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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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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ckpt 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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+ *.lz4 filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel 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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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.npz 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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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl 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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+ *.safetensors 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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+ *.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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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zst 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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+ *.aac filter=lfs diff=lfs merge=lfs -text
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+ *.flac filter=lfs diff=lfs merge=lfs -text
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+ *.mp3 filter=lfs diff=lfs merge=lfs -text
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+ *.ogg filter=lfs diff=lfs merge=lfs -text
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+ *.wav filter=lfs diff=lfs merge=lfs -text
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+ # Image files - uncompressed
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+ *.bmp filter=lfs diff=lfs merge=lfs -text
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+ *.gif filter=lfs diff=lfs merge=lfs -text
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+ *.png filter=lfs diff=lfs merge=lfs -text
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+ *.tiff filter=lfs diff=lfs merge=lfs -text
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+ # Image files - compressed
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+ *.jpg filter=lfs diff=lfs merge=lfs -text
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+ *.jpeg filter=lfs diff=lfs merge=lfs -text
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+ *.webp filter=lfs diff=lfs merge=lfs -text
LICENSE ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ This material and data is licensed under the terms of the Creative Commons Attribution 4.0
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+ International License (CC BY 4.0), The full text of the CC-BY 4.0 license is available at
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+ https://creativecommons.org/licenses/by/4.0/.
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+
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+ Notwithstanding the foregoing, this material and data may only be used, modified and distributed for
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+ the express purpose of training AI models, and subject to the foregoing restriction. In addition, this
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+ material and data may not be used in order to create audiovisual material that simulates the voice or
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+ likeness of the specific individuals appearing or speaking in such materials and data (a “deep-fake”).
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+ To the extent this paragraph is inconsistent with the CC-BY-4.0 license, the terms of this paragraph
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+ shall govern.
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+
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+ By downloading or using any of this material or data, you agree that the Project makes no
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+ representations or warranties in respect of the data, and shall have no liability in respect thereof. These
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+ disclaimers and limitations are in addition to any disclaimers and limitations set forth in the CC-BY-4.0
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+ license itself. You understand that the project is only able to make available the materials and data
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+ pursuant to these disclaimers and limitations, and without such disclaimers and limitations the project
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+ would not be able to make available the materials and data for your use.
README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ size_categories:
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+ - 1M<n<10M
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+ configs:
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+ - config_name: pre
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+ data_files:
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+ - pre/*/*.arrow
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+ - config_name: raw
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+ data_files:
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+ - raw/*/*.arrow
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+ - config_name: GK
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+ data_files:
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+ - pre/geekonomy/*.arrow
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+ - raw/geekonomy/*.arrow
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+ - config_name: GK_pre
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+ data_files: pre/geekonomy/*.arrow
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+ - config_name: GK_raw
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+ data_files: raw/geekonomy/*.arrow
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+ - config_name: OH
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+ data_files:
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+ - pre/osim-history/*.arrow
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+ - raw/osim-history/*.arrow
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+ - config_name: OH_pre
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+ data_files: pre/osim-history/*.arrow
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+ - config_name: OH_raw
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+ data_files: raw/osim-history/*.arrow
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+ - config_name: DK
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+ data_files:
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+ - pre/dor/*.arrow
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+ - raw/dor/*.arrow
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+ - config_name: DK_pre
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+ data_files: pre/dor/*.arrow
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+ - config_name: DK_raw
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+ data_files: raw/dor/*.arrow
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+ - config_name: YO
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+ data_files:
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+ - pre/Yo_the_podcast/*.arrow
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+ - raw/Yo_the_podcast/*.arrow
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+ - config_name: YO_pre
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+ data_files: pre/Yo_the_podcast/*.arrow
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+ - config_name: YO_raw
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+ data_files: raw/Yo_the_podcast/*.arrow
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+ - config_name: YV
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+ data_files:
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+ - pre/Yad_vashem/*.arrow
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+ - raw/Yad_vashem/*.arrow
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+ - config_name: YV_pre
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+ data_files: pre/Yad_vashem/*.arrow
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+ - config_name: YV_raw
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+ data_files: raw/Yad_vashem/*.arrow
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+
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+ ---
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+
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+ # HebDB
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+
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+ **Paper:** https://arxiv.org/abs/???
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+
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+ If you use our datasets, please use the following:
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+
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+
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+ ```
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+ @cite{
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+ Soon
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+ }
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+
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+ ```
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+
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+
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+
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+ ### Dataset Summary
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+
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+ A weakly supervised dataset for spoken language processing in the Hebrew language. HEBDB offers roughly 2500 hours of natural and spontaneous speech recordings in the Hebrew language, consisting of a large variety of speakers and topics. We provide raw recordings together with a pre-processed, weakly supervised, and filtered version. The goal of HEBDB is to further enhance research and development of spoken language processing tools for the Hebrew language.
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+
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+ Data variants are: `pre`, `raw`. Note variants share the same columns to ease the usage of dataset subsets but `raw` only use the columns: `fname`, `audio` and `is_raw`.
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+
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+ #### How do I download this?
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+
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+ ##### Using 🤗 Datasets
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ # pre only
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+ hebdb_pre = load_dataset("SLPRL-HUJI/HebDB", "pre")
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+
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+ # raw only
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+ hebdb_raw = load_dataset("SLPRL-HUJI/HebDB", "raw")
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+
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+ # One specific source(see code list below), both raw and pre
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+ geekonomy = load_dataset("SLPRL-HUJI/HebDB", "GK")
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+
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+ # One specific source, both raw and pre
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+ geekonomy_pre = load_dataset("SLPRL-HUJI/HebDB", "GK_pre")
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+ ```
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+
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+ To avoid downloading the entire dataset you can load it in streaming mode using `streaming=True`, for example:
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+
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+ ```python
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+ hebdb_pre = load_dataset("SLPRL-HUJI/HebDB", "pre", streaming=True)
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+ ```
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+
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+ You can also load and mix:
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+
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+ ```python
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+ from datasets import concatenate_datasets, load_dataset
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+
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+ geekonomy = load_dataset("SLPRL-HUJI/HebDB", "GK_pre")
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+ osim_history = load_dataset("SLPRL-HUJI/HebDB", "OH_pre")
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+
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+ # Concatenate both datasets
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+ concatenated = concatenate_datasets([geekonomy, osim_history])
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+
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+ ```
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+
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+ ### Sources
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+
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+ The 6 available sources are reported in the table below.
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+
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+ | code | name |
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+ |:------|:--------------------------|
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+ | GK | Geekonomy |
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+ | OH | Osim History |
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+ | DK | The Dor Kahn Experience |
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+ | YO | Yo! The podcast |
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+ | GQ | Good Question |
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+ | YV | Yad vashem |
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+
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+
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+
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+ ### Data Fields
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+
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+ The data have several fields:
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+ - `fname`: file name
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+ - `audio`:
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+ - `array`: array of audio samples
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+ - `sample_rate`: audio sampling rate
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+ - `path`: path to the audio file saved location
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+ - `is_raw`: Flag for raw/preprocessed
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+ - `raw`:
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+ - `fname`: origin raw file name
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+ - `start_sec`: start time mark in seconds
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+ - `end_sec`: end time mark in seconds
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+ - `source`: Source name
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+ - `n_samples`: Number of samples
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+ - `text`: Transcription
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+ - `normalized_text`: Normalized transcription (details in paper)
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+ - `score`: Transcription quality score obtained by forced aligner (details in paper)
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+
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+
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+
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+ ### Licensing Information
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+
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+ Data is licensed under the terms of the Creative Commons Attribution 4.0
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+ International License (CC BY 4.0), The full text of the CC-BY 4.0 license is available at
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+ https://creativecommons.org/licenses/by/4.0/.
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+
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+ ### Acknowledgements
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+
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+ This research work was supported by the Israel Innovation Authority, grant number 78563.
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