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* musa: correct MUSA SDK rc4.0.1 download URL Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> * Fix typo Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> --------- Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
61 lines
1.3 KiB
Markdown
61 lines
1.3 KiB
Markdown
# whisper.cpp/tests/librispeech
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[LibriSpeech](https://www.openslr.org/12) is a standard dataset for
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training and evaluating automatic speech recognition systems.
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This directory contains a set of tools to evaluate the recognition
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performance of whisper.cpp on LibriSpeech corpus.
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## Quick Start
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1. (Pre-requirement) Compile `whisper-cli` and prepare the Whisper
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model in `ggml` format.
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```
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$ # Execute the commands below in the project root dir.
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$ cmake -B build
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$ cmake --build build --config Release
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$ ./models/download-ggml-model.sh tiny
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```
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Consult [whisper.cpp/README.md](../../README.md) for more details.
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2. Download the audio files from LibriSpeech project.
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```
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$ make get-audio
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```
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3. Set up the environment to compute WER score.
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```
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$ pip install -r requirements.txt
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```
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For example, if you use `virtualenv`, you can set up it as follows:
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```
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$ python3 -m venv venv
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$ . venv/bin/activate
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$ pip install -r requirements.txt
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```
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4. Run the benchmark test.
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```
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$ make
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```
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## How-to guides
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### How to change the inference parameters
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Create `eval.conf` and override variables.
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```
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WHISPER_MODEL = large-v3-turbo
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WHISPER_FLAGS = --no-prints --threads 8 --language en --output-txt
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```
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Check out `eval.mk` for more details.
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