Datasets:
Tasks:
Automatic Speech Recognition
Sub-tasks:
keyword-spotting
Size:
10K<n<100K
ArXiv:
Tags:
speech-recognition
License:
Krazybeautiful85
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README (1).md
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---
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annotations_creators:
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- expert-generated
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- crowdsourced
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- machine-generated
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language_creators:
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- crowdsourced
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- expert-generated
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language:
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- en
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- fr
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- it
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- es
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- pt
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- de
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- nl
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- ru
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- pl
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- cs
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- ko
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- zh
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license:
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- cc-by-4.0
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multilinguality:
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- multilingual
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size_categories:
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- 10K<n<100K
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task_categories:
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- automatic-speech-recognition
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task_ids:
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- keyword-spotting
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pretty_name: MInDS-14
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language_bcp47:
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- en
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- en-GB
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- en-US
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- en-AU
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- fr
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- it
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- es
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- pt
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- de
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- nl
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- ru
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- pl
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- cs
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- ko
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- zh
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tags:
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- speech-recognition
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---
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# MInDS-14
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## Dataset Description
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- **Fine-Tuning script:** [pytorch/audio-classification](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification)
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- **Paper:** [Multilingual and Cross-Lingual Intent Detection from Spoken Data](https://arxiv.org/abs/2104.08524)
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- **Total amount of disk used:** ca. 500 MB
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MINDS-14 is training and evaluation resource for intent detection task with spoken data. It covers 14
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intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties.
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## Example
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MInDS-14 can be downloaded and used as follows:
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```py
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from datasets import load_dataset
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minds_14 = load_dataset("PolyAI/minds14", "fr-FR") # for French
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# to download all data for multi-lingual fine-tuning uncomment following line
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# minds_14 = load_dataset("PolyAI/all", "all")
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# see structure
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print(minds_14)
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# load audio sample on the fly
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audio_input = minds_14["train"][0]["audio"] # first decoded audio sample
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intent_class = minds_14["train"][0]["intent_class"] # first transcription
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intent = minds_14["train"].features["intent_class"].names[intent_class]
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# use audio_input and language_class to fine-tune your model for audio classification
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```
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## Dataset Structure
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We show detailed information the example configurations `fr-FR` of the dataset.
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All other configurations have the same structure.
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### Data Instances
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**fr-FR**
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- Size of downloaded dataset files: 471 MB
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- Size of the generated dataset: 300 KB
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- Total amount of disk used: 471 MB
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An example of a datainstance of the config `fr-FR` looks as follows:
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```
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{
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"path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav",
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"audio": {
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"path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav",
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"array": array(
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[0.0, 0.0, 0.0, ..., 0.0, 0.00048828, -0.00024414], dtype=float32
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),
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"sampling_rate": 8000,
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},
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"transcription": "je souhaite changer mon adresse",
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"english_transcription": "I want to change my address",
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"intent_class": 1,
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"lang_id": 6,
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}
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```
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### Data Fields
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The data fields are the same among all splits.
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- **path** (str): Path to the audio file
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- **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio
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- **transcription** (str): Transcription of the audio file
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- **english_transcription** (str): English transcription of the audio file
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- **intent_class** (int): Class id of intent
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- **lang_id** (int): Id of language
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### Data Splits
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Every config only has the `"train"` split containing of *ca.* 600 examples.
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## Dataset Creation
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Discussion of Biases
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Other Known Limitations
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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## Additional Information
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### Dataset Curators
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Licensing Information
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All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/).
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### Citation Information
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```
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@article{DBLP:journals/corr/abs-2104-08524,
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author = {Daniela Gerz and
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Pei{-}Hao Su and
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Razvan Kusztos and
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Avishek Mondal and
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Michal Lis and
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Eshan Singhal and
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Nikola Mrksic and
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Tsung{-}Hsien Wen and
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Ivan Vulic},
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title = {Multilingual and Cross-Lingual Intent Detection from Spoken Data},
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journal = {CoRR},
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volume = {abs/2104.08524},
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year = {2021},
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url = {https://arxiv.org/abs/2104.08524},
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eprinttype = {arXiv},
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eprint = {2104.08524},
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timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-2104-08524.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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### Contributions
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Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset
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gitattributes.txt
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bin.* filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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# Audio files - uncompressed
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*.pcm filter=lfs diff=lfs merge=lfs -text
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*.sam filter=lfs diff=lfs merge=lfs -text
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*.raw filter=lfs diff=lfs merge=lfs -text
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# Audio files - compressed
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*.flac filter=lfs diff=lfs merge=lfs -text
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