Entrada de voz privacy-first: Guía completa de speech-to-text local en 2026

Every time you use voice input on your phone or computer, your words are typically recorded, transmitted over the internet, and processed on a remote server. Google, Apple, Amazon, and Microsoft have all faced scrutiny over how they handle voice data — from contractors listening to recordings to indefinite data retention policies. For anyone who values privacy, this should be deeply concerning. Your voice carries not just words but biometric data, emotional state, and contextual information that can reveal far more than the text it produces.

The Privacy Problem with Voice Input

When you dictate a message using built-in speech recognition on most operating systems, your audio is sent to cloud servers for processing. This happens transparently — most users don't realize their voice is leaving their device. The audio may be stored for model improvement, reviewed by human contractors for quality assurance, or retained indefinitely in server logs. Even when companies anonymize this data, voice recordings contain inherent biometric markers that can potentially re-identify speakers.

The privacy implications extend beyond personal use. Professionals who dictate patient notes, legal documents, financial reports, or proprietary code are potentially exposing sensitive information every time they use cloud-based voice input. A doctor dictating a diagnosis, a lawyer reviewing case details, or an engineer describing a trade secret — all of this audio data passes through third-party infrastructure with varying levels of protection and varying data retention policies.

• Audio data is transmitted to third-party servers for processing
• Voice recordings may be stored indefinitely for model training
• Human reviewers at tech companies may listen to recordings
• Voice biometrics can potentially identify speakers even in 'anonymized' datasets
• Data retention policies vary widely and can change without notice

What Is Local Speech-to-Text?

Local speech-to-text (also called on-device STT or offline voice recognition) processes your voice entirely on your own computer or device. No audio data is transmitted over the internet. No third party ever hears or stores your recordings. The entire conversion from speech to text happens using a model that runs locally on your hardware — whether that's a laptop, desktop, or even a Raspberry Pi.

The breakthrough that made high-quality local STT possible was OpenAI's release of Whisper in September 2022. Whisper is an open-source speech recognition model trained on 680,000 hours of multilingual audio data. For the first time, a single model could match or exceed the accuracy of commercial cloud services while running entirely on consumer hardware. Since Whisper's release, the open-source community has produced optimized variants — faster-whisper, whisper.cpp, and others — that make local STT practical for real-time use.

Cloud vs Local STT: A Detailed Comparison

Choosing between cloud and local speech-to-text involves trade-offs across four key dimensions: accuracy, latency, privacy, and cost. Neither option is universally better — the right choice depends on your specific requirements, hardware, and how sensitive the content you're dictating is.

Accuracy

In 2024, cloud STT services had a noticeable accuracy advantage. By 2026, that gap has nearly closed. Whisper large-v3 running locally achieves word error rates within 1-2% of commercial cloud services for English. For less common languages, cloud services still hold an edge due to their continuously updated models, but the difference is shrinking with each Whisper release. For most practical voice input use cases — emails, messages, documents, code comments — local STT accuracy is indistinguishable from cloud.

Latency

Cloud STT adds network round-trip time — typically 100-500ms depending on your connection and the provider's server location. Local STT eliminates this entirely, but processing time depends on your hardware. On a modern machine with a decent GPU (RTX 3060 or better) or Apple Silicon (M1 or later), local Whisper processes speech in near-real-time. On older hardware or CPU-only systems, there can be noticeable delay for longer utterances. The tradeoff: cloud is consistently fast everywhere, local is fastest on good hardware but slower on weak hardware.

Privacy

This is where local STT wins absolutely and unequivocally. Cloud STT requires sending your audio to a third party — there is no way around this. Even with encryption in transit and privacy policies, you are trusting another organization with your voice data. Local STT requires zero trust: the model runs on your machine, the audio never touches a network interface, and there is mathematically zero chance of data exposure through the STT process itself. For regulated industries, sensitive content, or simply personal preference, local STT provides privacy guarantees that cloud services cannot match by design.

• Cloud advantages: Consistently fast, no hardware requirements, latest models, streaming support
• Cloud risks: Data transmitted to third parties, potential storage and review, privacy policy changes
• Local advantages: Complete privacy, zero network dependency, no ongoing costs, GDPR/HIPAA compliant by design
• Local limitations: Requires capable hardware, model updates are manual, slightly lower accuracy for some languages

Privacy Regulations Driving Change

Privacy regulations worldwide are increasingly treating voice data as sensitive personal information. The EU's General Data Protection Regulation (GDPR) classifies voice recordings as personal data, requiring explicit consent for processing and strict data minimization principles. The California Consumer Privacy Act (CCPA) gives residents the right to know what voice data is collected and request its deletion. HIPAA in the United States imposes severe penalties for unauthorized disclosure of patient information — including voice recordings of medical dictation.

For organizations in regulated industries, cloud-based voice input creates compliance complexity: you need to verify the provider's data processing agreements, ensure appropriate data residency, manage consent records, and handle data subject access requests. Local STT eliminates this entire compliance surface. If the data never leaves the device, there's no third-party processing to regulate, no data transfer to document, and no external data retention to audit. Some privacy officers are now mandating local STT for sensitive workflows simply because it removes an entire category of compliance risk.

The Best Local STT Options in 2026

The local STT ecosystem has matured significantly. Multiple high-quality options exist, each with different strengths. Here's a detailed look at the best choices available in 2026.

Whisper (OpenAI)

Whisper remains the gold standard for local speech recognition. Released by OpenAI as open source (MIT license), it supports 99 languages and achieves state-of-the-art accuracy. The large-v3 model delivers the best results but requires significant hardware resources. The medium and small models offer excellent accuracy-to-speed tradeoffs for real-time use. Whisper's main limitation is speed — the original Python implementation is not optimized for real-time transcription on most consumer hardware, which is why optimized variants exist.

faster-whisper

faster-whisper reimplements Whisper using CTranslate2, a fast inference engine for transformer models. The result is up to 4x faster transcription with the same accuracy as the original Whisper. It also uses significantly less memory, making it practical to run the large-v3 model on machines with 8GB of RAM. For most users seeking the best balance of speed, accuracy, and resource usage for local STT, faster-whisper is the recommended choice in 2026.

Vosk

Vosk takes a different approach — it's designed to be as lightweight as possible. Models are typically 50-300MB (compared to Whisper's 1-6GB), and it runs comfortably on devices with minimal resources, including Raspberry Pi and older laptops. Vosk supports 20+ languages and provides real-time streaming transcription out of the box. The trade-off is accuracy: Vosk doesn't match Whisper's quality, especially for complex vocabulary or noisy environments. But for basic dictation on resource-constrained devices, it's an excellent choice.

whisper.cpp

whisper.cpp is a C++ port of Whisper by Georgi Gerganov (the creator of llama.cpp). It eliminates the Python dependency entirely and provides native performance on all platforms. The standout feature is its optimization for Apple Silicon — on M1/M2/M3 Macs, whisper.cpp uses the Neural Engine and Metal GPU for significantly faster inference than any Python-based implementation. For macOS users, whisper.cpp is often the fastest local STT option available, with near-real-time transcription on Apple Silicon hardware.

How OpenTypeless Gives You the Choice

OpenTypeless is designed with a fundamental principle: you should choose your own speech-to-text provider based on your priorities. Need maximum privacy? Use a local Whisper instance through Ollama — your audio never leaves your machine. Need the fastest possible transcription? Use Groq's cloud Whisper with sub-second latency. Need the best accuracy for a specific language? Pick the provider that excels at that language. OpenTypeless supports 6 STT providers, and switching between them takes seconds — just change the provider in settings and enter your API key.

Setting Up Privacy-First Voice Input

Setting up fully private voice input with OpenTypeless takes just a few steps. First, install Ollama on your machine and pull a Whisper model. Then download OpenTypeless, select Ollama as your STT provider in settings, and optionally configure a local LLM for text polishing (also through Ollama). Once configured, every voice input — from pressing the hotkey to seeing polished text appear — happens entirely on your device. No internet connection required, no data transmitted, no privacy compromises. This is the setup recommended for anyone handling sensitive information: medical professionals, lawyers, engineers working with proprietary technology, or simply anyone who believes their voice data is nobody else's business.