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# whisper.cpp/tests/librispeech
[LibriSpeech](https://www.openslr.org/12) is a standard dataset for
training and evaluating automatic speech recognition systems.
This directory contains a set of tools to evaluate the recognition
performance of whisper.cpp on LibriSpeech corpus.
## Quick Start
1. (Pre-requirement) Compile `whisper-cli` and prepare the Whisper
model in `ggml` format.
```
$ # Execute the commands below in the project root dir.
$ cmake -B build
$ cmake --build build --config Release
$ ./models/download-ggml-model.sh tiny
```
Consult [whisper.cpp/README.md](../../README.md) for more details.
2. Download the audio files from LibriSpeech project.
```
$ make get-audio
```
3. Set up the environment to compute WER score.
```
$ pip install -r requirements.txt
```
For example, if you use `virtualenv`, you can set up it as follows:
```
$ python3 -m venv venv
$ . venv/bin/activate
$ pip install -r requirements.txt
```
4. Run the benchmark test.
```
$ make
```
## How-to guides
### How to change the inference parameters
Create `eval.conf` and override variables.
```
WHISPER_MODEL = large-v3-turbo
WHISPER_FLAGS = --no-prints --threads 8 --language en --output-txt
```
Check out `eval.mk` for more details.
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