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README.md

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