Google Chirp 2
Google Chirp 2 is 5th of 17 on the Ocular Dataset and 15th of 15 on time to first text.
Last updated Methodology by Dileep Chagam
- 2.59%
- Pipecat WER · 9th
- 3.80%
- Ocular WER · 5th
- 4.50s
- First text · 15th
- 568ms
- Final text · 15th
Likely range, with 95% confidence: Pipecat WER 2.31 to 2.89%, Ocular WER 2.93 to 4.60%.
Accuracy vs speed
Each dot is one model. Bottom-left is best: fewer errors and a faster final transcript.
Google Chirp 2 against every other model
Open on the metric Google Chirp 2 places highest on, with the other three a tab away. Lower is better on all of them, so the order runs best first.
Word error rate on eight licensed recordings provided by Ocular. Lower is better.
| # | Model | Ocular Dataset | clips | |
|---|---|---|---|---|
| 1 | Reson8 | 2.93% | 8 | |
| 2 | Cartesia Ink 2 | 3.09% | 8 | |
| 3 | Smallest Pulse | 3.46% | 8 | |
| 4 | GPT Realtime Whisper | 3.53% | 8 | |
| 5 | Google Chirp 2 | 3.80% | 8 | |
| 6 | Soniox STT-RT v5 | 3.92% | 8 | |
| 7 | Speechmatics Linden | 3.93% | 8 | |
| 8 | Deepgram Flux English | 4.18% | 8 | |
| 9 | Google Chirp 3 | 4.19% | 8 | |
| 10 | AssemblyAI Universal 3.5 Pro | 4.23% | 8 | |
| 11 | Deepgram Nova-3 | 4.39% | 8 | |
| 12 | Gradium | 4.45% | 8 | |
| 13 | GPT-4o Mini Transcribe | 4.45% | 8 | |
| 14 | AssemblyAI Universal 3.6 Pro | 4.50% | 8 | |
| 15 | Deepgram Flux Multilingual | 4.53% | 8 | |
| 16 | Inworld STT-1 | 10.60% | 8 | |
| 17 | GPT-4o Transcribe | 12.45% | 8 |
Counts are the clips each model returned usable text for, so they differ between models and between datasets.
- WER · Word error rate
- Share of words the model got wrong: replaced, missing or added. Lower is better.
- Pipecat Dataset
- 1,000 short public clips from Pipecat's STT benchmark dataset.
- Ocular Dataset
- 8 recordings from 4 real phone conversations, licensed from Ocular.
- Likely range · 95% range
- Where the model's true score probably falls, given how much audio was tested. Overlapping ranges are too close to call.
- TTFT · Time to first text
- How long after the audio starts streaming until the first words appear.
- TTFS · Final-text delay
- How long after the caller stops until the full transcript arrives. A voice agent waits for this before it replies.
First text and final text, at three percentiles
Measured on the same 206 streamed Ocular Dataset turns as every other model. P90 and P95 say what a caller meets on a bad turn, which a median hides.
| Measure | P50 | P90 | P95 |
|---|---|---|---|
| Time to first text | 4.50s | 6.40s | 8.15s |
| Final-text delay | 568ms* | 1.04s* | 1.22s* |
- P50 / P90 / P95 · Typical, slow and slowest turns
- Half of turns are faster than P50. Only 1 in 10 is slower than P90, and 1 in 20 slower than P95.
* Final-text delay is the observed time after stream close, because controlled finalization is not available for this model. Compare it with care against models that finalize on request.
Google Chirp 2 on the other two views
The same charts the benchmark page carries, with Google Chirp 2 ringed. Consistency across the two datasets is one reading; how fast a model starts against how fast it commits is the other.
Public clips vs real calls
Each dot is one model. Bottom-left is best on both datasets.
First words vs final transcript
Each dot is one model. Bottom-left is fastest at both.
How much audio it was tested on
- 1,000
- Pipecat clips scored
- 8
- Ocular recordings scored
- 205
- turns timed
- 1
- requests that failed
A clip counts only where the model returned usable text, so counts differ between models. Word error rate is measured the same way throughout: incorrect, missing, and extra words against the reference transcript.
Google Chirp 2 head to head
- vs AssemblyAI Universal 3.6 Pro
- vs AssemblyAI Universal 3.5 Pro
- vs Google Chirp 3
- vs Reson8
- vs GPT Realtime Whisper
- vs Cartesia Ink 2
- vs Speechmatics Linden
- vs Soniox STT-RT v5
- vs Inworld STT-1
- vs GPT-4o Mini Transcribe
- vs Deepgram Nova-3
- vs Smallest Pulse
- vs GPT-4o Transcribe
- vs Deepgram Flux English
- vs Deepgram Flux Multilingual
- vs Gradium
Frequently asked questions
Where does Google Chirp 2 place in the benchmark?
Google Chirp 2 places highest on the Ocular Dataset, 5th of 17 at 3.80%. Its weakest placing is time to first text, 15th of 15 at 4.50s.
How accurate is Google Chirp 2?
Google Chirp 2 transcribes the 1,000 public Pipecat clips at 2.59% word error rate and the eight licensed Ocular recordings at 3.80%. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
How fast is Google Chirp 2 in a live conversation?
First text arrives after 4.50s at the median, and the transcript is final 568ms after the speaker stops. Both are measured on the same 206 streamed turns as every other model, and the page shows P90 and P95 as well, which is what a caller meets on a bad turn.
How was Google Chirp 2 measured?
Every model runs the same two datasets under the same conditions: 1,000 public clips from Pipecat's STT benchmark dataset for accuracy at scale, and eight licensed conversation recordings from Ocular for real speech. Latency comes from 206 streamed turns, using each provider's own supported finalization contract. Google Chirp 2 returned usable text for 1,000 of the public clips.