AssemblyAI
Fast batch speech-to-text with rich metadata, strong jargon and mixed-language results, but overlap-heavy meetings can still lose too much.
Strong batch STT, but crosstalk is the weak spot
- You need batch transcription that returns completed JSON with word-level timestamps, confidence values, and speaker labels.
- You transcribe technical narration or jargon-heavy audio and want low WER on those clips.
- You need mixed-language batch transcription and can accept that this benchmark only covered a mostly-English code-switching sample.
- You need overlap-heavy meeting audio to retain every word.
Feature scores on this page: 33.2/100 (1 scored feature)
Our take
AssemblyAI completed all three batch jobs and stayed consistently fast, returning completed JSON with word-level timestamps, confidence, and speaker labels every time. It was excellent on the medical-jargon clip and best on the bilingual code-switching clip, but the overlapping-speech sample dropped 1,976 words, so it is a solid choice for structured batch transcription as long as overlap-heavy meetings are not the main workload.
In-Depth Review
Our detailed analysis of AssemblyAI — features, performance, and real-world testing.
Feature-by-Feature Breakdown
Robust Speech-to-Text TranscriptionToo much content is lost on crosstalk-heavy audio.33.16/100▾
Feature tested: Robust Speech-to-Text Transcription
Result: Failed (33.16/100)
Verdict: Too much content is lost on crosstalk-heavy audio.
Expected behavior: Transcribes difficult speech inputs into text, including overlap-heavy conversations, dense technical narration, and Spanish-English code-switching. The evidence came from the overlap-heavy meeting clip, the Gray's Anatomy clip, and a mostly-English mixed-language sample.
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): Input 1: Overlapping Speech / Crosstalk — crosstalk.wav, 65.39 MB, 2142.709 s, AMI EN2002a four-way overlap. — crosstalk.wav
Observed output: Output artifact (Text/code file): The completed transcript for the crosstalk clip finished successfully, but the scored run recorded 33.16% WER with 1,976 deletions and 4 detected speakers, so the transcript dropped too much content for record-quality use. — raw-response.json
Input artifact: Input artifact (Audio file): Input 1: Overlapping Speech / Crosstalk — crosstalk.wav, 65.39 MB, 2142.709 s, AMI EN2002a four-way overlap. — crosstalk.wav
Output artifact: Output artifact (Text/code file): The completed transcript for the crosstalk clip finished successfully, but the scored run recorded 33.16% WER with 1,976 deletions and 4 detected speakers, so the transcript dropped too much content for record-quality use. — raw-response.json
What changed: Audio file transformed into Text/code file
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): Input 2: Medical Jargon — medical_terms.mp3, clean single-speaker narration from Gray's Anatomy with many anatomical terms. — medical_terms.mp3
Observed output: Output artifact (Text/code file): The completed transcript for the medical-jargon clip was highly accurate: the benchmark recorded 3.78% WER and 100% recall of the scored medical terms. — raw-response-2.json
Input artifact: Input artifact (Audio file): Input 2: Medical Jargon — medical_terms.mp3, clean single-speaker narration from Gray's Anatomy with many anatomical terms. — medical_terms.mp3
Output artifact: Output artifact (Text/code file): The completed transcript for the medical-jargon clip was highly accurate: the benchmark recorded 3.78% WER and 100% recall of the scored medical terms. — raw-response-2.json
What changed: Audio file transformed into Text/code file
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): Input 3: Bilingual Code-Switching — mix_language.mp3, spontaneous Spanish-English conversation with English dominant in the sample. — mix_language.mp3
Observed output: Output artifact (Text/code file): The completed transcript for the bilingual clip was the strongest of the benchmarked engines on this input, with 21.04% WER and 72.5% Spanish token recall. — raw-response-3.json
Input artifact: Input artifact (Audio file): Input 3: Bilingual Code-Switching — mix_language.mp3, spontaneous Spanish-English conversation with English dominant in the sample. — mix_language.mp3
Output artifact: Output artifact (Text/code file): The completed transcript for the bilingual clip was the strongest of the benchmarked engines on this input, with 21.04% WER and 72.5% Spanish token recall. — raw-response-3.json
What changed: Audio file transformed into Text/code file
Why it matters / Conclusion: It can process overlap-heavy meetings, but the omission rate is high enough that this is not a safe transcript-of-record result.
Transcribes difficult speech inputs into text, including overlap-heavy conversations, dense technical narration, and Spanish-English code-switching. The evidence came from the overlap-heavy meeting clip, the Gray's Anatomy clip, and a mostly-English mixed-language sample.
Transcript Metadata ExportConsistently available on every run.▾
Feature tested: Transcript Metadata Export
Result: Passed
Verdict: Consistently available on every run.
Expected behavior: Returns structured transcript payloads with word-level timestamps, confidence values, and related JSON fields. The evidence shows these metadata fields present consistently across the tested audio runs.
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): INPUT: Overlapping Speech / Crosstalk — check returned payload structure and metadata. — crosstalk.wav
Observed output: Output artifact (Text/code file): Raw JSON payload showing word timestamps, confidence values, speaker labels, and 4 distinct speakers detected in the crosstalk run. — raw-response.json
Input artifact: Input artifact (Audio file): INPUT: Overlapping Speech / Crosstalk — check returned payload structure and metadata. — crosstalk.wav
Output artifact: Output artifact (Text/code file): Raw JSON payload showing word timestamps, confidence values, speaker labels, and 4 distinct speakers detected in the crosstalk run. — raw-response.json
What changed: Audio file transformed into Text/code file
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): INPUT: Medical Jargon — check returned payload structure and metadata. — medical_terms.mp3
Observed output: Output artifact (Text/code file): Raw JSON payload showing word timestamps, confidence values, speaker labels, and 1 distinct speaker detected in the medical-jargon run. — raw-response-2.json
Input artifact: Input artifact (Audio file): INPUT: Medical Jargon — check returned payload structure and metadata. — medical_terms.mp3
Output artifact: Output artifact (Text/code file): Raw JSON payload showing word timestamps, confidence values, speaker labels, and 1 distinct speaker detected in the medical-jargon run. — raw-response-2.json
What changed: Audio file transformed into Text/code file
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): INPUT: Bilingual Code-Switching — check returned payload structure and metadata. — mix_language.mp3
Observed output: Output artifact (Text/code file): Raw JSON payload showing word timestamps, confidence values, speaker labels, and 2 distinct speakers detected in the bilingual run. — raw-response-3.json
Input artifact: Input artifact (Audio file): INPUT: Bilingual Code-Switching — check returned payload structure and metadata. — mix_language.mp3
Output artifact: Output artifact (Text/code file): Raw JSON payload showing word timestamps, confidence values, speaker labels, and 2 distinct speakers detected in the bilingual run. — raw-response-3.json
What changed: Audio file transformed into Text/code file
Why it matters / Conclusion: This is a dependable structured-transcript export: timestamps, confidence, and deep JSON payloads were present on every run.
Returns structured transcript payloads with word-level timestamps, confidence values, and related JSON fields. The evidence shows these metadata fields present consistently across the tested audio runs.
Speaker DiarizationLabels are detected, but attribution correctness was not measured.▾
Feature tested: Speaker Diarization
Result: Partial
Verdict: Labels are detected, but attribution correctness was not measured.
Expected behavior: Adds speaker labels or speaker IDs to transcript output and reports the number of distinct speakers detected. It was exercised on crosstalk, medical-jargon, and bilingual clips, where speaker labels were surfaced consistently.
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): Input 1: Overlapping Speech / Crosstalk — four participants speaking over one another. — crosstalk.wav
Observed output: Output artifact (Text/code file): The transcript payload reported 4 distinct speaker labels on the crosstalk clip, matching the four-participant meeting structure, but label correctness was not scored. — raw-response.json
Input artifact: Input artifact (Audio file): Input 1: Overlapping Speech / Crosstalk — four participants speaking over one another. — crosstalk.wav
Output artifact: Output artifact (Text/code file): The transcript payload reported 4 distinct speaker labels on the crosstalk clip, matching the four-participant meeting structure, but label correctness was not scored. — raw-response.json
What changed: Audio file transformed into Text/code file
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): Input 2: Medical Jargon — single-speaker narration. — medical_terms.mp3
Observed output: Output artifact (Text/code file): The transcript payload reported 1 detected speaker on the medical-jargon clip, consistent with the single-speaker narration. — raw-response-2.json
Input artifact: Input artifact (Audio file): Input 2: Medical Jargon — single-speaker narration. — medical_terms.mp3
Output artifact: Output artifact (Text/code file): The transcript payload reported 1 detected speaker on the medical-jargon clip, consistent with the single-speaker narration. — raw-response-2.json
What changed: Audio file transformed into Text/code file
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): Input 3: Bilingual Code-Switching — two-speaker Spanish-English conversation. — mix_language.mp3
Observed output: Output artifact (Text/code file): The transcript payload reported 2 detected speakers on the bilingual clip, but speaker-attribution correctness was not independently measured. — raw-response-3.json
Input artifact: Input artifact (Audio file): Input 3: Bilingual Code-Switching — two-speaker Spanish-English conversation. — mix_language.mp3
Output artifact: Output artifact (Text/code file): The transcript payload reported 2 detected speakers on the bilingual clip, but speaker-attribution correctness was not independently measured. — raw-response-3.json
What changed: Audio file transformed into Text/code file
Why it matters / Conclusion: Speaker labels are exposed consistently, but this benchmark did not verify whether every attribution was correct.
Adds speaker labels or speaker IDs to transcript output and reports the number of distinct speakers detected. It was exercised on crosstalk, medical-jargon, and bilingual clips, where speaker labels were surfaced consistently.
Batch TranscriptionCompleted successfully on all three test runs.▾
Feature tested: Batch Transcription
Result: Partial
Verdict: Completed successfully on all three test runs.
Expected behavior: Accepts uploaded audio files, runs them through a batch transcription flow, and returns a completed transcript JSON response. The evidence covers overlapping speech, medical narration, and bilingual code-switching inputs reaching completion through the API path.
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): INPUT: Overlapping Speech / Crosstalk — crosstalk.wav (65.39 MB, 2142.709s) — crosstalk.wav
Observed output: Output artifact (Text/code file): Completed JSON response for the crosstalk run; the job reached status completed and returned a transcript payload. — raw-response.json
Input artifact: Input artifact (Audio file): INPUT: Overlapping Speech / Crosstalk — crosstalk.wav (65.39 MB, 2142.709s) — crosstalk.wav
Output artifact: Output artifact (Text/code file): Completed JSON response for the crosstalk run; the job reached status completed and returned a transcript payload. — raw-response.json
What changed: Audio file transformed into Text/code file
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): INPUT: Medical Jargon — medical_terms.mp3 (8.58 MB, 1123.944s) — medical_terms.mp3
Observed output: Output artifact (Text/code file): Completed JSON response for the medical-jargon run; the job reached status completed and returned a transcript payload. — raw-response-2.json
Input artifact: Input artifact (Audio file): INPUT: Medical Jargon — medical_terms.mp3 (8.58 MB, 1123.944s) — medical_terms.mp3
Output artifact: Output artifact (Text/code file): Completed JSON response for the medical-jargon run; the job reached status completed and returned a transcript payload. — raw-response-2.json
What changed: Audio file transformed into Text/code file
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): INPUT: Bilingual Code-Switching — mix_language.mp3 (22.19 MB, 1938.495s) — mix_language.mp3
Observed output: Output artifact (Text/code file): Completed JSON response for the bilingual run; the job reached status completed and returned a transcript payload. — raw-response-3.json
Input artifact: Input artifact (Audio file): INPUT: Bilingual Code-Switching — mix_language.mp3 (22.19 MB, 1938.495s) — mix_language.mp3
Output artifact: Output artifact (Text/code file): Completed JSON response for the bilingual run; the job reached status completed and returned a transcript payload. — raw-response-3.json
What changed: Audio file transformed into Text/code file
Why it matters / Conclusion: The batch API path was reliable across all three runs, with no manual intervention needed to reach completed JSON.
Accepts uploaded audio files, runs them through a batch transcription flow, and returns a completed transcript JSON response. The evidence covers overlapping speech, medical narration, and bilingual code-switching inputs reaching completion through the API path.
How it scored on the research's own criteria
The 3 evaluation dimensions from our hands-on research on AssemblyAI, each judged from recorded runs on 3 test inputs — the same verdicts the ranking page ranks on.
held up partial failed not exercised by this input
| Criterion | Verdict | What the runs showed | Per input | Proof |
|---|---|---|---|---|
| Output quality | Mixed3/5 | Accuracy is strong on clean medical narration, but it drops a lot on overlapping speech and settles in the middle on mixed-language audio. Because the best case is excellent while the hardest case is weak, the overall quality picture is mixed rather than consistently strong. | open proof ↗ | |
| Automation level | Strong5/5 | Each run completed the full upload → transcription → polling flow on its own and returned a finished result without operator intervention. The only missing detail is per-call timing, not whether the workflow itself ran end to end, so this stays at the top score. | open proof ↗ | |
| Input handling | Strong5/5 | It accepted all three uploaded files without objection and finished each run quickly, with low real-time factors around 0.016–0.018 and modest costs. That combination of reliable acceptance plus consistently fast batch processing supports the top score. | open proof ↗ |
Verdicts come verbatim from the study's recorded observations, never re-derived at render; a criterion with no recorded run shows Not exercised — this section cannot invent a score.
Official pricing
Benchmark runs used Universal-3.5 Pro; diarization stacks on top as an add-on.
The report says add-ons stack on top of the base rate, and multichannel audio is billed per channel.
Banner Preview
How the embed badge will look on your site
Embed HTML
Copy this code to your website source
Quick Integration Guide
- 1Copy the HTML code block above.
- 2Paste it into your site's HTML or CMS editor.
- 3Banner appears instantly on your page.
- 4Links back to your tool profile here.
Similar Tools
Discover more AI tools like AssemblyAI to enhance your workflow.
Comments (0)
Need a custom AI solution for this use case?
If you are looking to build a custom speech-to-text, transcription, or meeting transcription system for your business or internal workflow, email us at contact@futuresmart.ai.
Found something inaccurate or missing? We try to keep our AI research accurate and useful. If you found outdated information, an issue, or have a suggestion, email us at collaborate@aidemos.com.