Speech-to-text (STT), also called automatic speech recognition (ASR), is now a core component of call-centre software, voice assistants, meeting tools, field-service apps, and India-focused language products. The important choice is not simply whether a model is open or proprietary. It is whether the system delivers reliable transcripts, acceptable latency, predictable economics, and appropriate control for your users and data.
For an Indian startup, a benchmark on clean English audio is not enough. Your evaluation should include code-switching, regional accents, noisy mobile recordings, 8 kHz telephony, names and addresses, and languages such as Hindi, Tamil, Telugu, Bengali, Marathi, Kannada, Malayalam, Gujarati, and Punjabi. Use the framework below to compare vendors and self-hosted models before committing your architecture.
What you are actually comparing
A proprietary STT service usually combines a hosted acoustic model, language model, streaming infrastructure, scaling, monitoring, and product features behind an API. Examples include Google Cloud Speech-to-Text, Azure Speech, AWS Transcribe, Deepgram, AssemblyAI, and specialist Indian-language providers. You pay for usage and accept the provider’s limits on data handling, customisation, and model changes.
An open-source deployment gives you greater control over models such as Whisper variants, wav2vec 2.0 derivatives, and other community or commercial inference stacks. The model may be free to use, but production operation is not free: you still pay for GPUs, storage, networking, observability, engineering, upgrades, and incident response. For teams already deploying large language models locally, the operational patterns may be familiar, but real-time audio introduces its own constraints.
Compare accuracy beyond a single WER score
Word error rate (WER) is calculated as substitutions, deletions, and insertions divided by the number of reference words. It is useful, but it can hide failures that matter commercially. A transcript with a low overall WER may still misrecognise a medicine name, loan amount, customer ID, or village name.
Build a test set that reflects your product:
- Language mix: Separate English, Hindi, Hinglish, and each target regional language instead of reporting one blended score.
- Audio conditions: Include quiet rooms, traffic, fans, overlapping speech, low-quality microphones, and compressed WhatsApp-style audio.
- Domain vocabulary: Add product names, proper nouns, financial terms, medical terms, legal phrases, and local place names.
- Speaking styles: Test fast speech, elderly speakers, children, gender variation, and different regional accents.
- Business metrics: Track intent accuracy, entity accuracy, call disposition accuracy, and the percentage of transcripts needing human correction.
For Indian-language products, compare the model’s handling of code-switching rather than translating everything into English. A model that preserves a natural Hindi-English utterance may be more useful than one that produces a superficially fluent but incorrect translation. If your application also processes text in Hindi, review current open-source small language models for Hindi separately; text-model quality does not automatically predict speech performance.
Latency, streaming, and reliability
Batch transcription and live voice interaction have different requirements. For uploaded recordings, minutes of delay may be acceptable. For a voice bot, long pauses make the system feel broken even when the final transcript is accurate.
Measure:
- Time to first partial transcript and time to final transcript.
- Real-time factor (RTF): processing time divided by audio duration.
- Endpointing: how quickly the system detects that a speaker has stopped.
- Interim stability: whether partial text changes excessively.
- Availability and rate limits: including behaviour during traffic spikes.
- Recovery: reconnection, duplicate prevention, and handling of dropped audio frames.
Proprietary APIs generally provide mature WebSocket or streaming interfaces and elastic capacity. Self-hosted systems can match them, but only after careful batching, GPU allocation, queue design, autoscaling, and load testing. A model that is fast in a notebook may fail when hundreds of simultaneous calls compete for the same GPU.
Privacy, compliance, and data control
Ask exactly what happens to audio and transcripts. Review retention periods, regional processing, encryption, subprocessors, training policies, deletion controls, and administrator access. Do not rely on a generic “enterprise security” label.
Self-hosting is attractive for healthcare, financial services, government workflows, and internal corporate recordings because audio can remain in your VPC, data centre, or device. It also makes access controls and retention policies your responsibility. A hosted provider may offer stronger operational security than a small team can build, so compare the complete risk—not just whether the endpoint is external.
Consider a hybrid design: redact or segment sensitive audio locally, use a hosted service for less sensitive workloads, and retain a self-hosted fallback for restricted customers or outages. This approach is often more practical than forcing one model to serve every use case.
Total cost of ownership in India
Per-minute pricing is only one line item. Estimate monthly cost using your expected audio volume, concurrency, audio duration, and peak-to-average ratio.
Proprietary cost = transcription charges + streaming or feature add-ons + storage and egress + minimum commitments + integration work.
Self-hosted cost = GPU or CPU rental + idle capacity + storage and bandwidth + inference engineering + monitoring + model updates + annotation and evaluation + on-call support.
A hosted API usually wins during prototyping, irregular workloads, and early go-to-market. Self-hosting becomes more compelling when volume is steady, privacy requirements are strict, or per-minute margins are material. Calculate the break-even point using actual utilisation: a GPU running at 15% capacity can be more expensive than an API, even if its theoretical hourly rate looks low.
For teams using Google Cloud, Kubernetes, or similar infrastructure, deploying deep learning models on GKE can provide a useful operational pattern. But do not assume a general model-serving setup solves audio streaming, session affinity, or GPU scheduling automatically.
Customisation and Indian-language adaptation
Hosted providers may offer phrase hints, custom vocabularies, pronunciation dictionaries, or limited adaptation. These features are valuable when your main problem is terminology. They may not solve systematic errors caused by a dialect, noisy channel, or underrepresented language.
Open models provide more control over preprocessing, decoding, post-processing, and fine-tuning. You can train with labelled audio from your users, but obtain consent, remove sensitive information, balance speakers, and maintain a held-out evaluation set. Fine-tuning on a small or biased dataset can improve one accent while damaging general performance.
For Marathi, Telugu, Sanskrit, or other language-specific work, use a dedicated benchmark rather than assuming English-language results transfer. The same discipline applies to benchmarking NLP models for Telugu and Sanskrit and to dialect adaptation work such as fine-tuning AI models for Marathi dialects.
A practical decision framework
Choose a proprietary API when you need to launch quickly, have modest or unpredictable volume, require managed streaming, or lack ML infrastructure. Choose self-hosted open source when you need on-premise processing, edge inference, deep customisation, or predictable economics at sustained scale.
A hybrid path is often best:
1. Start with two hosted providers and one open model on the same test set.
2. Measure accuracy, latency, failure rate, and correction effort by language and audio type.
3. Launch with the option that minimises product risk, not merely the lowest quote.
4. Log anonymised errors and reassess at meaningful volume.
5. Move stable, high-volume workloads to self-hosting only when the savings or control justify the operational burden.
Evaluation checklist
Before signing a contract or deploying a model, confirm:
- The provider supports your required Indian languages and code-switching patterns.
- Pricing covers streaming, diarization, punctuation, storage, and custom vocabulary where needed.
- You can export audio, transcripts, timestamps, and confidence data.
- The system handles retries, partial results, silence, and dropped connections.
- Your test set includes real production-like audio with consent.
- Data retention, deletion, residency, and training-use terms are documented.
- Self-hosting estimates include idle capacity, GPU failures, upgrades, and on-call work.
The right answer is rarely “open source” or “proprietary” in the abstract. It is the system that meets your Indian-language accuracy and privacy requirements at a cost your product can sustain. Benchmark both paths on your own audio, keep the interfaces swappable, and revisit the decision as volume and model quality change.