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readme : update build instructions

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Georgi Gerganov 2024-12-08 15:48:14 +02:00
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@ -89,10 +89,11 @@ Now build the [main](examples/main) example and transcribe an audio file like th
```bash
# build the main example
make -j
cmake -B build
cmake --build build --config Release
# transcribe an audio file
./main -f samples/jfk.wav
./build/bin/main -f samples/jfk.wav
```
---
@ -265,11 +266,12 @@ Here are the steps for creating and using a quantized model:
```bash
# quantize a model with Q5_0 method
make -j quantize
./quantize models/ggml-base.en.bin models/ggml-base.en-q5_0.bin q5_0
cmake -B build
cmake --build build --config Release
./build/bin/quantize models/ggml-base.en.bin models/ggml-base.en-q5_0.bin q5_0
# run the examples as usual, specifying the quantized model file
./main -m models/ggml-base.en-q5_0.bin ./samples/gb0.wav
./build/bin/main -m models/ggml-base.en-q5_0.bin ./samples/gb0.wav
```
## Core ML support
@ -303,10 +305,6 @@ speed-up - more than x3 faster compared with CPU-only execution. Here are the in
- Build `whisper.cpp` with Core ML support:
```bash
# using Makefile
make clean
WHISPER_COREML=1 make -j
# using CMake
cmake -B build -DWHISPER_COREML=1
cmake --build build -j --config Release
@ -426,8 +424,8 @@ First, make sure you have installed `cuda`: https://developer.nvidia.com/cuda-do
Now build `whisper.cpp` with CUDA support:
```
make clean
GGML_CUDA=1 make -j
cmake -B build -DGGML_CUDA=1
cmake --build build -j --config Release
```
## Vulkan GPU support
@ -436,8 +434,8 @@ First, make sure your graphics card driver provides support for Vulkan API.
Now build `whisper.cpp` with Vulkan support:
```
make clean
make GGML_VULKAN=1 -j
cmake -B build -DGGML_VULKAN=1
cmake --build build -j --config Release
```
## BLAS CPU support via OpenBLAS
@ -448,28 +446,13 @@ First, make sure you have installed `openblas`: https://www.openblas.net/
Now build `whisper.cpp` with OpenBLAS support:
```
make clean
GGML_OPENBLAS=1 make -j
```
## BLAS CPU support via Intel MKL
Encoder processing can be accelerated on the CPU via the BLAS compatible interface of Intel's Math Kernel Library.
First, make sure you have installed Intel's MKL runtime and development packages: https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-download.html
Now build `whisper.cpp` with Intel MKL BLAS support:
```
source /opt/intel/oneapi/setvars.sh
mkdir build
cd build
cmake -DWHISPER_MKL=ON ..
WHISPER_MKL=1 make -j
cmake -B build -DGGML_BLAS=1
cmake --build build -j --config Release
```
## Ascend NPU support
Ascend NPU provides inference acceleration via [`CANN`](https://www.hiascend.com/en/software/cann) and AI cores.
Ascend NPU provides inference acceleration via [`CANN`](https://www.hiascend.com/en/software/cann) and AI cores.
First, check if your Ascend NPU device is supported:
@ -483,10 +466,8 @@ Then, make sure you have installed [`CANN toolkit`](https://www.hiascend.com/en/
Now build `whisper.cpp` with CANN support:
```
mkdir build
cd build
cmake .. -D GGML_CANN=on
make -j
cmake -B build -DGGML_CANN=1
cmake --build build -j --config Release
```
Run the inference examples as usual, for example:
@ -636,8 +617,9 @@ The [stream](examples/stream) tool samples the audio every half a second and run
More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).
```bash
make stream -j
./stream -m ./models/ggml-base.en.bin -t 8 --step 500 --length 5000
cmake -B build
cmake --build build --config Release
./build/bin/stream -m ./models/ggml-base.en.bin -t 8 --step 500 --length 5000
```
https://user-images.githubusercontent.com/1991296/194935793-76afede7-cfa8-48d8-a80f-28ba83be7d09.mp4