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--- |
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language: |
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- as |
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- bn |
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- en |
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- gu |
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- hi |
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- kn |
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- ml |
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- mr |
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- ne |
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- or |
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- pa |
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- ta |
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- te |
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license: cc-by-4.0 |
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task_categories: |
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- text-to-speech |
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- automatic-speech-recognition |
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size_categories: |
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- 100K<n<1M |
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tags: |
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- indic |
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- multilingual |
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- tts |
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- speech |
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--- |
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# Processed TTS Multilingual Data |
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Validated and quality-checked multilingual speech datasets for TTS training, covering 12+ Indian languages. |
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## Datasets Included |
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| Subset | Samples | Hours | Description | |
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|---|---|---|---| |
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| `indic_voices_r` | 239,684 | 548.8h | Indic Voices_R — IVR recordings | |
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| `rasa` | 201,509 | 361.2h | RASA — read speech (wiki, conv, book, news) | |
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| `indictts_iitm` | 155,236 | 253.6h | Indic TTS (IIT Madras) — studio TTS recordings at 48kHz | |
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| **Total** | **596,429** | **1,163.6h** | | |
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## Languages |
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Assamese (as), Bengali (bn), English (en), Gujarati (gu), Hindi (hi), Kannada (kn), Malayalam (ml), Marathi (mr), Nepali (ne), Odia (or), Punjabi (pa), Tamil (ta), Telugu (te) |
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## Structure |
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``` |
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├── indic_voices_r/ |
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│ ├── metadata.csv |
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│ └── audio/{lang}/*.wav |
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├── rasa/ |
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│ ├── metadata.csv |
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│ └── audio/{lang}/*.wav |
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└── indictts_iitm/ |
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├── metadata.csv |
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└── audio/{lang}/*.wav |
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``` |
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## Schema (metadata.csv) |
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Each subset has a `metadata.csv` with these columns: |
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| Field | Description | |
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|---|---| |
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| `file_name` | Relative path to audio file (e.g., `audio/bn/file.wav`) | |
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| `text` | Transcript text | |
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| `lang` | ISO 639-1 language code | |
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| `speaker_id` | Speaker identifier | |
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| `duration` | Audio duration in seconds | |
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| `source` | Original data source | |
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| `emotion` | Emotion label | |
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| `domain` | Text domain (wiki, conv, book, news, etc.) | |
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| `snr_db` | Signal-to-noise ratio in dB | |
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| `silence_ratio` | Fraction of silent frames | |
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| `clipping_ratio` | Fraction of clipped samples | |
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## Quality Checks Applied |
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All data has been validated through a 4-check pipeline: |
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1. **SNR + Silence + Duration** — reject low SNR (<10dB), excess silence (>35%), out-of-range duration (<1.5s or >30s), clipping (>1%) |
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2. **Speaking Rate** — reject abnormal speaking rates (<2 or >25 chars/sec) |
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3. **Text Normalization** — Unicode NFC normalization applied |
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4. **Audio Corruption** — reject empty, all-zeros, NaN/Inf, DC offset >0.1 |
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## Usage |
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```python |
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from datasets import load_dataset |
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# Load a specific subset |
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ds = load_dataset( |
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"PalakEngineerMaster/Processed_TTS_Multilingual_Data", |
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data_dir="rasa", |
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split="train", |
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) |
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# Access a sample |
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sample = ds[0] |
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print(sample["text"]) |
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# audio is at sample["file_name"] |
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``` |
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## Audio Format |
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- Format: WAV |
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- Sample rate: 16kHz (Indic Voices_R, RASA) / 48kHz (Indic TTS IIT M) |
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- Channels: mono |
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