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Georgi Gerganov 2022-10-28 22:09:40 +03:00 committed by GitHub
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@ -168,6 +168,16 @@ make medium
make large
```
## Memory usage
| Model | Disk | Mem | SHA |
| --- | --- | --- | --- |
| tiny | 75 MB | ~280 MB | `bd577a113a864445d4c299885e0cb97d4ba92b5f` |
| base | 142 MB | ~430 MB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
| small | 466 MB | ~1.0 GB | `55356645c2b361a969dfd0ef2c5a50d530afd8d5` |
| medium | 1.5 GB | ~2.6 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
| large | 2.9 GB | ~4.7 GB | `b1caaf735c4cc1429223d5a74f0f4d0b9b59a299` |
## Another example
Here is another example of transcribing a [3:24 min speech](https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg)
@ -290,16 +300,6 @@ the Accelerate framework utilizes the special-purpose AMX coprocessor available
In the future, `whisper.cpp` will support more sampling strategies.
## Memory usage
| Model | Disk | Mem |
| --- | --- | --- |
| tiny | 75 MB | ~280 MB |
| base | 142 MB | ~430 MB |
| small | 466 MB | ~1.0 GB |
| medium | 1.5 GB | ~2.6 GB |
| large | 2.9 GB | ~4.7 GB |
## Benchmarks
In order to have an objective comparison of the performance of the inference across different system configurations,