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What a useful Indian-language TTS benchmark should measure

Benchmarking text-to-speech models is more than generating a few audio clips and choosing the voice that sounds most pleasant. A credible comparison must separate text fidelity, intelligibility, naturalness, speed, cost, and robustness. These dimensions often trade off: a model may sound expressive but mispronounce names, or produce accurate speech while being too slow for a voice agent.

This matters particularly for Indian languages. Hindi, Bengali, Marathi, Tamil, Telugu, Kannada, Malayalam, Gujarati, Punjabi, Odia and other languages differ in script, phonology, stress and rhythm. Real deployments also contain code-mixed text, English product names, numerals, abbreviations, regional names and conversational spellings. A benchmark that uses only clean, short sentences will overstate production performance.

The same evaluation discipline is useful when building low-resource Indic NLP systems, where data coverage and linguistic diversity can affect results more than model size.

1. Define the benchmark before selecting models

Write down the intended use case first. A call-centre assistant, accessibility reader, audiobook tool and offline education application need different priorities.

Specify:

• Languages and scripts: Include the exact languages, script variants and dialects under test.
• Deployment setting: CPU, consumer GPU, mobile device or hosted inference endpoint.
• Voice requirements: Single-speaker consistency, multiple speakers, gender, age and speaking style.
• Quality target: For example, intelligibility for transactional calls versus expressive narration.
• Latency and cost limits: Record first-audio latency, real-time factor and estimated cost per minute.
• Reproducibility rules: Pin model revisions, software versions, hardware and decoding parameters.

Do not compare models only by their names. Confirm the model card, supported languages, licence, sample rate, speaker controls, required preprocessing and known limitations on Hugging Face. A model described as multilingual may offer very uneven quality across languages.

2. Build a representative, versioned test set

Create a held-out evaluation set that models actual Indian usage. Keep the text and metadata separate from model-specific preprocessing so every system receives an equivalent input.

A strong test set should include:

• Short prompts, long-form paragraphs, questions, commands and dialogues
• Common vocabulary alongside rare words and named entities
• Dates, times, currency, percentages, decimal numbers and phone numbers
• English words, acronyms, URLs and brand names used in Indian speech
• Formal, informal and code-mixed sentences
• Language-specific consonant clusters, vowel length contrasts and retroflex sounds
• Regional names, place names and domain terms such as banking, healthcare or education

Use native-language reviewers to validate spelling, punctuation and intended pronunciation. Store a canonical text, a normalised text, language tag, script tag, domain tag and pronunciation notes. Split by speaker, source and sentence family to prevent near-duplicate leakage.

For a first benchmark, 200-500 sentences per language can reveal major failures. For publication-quality comparisons, expand the set and report confidence intervals rather than a single aggregate score. Publicly releasing text, metadata and evaluation scripts—without private recordings or personal data—makes the result easier to audit.

3. Load models through the current Hugging Face interface

Hugging Face model APIs vary by architecture, so avoid assuming that a generic TTSModel class or a method such as synthesize() exists. Read each model card and use the documented pipeline, processor, tokenizer and vocoder. Some models work through transformers, while others require a repository-specific inference script or a dedicated library.

A typical setup may look like this:

python -m venv .venv
source .venv/bin/activate
pip install -U torch transformers datasets accelerate soundfile librosa jiwer pandas

Then load each model using its supported interface, pin a revision, and save generated audio as lossless WAV files. Record the exact command, commit hash, device, precision, batch size and generation settings. Use deterministic settings where the model permits them; if sampling is enabled, generate multiple seeds and report variability.

Before a large run, perform a smoke test with one prompt per language. Check sample rate, channel count, clipping, silence, file duration and whether the output is actually in the requested language.

4. Measure intelligibility and text fidelity

The most practical automatic check is to pass generated speech through a strong ASR system and compare the transcription with the reference. Calculate character error rate (CER) for scripts where word segmentation is unreliable, and word error rate (WER) where tokenisation is meaningful. Normalise punctuation, whitespace, digits and code-mixed tokens consistently before scoring.

Be careful: ASR-based scores measure the combined behaviour of TTS and ASR. A poor ASR model can unfairly penalise good speech, while a recogniser trained on similar synthetic audio can hide pronunciation problems. Use a capable Indic ASR baseline, publish its version, and manually review a stratified sample of errors.

Add targeted annotation for:

• Substituted or deleted phonemes
• Incorrect stress, tone or vowel length
• Number and date pronunciation
• Named-entity pronunciation
• Script or language switching errors

For high-stakes applications, report critical-error rates separately. Misreading a rupee amount, dosage, address or account number matters more than a minor grammatical variation.

5. Measure naturalness, speaker similarity and prosody

Human listening remains essential. Run a blinded evaluation with native speakers who understand the target language. Ask separate questions instead of forcing one overall score:

• How natural does the speech sound?
• How easy is it to understand?
• Is the pronunciation correct?
• Is the pacing and emphasis appropriate?
• Does the voice remain consistent across prompts?

Use a five- or seven-point scale, randomise model order, include repeated items to detect inattentive ratings, and report the number and background of raters. Provide headphones and calibrated instructions where possible. Report mean scores with confidence intervals, not just the highest MOS.

For objective analysis, measure duration, pause distribution, speech rate, silence ratio, pitch range and energy contours. These do not replace human ratings, but they help explain why a model sounds rushed, flat or unstable. If speaker identity matters, use a speaker-embedding similarity measure while acknowledging that such metrics can be biased by language and recording conditions.

6. Benchmark speed, memory and cost

Quality alone is not enough for Indian voice products. Measure:

• Time to first audio: Important for interactive systems
• End-to-end latency: Include text normalisation, model loading and vocoder time
• Real-time factor: Audio generation time divided by output duration
• Throughput: Audio minutes generated per minute of compute
• Peak memory: Record CPU RAM and GPU VRAM
• Cold-start and warm-start performance: Especially for serverless or autoscaled deployments
• Audio size and sample rate: Relevant to storage and network delivery

Run warm-up requests, then benchmark multiple prompt lengths in batches representative of production. Report hardware and precision so results are comparable. A smaller model with slightly lower MOS may be the better choice if it meets latency and cost targets.

These measurements become especially important when TTS is part of a voice agent for Indian businesses, where interruptions, response delay and noisy telephony channels shape user experience.

7. Analyse failures instead of hiding them in averages

Create an error taxonomy and review examples by language and text category. Useful categories include grapheme-to-phoneme errors, unsupported characters, code-mixing failures, incorrect number expansion, unnatural pauses, repeated audio, truncation, pronunciation drift and speaker instability.

Publish per-language results before macro averages. A single multilingual score can conceal a model that performs well in Hindi but fails in Malayalam or Odia. Include sample audio, transcripts, prompts and failure labels where licences and privacy rules allow. Do not release recordings containing personal data or identifiable speakers without consent.

Also check licence terms before commercial deployment. Model weights, training data, voice recordings and generated outputs may have different restrictions. Document whether the benchmark used public, synthetic or consented evaluation material.

8. A practical reporting template

Your final report should include:

• Model name, repository revision, licence and supported languages
• Test-set size, domains, scripts and speaker or source splits
• Text normalisation and phonemisation rules
• Hardware, software versions, precision and decoding settings
• CER/WER, human naturalness and intelligibility scores
• Latency, real-time factor, throughput and memory
• Per-language and per-category breakdowns
• Confidence intervals, rater details and statistical tests where appropriate
• Representative successes and failures
• A recommendation tied to a specific deployment scenario

Do not call a model “best” without defining the objective. A useful conclusion might be: “Model A offers the strongest intelligibility on Hindi customer-support prompts, while Model B has lower latency and better memory use for on-device Marathi.” That is more actionable than a single leaderboard number.

Recommended next steps for builders

Start with a small, versioned benchmark, validate the scoring pipeline manually, and expand only after the measurements are trustworthy. Keep separate development and final test sets, rerun baselines whenever preprocessing changes, and publish artefacts so others can reproduce the comparison.

For teams building public Indian-language tools, an open evaluation harness can be as valuable as a new model. It helps contributors identify gaps, supports Indian open-source AI developer projects, and gives funders and customers evidence beyond demo audio. If your benchmark reveals a real deployment opportunity, connect TTS quality to user outcomes—task completion, comprehension, accessibility or call resolution—rather than treating MOS as the product goal.