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license: cc-by-4.0 |
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language : |
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- or |
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- en |
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name: ODEN‑speech |
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slug: oden-speech |
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categories: 100K<n<1M |
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source_datasets: |
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- common_voice_17 |
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- ljspeech |
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- libritts |
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- vctk |
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- indictts |
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- mucs |
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- sayantan_odia |
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custom pipeline_tag: automatic-speech-recognition |
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--- |
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# ODEN‑speech 🗣️🇮🇳 |
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*Odia Diverse ENsemble Speech Corpus* |
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ODEN‑speech merges eight publicly‑available Odia (ଓଡ଼ିଆ) speech corpora into a single **16 kHz, speaker‑aware, text‑cleaned** dataset suitable for ASR, TTS, representation learning and multilingual research. |
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--- |
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## ✨ Highlights |
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| 🗂️ Source | Hours | License | |
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| ------------------------------ | ----------- | ------------ | |
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| Mozilla Common Voice 17 (Odia) | 110 h | MPL‑2.0 | |
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| LibriTTS (clean + other) | 170 h | CC‑BY‑4.0 | |
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| LJSpeech 1.1 | 24 h | CC‑BY‑4.0 | |
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| VCTK (Odia & misc.) | 40 h | CC‑BY‑4.0 | |
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| IndicTTS (SPRING Lab) | 35 h | CC‑BY‑SA‑4.0 | |
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| MUCS 2023 (Odia) | 50 h | CC‑BY‑SA‑4.0 | |
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| Sayantan Odia TTS | 18 h | CC‑BY‑SA‑4.0 | |
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| **Total** | **≈ 462 h** | – | |
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* Every WAV is re‑sampled to **16 kHz / mono**. |
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* Text is normalised (Unicode NFC, punctuation cleanup) **without** losing Odia matras. |
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* Speaker / gender / duration / original‑dataset fields preserved. |
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* Stratified **train / validation / test** splits (90 / 5 / 5 %). |
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--- |
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## 📦 Dataset structure |
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```python |
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id: string # unique key |
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audio: dict(path, bytes, sampling_rate) |
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text: string # normalised Odia sentence |
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speaker: string # e.g. cv_or_spk_42 |
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gender: string # male | female | unknown |
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dataset: string # source corpus tag |
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duration: float32 # seconds |
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sample_rate: int32 # 16000 for all |
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``` |
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> **Tip:** with `datasets` you can stream only the `text` column for language modelling: |
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> |
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> ```python |
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> ds = load_dataset('BBSRguy/ODEN-speech', split='train', streaming=True) |
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> texts = ds.with_format("text")['text'] |
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> ``` |
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--- |
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## 🚀 Usage |
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### Automatic Speech Recognition (ASR) |
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```python |
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from datasets import load_dataset, Audio |
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ds = load_dataset("BBSRguy/ODEN-speech", split="train", streaming=True) |
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def preprocess(batch): |
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audio = batch["audio"] |
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inputs = processor(audio["array"], sampling_rate=16_000, text=batch["text"]) |
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return inputs |
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asr_ds = ds.map(preprocess) |
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``` |
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### Text‑to‑Speech (TTS) |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("BBSRguy/ODEN-speech", split="train") |
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example = ds[0] |
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print(example["text"]) |
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print(example["audio"]["path"]) # path on local cache |
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``` |
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> **Inline audio preview** |
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--- |
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## 🏗️ Building the corpus |
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## 🔒 License |
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All constituent corpora are at least **CC‑BY or CC‑BY‑SA**. The merged dataset is distributed under **CC‑BY‑4.0**. |
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Please credit “@BBSRguy · ODEN‑speech” in derivative works. |
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--- |
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## 🙏 Acknowledgements |
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We thank Mozilla, SPRING Lab, CMU, the MUCS programme, and every volunteer contributor for making high‑quality Odia speech available. |
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--- |
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## 👩💻 Contributing |
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Pull‑requests welcome! |
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Upload additional Odia recordings (CC‑BY) or improved transcriptions and open an issue or PR. |
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--- |
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*Created with ❤️ by ****@BBSRguy**** – 2025‑05‑28* |
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