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--- |
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language: |
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- he |
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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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dataset_info: |
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features: |
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- name: uid |
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dtype: string |
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- name: file_id |
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dtype: string |
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- name: audio |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: sentence |
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dtype: string |
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- name: n_segment |
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dtype: int32 |
|
- name: duration_ms |
|
dtype: float32 |
|
- name: language |
|
dtype: string |
|
- name: sample_rate |
|
dtype: int32 |
|
- name: course |
|
dtype: string |
|
- name: sentence_length |
|
dtype: int32 |
|
- name: n_tokens |
|
dtype: int32 |
|
splits: |
|
- name: train |
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num_bytes: 17559119499.576 |
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num_examples: 75924 |
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download_size: 17274739665 |
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dataset_size: 17559119499.576 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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|
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|
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## Data Description |
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|
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Hebrew Speech Recognition dataset from [Campus IL](https://campus.gov.il/). |
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|
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Data was scraped from the Campus website, which contains video lectures from various courses in Hebrew. |
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Then subtitles were extracted from the videos and aligned with the audio. |
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Subtitles that are not on Hebrew were removed (WIP: need to remove non-Hebrew audio as well, e.g. using simple classifier). |
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Samples with duration less than 3 second were removed. |
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Total duration of the dataset is 152 hours. |
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Outliers in terms of the duration/char ratio were not removed, so it's possible to find suspiciously long or short sentences compared to the duration. |
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Note: if loading is slow, just clone it : |
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`git clone hebrew_speech_campus && cd hebrew_speech_campus && git lfs pull` |
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and load it from the folder `load_dataset("./hebrew_speech_campus")` |
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|
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## Data Format |
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|
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Audio files are in WAV format, 16kHz sampling rate, 16bit, mono. Ignore `path` field, use `audio.array` field value. |
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|
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## Data Usage |
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```python |
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from datasets import load_dataset |
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|
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ds = load_dataset("imvladikon/hebrew_speech_campus", split="train", streaming=True) |
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print(next(iter(ds))) |
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``` |
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|
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## Data Sample |
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``` |
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{'uid': '10c3eda27cf173ab25bde755d0023abed301fcfd', |
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'file_id': '10c3eda27cf173ab25bde755d0023abed301fcfd_13', |
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'audio': {'path': '/content/hebrew_speech_campus/data/from_another_angle-_mathematics_teaching_practices/10c3eda27cf173ab25bde755d0023abed301fcfd_13.wav', |
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'array': array([ 5.54326562e-07, 3.60812592e-05, -2.35188054e-04, ..., |
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2.34067178e-04, 1.55649337e-04, 6.32447700e-05]), |
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'sampling_rate': 16000}, |
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'sentence': 'ืืืืืจืื ืฆืจืืืื ืืงืืช ืขืืื ืืืจืืืช, ืืืืืืช ืืืืืืื ืื ืืืืืจ, ืืฉืื ืฆืจืื ืืืืืช ืืืืื', |
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'n_segment': 13, |
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'duration_ms': 6607.98193359375, |
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'language': 'he', |
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'sample_rate': 16000, |
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'course': 'from_another_angle-_mathematics_teaching_practices', |
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'sentence_length': 79, |
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'n_tokens': 13} |
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``` |
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|
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## Data Splits and Stats |
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Split: train |
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Number of samples: 75924 |
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|
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## Citation |
|
|
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Please cite the following if you use this dataset in your work: |
|
|
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``` |
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@misc{imvladikon2023hebrew_speech_campus, |
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author = {Gurevich, Vladimir}, |
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title = {Hebrew Speech Recognition Dataset: Campus}, |
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year = {2023}, |
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howpublished = \url{https://huggingface.co/datasets/imvladikon/hebrew_speech_campus}, |
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} |
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``` |
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