---
title: "Google Cloud"
type: "AI Tool"
url: "https://aidemos.com/tools/google-cloud-speech-to-text"
description: "Batch English audio got word-level timing and confidence, but diarization failed and code-switching was weak."
category: "audio-speech"
website: "https://cloud.google.com/speech-to-text/v2/docs"
published: "2026-08-24T00:52:41.334362+00:00"
updated: "2026-09-01T02:57:46.525906+00:00"
---

# Google Cloud

Timed batch transcripts for mostly English, jargon-heavy audio — but not for diarization or code-switching.

## TL;DR Verdict

**Good timed transcription for technical English, but weak where the use case gets hard**

**Where it wins:**
- you need batch STT with word-level timestamps and confidence
- your audio is mostly English and jargon-heavy
- you can tolerate Standard-tier pricing or want to benchmark offline batch jobs

**Main limitation:** you need reliable speaker diarization or speaker-separated transcripts

**Pricing:** Free tier $0 · V2 Standard — batch or real-time $0.016/min ($0.96/audio-hour) · V2 Dynamic Batch $0.004/min ($0.24/audio-hour) · Volume discounts as low as ~$0.004/min

`Batch STT` · `Word timestamps` · `No diarization` · `Weak code-switch`

**Website:** [Visit Google Cloud](https://cloud.google.com/speech-to-text/v2/docs)

> **Good timed transcription for technical English, but weak where the use case gets hard**
>
> Google Cloud Speech-to-Text is workable if you need batch transcripts with word-level timing and confidence for mostly English, jargon-heavy audio. The scored runs were much weaker on overlapping speech and code-switching, and diarization was not usable in the logged configuration. It is also priced at the Standard tier here, so this is not the cheapest path unless you re-test Dynamic Batch.

## Demo Recording

[Video: Google Cloud demo recording (download MP4)](https://cdn.futuresmart.ai/public/aidemos/297009249468463a89b97f14d024ca67.mov?v=1)
[▶️ Watch (streaming)](https://stream.futuresmart.ai/embed/06a420aa-4177-40b3-88dd-b4bcb8e9892b)
*Video — Terminal walkthrough of the benchmark run showing model selection and the three scored inputs.*

## Feature-by-Feature Breakdown

### Word-Level Transcript Metadata

**Verdict:** Timing and confidence are available, but speaker labels are not.

Produces transcript metadata at the word level for the tested long-form audio inputs, including timing offsets and, in one scored configuration, confidence values. The outputs were useful for captions, alignment, search, and review, but did not include speaker labels in these runs.

**Input:**

> **Audio**

**Output:**

**Input:**

> **Audio**

**Output:**

**Input:**

> **Audio**

**Output:**

**Bottom line:** Useful transcript metadata for captions, search, and review, but not enough for speaker-separated workflows.

### Speaker Diarization

**Verdict:** Diarization was not usable in the scored runs.

Attempts to assign speaker labels in transcribed audio for the benchmarked long-form runs, where diarization was requested but the outputs showed API rejection or unsupported-field behavior and no usable speaker attribution. The tested configurations did not return speaker labels.

**Input:**

> **Audio**

**Output:**

**Input:**

> **Audio**

**Output:**

**Input:**

> **Audio**

**Output:**

**Bottom line:** Do not rely on this configuration for meeting separation or speaker attribution.

### Technical Terminology Transcription

**Verdict:** Strong on the medical-jargon sample.

Transcribes speech with domain-specific jargon, demonstrated on the Gray's Anatomy narration where the scored transcript preserved technical terms with relatively low WER despite some insertion noise.

**Input:**

> **Audio**

**Output:**

**Bottom line:** This is the clearest strength in the scored set: technical terms were recognized well, even though the transcript still had insertion noise.

### Multilingual Code-Switch Transcription

**Verdict:** Weak on the bilingual sample as configured.

Handles speech that mixes languages, exercised on the bilingual code-switching sample where many Spanish tokens were dropped or anglicized. The benchmark notes the sample is heavily English-dominant, so the main evidence is reduced Spanish-token recall.

**Input:**

> **Audio**

**Output:**

**Bottom line:** Do not treat this as reliable multilingual support in the tested configuration.

### Batch Speech Transcription

**Verdict:** Completed all three batch jobs, but quality varied sharply by audio type.

Transcribes long-form audio in batch mode, exercised on the medical-jargon, overlapping-speech, and bilingual code-switching inputs. The benchmarked runs completed the transcription job, though output quality varied on harder audio.

**Input:**

> **Audio**

**Output:**

**Input:**

> **Audio**

**Output:**

**Input:**

> **Audio**

**Output:**

**Input:**

> **Audio**

**Output:**

**Bottom line:** It reliably finishes the batch job, but hard audio exposes large quality swings.

## Official pricing

Standard-tier costs were used for the benchmark; Dynamic Batch is cheaper but was not exercised here.

| Plan | Price | Notes |
| --- | --- | --- |
| Free tier | $0 | 60 audio-minutes per month; applies across V1 and V2. |
| V2 Standard — batch or real-time ★ (tested) | $0.016/min ($0.96/audio-hour) | Pay-as-you-go; this benchmark was billed at this tier. |
| V2 Dynamic Batch | $0.004/min ($0.24/audio-hour) | Lower-urgency processing with no latency guarantee; 75% cheaper than Standard. |
| Volume discounts | as low as ~$0.004/min | Requires sales contact for large monthly commitments. |
| V1 API | $0.016/min | Same headline rate as Standard. |

*Prices are from the vendor pricing page and reflect the benchmark's Standard-tier billing path.*

## Is It Right For You?

**Use it if**
- you need batch STT with word-level timestamps and confidence
- your audio is mostly English and jargon-heavy
- you can tolerate Standard-tier pricing or want to benchmark offline batch jobs

**Skip it if**
- you need reliable speaker diarization or speaker-separated transcripts
- you need strong multilingual or balanced code-switch transcription
- your workload is overlap-heavy meetings or crosstalk
- you are cost-sensitive and will not re-test Dynamic Batch

## Classification

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

## Frequently Asked Questions

**Q: Does Google Cloud Speech-to-Text return speaker diarization in these runs?**

No. The request configs enabled diarization, but the scored runs logged diarization rejection or unsupported-field notes and returned no speaker labels.

**Q: How accurate was it on technical jargon?**

On the medical-jargon sample, it scored 13.09% WER and recalled all 9 scored jargon terms, though it still inserted 80 extra words.

**Q: How did it handle overlapping speech?**

Poorly. On the crosstalk sample, it scored 43.50% WER with 2614 deletions and 50 insertions, so a large share of words were omitted.

**Q: How did it handle code-switching?**

Poorly. On the bilingual sample, it scored 56.13% WER and only 5.0% Spanish token recall (4 of 80 types). The benchmark notes that the sample is about 95.5% English, so Spanish token recall is the more meaningful signal.

**Q: Does it provide word-level timestamps and confidence?**

Yes. All three raw responses included per-word timing entries and confidence values, but no speaker labels were present in the scored outputs.

**Q: How much does it cost?**

Standard tier is $0.016/min ($0.96/hour). The free tier is 60 audio-minutes per month, and V2 Dynamic Batch is $0.004/min ($0.24/hour) with no latency guarantee.

## Similar Tools

AI tools similar to Google Cloud:

- [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.
- [AWS Transcribe](https://aidemos.com/tools/aws-transcribe) — Batch speech-to-text with word-level metadata, but accuracy drops on overlap and code-switching.
- [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 word-level metadata and speaker labels, 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 transcription, batch transcription, or audio transcription pipeline 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).