diff --git a/docs/quick-start/assets/NuGet-Local-Whisper.png b/docs/quick-start/assets/NuGet-Local-Whisper.png new file mode 100644 index 00000000..f811d115 Binary files /dev/null and b/docs/quick-start/assets/NuGet-Local-Whisper.png differ diff --git a/docs/utilities/local-whisper.md b/docs/utilities/local-whisper.md index 491e5398..276faf68 100644 --- a/docs/utilities/local-whisper.md +++ b/docs/utilities/local-whisper.md @@ -5,8 +5,22 @@ Whisper, an advanced automatic speech recognition (ASR) system developed by OpenAI, represents a significant leap forward in speech technology. This system was trained on an enormous dataset comprising 680,000 hours of supervised data, which includes a wide range of languages and tasks, all sourced from the web. The diversity and scale of this dataset play a crucial role in enhancing Whisper's ability to accurately recognize and transcribe speech. As a result, it exhibits improved robustness in dealing with various accents, background noise, and complex technical language, making it a versatile and reliable tool for a broad spectrum of applications. ### Get started with Local Whisper +To begin using the local Whisper model, the Whisper.net library must be added as a dependency. This can be achieved through: +- NuGet Manager +
+ ![NuGet Manager](assets/NuGet-Local-Whisper.png) +- Package Manager Console +```powershell +Install-Package Whisper.net +Install-Package Whisper.net.Runtime +``` +- Add a package reference in your csproj +``` + + +``` -BotSharp offers support for the following Whisper model types through the use of plug-ins: +The following Whisper model types would be available through the use of plug-ins: - Tiny - TinyEn @@ -31,4 +45,4 @@ The transcript will be displayed in the response. ![Response of Whisper Model](assets/Result-Local-Whisper.png) ### Response Time -When using a CPU locally, the response time is impressively fast. For instance, it can transcribe a 10-minute audio clip into text in approximately 30 seconds. For shorter audio files, ranging from 3 to 5 minutes, the transcription response is even quicker. \ No newline at end of file +When using a CPU locally, the response time is impressively fast. For instance, it can transcribe a 10-minute audio clip into text in approximately 30 seconds. For shorter audio files, ranging from 3 to 5 minutes in duration, the transcription response is around a few seconds or even quicker. \ No newline at end of file