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README.md

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+---
+license: cc-by-4.0
+language:
+- as
+- bn
+- gu
+- hi
+- kn
+- ml
+- mr
+- ne
+- or
+- pa
+- ta
+- te
+- ur
+task_categories:
+- automatic-speech-recognition
+- audio-classification
+tags:
+- speech
+- conversational
+- multilingual
+- indian-languages
+- diarization
+- asr
+- tts
+pretty_name: Multilingual Indian Conversational Speech
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: metadata.jsonl
+---
+
+# Multilingual Indian Conversational Speech
+
+A dataset of **naturalistic, spontaneous two-speaker conversations** across
+**13 Indian languages**, with segment-level transcripts, speaker labels, and
+timestamps. Designed for ASR, TTS, speaker diarization, and conversational
+speech research.
+
+## Languages (13)
+
+Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali,
+Odia, Punjabi, Tamil, Telugu, Urdu.
+
+## Content
+
+Conversations resemble real-world interactions across multiple domains:
+Technology / customer support, Financial services, Healthcare, Food &
+delivery, and Retail & commerce. Speech is conversational and spontaneous —
+with natural turn-taking, code-switching (English mixed with the local
+language), interruptions, and expressive prosody.
+
+## Structure
+
+```
+audio/                    full conversation recordings (WAV), one per language
+metadata.jsonl            segment-level annotations referencing the recordings
+```
+
+Each row in `metadata.jsonl` is one **utterance segment** (1–20 seconds) that
+references its source recording in `audio/` with start/end timestamps.
+
+## Schema
+
+| Field              | Type   | Description                                             |
+|--------------------|--------|---------------------------------------------------------|
+| `segment_id`       | string | Segment identifier within a recording (e.g. `SEG-001`). |
+| `recording_id`     | string | Source recording ID (e.g. `REC-ASM-HLT-011`).           |
+| `audio_file`       | string | Relative path to the full recording in `audio/`.        |
+| `language`         | string | Language of the conversation.                           |
+| `speaker_id`       | string | Speaker label within the conversation (`SPK_01`, `SPK_02`). |
+| `speaker_gender`   | string | `Male` / `Female` where known.                          |
+| `start_time`       | string | Segment start (`HH:MM:SS.mmm`).                         |
+| `end_time`         | string | Segment end (`HH:MM:SS.mmm`).                           |
+| `start_seconds`    | float  | Segment start in seconds.                               |
+| `end_seconds`      | float  | Segment end in seconds.                                 |
+| `duration_seconds` | float  | Segment duration in seconds.                            |
+| `transcript`       | string | Verbatim transcript in the native script.               |
+| `domain`           | string | Conversation domain (Technology, Healthcare, etc.).     |
+| `sample_rate`      | int    | Audio sample rate (Hz).                                 |
+| `channels`         | int    | Number of audio channels.                               |
+| `bit_depth`        | int    | Audio bit depth.                                        |
+| `source_recording` | string | Original recording filename.                            |
+
+## Audio
+
+- Uncompressed WAV, mostly 48 kHz / 16-bit (some 44.1 kHz and 24-bit).
+- One full conversation recording per language; segment rows reference offsets
+  within it, so clips can be extracted on demand using `start_seconds` and
+  `end_seconds`.
+
+## Notes
+
+- Transcripts include natural English code-switching, common in Indian
+  conversational speech.
+- Timestamps have been normalized to a consistent `HH:MM:SS.mmm` format.
+- This is a sample release; larger collections follow the same schema.
+
+## Extracting a segment clip (example)
+
+```python
+import soundfile as sf, json
+row = json.loads(open("metadata.jsonl").readline())
+data, sr = sf.read(row["audio_file"])
+clip = data[int(row["start_seconds"]*sr):int(row["end_seconds"]*sr)]
+sf.write("segment.wav", clip, sr)
+```

audio/Assamese.wav

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audio/Bengali.wav

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