Fact · Speech recognition
0.21real-time factor
In the paper's Table 1, Emformer RNN-T model D had a real-time factor of 0.21 when run on a single-core CPU of an Android device, about 4.76 times faster than real time.
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In the source's own words
“Emformer-based RNN-T Encoder Cells D 20 x (8 |H|, 512|W |, 5|C|) 2x512 77.4 1.50 2.14 11.8 0.21 ... we focus on first-pass, single-core CPU-based model evaluation on Android devices”
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition, Yuan Shangguan et al., Interspeech 2021, 2021, Section 3, Table 1.
Open the original (DOI 10.21437/Interspeech.2021-1887)- What it measures
- Table 1 compares four streaming RNN-T models by real-time factor, the time needed to process audio frames divided by the length of the audio. The paper says it evaluated single-core CPU inference on Android devices.
- What it does not show
- The Android device model is not named, and the speed is for one CPU core on phones, not for a desktop or server. The test data is an in-house voice command dataset.
In the data
A row of Speech Recognition Speed: Real-Time Factor Data, as Shangguan et al. 2021, model D: 5 times faster than real time.
- System
- Emformer RNN-T model D
- Kind
- Research system
- Hardware
- Single-core CPU, Android device
- Test set
- In-house voice command dataset
- Year
- 2021
- Note
- 1 divided by the real-time factor 0.21 is 4.76 times faster than real time.