Ninada (निनाद) v4.0 — 24,000 Hz Native Per-Language Studio VITS Specialists

Ninada (Nināda — Sanskrit निनाद, deep resonant sound) v4.0 is a 24,000 Hz Native Per-Language Studio Neural TTS suite (~27.9 MB active W8A32+FP16 ONNX per language, 372.7 ms native CPU median TTFA, 0.197x RTF) with 6 dedicated Studio Male + 6 dedicated Studio Female specialists (Sanskrit [sa], Kannada [kn], Hindi [hi], Tamil [ta], Telugu [te], Malayalam [ml]) trained end-to-end on each language's own native single-speaker 24,000 Hz studio recordings (ai4bharat/Rasa) and native Unicode script tokens.

• Live Off-Main-Thread Browser AudioWorklet Studio: https://hemanth.github.io/ninada/
• GitHub Repository & Streaming SDKs (JS + Python): https://github.com/hemanth/ninada
• Curated 24 kHz Studio Split Manifests: https://huggingface.co/datasets/gnumanth/ninada-sanskrit-indic

Architecture & Native Dataset-Matched Training

• Zero Cross-Language Accent Bleed: Every language (sa, kn, hi, ta, te, ml) has its own dedicated 24,000 Hz StudioVitsModel (RelativePositionTransformer + StochasticDurationPredictor Rational-Quadratic Spline + ResidualCouplingBlock normalizing flow + HiFi-GAN v1 [8, 8, 2, 2] upsampling generator, HOP_LENGTH = 256) trained strictly on that language's own native ai4bharat/Rasa single-speaker 24 kHz studio recordings (13700 total studio training clips across Male & Female speakers).
• 100% Native Script Orthography (vocabs.json):
  • kn: Native Kannada script (85 tokens) trained on ai4bharat/Rasa Kannada (SYSPIN/vits_Kannada_{Male,Female} frozen 500k-step TextEncoder + 24 kHz studio vocoder adaptation).
  • sa: Classical Sanskrit Devanagari (144 tokens including ऋ, ॠ, ऌ, ळ, ऽ, ः, ।, ॥, ॐ) trained exclusively on ai4bharat/Rasa Sanskrit (SYSPIN/vits_Marathi_{Male,Female} warm-start — zero Hindi dataset or Hindi voice sharing).
  • te: Native Telugu script (110 tokens) trained on ai4bharat/Rasa Telugu (SYSPIN/vits_Telugu_{Male,Female} warm-start).
  • hi: Native Hindi Devanagari (120 tokens) trained on ai4bharat/Rasa Hindi (SYSPIN/vits_Hindi_{Male,Female} warm-start).
  • ta: Native Tamil script (61 tokens including ழ, ற, ன) trained on ai4bharat/Rasa Tamil (SYSPIN/vits_Kannada_{Male,Female} Brahmic +0x0100 embedding isomorphism).
  • ml: Native Malayalam script (101 tokens including atomic chillus ൺ ൻ ർ ൽ ൾ ൿ) trained on ai4bharat/Rasa Malayalam (SYSPIN/vits_Kannada_{Male,Female} Brahmic -0x0080 embedding isomorphism).
• ~27.9 MB Browser W8A32+FP16 ONNX + Offline CacheStorage: Each exported onnx/{sa,kn,hi,ta,te,ml}.onnx and onnx/{sa,kn,hi,ta,te,ml}_female.onnx is ~27.9 MB (0.61 dB deterministic MCD parity vs PyTorch FP32), persisted in CacheStorage + IndexedDB (0 network refetches on reload or tab revisit) and streamed via 24 kHz AudioWorklet ring buffer with 0 underruns.

Held-Out 24 kHz Studio Test Leaderboard (N = 240 Utterances; B = 1,000 Bootstrap 95% CIs)

System	Active Footprint (↓)	Sample Rate	Coverage (↑)	TTFA p50 (ms, ↓)	Mean RTF (↓)	MCD 0–8k (dB, ↓)	Studio 8–12k HF Err (dB, ↓)	Log-F0 RMSE (c, ↓)	Dur MAE (s, ↓)
Base facebook/mms-tts-hin (Raw)	138.8 MB	16 kHz	35.0%	1.0	0.030x	7.97 [7.69, 8.24]	66.24 [65.48, 66.95]	1018.47 [987.17, 1050.48]	3.21 [2.89, 3.54]
Meta MMS 5-Specialist Fleet (5x VITS)	694.0 MB	16 kHz	100.0%	60.2	0.024x	5.09 [5.03, 5.15]	65.90 [65.49, 66.33]	676.29 [661.53, 690.29]	1.34 [1.21, 1.48]
Ninada Tier-2 Zero-Dep Klatt Fallback	0.0 MB	24 kHz	100.0%	104.0	0.0305x	10.38 [10.26, 10.50]	41.94 [41.27, 42.56]	613.77 [597.33, 630.07]	0.98 [0.88, 1.08]
Ninada v4.0 Untuned Warm-Start (step=0)	112.0 MB	24 kHz	100.0%	189.2	0.047x	4.60 [4.54, 4.65]	11.01 [10.68, 11.35]	639.61 [626.39, 653.49]	0.95 [0.86, 1.04]
Ninada v4.0 Native Studio PyTorch (FP32)	112.0 MB	24 kHz	100.0%	45.3	0.040x	3.40 [3.37, 3.43]	6.34 [6.16, 6.52]	529.20 [514.68, 542.48]	0.57 [0.50, 0.63]
Ninada v4.0 Native W8A32+FP16 ONNX (Browser)	27.9 MB	24 kHz	100.0%	372.7	0.197x	3.39 [3.36, 3.43]	6.37 [6.19, 6.57]	530.38 [517.00, 543.32]	0.56 [0.50, 0.62]

24 kHz Streaming Quickstart

import ninada from 'ninada';

const voice = await ninada('kannada', {
  models: {
    kn: 'https://huggingface.co/gnumanth/ninada/resolve/main/onnx/kn.onnx',
    sa: 'https://huggingface.co/gnumanth/ninada/resolve/main/onnx/sa.onnx',
  },
});
for await (const chunk of voice.stream('ನಮಸ್ಕಾರ, ಇಂದು ಬೆಂಗಳೂರಿನಲ್ಲಿ ಹವಾಮಾನ ತುಂಬಾ ಚೆನ್ನಾಗಿದೆ.')) {
  playPcm24k(chunk.pcm, chunk.sampleRate); // 24,000 Hz native Kannada studio stream!
}

from ninada import Ninada

tts = Ninada(lang="sa")
for chunk in tts.stream("प्रज्ञापूर्वकव्यापारवत्त्वं जीवत्वम् । यथार्थज्ञानं प्रमाणम् ॥"):
    play(chunk["pcm"], chunk["sample_rate"])  # 24,000 Hz float32 native Sanskrit PCM