--- license: cc-by-4.0 task_categories: - text-to-speech language: - en - te tags: - tts - speech - indian-languages - telugu - indian-english - emotion - single-speaker pretty_name: Indian English + Telugu Single-Speaker TTS (emotion-tagged) size_categories: - n<1K configs: - config_name: indian_english data_files: - split: train path: indian_english/train-* - split: validation path: indian_english/validation-* - split: test path: indian_english/test-* - config_name: telugu data_files: - split: train path: telugu/train-* - split: validation path: telugu/validation-* - split: test path: telugu/test-* --- # Indian English + Telugu Single-Speaker TTS Dataset (emotion-tagged) Clean audio clips sourced from YouTube, transcribed with **Sarvam** ASR, segmented with diarization, and labeled with emotion/style tags. Built as a data-quality / curation exercise. > **"Single-speaker"** means **each clip contains exactly one speaker** (verified by > diarization and speaker-embedding similarity). The dataset spans **9 distinct speakers > total** (4 English, 5 Telugu), tracked via `speaker_id`. ## Contents - **Indian English** (`indian_english`): 30.17 min, 160 clips, 4 speakers; emotions: {'angry': 30, 'neutral': 29, 'calm': 29, 'sad': 29, 'excited': 29, 'fearful': 8, 'happy': 6} - **Telugu** (`telugu`): 30.08 min, 150 clips, 5 speakers; emotions: {'calm': 27, 'neutral': 27, 'angry': 27, 'excited': 27, 'sad': 27, 'fearful': 6, 'happy': 6, 'surprised': 3} Total: **60.25 minutes**. ## Evaluation - **Single-speaker check** (ECAPA-TDNN embeddings): same-speaker cosine 0.74 vs different-speaker 0.21 (separation 0.52, verification AUC 0.96 / EER 9.1%; 0/11 candidate speakers flagged). - **Transcript reliability**: an independent recogniser (Whisper) agrees with the English Sarvam transcripts at 5.5% word error (n=40). For Telugu, a Telugu-specialised Indic recogniser brings word error against the Sarvam transcripts to 47%, down from 76% with a generic multilingual model; Telugu is best judged by forced alignment and the human listening pass rather than by cross-ASR alone. - **Emotion-tag reliability** (sarvam-30b vs sarvam-105b on 120 clips): 65% agreement, Cohen's κ 0.55. - **Phoneme coverage**: English 39 (100%), Telugu 44 (88%). - **Perceptual quality** (DNSMOS OVRL, published set): EN 3.09 (58% pass>3.0), TE 3.16 (81% pass>3.0). Filter `dnsmos_pass=True` for a stricter subset. - **Transcript–audio alignment** (MMS forced-align): median confidence EN 0.954, TE 0.937. - **Emotion-label agreement** (Krippendorff alpha): 0.44 between the two LLM raters. Adding two acoustic SER models drops the three-rater alpha near zero, because those models cluster toward neutral and were not trained for Telugu, so the emotion labels are treated as weakly supervised and ship with a confidence. Per-clip VAD (valence, arousal, dominance) is included. - **Validation by a larger model** (sarvam-105b re-judging the 30b tags, 310 published clips): 89% of transcripts judged clean, 81% suitable to train on, and the emotion endorsed on 26%. Each clip also carries a topic; the set is mostly storytelling (mythology, folk tales, audiobook fiction). - **Audio bandwidth**: the source recordings are band-limited (median 99% energy roll-off ~4.1 kHz for English, ~2.6 kHz for Telugu), so although the files are stored at 24 kHz they suit standard 16–24 kHz TTS rather than full-band synthesis. - **Human listening audit** (80 clips, 40 per language): 37/40 English and 35/40 Telugu transcripts matched the audio exactly, the rest off by minor punctuation or a single word, and no clip was judged unusable for TTS. See the project report (GitHub repo) for full methodology and figures. ## Schema `audio` (24 kHz mono), `text` (raw transcript), `annotated_text` (English code-switch spans bracketed, truncation marked with an em dash), `normalized_text` (language-aware: numbers and abbreviations expanded for TTS reading), `language`, `emotion` (neutral, happy, sad, angry, excited, calm, fearful, surprised), `style` (narrative, conversational, formal, expressive, whisper), `emotion_confidence`, `tag_source` (`auto`/`human`), `topic`, `speaker_id`, `gender`, `accent`, `duration`; quality scores (`snr_db`, `dnsmos_ovrl/sig/bak`, `dnsmos_pass`, `squim_*`, `mms_align_score`, `overlap_flag`, `llm_tts_suitable`); VAD (`valence`/`arousal`/`dominance`); annotation flags (below); and provenance (`source_video_id/url/channel`, `license`, `segment_start/end`, `sample_rate`). ## Annotation flags Each clip records what is imperfect about it, so users can filter rather than trust blindly. The two audio-quality flags are automatically inferred proxies, not verified audible-noise labels: `quality_flag` (a quality concern: DNSMOS < 3.0, or SNR < 18 dB, or elevated energy in pauses) and `low_quality_audio` (clearly degraded: DNSMOS < 2.8). The rest: `has_truncation` (ends mid-utterance), `has_codemix` (preserved English in a regional clip; 0 in practice, since Sarvam ASR transliterates English into Telugu script), `has_laughter` (audible laughter, set by a listening pass), `emotion_low_confidence` (tag confidence < 0.55), `transcript_review_needed` (judge-flagged or alignment < 0.85), `overlap_flag` (possible second voice). `annotation_flags` is the pipe-joined list per clip. ## Filtering recommendations - Studio-like subset: `dnsmos_pass == true and quality_flag == false and has_truncation == false` - Expressive subset: `emotion_confidence > 0.7 and emotion != "neutral"` - Storytelling subset: `topic in ('mythology', 'folktale', 'fiction')` - Review queue: `transcript_review_needed == true or emotion_low_confidence == true` ## How it was built 1. Curated single-speaker YouTube sources (audiobooks, lectures, news, storytelling). 2. **Sarvam batch STT** (`saaras:v3`) with diarization + timestamps for structure. 3. Silence-snapped segmentation into 3–25 s clips (single speaker only). 4. **Sarvam realtime STT** (`saarika:v2.5`) per clip for clip-accurate transcripts. 5. Automated quality gates (clipping, SNR, silence, music/noise bed, ASR confidence, dedup). 6. Hybrid emotion tagging: per-speaker-normalized acoustic features + Sarvam LLM, with an acoustic whisper override. 7. Human review (listen, fix transcripts, relabel); **human labels override automated ones**. 8. Light loudness normalization (dynamics preserved), balanced emotion selection. ## Audio 24 kHz mono WAV. Loudness lightly normalized (~-20 LUFS, peak −1 dBFS) WITHOUT limiting, so the prosodic dynamics that carry emotion are preserved. ## Ethics & licensing Sourced from YouTube for research; clips are short and transformative. Per-clip provenance (`source_url`, `source_channel`, `license`) is retained. Respect the original creators' rights; remove clips on request. ## Limitations Emotion tags are weakly supervised (acoustic features + LLM, partly human-verified) and can miss subtle prosody. The source audio is band-limited, so it suits standard 16–24 kHz TTS rather than full-band synthesis, and the English half leans heavily on one narrator. See the project report for the iteration history.