From 74dc0dfb23d6f06a00400d3c2a202825888dbe0a Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Thu, 9 Nov 2023 15:36:28 +0000 Subject: [PATCH] =?UTF-8?q?Sync=20with=20whisper.cpp=20v1.4.3=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .github/workflows/publish-docker.yml | 4 ++-- .github/workflows/sync-whisper.yml | 2 +- README.md | 33 ++++++++++++--------------- assets/logo.png | Bin 0 -> 1408400 bytes 4 files changed, 18 insertions(+), 21 deletions(-) create mode 100644 assets/logo.png diff --git a/.github/workflows/publish-docker.yml b/.github/workflows/publish-docker.yml index 9db71dc..aa5c7fa 100644 --- a/.github/workflows/publish-docker.yml +++ b/.github/workflows/publish-docker.yml @@ -47,12 +47,12 @@ jobs: run: | source $GITHUB_ENV # Always set the default tags - DEFAULT_TAGS="dublok/stt:${GITHUB_REF_NAME},dublok/stt:${SHA_SHORT}" + DEFAULT_TAGS="dublok/whisperdock:${GITHUB_REF_NAME},dublok/whisperdock:${SHA_SHORT}" echo "TAGS=${DEFAULT_TAGS}" >> $GITHUB_ENV # Check if GITHUB_REF_NAME starts with 'v' and if so, append 'latest' if [[ $GITHUB_REF_NAME == v* ]]; then - LATEST_TAGS="${DEFAULT_TAGS},dublok/stt:latest" + LATEST_TAGS="${DEFAULT_TAGS},dublok/whisperdock:latest" echo "TAGS=${LATEST_TAGS}" >> $GITHUB_ENV fi diff --git a/.github/workflows/sync-whisper.yml b/.github/workflows/sync-whisper.yml index d14a40c..c341578 100644 --- a/.github/workflows/sync-whisper.yml +++ b/.github/workflows/sync-whisper.yml @@ -111,7 +111,7 @@ jobs: steps: - name: 🛒 Checkout repository for sync run: | - git clone https://x-access-token:${{ secrets.PAT }}@github.com/ErcinDedeoglu/stt.git . + git clone https://x-access-token:${{ secrets.PAT }}@github.com/ErcinDedeoglu/whisperdock.git . - name: 🔄 Sync whisper.cpp repository to src/whisper run: | diff --git a/README.md b/README.md index c2ca519..a05de71 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,14 @@ -# [Speech-to-Text (STT) Transcription Service 🎤](https://github.com/ErcinDedeoglu/stt) +# [WhisperDock - Speech-to-Text Service 🎤](https://github.com/ErcinDedeoglu/WhisperDock) This repository hosts the Dockerized speech-to-text transcription service, which utilizes Whisper C++ alongside Python to provide an API for audio file transcription. -
+
## Background and Motivation -In the rapidly advancing field of machine learning, access to efficient and robust tools for everyday applications is essential. Speech-to-text transcription is one of the areas that has seen significant improvements, but deploying these models quickly and efficiently remains a challenge. Whisper C++, a high-performance transcription tool, has emerged as a powerful option, yet it still requires a streamlined pathway to deployment. +Access to efficient and robust tools for everyday applications is essential in the rapidly advancing field of machine learning. Speech-to-text transcription is one of the areas that has seen significant improvements, but deploying these models quickly and efficiently remains a challenge. Whisper C++, a high-performance transcription tool, has emerged as a powerful option, yet it still requires a streamlined pathway to deployment. -This repository was created out of a necessity to bridge the gap between the development of speech-to-text models and their deployment in real-world applications. Many existing solutions require extensive setup, intricate knowledge of systems, and can be time-consuming to deploy, creating a barrier for developers, researchers, and businesses who want to integrate transcription capabilities into their services. +This repository was created out of a necessity to bridge the gap between the development of speech-to-text models and their deployment in real-world applications. Many existing solutions require extensive setup and intricate knowledge of systems and can be time-consuming to deploy, creating a barrier for developers, researchers, and businesses who want to integrate transcription capabilities into their services. The Speech-to-Text Transcription Service aims to provide a fast, reliable, and easy-to-use solution for deploying Whisper C++ models. By containerizing the service with Docker, we significantly reduce the complexity of deployment and make it possible to launch a transcription service that is both scalable and accessible. @@ -17,7 +17,7 @@ Here are some of the key motivations behind this project: - **Speed of Deployment**: By providing a Dockerized solution, we enable rapid deployment of the transcription service, allowing users to go from zero to a fully functioning service in minutes. - **Ease of Use**: The provided APIs and Docker setup are designed to be as simple as possible, requiring minimal configuration and allowing for easy integration into existing workflows. - **Accessibility**: Making Whisper C++ easily deployable opens up more opportunities for developers and organizations of all sizes to utilize state-of-the-art transcription technology. -- **Continuous Integration and Delivery**: With GitHub Actions, updates and improvements are integrated seamlessly, ensuring that the service remains up-to-date with the latest advancements from the Whisper C++ repository. +- **Continuous Integration and Delivery**: With GitHub Actions, updates, and improvements are integrated seamlessly, ensuring the service remains up-to-date with the latest advancements from the Whisper C++ repository. In contributing to this repository, I hope to empower individuals and organizations to harness the capabilities of Whisper C++ without the overhead of complex deployment processes, thus fostering innovation and development in the field of speech recognition. @@ -31,14 +31,14 @@ For quick deployment, use the Docker images provided in the Docker registry. For the latest stable version: ```bash -docker pull dublok/stt:latest -docker run -p 5000:5000 dublok/stt:latest +docker pull dublok/whisperdock:latest +docker run -p 5000:5000 dublok/whisperdock:latest ``` For the nightly build (unstable but with early access to new features): ```bash -docker pull dublok/stt:main -docker run -p 5000:5000 dublok/stt:main +docker pull dublok/whisperdock:main +docker run -p 5000:5000 dublok/whisperdock:main ``` The service should now be accessible at `http://localhost:5000`. @@ -47,21 +47,19 @@ The service should now be accessible at `http://localhost:5000`. 1. Clone the repository: ```bash -git clone https://github.com/ErcinDedeoglu/stt +git clone https://github.com/ErcinDedeoglu/WhisperDock ``` 2. Build the Docker image: ```bash -docker build -t stt-service . +docker build -t whisperdock . ``` 3. Run the container: ```bash -docker run -p 5000:5000 stt-service +docker run -p 5000:5000 whisperdock ``` -Certainly! Below is an updated README section that includes an example response from the API after submitting an audio file for transcription. - --- ## API Usage @@ -72,7 +70,7 @@ To transcribe audio, make a POST request to the `/transcribe` endpoint with the curl -X POST -F 'file=@/path/to/your/audio.wav' http://localhost:5000/transcribe ``` -Make sure your audio file is in WAV format with a sample rate of 16kHz. +Ensure your audio file is in WAV format with a sample rate of 16kHz. ### Example Response @@ -84,7 +82,7 @@ Upon successful transcription, the service will return a JSON response containin { "start_time": "00:00:00.000", "end_time": "00:00:03.000", - "text": "Welcome to our speech to text service." + "text": "Welcome to our speech-to-text service." }, { "start_time": "00:00:03.500", @@ -135,5 +133,4 @@ This project uses GitHub Actions for continuous integration, which automates the ## License -This Speech-to-Text Transcription Service is made available under the [CC0 1.0 Universal](LICENSE) public domain dedication. - +This Speech-to-Text Transcription Service is available under the [CC0 1.0 Universal](https://github.com/ErcinDedeoglu/WhisperDock/blob/main/LICENSE) public domain dedication. diff --git a/assets/logo.png b/assets/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..8d6041159875e841b8fe0b4679a2ce28ab1f6726 GIT binary patch literal 1408400 zcmV)GK)%0;P)00001b5ch_0Itp) z=>PyA07*naRCr!|y@zrf$+B%(gd!B1vcC zR8?kVgolgm+dZCq{rdLt>C>~_{oVK7?hX$RcMlH_J6?W=-?`r5;Zgp6aB#3YIC!%A_U&6f^W@3F?)dm< zcXRV4|9|@QX+Hnt$!_=U`?vg`&mJ8f_PvLP-Itp$yQfc2cPA$&dGG1z88_VB-`?&H zkB)Ya5BIy9FW0+^i_6`2?sM>DcYk-6ar1dz%)^&2Uv`fVkGp5jE_c^AH@QB~ba#KB zzcVh!d3Algd-38$=I-$5F!S;0)2H3b7cV-#%)%{o z?|ypzVs~}*IoIdeKYngqc4!nl}c){=33MQ)A`^VzQDRX&q_aD0;Q zGl$#{nPUteKYYxZ_8Grt|M(u);f0(bi^@5BkNHOakB*KbQ`wg@x6JXw{r&bFjPdcY z*HK=Pb=K$Zj?W!r&)?i!M{d_==DLiN>+xOIpX)F`>mK9zqi<$jnRn)~YkxZ9>%CZS zWn0@G6A+tOS>&SKZ$sXdqydu-bCy@ij$OQXCwvd^^*TnjxJS%3O8`s>?Q^!h=aDb(&s-vF?3=Um 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