---
title: "AWS"
type: "AI Tool"
url: "https://aidemos.com/tools/aws-transcribe"
description: "AWS Transcribe returned word timings, confidence, and speaker labels in batch, but overlap hurt accuracy and code-switching kept only 30% of Spanish tokens."
category: "audio-speech"
website: "https://docs.aws.amazon.com/transcribe/latest/dg/"
published: "2026-08-20T12:32:56.030359+00:00"
updated: "2026-09-01T02:59:29.321895+00:00"
---

# AWS

Batch speech-to-text with word-level metadata, but accuracy drops on overlap and code-switching.

## TL;DR Verdict

**Reliable batch plumbing, uneven transcripts**

**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 upload → poll → fetch workflow
- you are transcribing mostly English or technical jargon and can tolerate weaker performance on overlap-heavy or code-switched 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 API` · `Word-level metadata` · `Speaker labels` · `$0.006/min`

**Website:** [Visit AWS](https://docs.aws.amazon.com/transcribe/latest/dg/)

## Cross-input benchmark results

Batch transcription on crosstalk, medical jargon, and bilingual code-switching.

- **1/3** Medical Jargon — WER 3.63%; jargon recall 77.8%; missed cancellous and trabeculae.
- **2/3** Bilingual Code-Switching — WER 23.06%; Spanish token recall 30.0%; many Spanish tokens were dropped or anglicized.
- **3/3** Overlapping Speech / Crosstalk — WER 33.88%; 4 speaker labels were detected, but attribution correctness was not scored.

> **Reliable batch plumbing, uneven transcripts**
>
> AWS Transcribe returned a complete developer payload on every scored run: word-level timing, confidence, and speaker labels were present, and the batch workflow completed cleanly at about $0.36/audio-hour. It was excellent on the medical-jargon clip, but it dropped heavily on overlapping speech and recovered only 30.0% of Spanish tokens in the code-switching sample. This benchmark also only verified speaker-label presence and count, not diarization attribution, so I would treat it as solid English-centric batch STT rather than a safe default for multilingual or overlap-heavy audio.

## Demo Recording

[Video: AWS demo recording (download MP4)](https://cdn.futuresmart.ai/public/aidemos/673a36ed26d7481dafceff1ec3fb9103.mov?v=1)
[▶️ Watch (streaming)](https://stream.futuresmart.ai/embed/3ce3f8ed-ca44-4966-b28f-180848ecbf44)
*Video — Tutorial recording of the AWS Transcribe benchmark workflow.*

## Feature-by-Feature Breakdown

### Batch Speech-to-Text Transcription

**Verdict:** Mixed

Converts staged long-form audio into full transcripts. It was exercised on overlapping crosstalk, medical-jargon, and bilingual code-switching clips.

**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.

### Structured Transcription Metadata Export

**Verdict:** Strong developer payload shape, but transcript quality varies sharply by audio type.

Returns batch transcription output together with word-level timestamps, confidence values, and speaker-label fields, often in a developer-facing JSON shape. It was exercised on medical-jargon narration, four-speaker crosstalk, and bilingual code-switching clips.

**Input:**

> **Audio**

**Output:**

**Input:**

> **Audio**

**Output:**

**Input:**

> **Audio**

**Output:**

**Bottom line:** Best when you need a batch transcript plus metadata in a stable JSON shape; not best when you need uniformly accurate transcription across hard audio, and not enough to prove diarization attribution on its own.

### Asynchronous Batch Job Orchestration

**Verdict:** End-to-end batch orchestration worked on all three runs.

Submits audio as a signed batch request, stages it in S3, polls for completion, and retrieves the finished transcription result. It was exercised with a local AWS profile when available and credential prompting at runtime.

**Input:**

> **Image**

**Output:**

**Input:**

> **Image**

**Output:**

**Input:**

> **Image**

**Output:**

**Bottom line:** The batch workflow itself was stable across all three runs, but the benchmark did not measure call-by-call HTTP timings, so this confirms end-to-end automation rather than internal stage latency.

## AWS Transcribe pricing

Standard batch is flat-rate and was the mode used in this benchmark.

| 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; this benchmark used standard batch. |
| Standard Streaming | $0.0001667/sec ($0.010002/min) | Flat rate shown in the report, but this benchmark was batch-only and did not measure streaming latency. |

*Standard transcription is billed in 1-second increments with no minimum. The report also says the free tier is 60 audio minutes per month for 12 months. Add-ons such as automatic content redaction and custom language models are priced separately.*

## 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 upload → poll → fetch workflow
- you are transcribing mostly English or technical jargon and can tolerate weaker performance on overlap-heavy or code-switched 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 direct local-file transcription without S3 staging
- you need measured streaming latency from this benchmark

## Classification

- **Category:** audio-speech
- **Subcategory:** audio-to-text
- **Type:** speech
- **Built for:** Other

## Frequently Asked Questions

**Q: Does AWS Transcribe require S3 staging?**

Yes in this benchmark. The request configuration notes that audio must be staged in S3 first, then the pipeline uses AWS-signed batch requests to start and fetch the job.

**Q: What metadata does AWS Transcribe return?**

On all three scored runs the payload included word-level timestamps, confidence, and speaker_labels. Payload depth was 3/3 on every input.

**Q: Does AWS Transcribe support speaker labels?**

Yes. Speaker labels were returned on every run, and the previewed counts were 4 speakers on crosstalk, 1 on medical jargon, and 3 on the bilingual sample. The benchmark did not measure whether those labels were attributed correctly.

**Q: How did AWS Transcribe handle overlapping speech?**

Poorly. On the four-speaker crosstalk sample it scored 33.88% WER, returned 5,460 words versus 7,579 reference words, and dropped a large amount of content.

**Q: How did AWS Transcribe handle bilingual code-switching?**

Weakly on Spanish content. The benchmark recorded 23.06% WER overall and only 30.0% Spanish token recall, with many Spanish tokens dropped or anglicized.

**Q: How much does AWS Transcribe cost?**

Standard batch is $0.006/min ($0.36/audio-hour) and the report says the free tier is 60 audio minutes per month for 12 months. The benchmarked standard batch mode is flat-rate with no volume tiers.

## Similar Tools

AI tools similar to AWS:

- [AssemblyAI](https://aidemos.com/tools/assemblyai-speech-to-text) — Fast batch speech-to-text with rich metadata, strong jargon and mixed-language results, but overlap-heavy meetings can still lose too much.
- [Speechmatics](https://aidemos.com/tools/speechmatics) — Strong batch STT for hard English audio, but weak on code-switching as configured.
- [OpenAI](https://aidemos.com/tools/openai) — Batch speech-to-text with word timestamps, but a strict upload cap and weak multilingual performance make it a mixed fit for hard audio.
- [ElevenLabs Scribe](https://aidemos.com/tools/elevenlabs-scribe) — Fast batch speech-to-text with word-level metadata, strongest on jargon and weaker on overlap/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 crosstalk and code-switching.
- [Gladia](https://aidemos.com/tools/gladia) — Batch STT with rich word-level metadata and strong jargon recall, but overlap handling is weak and bilingual WER needs a mono-downmixed rerun.
- [Deepgram](https://aidemos.com/tools/deepgram) — Batch speech-to-text with rich metadata, but weak on crosstalk and code-switching.

## Need a custom AI solution for this use case?

If you are looking to build a custom speech-to-text, audio transcription, or subtitle generation system 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).