IndicWav2Vec: Robust ASR for Indic Languages

• IndicWav2Vec is a self-supervised transformer-based model designed for high-coverage ASR in over 40 low-resource Indic languages.
• The model employs extensive multilingual pretraining and contrastive learning to reduce error rates and enhance phonetic alignment.
• Advanced decoding with language model fusion and efficient on-device adaptation techniques ensures robust, production-quality transcripts.

IndicWav2Vec is a family of self-supervised, transformer-based speech representation learning models designed for automatic speech recognition (ASR) over a broad spectrum of low-resource Indic languages. Extending the wav2vec 2.0 framework, these models leverage large-scale multilingual and domain-varied corpora from Indian linguistic families to create robust, transferable ASR systems. Three major lines of research and engineering—Open-Speech EkStep's CLSRIL-23, AI4Bharat's large-scale pretraining, and the Vakyansh/Gram Vaani deployment studies—establish IndicWav2Vec as the de facto foundation for modern, high-coverage Indic ASR (Javed et al., 2021, Gupta et al., 2021, Chadha et al., 2022, Chauhan et al., 18 Dec 2025).

1. Corpus Construction and Preprocessing

IndicWav2Vec models depend critically on massively multilingual and high-quality audio data curation. Training corpora span up to 40 Indian languages, extracted from sources such as Newsonair (multilingual news), YouTube Creative Commons, and specialized rural telephony collections (Gram Vaani). Datasets are processed via:

• Resampling to 16 kHz mono, 16-bit PCM.
• WebRTC VAD (aggressiveness = 2) for voice activity segmentation in 1–30 s segments.
• SNR-based chunk rejection, typically discarding utterances with $<$15–25 dB SNR (Chadha et al., 2022, Gupta et al., 2021, Javed et al., 2021).
• Speaker and gender balancing using 256-dimensional Resemblyzer embeddings + HDBSCAN clustering, with SVM(RBF) gender detection (Chadha et al., 2022).
• Cleaning via script normalization, removal of excessive non-native glyphs, and hand annotation for especially low-resource language pairs.

For Gram Vaani clinical audio, unique pre-processing involves upsampling raw telephony (8 kHz $\rightarrow$ 16 kHz) and segmenting domain-specific Hindi dialect speech (Chauhan et al., 18 Dec 2025).

2. Model Architecture and Learning Objectives

IndicWav2Vec adopts the wav2vec 2.0 paradigm with modifications for multilinguality:

• Feature Encoder: Stack of 7–12 1D convolutional layers. Example: 12 layers in CLSRIL-23; 7 layers in AI4Bharat models; downsampling occurs at each stage.
• Context Network: 12–24 layer Transformer encoder (BASE: $d=768$, 8 heads; LARGE: $d=1024$, 16 heads).
• Quantization Module: Product quantization with $G=2$ codebooks, each $V=320$ entries; Gumbel-softmax is used to enable backpropagation through discrete code selection.
• Output Layer: CTC (Connectionist Temporal Classification) head for ASR fine-tuning, applied over contextualized transformer outputs.

Training employs a dual-loss regime: $\mathcal{L} = \mathcal{L}_m + \alpha \mathcal{L}_d$ where $\mathcal{L}_m$ is the contrastive loss (discriminating true versus distractor quantized latents) and $\mathcal{L}_d$ is a diversity regularization promoting uniform codebook activation. In practice, $\alpha=0.1$ is optimal. Only masked spans (up to 49% masking, span $\approx$ 200 ms) are subject to contrastive prediction (Gupta et al., 2021, Javed et al., 2021).

Fine-tuning for ASR uses the CTC loss on paired audio-text, with the feature encoder typically frozen and optimization performed over the transformer and CTC layers.

3. Multilingual Pretraining Strategies and Transfer

Pretraining indicwav2vec on a multilingual pool (up to 17,000 hours across 40 languages) provides two compelling advantages:

• Cross-lingual Phonetic Alignment: The shared quantizer enforces codebook entry reuse, inducing joint phonetic subspaces across languages. Clustering experiments demonstrate high overlap in codebook utilization for closely related scripts (e.g., Hindi–Urdu–Punjabi cluster) (Gupta et al., 2021, Javed et al., 2021).
• Balanced Language Sampling: To prevent domination by high-resource languages, probability for sampling a language $\ell$ is $p_\ell \propto (n_\ell/N)^\alpha$ with $\alpha=0.7$, proven empirically optimal.

Ablation studies confirm that multilingual pretraining outperforms monolingual: WER and CER on Hindi decrease by 1.5% and 0.9%, respectively (greedy decoding); larger reductions are observed for lower-resource languages such as Gujarati and Telugu (Gupta et al., 2021). The performance gains are more pronounced when limited fine-tuning data is available.

4. LLM Fusion and Decoding

ASR performance is substantially improved by integrating external LMs during decoding, notably KenLM 5-gram or 6-gram models, and optionally neural Transformer LMs for rescoring (Chadha et al., 2022, Javed et al., 2021). Decoding is conducted via beam search: $$y^{*} = \arg\max [\log p_{\mathrm{AM}}(y) + \alpha \log p_{\mathrm{LM}}(y) + \beta |y|]$$ where $\alpha$ and $\beta$ are tuned on validation data, and $p_{\mathrm{AM}}$ is the acoustic model probability (from CTC).

Use of diverse-domain LMs, rather than merely large ones, provides the best error reduction: CER drops by over 28% and WER by approximately 36% after LM decoding across 18 Indic languages (Dhuriya et al., 2022). Domain-matched LMs are critical—the use of mismatched LMs (e.g., generic LM for Sanskrit) can dramatically increase WER (e.g., from 19% to 45%) (Chadha et al., 2022).

Final ASR pipelines include punctuation restoration modules (e.g., IndicBERT token classification) and rule-based inverse text normalization (WFSTs) to yield production-quality transcripts.

5. On-Device and Domain-Specific Adaptation

To bridge the "reality gap" on challenging domains (e.g., rural clinical audio), parameter-efficient adaptation strategies are prerequisite for privacy and efficiency:

• Low-Rank Adaptation (LoRA): Only low-rank matrices $\{A,B\}$ (per transformer layer) are trained, with original model weights frozen. This compresses the updated parameter count by up to $10^4\times$, enabling edge-device continual learning (Chauhan et al., 18 Dec 2025).
• Experience Replay: Multi-domain replay buffers—one general domain, one domain-specific—are mixed during LoRA adaptation to mitigate catastrophic forgetting as measured by post-adaptation WER increases. The multi-buffer method yields a 47% reduction in forgetting compared to naive adaptation.

Results on Gram Vaani domain: baseline WER of 40.94% (pretrained IndicWav2Vec) is reduced to 33.94% with LoRA + multi-domain replay, a 17.1% relative improvement. The same approach keeps generic domain WER increases ("Forgetting_V") under tight control (Chauhan et al., 18 Dec 2025).

Method	Target WER	Rel. Improvement	Gen WER	Forgetting
Baseline	40.94%	–	11.57%	–
Naive LoRA (V1.1)	34.00%	17.0%	17.61%	+6.04%
Multi-Replay (V3.1)	33.94%	17.1%	14.78%	+3.21%

On-device adaptation is computationally lightweight (update per 2,000 utterances in minutes on Nvidia T4 or equivalent CPU), requires no server offloading, and maintains data privacy as all sensitive audio/text remains local.

6. Performance Benchmarks and Comparative Results

IndicWav2Vec models deliver SOTA WERs for numerous Indic languages. Representative test-set WER/CER scores (with LM, best pipeline) (Gupta et al., 2021, Chadha et al., 2022, Javed et al., 2021):

Language	WER (%)	CER (%)
Hindi	10.5	(MUCS)
Gujarati	11.7	(MSR)
Tamil	13.6	(MSR)
Nepali	9.7	(OpenSLR)
Bengali	10.6	(OpenSLR)
Sinhala	18.6	(OpenSLR)
Kannada	26.94	(OS)
Telugu	20.60	(MU)

Relative improvement over prior ASR baselines ranges from 20% to 50% absolute in extremely low-resource settings, with the architecture and training regime enabling <20% WER even on languages where labeled data is <10 hours (Javed et al., 2021, Gupta et al., 2021).

7. Engineering Practices, Open Resources, and Future Directions

All major IndicWav2Vec lines provide open-source code, recipes, and checkpoints (Javed et al., 2021, Gupta et al., 2021, Chadha et al., 2022). Engineering and deployment choices prioritize:

• Wav2vec2-base models for speed and real-time inference.
• Data- and domain-specific LMs for robustness.
• Unicode NFD normalization and WFST text normalization for script/vocabulary sparsity reduction.
• Custom fairseq-based platforms and W&B for experiment tracking.

Identified research directions include unsupervised ASR for zero-resource languages, optimizing quantizer size for ultra-low-resource adaptation, and further architectural growth (larger transformers, end-to-end LM/AM joint learning). Iterative pseudo-labeling on raw unsegmented audio yields little benefit under current constraints due to noisy label propagation.

IndicWav2Vec serves as both a state-of-the-art monolingual and multilingual ASR backbone for India's linguistic diversity, supporting robust transfer, on-device adaptability, and open innovation for the global speech community (Gupta et al., 2021, Chadha et al., 2022, Chauhan et al., 18 Dec 2025, Javed et al., 2021).