--- title: "AWS Transcribe" type: "AI Tool" url: "https://aidemos.com/tools/aws-transcribe" description: "We scored AWS Transcribe on English, medical, overlap, and Spanish-English clips: full payloads and jargon were strong, but overlap and Spanish broke down." category: "audio-speech" website: "https://docs.aws.amazon.com/transcribe/latest/dg/" published: "2026-08-20T12:32:56.030359+00:00" updated: "2026-08-20T12:48:34.847454+00:00" --- # AWS Transcribe Batch speech-to-text with timestamps and speaker labels, but weak on multilingual audio. ## TL;DR Verdict **Solid batch API, but not a safe multilingual pick as configured** **Where it wins:** - you need a batch transcription API that returns word-level timestamps, confidence, and speaker labels - you can stage audio in S3 and run an async job/poll workflow - you are transcribing mostly English or technical jargon and can tolerate weaker performance on harder audio **Main limitation:** you need reliable multilingual or code-switching transcription **Pricing:** Free tier $0 · Standard Batch $0.0001000/sec ($0.006/min) · Standard Streaming $0.0001667/sec ($0.010002/min) `Batch STT` · `Word timestamps` · `Speaker labels` · `Code-switching tested` **Website:** [Visit AWS Transcribe](https://docs.aws.amazon.com/transcribe/latest/dg/) ## Criterion-to-evidence map Three hard-audio inputs, the key metric, and the primary artifact(s) to inspect. - **1** Overlapping speech / crosstalk — WER 33.88%; 4 speaker labels detected; heavy deletions against a 7579-word reference. - **2** Medical jargon — WER 3.63%; jargon recall 77.8%; missed 'cancellous' and 'trabeculae'. - **3** Bilingual code-switching — WER 23.06%; Spanish token recall 30.0%; weak on Spanish words. > **Solid batch API, but not a safe multilingual pick as configured** > > AWS Transcribe returned a complete developer payload on every scored run and handled the medical jargon clip well, but the overlap sample lost a lot of content and the Spanish-English sample showed weak Spanish recovery. In this benchmark it looks like a dependable batch transcription API for English-centric workflows, not a strong choice for multilingual or code-switching audio without further tuning. ## Demo Recording [Video: AWS Transcribe demo recording](https://cdn.futuresmart.ai/public/aidemos/7ce1dbc90c3641c5854e020d00f63259.mov?v=1) *Video — Tutorial recording of the AWS Transcribe benchmark workflow.* ## Feature-by-Feature Breakdown ### Batch Speech-to-Text Transcription **Verdict:** Mixed Runs asynchronous batch transcription jobs on long-form audio staged in AWS and returns full transcripts. It was exercised on overlapping crosstalk, medical-jargon, and bilingual code-switching audio, with results varying by difficulty. **Input:** > **Audio** **Output:** **Input:** > **Audio** **Output:** **Input:** > **Audio** **Output:** **Input:** > **Audio** **Output:** **Input:** > **Audio** **Output:** **Input:** > **Audio** **Output:** **Bottom line:** Best on the medical-jargon clip, acceptable on the code-switching clip in headline WER, and clearly weakest on crosstalk because of heavy deletions. ### Word-Level Metadata Export **Verdict:** Strong Returns developer-facing JSON with word-level timings, confidence values, and speaker labels. Across the scored runs, the payload consistently exposed these fields and supported downstream tooling. **Input:** > **Audio** **Output:** **Input:** > **Audio** **Output:** **Input:** > **Audio** **Output:** **Bottom line:** Consistent across every scored run and useful for downstream tooling, but the report only verifies that labels exist and counts them — it does not verify diarization attribution. ## AWS Transcribe pricing Standard batch and streaming are flat-rate; the benchmarked runs here used batch transcription. | Plan | Price | Notes | | --- | --- | --- | | Free tier | $0 | 60 audio minutes/month for 12 months; unused minutes do not roll over. | | Standard Batch ★ (tested) | $0.0001000/sec ($0.006/min) | Flat rate, no volume tiers; billed in 1-second increments; up to two channels included. | | Standard Streaming | $0.0001667/sec ($0.010002/min) | Flat rate, no volume tiers. | *AWS's own pricing feed shows standard transcription uses a single rate dimension with no volume tiers; add-ons such as PII redaction and custom language models are billed separately. The benchmarked cost figures in this report are list price multiplied by measured duration.* ## Is It Right For You? **Use it if** - you need a batch transcription API that returns word-level timestamps, confidence, and speaker labels - you can stage audio in S3 and run an async job/poll workflow - you are transcribing mostly English or technical jargon and can tolerate weaker performance on harder audio **Skip it if** - you need reliable multilingual or code-switching transcription - you need verified diarization attribution rather than just detected speaker labels - you need measured streaming latency from this benchmark - you need direct local-file transcription without S3 staging ## Classification - **Category:** audio-speech - **Subcategory:** audio-to-text - **Type:** speech - **Built for:** Other ## Frequently Asked Questions **Q: How much does AWS Transcribe cost?** The report sources AWS's own pricing feed and lists standard batch transcription at $0.006/minute ($0.36/hour), standard streaming at about $0.010002/minute, and a free tier of 60 audio minutes per month for 12 months. **Q: Does AWS Transcribe require S3?** Yes. The report says audio must be staged in S3 first before the batch transcription job runs. **Q: Does it return word-level timestamps and confidence?** Yes. All three tested runs exposed word timestamps and confidence fields in the raw JSON response. **Q: Does it support speaker labels?** Yes. Speaker labels were detected on every scored run, with 4 distinct speakers on the crosstalk and medical-jargon inputs and 3 distinct speakers on the bilingual input. The report does not measure whether those labels were attributed correctly. **Q: How did it handle overlapping speech?** Poorly. On the crosstalk run it scored 33.88% WER, returned 5460 words against a 7579-word reference, and dropped 2162 words. **Q: How did it handle multilingual or code-switching audio?** Weakly as configured. On the Spanish-English sample the benchmark summary recorded only 30.0% Spanish token recall, so the report recommends re-testing with explicit multilingual settings before trusting it for bilingual audio. ## Similar Tools AI tools similar to AWS Transcribe: - [Deepgram Nova-3](https://aidemos.com/tools/deepgram-nova-3) — Batch speech-to-text with rich metadata, but weak on crosstalk and code-switching. - [AssemblyAI Universal](https://aidemos.com/tools/assemblyai-universal) — Fast batch STT with strong metadata and mixed-language performance, but overlap-heavy meetings can drop too many words. - [ElevenLabs Scribe](https://aidemos.com/tools/elevenlabs-scribe) — Fast batch transcription with rich metadata, strongest on jargon and weaker on overlap and code-switching. - [Rev AI](https://aidemos.com/tools/rev-ai) — Low-cost batch speech-to-text with structured JSON, word timestamps, and speaker labels, but mixed accuracy on hard audio and weak multilingual recall. - [GroqCloud Whisper Large-v3](https://aidemos.com/tools/groqcloud-whisper-large-v3) — Fast batch transcription for under-cap audio, with strong jargon handling but a hard 25 MB ceiling. ## Need a custom AI solution for this use case? If you are looking to build a custom speech-to-text transcription, timestamping, or speaker diarization workflow for your business or internal workflow, email us at [contact@futuresmart.ai](mailto: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](mailto:collaborate@aidemos.com).