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--- |
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language: |
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- he |
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license: mit |
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task_categories: |
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- automatic-speech-recognition |
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tags: |
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- speech-recognition |
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- lyrics |
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- hebrew |
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- music |
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pretty_name: Caspi STT Benchmark |
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size_categories: |
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- n<1K |
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--- |
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# Caspi STT Benchmark |
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Hebrew speech-to-text (STT) evaluation dataset built from **Mati Caspi** songs: YouTube audio plus reference lyrics, packaged for Hugging Face. |
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## Dataset description |
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- **Audio**: 16 kHz mono WAV segments (one row per track or segment). |
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- **Text**: Reference transcript (lyrics or song title when lyrics are missing). |
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- **Metadata**: `id`, `youtube_id`, `title`, `song_name`. |
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Intended for STT benchmarking: compare model transcriptions to `text` (e.g. WER/CER). |
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## How to use |
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```python |
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from datasets import load_dataset, Audio |
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ds = load_dataset("ozlabs/caspi", split="train") |
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ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) |
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# Example row |
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ex = ds[0] |
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# ex["audio"] → decoded array; ex["text"] → reference transcript |
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``` |
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## Source |
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- Audio: extracted from YouTube (playlists) at 16 kHz mono. |
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- Lyrics: provided manually or via Shazam; |
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## License |
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MIT (or as specified in the repo). Audio and lyrics are used for research/evaluation; respect YouTube and lyric providers’ terms. |
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