Fluen Studio Transcription Engines Overview

This is a comparison designed to help you decide which transcription engine works best for your specific use case.

At Fluen Studio, we intentionally offer multiple AI transcription engines because no single engine is objectively “the best” in all situations. Each one has its own strengths, weaknesses, and behavioral quirks, and the right choice depends on factors such as audio quality, language mix, turnaround needs, and how the transcript will be used downstream.

It’s also important to clarify how transcription fits into the bigger picture.

The engines compared in this article only generate the raw transcript. In Fluen Studio, that raw output is then processed by a separate and equally important system: the Segmentation Engine. This engine, based on advanced NLP and LLM techniques, restructures the transcript into readable, well-timed subtitles. It breaks sentences across one or two lines at sensible points, follows language-specific syntactic rules, and places line breaks much like a professional human subtitler would.

This is especially critical for long-form, pre-recorded media, where readability matters just as much as raw accuracy.

With that context in mind, here’s how the transcription engines available in Fluen Studio compare.

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Transcription Engines Compared

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Engine-by-Engine Overview

OpenAI Whisper

OpenAI Whisper is widely regarded as one of the most linguistically accurate transcription engines available today. It performs particularly well with multilingual content, proper names, acronyms, and punctuation, often producing text that already feels “edited” rather than raw.

Where Whisper is less strong is timing precision and true code-switching. While it may correctly recognize foreign words embedded in another language, it doesn’t reliably detect intentional language switching within the same sentence.

Typical strengths

• High transcription accuracy
• Excellent punctuation and prose quality
• Strong multilingual support

Typical limitations

• Weaker timing alignment
• Inconsistent handling of mixed-language speech

AssemblyAI

AssemblyAI focuses on reliability, speed, and structural features. It is extremely consistent across different files and handles diarization and custom terminology well. Its output tends to be strictly verbatim, which can result in less natural punctuation and flow. This makes it a solid choice when accuracy must be predictable and processing speed matters more than stylistic polish.

Typical strengths

• Very fast processing
• High reliability
• Speaker diarization and term base support

Typical limitations

• Literal, rigid prose
• Less natural punctuation

Deepgram Nova 3

Deepgram Nova 3 stands out for its timing precision and robustness. It produces some of the best word and sentence alignment available, making it particularly suitable for subtitle workflows where timing accuracy is critical.

It is also the only engine in this comparison that supports true code-switching, meaning it can correctly detect and transcribe multiple languages within the same sentence (for a defined set of languages).

Typical strengths

• Excellent timing accuracy
• Strong performance in noisy environments
• Native code-switching support

Typical limitations

• More verbatim writing style
• More limited language coverage than Whisper

Deepgram’s Whisper

Deepgram’s Whisper implementation delivers the same high-quality transcription standards as OpenAI Whisper, particularly when it comes to linguistic accuracy, readability, and foreign-language handling. It produces clean, natural text that works very well as a foundation for professional subtitles.

One area where it stands out is formatting consistency. Numerals, measurements, and technical formats (such as units, quantities, and abbreviations) are often handled more cleanly and consistently, which can be especially valuable in educational, technical, or data-heavy content.

In addition, unlike OpenAI Whisper, it also supports speaker diarization, making it suitable for multi-speaker recordings where identifying speakers matters.

The main trade-off is processing speed. Deepgram’s Whisper is the slowest engine in this comparison, which makes it better suited for quality-focused workflows rather than high-throughput scenarios.

Typical strengths

• Whisper-level linguistic accuracy
• Clean handling of numbers, measurements, and formatted data
• Natural, readable prose
• Speaker diarization support

Typical limitations

• Slower processing times compared to other engines

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Practical Scenarios: Which Engine Fits Best?

Below are some real-world, pre-recorded media scenarios and how different engines typically perform in each case.

Long-form educational or training content

Imagine a 90-minute recorded online course, internal training session, or university lecture. The audio is generally clean, speakers talk continuously, and the end goal is highly readable subtitles that people will watch for extended periods of time.

In this case, transcription accuracy and sentence flow matter more than raw timing precision.

Best fit: OpenAI Whisper or Deepgram Nova 3. Whisper produces clean, well-punctuated text, while Nova 3 provides excellent alignment. In both cases, Fluen Studio’s Segmentation Engine ensures the final subtitles remain readable and well structured.

Interviews, panels, and recorded meetings

Consider a recorded panel discussion or interview with two or more speakers. Identifying who is speaking matters, especially if subtitles will be edited, reviewed, or reused for transcripts. Here, diarization and consistency are often more important than prose elegance.

Best fit: AssemblyAI or Deepgram Nova 3. Both support speaker diarization and handle conversational speech well. AssemblyAI is particularly reliable at scale, while Nova 3 offers better timing precision.

Mixed-language content and code-switching

Some real-world content naturally switches languages. For example, a speaker moving between English and Spanish in the same sentence, or a technical talk where foreign terms are intentionally used mid-speech. Most transcription engines struggle with this.

Best fit: Deepgram Nova 3. It is the only engine in this comparison that reliably supports true code-switching, making it the safest choice for multilingual speech within the same audio segment.

Large volumes with tight turnaround times

Think of a media team processing dozens or hundreds of recorded videos per week, where speed, predictability, and consistency matter more than stylistic refinement.

Best fit: AssemblyAI. Its fast processing speed and high reliability make it well suited for high-throughput workflows.

Content where text quality matters more than structure

Some content is reused beyond subtitles. For example, transcripts published as articles, documentation, or searchable archives. In these cases, clean punctuation and natural sentence flow reduce editing effort.

Best fit: OpenAI Whisper. It produces text that often feels closer to edited prose than raw transcription.

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Accuracy Matters, but Readability Matters More

Raw transcription accuracy is important, but it’s not the only thing that matters. For subtitles, especially in long-form content, readability is just as critical.

That’s why Fluen Studio separates the process into two stages: a transcription engine generates the raw text, and then a Segmentation Engine restructures it into well-timed, readable subtitle cues.

This means that even if you choose an engine with slightly lower accuracy, the final subtitles can still be excellent, because the segmentation layer handles the heavy lifting of making them readable and natural.

When choosing an engine, consider not just accuracy, but also timing precision, language coverage, processing speed, and how the transcript will be used downstream.

If the first result isn’t ideal, you can always reprocess with a different engine at no extra cost. That flexibility is by design.