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Wenbo Cao 2024-08-17 15:55:35 -05:00
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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
<br/>
![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
```
<PackageReference Include="Whisper.net" Version="1.5.0" />
<PackageReference Include="Whisper.net.Runtime" Version="1.5.0" />
```
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
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![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.
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.