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TieIncred/
VaaniCall

Name: VaaniCall
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nemo

automatic-speech-recognition

zero-shot-language-identification

rnn-t

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VaaniCall

Call-grade speech recognition for 11 Indian languages.
By Tausif Iqbal. Built on NVIDIA NeMo FastConformer, fine-tuned from VAANI for PSTN, VoIP, and contact-center audio.

VaaniCall transcribes a phone call without a language code. It infers language and script, and it is trained through a 21-stage telephony pipeline so AMR, packet loss, and handset noise look like training data — not a domain shift.

Supported languages: Hindi, Bengali, Tamil, Telugu, Kannada, Malayalam, Marathi, Gujarati, Punjabi, Odia, English

Why VaaniCall


Zero-shot language ID	No `lang=` argument. One model, eleven languages, native scripts.
Telephony-hardened	21-stage GPU augmentation: codecs, burst loss, VAD bite, handset EQ, room IR.
Holds on clean audio	Phone fine-tune does not tax clean WER (24.20% → 24.06% on IndicVoices).
Beats Whisper on Indic	3.5× lower overall WER than Whisper large-v3 on IndicVoices.
FastConformer + RNNT	Streaming-friendly NVIDIA NeMo `EncDecRNNTBPE` checkpoint (`.nemo`).

Performance at a glance

All numbers are Word Error Rate (lower is better). Evaluations use 500 clips per language.

Overall WER: VaaniCall vs Whisper large-v3 on IndicVoices and FLEURS

Benchmark	Condition	VaaniCall	Whisper large-v3	Relative WER cut
IndicVoices	Clean	24.20%	84.07%	71%
IndicVoices	Telephonic	24.06%	84.50%	72%
Google FLEURS	Clean	23.52%	65.23%	64%

Whisper large-v3 still leads on English. VaaniCall is the Indic-language model.

VaaniCall WER stays flat from clean to telephonic audio

Benchmarks

vs Whisper large-v3 — IndicVoices, telephonic

Per-language telephonic WER vs Whisper large-v3

IndicVoices · telephonic · full table

Language	N	VaaniCall WER ↓	VaaniCall CER ↓	Whisper WER	Whisper CER
Bengali	500	15.41%	4.93%	73.87%	38.04%
English	500	13.26%	4.56%	7.80%	3.04%
Gujarati	500	21.46%	7.56%	61.48%	29.72%
Hindi	500	16.04%	6.87%	30.31%	15.61%
Kannada	500	38.01%	10.96%	98.03%	53.05%
Malayalam	500	40.70%	11.53%	138.83%	108.41%
Marathi	500	17.10%	6.30%	93.50%	47.21%
Odia	500	31.52%	10.40%	110.63%	97.36%
Punjabi	500	14.46%	6.03%	86.10%	54.26%
Tamil	500	33.82%	9.84%	80.72%	40.71%
Telugu	500	28.45%	8.63%	146.64%	97.12%
Overall	5500	24.06%	8.26%	84.50%	56.30%

IndicVoices · clean · full table

Language	N	VaaniCall WER ↓	VaaniCall CER ↓	Whisper WER	Whisper CER
Bengali	500	15.16%	4.96%	73.36%	36.81%
English	500	14.14%	4.67%	8.37%	3.12%
Gujarati	500	20.02%	6.68%	56.27%	25.96%
Hindi	500	16.45%	6.77%	30.71%	15.20%
Kannada	500	38.26%	11.02%	99.44%	52.16%
Malayalam	500	41.79%	12.11%	145.50%	114.81%
Marathi	500	16.76%	5.80%	92.38%	46.12%
Odia	500	31.85%	10.63%	107.96%	95.15%
Punjabi	500	15.71%	6.59%	89.11%	58.95%
Tamil	500	34.50%	10.22%	77.39%	38.11%
Telugu	500	28.39%	8.73%	141.52%	96.52%
Overall	5500	24.20%	8.31%	84.07%	56.30%

vs Whisper large-v3 — Google FLEURS (out of domain)

Held-out read speech. Odia is not in FLEURS.

FLEURS out-of-domain WER vs Whisper large-v3

FLEURS · clean · full table

Language	N	VaaniCall WER ↓	VaaniCall CER ↓	Whisper WER	Whisper CER
Bengali	500	26.00%	9.22%	77.60%	32.63%
English	500	15.16%	7.88%	4.47%	1.78%
Gujarati	500	24.94%	8.22%	54.99%	22.19%
Hindi	500	15.70%	6.18%	27.61%	9.63%
Kannada	500	25.80%	7.95%	72.85%	22.87%
Malayalam	500	37.55%	10.36%	130.76%	102.00%
Marathi	500	22.32%	7.18%	76.52%	22.61%
Punjabi	500	16.53%	6.06%	74.63%	35.88%
Tamil	500	32.78%	9.84%	47.73%	12.11%
Telugu	500	29.19%	9.99%	126.29%	74.64%
Overall	5000	23.52%	8.34%	65.23%	34.04%

Tamil CER is lower for Whisper (12.11%) despite a higher WER — typically an artifact of shorter predicted strings. VaaniCall still wins WER.

Per-language profile

VaaniCall WER by language on telephonic IndicVoices

Strongest Indic languages on phone audio: Punjabi 14.5% · Bengali 15.4% · Hindi 16.0% · Marathi 17.1%.

vs VAANI base — the telephony fine-tune

VaaniCall is a telephony specialist on top of VAANI. On phone-channel audio it improves 9 of 10 Indic languages. On clean audio the two models are statistically tied (24.37% vs 24.38% WER).

WER reduction versus VAANI base under telephonic conditions

vs VAANI base · telephonic

Language	N	VaaniCall WER ↓	VAANI WER	ΔWER	VaaniCall CER ↓	VAANI CER	ΔCER
Bengali	500	19.85%	21.70%	+1.85	7.74%	9.36%	+1.62
Gujarati	500	26.27%	28.97%	+2.70	10.72%	11.50%	+0.78
Hindi	500	19.65%	20.28%	+0.63	8.92%	9.22%	+0.30
Kannada	500	44.44%	47.03%	+2.59	14.93%	15.73%	+0.80
Malayalam	500	47.67%	48.51%	+0.84	16.46%	16.79%	+0.33
Marathi	500	23.65%	25.12%	+1.46	9.29%	10.02%	+0.73
Odia	500	35.93%	34.56%	−1.37	13.65%	13.28%	−0.37
Punjabi	500	20.26%	20.78%	+0.51	9.50%	9.71%	+0.21
Tamil	500	40.88%	41.84%	+0.96	14.69%	15.10%	+0.41
Telugu	500	32.17%	33.04%	+0.88	11.41%	11.75%	+0.34
Overall	5000	29.96%	31.04%	+1.08	11.96%	12.46%	+0.51

Δ = VAANI − VaaniCall. Positive Δ → VaaniCall is better. English excluded (not a VAANI fine-tune target).

vs VAANI base · clean

Language	N	VaaniCall WER	VAANI WER	ΔWER	VaaniCall CER	VAANI CER	ΔCER
Bengali	500	14.94%	15.07%	+0.13	5.01%	4.97%	−0.04
Gujarati	500	21.11%	21.93%	+0.82	7.38%	7.61%	+0.24
Hindi	500	16.47%	16.39%	−0.08	6.78%	6.75%	−0.03
Kannada	500	37.91%	38.78%	+0.87	10.99%	11.16%	+0.17
Malayalam	500	40.37%	40.63%	+0.26	11.46%	11.60%	+0.14
Marathi	500	17.05%	17.01%	−0.04	5.38%	5.33%	−0.05
Odia	500	31.18%	28.49%	−2.69	9.68%	9.15%	−0.53
Punjabi	500	14.17%	14.38%	+0.21	5.93%	5.86%	−0.07
Tamil	500	34.26%	34.49%	+0.24	9.82%	9.87%	+0.05
Telugu	500	27.12%	27.55%	+0.43	7.95%	8.07%	+0.12
Overall	5000	24.37%	24.38%	+0.01	8.17%	8.18%	+0.01

Results are statistically equivalent on clean audio — the fine-tune does not spend general accuracy to buy telephony robustness.

vs AI4Bharat IndicConformer

IndicConformer is a strong general-purpose Indic ASR and still leads this comparison. VaaniCall is optimized for call-channel robustness and zero-shot language ID, not for matching a dedicated per-language conformer on clean read speech.

Condition	VaaniCall	IndicConformer	Gap
Telephonic	29.38%	26.11%	−3.26 pp
Clean	24.56%	20.63%	−3.93 pp

Largest telephonic gaps: Malayalam −5.61 pp, Telugu −5.49 pp. English excluded.

vs IndicConformer · telephonic

Language	N	VaaniCall WER	IndicConformer WER ↓	ΔWER	VaaniCall CER	IndicConformer CER ↓	ΔCER
Bengali	500	18.99%	16.81%	−2.18	7.29%	6.65%	−0.63
Gujarati	500	25.31%	22.48%	−2.83	9.89%	9.46%	−0.43
Hindi	500	19.61%	17.27%	−2.34	9.25%	7.86%	−1.40
Kannada	500	44.50%	43.22%	−1.28	15.24%	13.92%	−1.33
Malayalam	500	45.91%	40.30%	−5.61	15.74%	12.75%	−2.99
Marathi	500	22.31%	18.78%	−3.53	8.27%	7.09%	−1.18
Odia	500	36.38%	32.34%	−4.04	13.86%	12.42%	−1.44
Punjabi	500	19.16%	16.48%	−2.68	8.75%	8.74%	−0.01
Tamil	500	38.82%	35.30%	−3.52	13.53%	11.34%	−2.18
Telugu	500	34.36%	28.87%	−5.49	12.52%	10.10%	−2.41
Overall	5000	29.38%	26.11%	−3.26	11.62%	10.15%	−1.47

vs IndicConformer · clean

Language	N	VaaniCall WER	IndicConformer WER ↓	ΔWER	VaaniCall CER	IndicConformer CER ↓	ΔCER
Bengali	500	15.42%	12.79%	−2.64	4.96%	4.00%	−0.95
Gujarati	500	20.37%	15.24%	−5.13	7.09%	5.11%	−1.98
Hindi	500	15.97%	14.23%	−1.74	6.58%	5.68%	−0.89
Kannada	500	37.67%	33.94%	−3.73	11.10%	9.40%	−1.70
Malayalam	500	41.67%	35.23%	−6.43	12.06%	9.67%	−2.39
Marathi	500	16.08%	13.02%	−3.05	5.26%	4.28%	−0.99
Odia	500	30.82%	25.86%	−4.96	9.86%	8.01%	−1.85
Punjabi	500	15.00%	10.65%	−4.35	6.48%	4.21%	−2.27
Tamil	500	34.76%	30.35%	−4.41	10.31%	8.52%	−1.79
Telugu	500	27.90%	24.43%	−3.47	8.49%	6.92%	−1.57
Overall	5000	24.56%	20.63%	−3.93	8.40%	6.74%	−1.66

How it works

Phone audio into FastConformer encoder, RNNT decoder, transcript with no language ID

Training

Two-phase fine-tune from the VAANI FastConformer checkpoint.

Phase 1 language alignment, phase 2 acoustic refinement

Language alignment — encoder frozen; decoder learns script and language mapping.
Acoustic refinement — last five encoder layers plus the full decoder unfrozen for telephonic acoustics.

Data

Trained on AI4Bharat IndicVoices and VAANI: 50,000 train / 5,000 val / 5,000 test clips per language (mean duration ~18 s).

Telephony augmentation

Every training batch can pass through a 21-stage, GPU-accelerated call-channel simulator.

Talker, handset, codec, network, and line stages of the augmentation pipeline

All 21 stages

#	Stage	Prob	What it does
0	Identity setup	100%	Tier: Premium / Standard / Legacy. Locks mic profile and codec behavior.
1	Formant shift	25%	Vocal-tract warp (0.95–1.05×) so the model does not memorize speakers.
2	Speed perturbation	30%	Tempo + pitch together (0.9–1.1×).
3	Room IR	50%	GPU conv1d reverb, partial RMS match, up to 20 ms pre-delay.
4	Background noise	90%	WHAM! floor (15–25 dB SNR) + MUSAN chatter (40%, 10–20 dB).
5	Mic profile	100%	Flat / mid / low / muffled EQ + tanh saturation.
6	Mic plosives	35%	Breath thumps on transients, 20–50 Hz.
7	Handling noise	45%	Handset friction, lowpass @ 2.5 kHz, 25–35 dB SNR.
8	Vocal EQ	75%	Presence peak 900–1600 Hz; optional nasal / low-mid dip.
9	Volume drift	20%	Interpolated gain envelope, ±3 dB.
10	Pre-codec AGC	85%	Noise-aware rolling-RMS AGC, gain capped at 3.5×.
11	Codec roundtrip	100%	pcm_alaw 35% / amrnb 35% / amrwb 15% / opus 10% / pcm_mulaw 5%.
12	Loudness recovery	100%	Peak targets: Premium −8 / Standard −4 / Legacy −1 dBFS.
13	Gateway VAD clipping	35%	IVR VAD bite: 15–45 ms off 40% of onsets.
14	Network loss	40%	Gilbert–Elliott burst loss, 20 ms packets, optional PLC smear.
15	Stutter / jitter	25%	5–40 ms micro-dropouts to 0–10% amplitude.
16	Clock drift	15%	Per-100 ms resample warp 0.998–1.002×.
17	IMD distortion	35%	Quadratic + cubic harmonics.
18	Soft clipping	70%	Handset tanh above 0.5–0.85.
19	Receiver hiss	40–85%	Hardware floor from −85 to −54 dBFS by tier.
20	Filter mode	100%	Static 75% / dynamic 25%; NB or WB band limits.
21	Mastering	100%	Gain, smart nudge, tanh, brick-wall at ±0.98.

Quick start

python

from nemo.collections.asr.models import EncDecRNNTBPEModel

model = EncDecRNNTBPEModel.from_pretrained(
    model_name="TieIncred/VaaniCall"
)

hypotheses = model.transcribe(["sample.wav"], return_hypotheses=True)
print(hypotheses[0].text)

No language flag. 16 kHz mono WAV is the happy path; typical telephony (8 kHz, μ-law / AMR) is the training domain.

Intended use

Inbound / outbound contact-center transcription in Indic languages
VoIP and PSTN call analytics
Multilingual IVR and voice-bot post-processing
Offline batch transcription of call recordings

Not intended for: medical or legal dictation, real-time emergency dispatch, or English-only broadcast ASR (use Whisper or a dedicated English model).

Limitations

English trails Whisper large-v3 (expected: this checkpoint is Indic-first).
Malayalam, Kannada, Tamil remain the hardest languages (WER 34–41% on IndicVoices).
Odia slightly regresses vs the VAANI base under telephony (−1.37 pp).
IndicConformer is still stronger as a general Indic ASR.
Code-mixed utterances and heavy music-on-hold are not separately benchmarked.
Evaluations are 500 clips / language; treat per-language gaps of <1 pp as noise.

Citation

If you use VaaniCall, please cite this model and VAANI, the base the fine-tune starts from:

bibtex

@misc{iqbal2026vaanicall,
  title={VaaniCall: Multilingual Telephony ASR for Indic Languages},
  author={Iqbal, Tausif},
  year={2026},
  howpublished={Hugging Face},
  url={https://huggingface.co/TieIncred/VaaniCall}
}

bibtex

@misc{pulikodan2026vaanicapturinglanguagelandscape,
  title={VAANI: Capturing the language landscape for an inclusive digital India},
  author={Sujith Pulikodan and Abhayjeet Singh and Agneedh Basu and Nihar Desai and Pavan Kumar J and Pranav D Bhat and Raghu Dharmaraju and Ritika Gupta and Sathvik Udupa and Saurabh Kumar and Sumit Sharma and Vaibhav Vishwakarma and Visruth Sanka and Dinesh Tewari and Harsh Dhand and Amrita Kamat and Sukhwinder Singh and Shikhar Vashishth and Partha Talukdar and Raj Acharya and Prasanta Kumar Ghosh},
  year={2026},
  eprint={2603.28714},
  archivePrefix={arXiv},
  primaryClass={eess.AS},
  url={https://arxiv.org/abs/2603.28714}
}

Also cite IndicVoices when reporting numbers on that test set.

VaaniCall · Tausif Iqbal · Apache 2.0