If you’re choosing an automatic transcription model, OpenAI Whisper is usually one of the first names that come up. It’s accurate, multilingual and surprisingly robust in real-world conditions.
But Whisper isn’t the only open-source option.
Models like Vosk, Kaldi and Coqui STT offer different trade-offs around performance, hardware requirements, and customisation. The ‘best’ model depends less on headline accuracy and more on how you plan to use transcription day-to-day. When comparing Whisper vs Vosk, it’s important to consider which model delivers the most accurate results and best accuracy for your specific needs, such as language support, audio quality and the intended use of the transcriptions.
In this article, we’ll:
- Compare Whisper with other leading open-source transcription models
- Explain where each one fits
- Shows why transcription quality alone is rarely the real bottleneck for teams
At a Glance: Which Model Is Best for What?
Quick takeaways
- Whisper delivers the best out-of-the-box accuracy and language coverage
- Vosk is ideal for lightweight or offline use
- Kaldi offers maximum control, but only for expert teams
- Coqui STT suits privacy-first or community-driven projects
When choosing between models, consider how efficiently they handle large volumes of audio and long files, key factors for businesses processing extensive media or requiring scalable, cost-effective transcription.
None of these tools, on their own, solves the problem of meeting follow-up, alignment or decision capture.
OpenAI Whisper
Whisper is an open-source automatic speech recognition (ASR) model developed by OpenAI and a system trained on a large, diverse multilingual dataset, specifically, 680,000 hours of web-collected training data. This extensive and varied training data improves the model’s robustness across different languages, accents and background noise. As a result, Whisper is capable of handling multiple tasks, including both speech and language recognition, making it a versatile and general-purpose solution for a wide range of audio and language processing functions.
Why Whisper stands out
- Supports transcription in ~99 languages
- Automatically detects the spoken language
- Handles accents, background noise and mixed-language audio well
- Can translate non-English speech directly into English
- Available in different models, including a base model for faster performance and large models for best accuracy, so users can choose the best fit for their needs
In practical terms, Whisper is often dramatically more accurate than earlier open-source models, especially in messy real-world audio.
Trade-offs to be aware of
- High hardware requirements for fast processing
- Produces raw transcripts, not structured outputs
- No built-in summaries, actions or decisions
Whisper is an excellent foundation model, but teams still need additional tooling to turn transcripts into something useful.
Note: When choosing between Whisper’s different model sizes, there is a trade-off between speed and accuracy. Larger models offer higher accuracy but require more processing power and are slower, while smaller models are faster but may be less accurate.
Vosk
Vosk is designed for efficiency.
Its models are small (around 50 MB per language), fast and capable of running on low-power devices or offline environments.
Where Vosk works best
- Mobile or embedded devices
- Offline transcription
- Simple voice interfaces
- Environments with limited compute resources
Where it falls short
- Fewer supported languages than Whisper
- Lower accuracy in complex or noisy audio
- Less reliable language switching
Vosk is a strong choice when performance and footprint matter more than transcription quality.
Kaldi
Kaldi isn’t a product; it’s an entire toolkit.
It gives you full control over acoustic models, language models and decoding pipelines. That power comes with serious complexity.
Strengths
- Extremely customisable
- Proven in research and specialist environments
- Flexible for niche or highly tuned use cases
Real-world drawbacks
- Very steep learning curve
- Long setup times
- Significant infrastructure overhead
Kaldi makes sense for research teams or organisations building bespoke speech systems, not for most business use cases.
Coqui STT
Coqui STT evolved from Mozilla’s DeepSpeech project and focuses on local, privacy-friendly speech recognition. Compared to other open source models, Coqui STT stands out for its emphasis on privacy and local processing, whereas some alternatives may require more complex technical setup or offer broader language support.
What it’s good at
- On-device transcription
- Community-led development
- Open tooling for voice interfaces
Limitations
- Fewer updates in recent years
- Smaller ecosystem
- Accuracy lags behind Whisper in many languages
Coqui STT is best suited to teams prioritising local processing and open governance over cutting-edge accuracy.
Side-by-Side Comparison
| Model | Accuracy | Languages | Hardware needs | Best for | Speed (Long Files) |
| Whisper | Very high (best accuracy in tests on complex audio files) | ~99 | High (GPU recommended) | Multilingual accuracy, complex audio environments | Slower on long files; may struggle with subtitle synchronization |
| Vosk | Medium | 20+ | Low | Lightweight/offline use, quick subtitle generation | Faster than Whisper on long files and audio files |
| Kaldi | High (tunable) | Custom | Very high | Expert custom builds | Depends on configuration |
| Coqui STT | Medium-high | Multilingual | Medium | Privacy-first setups | Moderate |
Tests were conducted on the same audio files, including long files, to compare transcription speed and accuracy. File-level and audio file-level metrics were used, such as Word Error Rate (WER), which is calculated by dividing the number of errors by the total number of words in the reference file. Vosk demonstrated a clear speed advantage for generating subtitles and transcribing audio files, making it ideal for quick subtitle generation, while Whisper achieved the best accuracy, especially in complex audio environments, but may have issues with subtitle synchronisation.
The Missing Layer: What Happens After Transcription?
All of these models answer the same question: ‘What was said?‘
It’s important to note that while transcription models accurately capture what was said, they don’t provide structure, summaries or actionable insights.
Most teams are actually struggling with a different one: ‘What do we do next?‘
Raw transcripts don’t:
- Capture decisions
- Assign action items
- Create summaries people actually read
- Keep teams aligned after meetings
This is where transcription models stop, and meeting intelligence begins.
Transcription Model Security and Privacy
When it comes to automatic speech recognition (ASR) in business, especially in sectors like finance, where sensitive information is routinely discussed, security and privacy are non-negotiable. The choice between open-source models like the Whisper model and cloud-based solutions such as Google Speech can have a significant impact on how your data is handled and protected.
With open-source models like Whisper, you have full control over your data. You can deploy the model locally, ensuring that audio files and transcripts never leave your secure environment. This is a major advantage for businesses that need to comply with strict data protection regulations or simply want to minimise the risk of data breaches. In contrast, cloud-based transcription services may expose your data to third-party servers, increasing the risk of unauthorised access or leaks, an important consideration for any business handling confidential client information.
Background noise is another real-world challenge that can affect model accuracy. The best models, such as Whisper and the Vosk model, use cutting-edge technologies to filter out noise and focus on the speaker’s voice, resulting in more accurate transcriptions even in less-than-ideal environments. For businesses transcribing audio from meetings, calls or field recordings, this can make the difference between a usable transcript and a confusing jumble of words.
Model accuracy is typically measured by the Word Error Rate (WER), which uses the Levenshtein distance to compare the predicted transcript to the actual spoken words. A lower WER means better performance and more reliable results, crucial when transcribing technical terms, financial data or legal discussions. The Whisper model, for example, is known for its high accuracy and low WER, making it a strong choice for projects where precision matters.
Advanced features like speaker identification can further enhance the value of your transcripts. The Vosk model, for instance, can distinguish between different speakers, which is invaluable for meeting transcription, interviews or any scenario where tracking who said what is important. Support for multiple languages and different accents is another key factor, especially for businesses operating in diverse markets or with international teams. The Whisper model stands out here, supporting over 100 languages and handling a wide range of accents with ease.
Model size also plays a role in choosing the right solution. Larger models generally deliver better quality and higher accuracy, but they require more computational resources and may not be suitable for real-time transcription or devices with limited GPU memory. Smaller models, on the other hand, are significantly faster and can be ideal for real-time applications, though they may sacrifice some accuracy. The Whisper model offers different sizes, allowing developers to balance speed and quality based on their specific needs.
Finally, the best transcription solution depends on your project’s unique requirements. Developers and business leaders should consider factors like efficiency, control, voice quality and the types of audio files they need to process. Fine-tuning models on your own data can further improve performance, especially for industry-specific jargon or technical terms. While the Whisper model is often the go-to for high accuracy and advanced features, other models like Vosk may offer better performance for certain languages or environments.
When selecting a transcription model, weigh your needs for security, privacy, accuracy, speed and advanced features. Open-source models like Whisper give you full control and high accuracy, while other models may excel in specific niches. The right choice will help you transcribe speech efficiently, securely and with the quality your business demands.
How Jamy Uses Transcription Models Differently
Jamy doesn’t compete with Whisper, Vosk or Kaldi at the model level.
Instead, it sits on top of transcription, turning speech into structured outcomes.
With Jamy:
- Language detection happens automatically
- Meetings are summarised, not dumped as text
- Decisions and actions are extracted and assigned
- Reports are generated consistently across teams
That means transcription becomes an input, not the final deliverable.
Teams don’t need to choose between models, tune parameters or manage outputs manually. They just get clear follow-ups, regardless of which language the meeting was in.
How to Choose the Right Model
When comparing transcription options, ask:
- Do we need raw text or usable outputs?
- Do we have the technical resources to manage models?
- Are we working across multiple languages?
- Is privacy or local deployment critical?
- How much time do we spend after meetings cleaning things up?
In the long run, Whisper may outperform Vosk due to its higher accuracy and extensive training data. However, developers often use a hybrid approach: Vosk provides immediate feedback, while Whisper runs in the background to correct and improve the final transcription.
For many teams, the answer isn’t ‘Which model is best?‘, it’s ‘Why are we still doing this manually?‘
Key Takeaways
- Advances in transcription technology have enabled high accuracy and efficiency across diverse business and audio processing use cases.
- Whisper is the strongest open-source transcription model for multilingual accuracy
- Vosk and Kaldi serve very specific technical use cases
- Coqui STT suits privacy-driven environments
- Transcription quality alone doesn’t solve meeting fatigue or follow-up issues
- Tools like Jamy turn transcription into action, not just text
Give Jamy a try
Looking for a solution that transcribes in real-time? Download and use Jamy for free today.
FAQs
Is Whisper better than other open-source transcription models?
For most multilingual, real-world use cases, yes. Whisper generally delivers higher accuracy and better language handling than other open-source options.
Do I need a GPU to use Whisper?
Not strictly, but performance improves significantly with GPU support. Without it, transcription can be slow for longer files.
Can these models replace meeting notes?
They can replace manual transcription, not decision-making or follow-up. You still need structure, summaries and actions extracted from the transcript.
How does Jamy fit into this?
Jamy uses transcription as a foundation, then adds summaries, decisions, tasks and reports so teams don’t have to turn raw text into outcomes themselves.
