+```python
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+fleurs = load_dataset("google/fleurs", "hi_in", split="train")
+dataloader = DataLoader(fleurs, 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).
+
+### Example scripts
+
+Train your own CTC or Seq2Seq Automatic Speech Recognition models on FLEURS with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
+
+Fine-tune your own Language Identification models on FLEURS with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification)
+
+### 1. Speech Recognition (ASR)
+
+```py
+from datasets import load_dataset
+
+fleurs_asr = load_dataset("google/fleurs", "af_za")  # for Afrikaans
+# to download all data for multi-lingual fine-tuning uncomment following line
+# fleurs_asr = load_dataset("google/fleurs", "all")
+
+# see structure
+print(fleurs_asr)
+
+# load audio sample on the fly
+audio_input = fleurs_asr["train"][0]["audio"]  # first decoded audio sample
+transcription = fleurs_asr["train"][0]["transcription"]  # first transcription
+# use `audio_input` and `transcription` to fine-tune your model for ASR
+
+# for analyses see language groups
+all_language_groups = fleurs_asr["train"].features["lang_group_id"].names
+lang_group_id = fleurs_asr["train"][0]["lang_group_id"]
+
+all_language_groups[lang_group_id]
+```
+
+### 2. Language Identification
+
+LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all.
+
+```py
+from datasets import load_dataset
+
+fleurs_langID = load_dataset("google/fleurs", "all") # to download all data
+
+# see structure
+print(fleurs_langID)
+
+# load audio sample on the fly
+audio_input = fleurs_langID["train"][0]["audio"]  # first decoded audio sample
+language_class = fleurs_langID["train"][0]["lang_id"]  # first id class
+language = fleurs_langID["train"].features["lang_id"].names[language_class]
+
+# use audio_input and language_class to fine-tune your model for audio classification
+```
+
+### 3. Retrieval
+
+Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult.
+
+```py
+from datasets import load_dataset
+
+fleurs_retrieval = load_dataset("google/fleurs", "af_za")  # for Afrikaans
+# to download all data for multi-lingual fine-tuning uncomment following line
+# fleurs_retrieval = load_dataset("google/fleurs", "all")
+
+# see structure
+print(fleurs_retrieval)
+
+# load audio sample on the fly
+audio_input = fleurs_retrieval["train"][0]["audio"]  # decoded audio sample
+text_sample_pos = fleurs_retrieval["train"][0]["transcription"]  # positive text sample
+text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples
+
+# use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval
+```
+
+Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech.
+
+## Dataset Structure
+
+We show detailed information the example configurations `af_za` of the dataset.
+All other configurations have the same structure.
+
+### Data Instances
+
+**af_za**
+- Size of downloaded dataset files: 1.47 GB
+- Size of the generated dataset: 1 MB
+- Total amount of disk used: 1.47 GB
+
+An example of a data instance of the config `af_za` looks as follows:
+
+```
+{'id': 91,
+ 'num_samples': 385920,
+ 'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav',
+ 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav',
+  'array': array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
+         -1.1205673e-04, -8.4638596e-05, -1.2731552e-04], dtype=float32),
+  'sampling_rate': 16000},
+ 'raw_transcription': 'Dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin',
+ 'transcription': 'dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin',
+ 'gender': 0,
+ 'lang_id': 0,
+ 'language': 'Afrikaans',
+ 'lang_group_id': 3}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+- **id** (int): ID of audio sample
+- **num_samples** (int): Number of float values
+- **path** (str): Path to the audio file
+- **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio
+- **raw_transcription** (str): The non-normalized transcription of the audio file
+- **transcription** (str): Transcription of the audio file
+- **gender** (int): Class id of gender
+- **lang_id** (int): Class id of language
+- **lang_group_id** (int): Class id of language group
+
+### Data Splits
+
+Every config only has the `"train"` split containing of *ca.* 1000 examples, and a `"validation"` and `"test"` split each containing of *ca.* 400 examples.
+
+## Dataset Creation
+
+We collect between one and three recordings for each sentence (2.3 on average), and buildnew train-dev-test splits with 1509, 150 and 350 sentences for
+train, dev and test respectively.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos).
+
+### Discussion of Biases
+
+Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through FLEURS should generalize to all languages.
+
+### Other Known Limitations
+
+The dataset has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on FLEURS should still correlate well with actual progress made for speech understanding.
+
+## Additional Information
+
+All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/).
+
+### Citation Information
+
+You can access the FLEURS paper at https://arxiv.org/abs/2205.12446.
+Please cite the paper when referencing the FLEURS corpus as:
+
+```
+@article{fleurs2022arxiv,
+  title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},
+  author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},
+  journal={arXiv preprint arXiv:2205.12446},
+  url = {https://arxiv.org/abs/2205.12446},
+  year = {2022},
+```
+
+### Contributions
+
+Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@aconneau](https://github.com/aconneau) for adding this dataset.