Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/mutablelogic/go-whisper
Speech-to-Text in golang
https://github.com/mutablelogic/go-whisper
golang speech-recognition speech-to-text whisper
Last synced: 1 day ago
JSON representation
Speech-to-Text in golang
- Host: GitHub
- URL: https://github.com/mutablelogic/go-whisper
- Owner: mutablelogic
- License: apache-2.0
- Created: 2022-12-01T19:17:47.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-21T21:07:13.000Z (18 days ago)
- Last Synced: 2024-12-31T18:25:15.724Z (8 days ago)
- Topics: golang, speech-recognition, speech-to-text, whisper
- Language: Go
- Homepage: https://pkg.go.dev/github.com/mutablelogic/go-whisper
- Size: 8.04 MB
- Stars: 84
- Watchers: 2
- Forks: 9
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# go-whisper
Speech-to-Text in golang. This is an early development version.
* `cmd` contains an OpenAI-API compatible service
* `pkg` contains the `whisper` service and client
* `sys` contains the `whisper` bindings to the `whisper.cpp` library
* `third_party` is a submodule for the whisper.cpp source
## Running
You can either run the whisper service as a CLI command or in a docker container.
There are docker images for arm64 and amd64 (Intel). The arm64 image is built for
Jetson GPU support specifically, but it will also run on Raspberry Pi's.
In order to utilize a NVIDIA GPU, you'll need to install the
[NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) first.
A docker volume should be created called "whisper" can be used for storing the Whisper language
models. You can see which models are available to download locally [here](https://huggingface.co/ggerganov/whisper.cpp).
The following command will run the server on port 8080 for an NVIDIA GPU:
```bash
docker run \
--name whisper-server --rm \
--runtime nvidia --gpus all \ # When using a NVIDIA GPU
-v whisper:/data -p 8080:80 \
ghcr.io/mutablelogic/go-whisper:latest
```
The API is then
available at `http://localhost:8080/v1` and it generally conforms to the
[OpenAI API](https://platform.openai.com/docs/api-reference/audio) spec.
### Sample Usage
In order to download a model, you can use the following command (for example):
```bash
curl -X POST -H "Content-Type: application/json" -d '{"Path" : "ggml-medium-q5_0.bin" }' localhost:8080/v1/models\?stream=true
```
To list the models available, you can use the following command:
```bash
curl -X GET localhost:8080/v1/models
```
To delete a model, you can use the following command:
```bash
curl -X DELETE localhost:8080/v1/models/ggml-medium-q5_0
```
To transcribe a media file into it's original language, you can use the following command:
```bash
curl -F model=ggml-medium-q5_0 -F file=@samples/jfk.wav localhost:8080/v1/audio/transcriptions\?stream=true
```
To translate a media file into a different language, you can use the following command:
```bash
curl -F model=ggml-medium-q5_0 -F file=@samples/de-podcast.wav -F language=en localhost:8080/v1/audio/translations\?stream=true
```
There's more information on the API [here](doc/API.md).
## Building
If you are building a docker image, you just need make and docker installed:
* `DOCKER_REGISTRY=docker.io/user make docker` - builds a docker container with the
server binary, tagged to a specific registry
If you want to build the server yourself for your specific combination of hardware (for example,
on MacOS), you can use the `Makefile` in the root directory and have the following dependencies
met:
* Go 1.22
* C++ compiler
* FFmpeg 6.1 libraries (see [here](doc/build.md) for more information)
* For CUDA, you'll need the CUDA toolkit installed including the `nvcc` compiler
The following `Makefile` targets can be used:
* `make server` - creates the server binary, and places it in the `build` directory. Should
link to Metal on macOS
* `GGML_CUDA=1 make server` - creates the server binary linked to CUDA, and places it
in the `build` directory. Should work for amd64 and arm64 (Jetson) platforms
See all the other targets in the `Makefile` for more information.
## Developing
TODO
## Status
Still in development. See this [issue](https://github.com/mutablelogic/go-whisper/issues/1) for
remaining tasks to be completed.
## Contributing & Distribution
__This module is currently in development and subject to change.__
Please do file feature requests and bugs [here](https://github.com/mutablelogic/go-whisper/issues).
The license is Apache 2 so feel free to redistribute. Redistributions in either source
code or binary form must reproduce the copyright notice, and please link back to this
repository for more information:
> __go-whisper__\
> [https://github.com/mutablelogic/go-whisper/](https://github.com/mutablelogic/go-whisper/)\
> Copyright (c) 2023-2024 David Thorpe, All rights reserved.
>
> __whisper.cpp__\
> [https://github.com/ggerganov/whisper.cpp](https://github.com/ggerganov/whisper.cpp)\
> Copyright (c) 2023-2024 The ggml authors
This software links to static libraries of [whisper.cpp](https://github.com/ggerganov/whisper.cpp) licensed under
the [MIT License](http://www.gnu.org/licenses/old-licenses/lgpl-2.1.html).