Gladia
Batch STT with rich JSON metadata and strong jargon recall, but weak overlap handling and a channel-duplication caveat on bilingual audio.
Strong metadata and jargon recall, but not a clean win on hard overlap
- You need a batch STT API that returns transcripts plus word-level timestamps, confidence, and speaker labels.
- You care about technical jargon and proper-term recall on hard audio.
- You want mixed-language transcript coverage and can rerun stereo or code-switching cases with mono downmix or explicit channel control before trusting the WER.
- You need reliable overlap handling on crosstalk-heavy audio.
Our take
Gladia is compelling as a batch STT API because it consistently returns word-level metadata and did especially well on the medical jargon sample. But the crosstalk transcript was incomplete, and the bilingual run's WER is not trustworthy because the response duplicated both audio channels; rerun with mono downmix or explicit channel control before relying on that score.
In-Depth Review
Our detailed analysis of Gladia — features, performance, and real-world testing.
Feature-by-Feature Breakdown
Batch TranscriptionMixed▾
Feature tested: Batch Transcription
Result: Partial
Verdict: Mixed
Expected behavior: Accepts pre-recorded audio and returns transcripts in batch mode. The tested inputs included three long-form audio samples, showing the endpoint can process multiple recordings in one run.
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): Overlapping speech / crosstalk sample. — crosstalk.wav
Observed output: Output artifact (Text/code file): Returned a completed transcript for the crosstalk sample, but the scored run was weak: 5,455 words returned against a 7,579-word reference, with WER 37.35%, 529 substitutions, 2,213 deletions, and 89 insertions. — raw-response-3.json
Input artifact: Input artifact (Audio file): Overlapping speech / crosstalk sample. — crosstalk.wav
Output artifact: Output artifact (Text/code file): Returned a completed transcript for the crosstalk sample, but the scored run was weak: 5,455 words returned against a 7,579-word reference, with WER 37.35%, 529 substitutions, 2,213 deletions, and 89 insertions. — raw-response-3.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): Medical jargon narration sample. — medical_terms.mp3
Observed output: Output artifact (Text/code file): Returned a completed transcript for the medical sample with WER 4.07% and 2,738 words against a 2,728-word reference; the run also preserved all scored jargon terms. — raw-response-2.json
Input artifact: Input artifact (Audio file): Medical jargon narration sample. — medical_terms.mp3
Output artifact: Output artifact (Text/code file): Returned a completed transcript for the medical sample with WER 4.07% and 2,738 words against a 2,728-word reference; the run also preserved all scored jargon 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): Bilingual code-switching sample. — mix_language.mp3
Observed output: Output artifact (Text/code file): Returned a completed transcript for the bilingual sample, but the output duplicated both channels and produced 10,765 words against a 6,517-word reference, so the 88.45% WER is not a trustworthy accuracy result until the run is repeated with mono downmix or explicit channel control. — raw-response.json
Input artifact: Input artifact (Audio file): Bilingual code-switching sample. — mix_language.mp3
Output artifact: Output artifact (Text/code file): Returned a completed transcript for the bilingual sample, but the output duplicated both channels and produced 10,765 words against a 6,517-word reference, so the 88.45% WER is not a trustworthy accuracy result until the run is repeated with mono downmix or explicit channel control. — raw-response.json
What changed: Audio file transformed into Text/code file
Why it matters / Conclusion: Reliable as a batch endpoint, but transcript quality is uneven across hard audio and the bilingual score must be rerun before it can be cited.
Accepts pre-recorded audio and returns transcripts in batch mode. The tested inputs included three long-form audio samples, showing the endpoint can process multiple recordings in one run.
Structured Transcript OutputStrong▾
Feature tested: Structured Transcript Output
Result: Passed
Verdict: Strong
Expected behavior: Returns machine-readable transcript results with downstream-friendly metadata such as word-level timestamps, confidence values, speaker labels, and channel count. The tested runs on crosstalk, jargon, and bilingual audio showed the structured payload is consistently present and integration-ready.
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): Overlapping speech / crosstalk sample. — crosstalk.wav
Observed output: Output artifact (Text/code file): Developer metadata was present in the response: word timestamps yes, confidence yes, speaker labels yes, with 12,968 timed tokens and 4 distinct speaker labels. — raw-response-3.json
Input artifact: Input artifact (Audio file): Overlapping speech / crosstalk sample. — crosstalk.wav
Output artifact: Output artifact (Text/code file): Developer metadata was present in the response: word timestamps yes, confidence yes, speaker labels yes, with 12,968 timed tokens and 4 distinct speaker labels. — raw-response-3.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): Medical jargon narration sample. — medical_terms.mp3
Observed output: Output artifact (Text/code file): Developer metadata was present in the response: word timestamps yes, confidence yes, speaker labels yes, with 5,854 timed tokens and 1 distinct speaker. — raw-response-2.json
Input artifact: Input artifact (Audio file): Medical jargon narration sample. — medical_terms.mp3
Output artifact: Output artifact (Text/code file): Developer metadata was present in the response: word timestamps yes, confidence yes, speaker labels yes, with 5,854 timed tokens and 1 distinct speaker. — 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): Bilingual code-switching sample. — mix_language.mp3
Observed output: Output artifact (Text/code file): Developer metadata was present in the response: word timestamps yes, confidence yes, speaker labels yes, with 26,166 timed tokens, 5 distinct speakers, and two channels recorded in the payload. — raw-response.json
Input artifact: Input artifact (Audio file): Bilingual code-switching sample. — mix_language.mp3
Output artifact: Output artifact (Text/code file): Developer metadata was present in the response: word timestamps yes, confidence yes, speaker labels yes, with 26,166 timed tokens, 5 distinct speakers, and two channels recorded in the payload. — raw-response.json
What changed: Audio file transformed into Text/code file
Why it matters / Conclusion: Consistent, integration-friendly metadata export across every tested input.
Returns machine-readable transcript results with downstream-friendly metadata such as word-level timestamps, confidence values, speaker labels, and channel count. The tested runs on crosstalk, jargon, and bilingual audio showed the structured payload is consistently present and integration-ready.
Mixed-Language TranscriptionPromising but caveated▾
Feature tested: Mixed-Language Transcription
Result: Partial
Verdict: Promising but caveated
Expected behavior: Handles mixed English/Spanish audio without requiring a special code-switching flag. The bilingual sample produced Spanish-token coverage, providing evidence for mixed-language coverage.
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): Spontaneous bilingual Spanish-English conversation. — mix_language.mp3
Observed output: Output artifact (Text/code file): The bilingual run returned 10,765 words against a 6,517-word reference and exposed 56.2% Spanish token recall, but the response duplicated both channels, so the 88.45% WER is an artefact rather than a trustworthy accuracy result. — raw-response.json
Input artifact: Input artifact (Audio file): Spontaneous bilingual Spanish-English conversation. — mix_language.mp3
Output artifact: Output artifact (Text/code file): The bilingual run returned 10,765 words against a 6,517-word reference and exposed 56.2% Spanish token recall, but the response duplicated both channels, so the 88.45% WER is an artefact rather than a trustworthy accuracy result. — raw-response.json
What changed: Audio file transformed into Text/code file
Why it matters / Conclusion: Useful for mixed-language coverage, but this benchmark needs a rerun before the bilingual WER can be trusted.
Handles mixed English/Spanish audio without requiring a special code-switching flag. The bilingual sample produced Spanish-token coverage, providing evidence for mixed-language coverage.
Speaker-Aware TranscriptionMixed▾
Feature tested: Speaker-Aware Transcription
Result: Partial
Verdict: Mixed
Expected behavior: Produces transcript output that preserves speaker labels and works on overlap audio while also continuing through bilingual runs. The tested overlap sample and bilingual runs showed speaker labeling and code-switching coverage in the transcript output.
Test case: Audio file → Image
Input type: Audio file
Input used: Input artifact (Audio file): Four-way overlapping meeting audio (AMI EN2002a), 65.39 MB, 2142.709 s. — crosstalk.wav
Observed output: Output artifact (Image): Run metrics report diarization detected with 4 distinct speaker labels, matching the 4 true participants in EN2002a. — 03-terminal-metrics-3.png
Input artifact: Input artifact (Audio file): Four-way overlapping meeting audio (AMI EN2002a), 65.39 MB, 2142.709 s. — crosstalk.wav
Output artifact: Output artifact (Image): Run metrics report diarization detected with 4 distinct speaker labels, matching the 4 true participants in EN2002a. — 03-terminal-metrics-3.png
What changed: Audio file transformed into Image
Why it matters / Conclusion: Useful for diarization-aware workflows and some mixed-language coverage, but speaker attribution correctness was not measured and the bilingual benchmark needs a rerun before its accuracy can be trusted.
Produces transcript output that preserves speaker labels and works on overlap audio while also continuing through bilingual runs. The tested overlap sample and bilingual runs showed speaker labeling and code-switching coverage in the transcript output.
Vendor pricing
Starter matches the list-rate used in the benchmark; Growth lowers the per-hour price on commitment, and Enterprise is custom.
Rates are from the vendor pricing page and differ between async and real-time usage.
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