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* tests : add script to benchmark whisper.cpp on LibriSpeech corpus LibriSpeech is a widely-used benchmark dataset for training and testing speech recognition models. This adds a set of scripts to measure the recognition accuracy of whisper.cpp models, following the common benchmark standards. Signed-off-by: Fujimoto Seiji <fujimoto@ceptord.net> * Document how to prepare `whisper-cli` and model files Feedback from Daniel Bevenius. This adds a short code example how to prepare the `whisper-cli` command, to make the initial setup step a little bit clearer. Signed-off-by: Fujimoto Seiji <fujimoto@ceptord.net> * tests : Simplify how to set up Python environment Based on a feedback from Georgi Gerganov. Instead of setting up a virtual environment in Makefile, let users set up the Python environment. This is better since users may have their own preferred workflow/toolkit. Signed-off-by: Fujimoto Seiji <fujimoto@ceptord.net> --------- Signed-off-by: Fujimoto Seiji <fujimoto@ceptord.net> |
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| normalizers | ||
| .gitignore | ||
| eval.mk | ||
| eval.py | ||
| Makefile | ||
| README.md | ||
| requirements.txt | ||
whisper.cpp/tests/librispeech
LibriSpeech 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
-
(Pre-requirement) Compile
whisper-cliand prepare the Whisper model inggmlformat.$ # Execute the commands below in the project root dir. $ cmake -B build $ cmake --build build --config Release $ ./models/download-ggml-model.sh tinyConsult whisper.cpp/README.md for more details.
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Download the audio files from LibriSpeech project.
$ make get-audio -
Set up the environment to compute WER score.
$ pip install -r requirements.txtFor example, if you use
virtualenv, you can set up it as follows:$ python3 -m venv venv $ . venv/bin/activate $ pip install -r requirements.txt -
Run the benchmark test.
$ make
How-to guides
How to change the inferece 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.