base on Port of OpenAI's Whisper model in C/C++ # whisper.cpp  [](https://github.com/ggerganov/whisper.cpp/actions) [](https://opensource.org/licenses/MIT) [](https://conan.io/center/whisper-cpp) [](https://www.npmjs.com/package/whisper.cpp/) Stable: [v1.7.3](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.7.3) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126) High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model: - Plain C/C++ implementation without dependencies - Apple Silicon first-class citizen - optimized via ARM NEON, Accelerate framework, Metal and [Core ML](#core-ml-support) - AVX intrinsics support for x86 architectures - VSX intrinsics support for POWER architectures - Mixed F16 / F32 precision - [Integer quantization support](#quantization) - Zero memory allocations at runtime - [Vulkan support](#vulkan-gpu-support) - Support for CPU-only inference - [Efficient GPU support for NVIDIA](#nvidia-gpu-support) - [OpenVINO Support](#openvino-support) - [Ascend NPU Support](#ascend-npu-support) - [C-style API](https://github.com/ggerganov/whisper.cpp/blob/master/include/whisper.h) Supported platforms: - [x] Mac OS (Intel and Arm) - [x] [iOS](examples/whisper.objc) - [x] [Android](examples/whisper.android) - [x] [Java](bindings/java/README.md) - [x] Linux / [FreeBSD](https://github.com/ggerganov/whisper.cpp/issues/56#issuecomment-1350920264) - [x] [WebAssembly](examples/whisper.wasm) - [x] Windows ([MSVC](https://github.com/ggerganov/whisper.cpp/blob/master/.github/workflows/build.yml#L117-L144) and [MinGW](https://github.com/ggerganov/whisper.cpp/issues/168)] - [x] [Raspberry Pi](https://github.com/ggerganov/whisper.cpp/discussions/166) - [x] [Docker](https://github.com/ggerganov/whisper.cpp/pkgs/container/whisper.cpp) The entire high-level implementation of the model is contained in [whisper.h](include/whisper.h) and [whisper.cpp](src/whisper.cpp). The rest of the code is part of the [`ggml`](https://github.com/ggerganov/ggml) machine learning library. Having such a lightweight implementation of the model allows to easily integrate it in different platforms and applications. As an example, here is a video of running the model on an iPhone 13 device - fully offline, on-device: [whisper.objc](examples/whisper.objc) https://user-images.githubusercontent.com/1991296/197385372-962a6dea-bca1-4d50-bf96-1d8c27b98c81.mp4 You can also easily make your own offline voice assistant application: [command](examples/command) https://user-images.githubusercontent.com/1991296/204038393-2f846eae-c255-4099-a76d-5735c25c49da.mp4 On Apple Silicon, the inference runs fully on the GPU via Metal: https://github.com/ggerganov/whisper.cpp/assets/1991296/c82e8f86-60dc-49f2-b048-d2fdbd6b5225 ## Quick start First clone the repository: ```bash git clone https://github.com/ggerganov/whisper.cpp.git ``` Navigate into the directory: ``` cd whisper.cpp ``` Then, download one of the Whisper [models](models/README.md) converted in [`ggml` format](#ggml-format). For example: ```bash sh ./models/download-ggml-model.sh base.en ``` Now build the [whisper-cli](examples/cli) example and transcribe an audio file like this: ```bash # build the project cmake -B build cmake --build build --config Release # transcribe an audio file ./build/bin/whisper-cli -f samples/jfk.wav ``` --- For a quick demo, simply run `make base.en`. The command downloads the `base.en` model converted to custom `ggml` format and runs the inference on all `.wav` samples in the folder `samples`. For detailed usage instructions, run: `./build/bin/whisper-cli -h` Note that the [whisper-cli](examples/cli) example currently runs only with 16-bit WAV files, so make sure to convert your input before running the tool. For example, you can use `ffmpeg` like this: ```bash ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav ``` ## More audio samples If you want some extra audio samples to play with, simply run: ``` make -j samples ``` This will download a few more audio files from Wikipedia and convert them to 16-bit WAV format via `ffmpeg`. You can download and run the other models as follows: ``` make -j tiny.en make -j tiny make -j base.en make -j base make -j small.en make -j small make -j medium.en make -j medium make -j large-v1 make -j large-v2 make -j large-v3 make -j large-v3-turbo ``` ## Memory usage | Model | Disk | Mem | | ------ | ------- | ------- | | tiny | 75 MiB | ~273 MB | | base | 142 MiB | ~388 MB | | small | 466 MiB | ~852 MB | | medium | 1.5 GiB | ~2.1 GB | | large | 2.9 GiB | ~3.9 GB | ## Quantization `whisper.cpp` supports integer quantization of the Whisper `ggml` models. Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently. Here are the steps for creating and using a quantized model: ```bash # quantize a model with Q5_0 method 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 ./build/bin/whisper-cli -m models/ggml-base.en-q5_0.bin ./samples/gb0.wav ``` ## Core ML support On Apple Silicon devices, the Encoder inference can be executed on the Apple Neural Engine (ANE) via Core ML. This can result in significant speed-up - more than x3 faster compared with CPU-only execution. Here are the instructions for generating a Core ML model and using it with `whisper.cpp`: - Install Python dependencies needed for the creation of the Core ML model: ```bash pip install ane_transformers pip install openai-whisper pip install coremltools ``` - To ensure `coremltools` operates correctly, please confirm that [Xcode](https://developer.apple.com/xcode/) is installed and execute `xcode-select --install` to install the command-line tools. - Python 3.10 is recommended. - MacOS Sonoma (version 14) or newer is recommended, as older versions of MacOS might experience issues with transcription hallucination. - [OPTIONAL] It is recommended to utilize a Python version management system, such as [Miniconda](https://docs.conda.io/en/latest/miniconda.html) for this step: - To create an environment, use: `conda create -n py310-whisper python=3.10 -y` - To activate the environment, use: `conda activate py310-whisper` - Generate a Core ML model. For example, to generate a `base.en` model, use: ```bash ./models/generate-coreml-model.sh base.en ``` This will generate the folder `models/ggml-base.en-encoder.mlmodelc` - Build `whisper.cpp` with Core ML support: ```bash # using CMake cmake -B build -DWHISPER_COREML=1 cmake --build build -j --config Release ``` - Run the examples as usual. For example: ```text $ ./build/bin/whisper-cli -m models/ggml-base.en.bin -f samples/jfk.wav ... whisper_init_state: loading Core ML model from 'models/ggml-base.en-encoder.mlmodelc' whisper_init_state: first run on a device may take a while ... whisper_init_state: Core ML model loaded system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | COREML = 1 | ... ``` The first run on a device is slow, since the ANE service compiles the Core ML model to some device-specific format. Next runs are faster. For more information about the Core ML implementation please refer to PR [#566](https://github.com/ggerganov/whisper.cpp/pull/566). ## OpenVINO support On platforms that support [OpenVINO](https://github.com/openvinotoolkit/openvino), the Encoder inference can be executed on OpenVINO-supported devices including x86 CPUs and Intel GPUs (integrated & discrete). This can result in significant speedup in encoder performance. Here are the instructions for generating the OpenVINO model and using it with `whisper.cpp`: - First, setup python virtual env. and install python dependencies. Python 3.10 is recommended. Windows: ```powershell cd models python -m venv openvino_conv_env openvino_conv_env\Scripts\activate python -m pip install --upgrade pip pip install -r requirements-openvino.txt ``` Linux and macOS: ```bash cd models python3 -m venv openvino_conv_env source openvino_conv_env/bin/activate python -m pip install --upgrade pip pip install -r requirements-openvino.txt ``` - Generate an OpenVINO encoder model. For example, to generate a `base.en` model, use: ``` python convert-whisper-to-openvino.py --model base.en ``` This will produce ggml-base.en-encoder-openvino.xml/.bin IR model files. It's recommended to relocate these to the same folder as `ggml` models, as that is the default location that the OpenVINO extension will search at runtime. - Build `whisper.cpp` with OpenVINO support: Download OpenVINO package from [release page](https://github.com/openvinotoolkit/openvino/releases). The recommended version to use is [2023.0.0](https://github.com/openvinotoolkit/openvino/releases/tag/2023.0.0). After downloading & extracting package onto your development system, set up required environment by sourcing setupvars script. For example: Linux: ```bash source /path/to/l_openvino_toolkit_ubuntu22_2023.0.0.10926.b4452d56304_x86_64/setupvars.sh ``` Windows (cmd): ```powershell C:\Path\To\w_openvino_toolkit_windows_2023.0.0.10926.b4452d56304_x86_64\setupvars.bat ``` And then build the project using cmake: ```bash cmake -B build -DWHISPER_OPENVINO=1 cmake --build build -j --config Release ``` - Run the examples as usual. For example: ```text $ ./build/bin/whisper-cli -m models/ggml-base.en.bin -f samples/jfk.wav ... whisper_ctx_init_openvino_encoder: loading OpenVINO model from 'models/ggml-base.en-encoder-openvino.xml' whisper_ctx_init_openvino_encoder: first run on a device may take a while ... whisper_openvino_init: path_model = models/ggml-base.en-encoder-openvino.xml, device = GPU, cache_dir = models/ggml-base.en-encoder-openvino-cache whisper_ctx_init_openvino_encoder: OpenVINO model loaded system_info: n_threads = 4 / 8 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 | COREML = 0 | OPENVINO = 1 | ... ``` The first time run on an OpenVINO device is slow, since the OpenVINO framework will compile the IR (Intermediate Representation) model to a device-specific 'blob'. This device-specific blob will get cached for the next run. For more information about the Core ML implementation please refer to PR [#1037](https://github.com/ggerganov/whisper.cpp/pull/1037). ## NVIDIA GPU support With NVIDIA cards the processing of the models is done efficiently on the GPU via cuBLAS and custom CUDA kernels. First, make sure you have installed `cuda`: https://developer.nvidia.com/cuda-downloads Now build `whisper.cpp` with CUDA support: ``` cmake -B build -DGGML_CUDA=1 cmake --build build -j --config Release ``` ## Vulkan GPU support Cross-vendor solution which allows you to accelerate workload on your GPU. First, make sure your graphics card driver provides support for Vulkan API. Now build `whisper.cpp` with Vulkan support: ``` cmake -B build -DGGML_VULKAN=1 cmake --build build -j --config Release ``` ## BLAS CPU support via OpenBLAS Encoder processing can be accelerated on the CPU via OpenBLAS. First, make sure you have installed `openblas`: https://www.openblas.net/ Now build `whisper.cpp` with OpenBLAS support: ``` 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. First, check if your Ascend NPU device is supported: **Verified devices** | Ascend NPU | Status | |:-----------------------------:|:-------:| | Atlas 300T A2 | Support | Then, make sure you have installed [`CANN toolkit`](https://www.hiascend.com/en/software/cann/community) . The lasted version of CANN is recommanded. Now build `whisper.cpp` with CANN support: ``` cmake -B build -DGGML_CANN=1 cmake --build build -j --config Release ``` Run the inference examples as usual, for example: ``` ./build/bin/whisper-cli -f samples/jfk.wav -m models/ggml-base.en.bin -t 8 ``` *Notes:* - If you have trouble with Ascend NPU device, please create a issue with **[CANN]** prefix/tag. - If you run successfully with your Ascend NPU device, please help update the table `Verified devices`. ## Installing with Conan You can install pre-built binaries for whisper.cpp or build it from source using [Conan](https://conan.io/). Use the following command: ``` conan install --requires="whisper-cpp/[*]" --build=missing ``` For detailed instructions on how to use Conan, please refer to the [Conan documentation](https://docs.conan.io/2/). ## Limitations - Inference only ## Real-time audio input example This is a naive example of performing real-time inference on audio from your microphone. The [stream](examples/stream) tool samples the audio every half a second and runs the transcription continuously. More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10). ```bash 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 ## Confidence color-coding Adding the `--print-colors` argument will print the transcribed text using an experimental color coding strategy to highlight words with high or low confidence: ```bash ./build/bin/whisper-cli -m models/ggml-base.en.bin -f samples/gb0.wav --print-colors ``` <img width="965" alt="image" src="https://user-images.githubusercontent.com/1991296/197356445-311c8643-9397-4e5e-b46e-0b4b4daa2530.png"> ## Controlling the length of the generated text segments (experimental) For example, to limit the line length to a maximum of 16 characters, simply add `-ml 16`: ```text $ ./build/bin/whisper-cli -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml 16 whisper_model_load: loading model from './models/ggml-base.en.bin' ... system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ... [00:00:00.000 --> 00:00:00.850] And so my [00:00:00.850 --> 00:00:01.590] fellow [00:00:01.590 --> 00:00:04.140] Americans, ask [00:00:04.140 --> 00:00:05.660] not what your [00:00:05.660 --> 00:00:06.840] country can do [00:00:06.840 --> 00:00:08.430] for you, ask [00:00:08.430 --> 00:00:09.440] what you can do [00:00:09.440 --> 00:00:10.020] for your [00:00:10.020 --> 00:00:11.000] country. ``` ## Word-level timestamp (experimental) The `--max-len` argument can be used to obtain word-level timestamps. Simply use `-ml 1`: ```text $ ./build/bin/whisper-cli -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml 1 whisper_model_load: loading model from './models/ggml-base.en.bin' ... system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ... [00:00:00.000 --> 00:00:00.320] [00:00:00.320 --> 00:00:00.370] And [00:00:00.370 --> 00:00:00.690] so [00:00:00.690 --> 00:00:00.850] my [00:00:00.850 --> 00:00:01.590] fellow [00:00:01.590 --> 00:00:02.850] Americans [00:00:02.850 --> 00:00:03.300] , [00:00:03.300 --> 00:00:04.140] ask [00:00:04.140 --> 00:00:04.990] not [00:00:04.990 --> 00:00:05.410] what [00:00:05.410 --> 00:00:05.660] your [00:00:05.660 --> 00:00:06.260] country [00:00:06.260 --> 00:00:06.600] can [00:00:06.600 --> 00:00:06.840] do [00:00:06.840 --> 00:00:07.010] for [00:00:07.010 --> 00:00:08.170] you [00:00:08.170 --> 00:00:08.190] , [00:00:08.190 --> 00:00:08.430] ask [00:00:08.430 --> 00:00:08.910] what [00:00:08.910 --> 00:00:09.040] you [00:00:09.040 --> 00:00:09.320] can [00:00:09.320 --> 00:00:09.440] do [00:00:09.440 --> 00:00:09.760] for [00:00:09.760 --> 00:00:10.020] your [00:00:10.020 --> 00:00:10.510] country [00:00:10.510 --> 00:00:11.000] . ``` ## Speaker segmentation via tinydiarize (experimental) More information about this approach is available here: https://github.com/ggerganov/whisper.cpp/pull/1058 Sample usage: ```py # download a tinydiarize compatible model ./models/download-ggml-model.sh small.en-tdrz # run as usual, adding the "-tdrz" command-line argument ./build/bin/whisper-cli -f ./samples/a13.wav -m ./models/ggml-small.en-tdrz.bin -tdrz ... main: processing './samples/a13.wav' (480000 samples, 30.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, tdrz = 1, timestamps = 1 ... ... [00:00:00.000 --> 00:00:03.800] Okay Houston, we've had a problem here. [SPEAKER_TURN] [00:00:03.800 --> 00:00:06.200] This is Houston. Say again please. [SPEAKER_TURN] [00:00:06.200 --> 00:00:08.260] Uh Houston we've had a problem. [00:00:08.260 --> 00:00:11.320] We've had a main beam up on a volt. [SPEAKER_TURN] [00:00:11.320 --> 00:00:13.820] Roger main beam interval. [SPEAKER_TURN] [00:00:13.820 --> 00:00:15.100] Uh uh [SPEAKER_TURN] [00:00:15.100 --> 00:00:18.020] So okay stand, by thirteen we're looking at it. [SPEAKER_TURN] [00:00:18.020 --> 00:00:25.740] Okay uh right now uh Houston the uh voltage is uh is looking good um. [00:00:27.620 --> 00:00:29.940] And we had a a pretty large bank or so. ``` ## Karaoke-style movie generation (experimental) The [whisper-cli](examples/cli) example provides support for output of karaoke-style movies, where the currently pronounced word is highlighted. Use the `-wts` argument and run the generated bash script. This requires to have `ffmpeg` installed. Here are a few _"typical"_ examples: ```bash ./build/bin/whisper-cli -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -owts source ./samples/jfk.wav.wts ffplay ./samples/jfk.wav.mp4 ``` https://user-images.githubusercontent.com/1991296/199337465-dbee4b5e-9aeb-48a3-b1c6-323ac4db5b2c.mp4 --- ```bash ./build/bin/whisper-cli -m ./models/ggml-base.en.bin -f ./samples/mm0.wav -owts source ./samples/mm0.wav.wts ffplay ./samples/mm0.wav.mp4 ``` https://user-images.githubusercontent.com/1991296/199337504-cc8fd233-0cb7-4920-95f9-4227de3570aa.mp4 --- ```bash ./build/bin/whisper-cli -m ./models/ggml-base.en.bin -f ./samples/gb0.wav -owts source ./samples/gb0.wav.wts ffplay ./samples/gb0.wav.mp4 ``` https://user-images.githubusercontent.com/1991296/199337538-b7b0c7a3-2753-4a88-a0cd-f28a317987ba.mp4 --- ## Video comparison of different models Use the [scripts/bench-wts.sh](https://github.com/ggerganov/whisper.cpp/blob/master/scripts/bench-wts.sh) script to generate a video in the following format: ```bash ./scripts/bench-wts.sh samples/jfk.wav ffplay ./samples/jfk.wav.all.mp4 ``` https://user-images.githubusercontent.com/1991296/223206245-2d36d903-cf8e-4f09-8c3b-eb9f9c39d6fc.mp4 --- ## Benchmarks In order to have an objective comparison of the performance of the inference across different system configurations, use the [whisper-bench](examples/bench) tool. The tool simply runs the Encoder part of the model and prints how much time it took to execute it. The results are summarized in the following Github issue: [Benchmark results](https://github.com/ggerganov/whisper.cpp/issues/89) Additionally a script to run whisper.cpp with different models and audio files is provided [bench.py](scripts/bench.py). You can run it with the following command, by default it will run against any standard model in the models folder. ```bash python3 scripts/bench.py -f samples/jfk.wav -t 2,4,8 -p 1,2 ``` It is written in python with the intention of being easy to modify and extend for your benchmarking use case. It outputs a csv file with the results of the benchmarking. ## `ggml` format The original models are converted to a custom binary format. This allows to pack everything needed into a single file: - model parameters - mel filters - vocabulary - weights You can download the converted models using the [models/download-ggml-model.sh](models/download-ggml-model.sh) script or manually from here: - https://huggingface.co/ggerganov/whisper.cpp - https://ggml.ggerganov.com For more details, see the conversion script [models/convert-pt-to-ggml.py](models/convert-pt-to-ggml.py) or [models/README.md](models/README.md). ## [Bindings](https://github.com/ggerganov/whisper.cpp/discussions/categories/bindings) - [x] Rust: [tazz4843/whisper-rs](https://github.com/tazz4843/whisper-rs) | [#310](https://github.com/ggerganov/whisper.cpp/discussions/310) - [x] JavaScript: [bindings/javascript](bindings/javascript) | [#309](https://github.com/ggerganov/whisper.cpp/discussions/309) - React Native (iOS / Android): [whisper.rn](https://github.com/mybigday/whisper.rn) - [x] Go: [bindings/go](bindings/go) | [#312](https://github.com/ggerganov/whisper.cpp/discussions/312) - [x] Java: - [GiviMAD/whisper-jni](https://github.com/GiviMAD/whisper-jni) - [x] Ruby: [bindings/ruby](bindings/ruby) | [#507](https://github.com/ggerganov/whisper.cpp/discussions/507) - [x] Objective-C / Swift: [ggerganov/whisper.spm](https://github.com/ggerganov/whisper.spm) | [#313](https://github.com/ggerganov/whisper.cpp/discussions/313) - [exPHAT/SwiftWhisper](https://github.com/exPHAT/SwiftWhisper) - [x] .NET: | [#422](https://github.com/ggerganov/whisper.cpp/discussions/422) - [sandrohanea/whisper.net](https://github.com/sandrohanea/whisper.net) - [NickDarvey/whisper](https://github.com/NickDarvey/whisper) - [x] Python: | [#9](https://github.com/ggerganov/whisper.cpp/issues/9) - [stlukey/whispercpp.py](https://github.com/stlukey/whispercpp.py) (Cython) - [AIWintermuteAI/whispercpp](https://github.com/AIWintermuteAI/whispercpp) (Updated fork of aarnphm/whispercpp) - [aarnphm/whispercpp](https://github.com/aarnphm/whispercpp) (Pybind11) - [abdeladim-s/pywhispercpp](https://github.com/abdeladim-s/pywhispercpp) (Pybind11) - [x] R: [bnosac/audio.whisper](https://github.com/bnosac/audio.whisper) - [x] Unity: [macoron/whisper.unity](https://github.com/Macoron/whisper.unity) ## Examples There are various examples of using the library for different projects in the [examples](examples) folder. Some of the examples are even ported to run in the browser using WebAssembly. Check them out! | Example | Web | Description | | --------------------------------------------------- | ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- | | [whisper-cli](examples/cli) | [whisper.wasm](examples/whisper.wasm) | Tool for translating and transcribing audio using Whisper | | [whisper-bench](examples/bench) | [bench.wasm](examples/bench.wasm) | Benchmark the performance of Whisper on your machine | | [whisper-stream](examples/stream) | [stream.wasm](examples/stream.wasm) | Real-time transcription of raw microphone capture | | [whisper-command](examples/command) | [command.wasm](examples/command.wasm) | Basic voice assistant example for receiving voice commands from the mic | | [whisper-server](examples/server) | | HTTP transcription server with OAI-like API | | [whisper-talk-llama](examples/talk-llama) | | Talk with a LLaMA bot | | [whisper.objc](examples/whisper.objc) | | iOS mobile application using whisper.cpp | | [whisper.swiftui](examples/whisper.swiftui) | | SwiftUI iOS / macOS application using whisper.cpp | | [whisper.android](examples/whisper.android) | | Android mobile application using whisper.cpp | | [whisper.nvim](examples/whisper.nvim) | | Speech-to-text plugin for Neovim | | [generate-karaoke.sh](examples/generate-karaoke.sh) | | Helper script to easily [generate a karaoke video](https://youtu.be/uj7hVta4blM) of raw audio capture | | [livestream.sh](examples/livestream.sh) | | [Livestream audio transcription](https://github.com/ggerganov/whisper.cpp/issues/185) | | [yt-wsp.sh](examples/yt-wsp.sh) | | Download + transcribe and/or translate any VOD [(original)](https://gist.github.com/DaniruKun/96f763ec1a037cc92fe1a059b643b818) | | [wchess](examples/wchess) | [wchess.wasm](examples/wchess) | Voice-controlled chess | ## [Discussions](https://github.com/ggerganov/whisper.cpp/discussions) If you have any kind of feedback about this project feel free to use the Discussions section and open a new topic. You can use the [Show and tell](https://github.com/ggerganov/whisper.cpp/discussions/categories/show-and-tell) category to share your own projects that use `whisper.cpp`. If you have a question, make sure to check the [Frequently asked questions (#126)](https://github.com/ggerganov/whisper.cpp/discussions/126) discussion. ", Assign "at most 3 tags" to the expected json: {"id":"1414","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"