> ## Documentation Index
> Fetch the complete documentation index at: https://visionagents.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Fast-Whisper

[Fast-Whisper](https://github.com/guillaumekln/faster-whisper) is a high-performance local STT using CTranslate2. Provides 2-4x faster inference than standard Whisper with support for CPU and GPU acceleration.

<Info>
  Vision Agents uses [Stream Video](https://getstream.io/video/) for real-time WebRTC transport by default. [External WebRTC transports](/integrations/introduction-to-integrations#edge-transport) are supported as well. Most AI providers offer free tiers to get started.
</Info>

## Installation

```sh theme={null}
uv add "vision-agents[fast-whisper]"
```

## Quick Start

```python theme={null}
from vision_agents.core import Agent, User
from vision_agents.plugins import fast_whisper, gemini, elevenlabs, getstream

agent = Agent(
    edge=getstream.Edge(),
    agent_user=User(name="Assistant", id="agent"),
    instructions="You are a helpful assistant.",
    llm=gemini.LLM("gemini-3-flash-preview"),
    stt=fast_whisper.STT(model_size="base"),
    tts=elevenlabs.TTS(),
)
```

<Note>
  Fast-Whisper runs locally. No API key required. Models download automatically
  on first use.
</Note>

## Parameters

| Name | Type | Default | Description |
| - | - | - | - |
| `model_size` | `str` | `"base"` | Model size (`"tiny"`, `"base"`, `"small"`, `"medium"`, `"large-v3"`) |
| `language` | `str` | `None` | Language code or `None` for auto-detect |
| `device` | `str` | `"cpu"` | Device (`"cpu"`, `"cuda"`, `"auto"`) |
| `compute_type` | `str` | `"int8"` | Precision (`"int8"`, `"float16"`, `"float32"`) |

## Model Sizes

| Model | Speed | Use Case |
| - | - | - |
| `tiny` | Fastest | Real-time, resource-constrained |
| `base` | Very Fast | General purpose |
| `small` | Fast | Balanced |
| `medium` | Moderate | Higher accuracy |
| `large-v3` | Slower | Maximum accuracy |

## Optimization

```python theme={null}
# CPU (default) - use int8 for best performance
stt = fast_whisper.STT(device="cpu", compute_type="int8")

# GPU - use float16 for speed and accuracy
stt = fast_whisper.STT(device="cuda", compute_type="float16")
```

## Next Steps

<CardGroup cols={2}>
  <Card title="Build a Voice Agent" icon="microphone" href="/introduction/voice-agents">
    Get started with voice
  </Card>

  <Card title="Build a Video Agent" icon="video" href="/introduction/video-agents">
    Add video processing
  </Card>
</CardGroup>


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