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

# MLX Transcribe Tools

> MLX Transcribe: A tool for transcribing audio files using MLX Whisper.

<Warning>
  Run this example on macOS or Linux with a supported MLX backend. Windows is not supported. Linux requires one of `mlx[cpu]`, `mlx[cuda12]`, or `mlx[cuda13]`. See [MLX installation](https://ml-explore.github.io/mlx/build/html/install.html).
</Warning>

```python mlx_transcribe_tools.py theme={null}
"""
MLX Transcribe: A tool for transcribing audio files using MLX Whisper

Requirements:
1. ffmpeg - Install using:
   - macOS: `brew install ffmpeg`
   - Ubuntu: `sudo apt-get install ffmpeg`
   - Windows: Download from https://ffmpeg.org/download.html

2. mlx-whisper library:
   uv pip install mlx-whisper

Example Usage:
- Place your audio files in the 'storage/audio' directory
    Eg: download https://www.ted.com/talks/reid_hoffman_and_kevin_scott_the_evolution_of_ai_and_how_it_will_impact_human_creativity
- Run this script to transcribe audio files
- Supports various audio formats (mp3, mp4, wav, etc.)
"""

from pathlib import Path

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.mlx_transcribe import MLXTranscribeTools

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------


# Get audio files from storage/audio directory
agno_root_dir = Path(__file__).parent.parent.parent.resolve()
audio_storage_dir = agno_root_dir.joinpath("storage/audio")

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    if not audio_storage_dir.exists():
        audio_storage_dir.mkdir(exist_ok=True, parents=True)

    agent = Agent(
        name="Transcription Agent",
        model=OpenAIChat(id="gpt-4o"),
        tools=[MLXTranscribeTools(base_dir=audio_storage_dir)],
        instructions=[
            "To transcribe an audio file, use the `transcribe` tool with the name of the audio file as the argument.",
            "You can find all available audio files using the `read_files` tool.",
        ],
        markdown=True,
    )

    agent.print_response(
        "Summarize the reid hoffman ted talk, split into sections", stream=True
    )
```

## Run the Example

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno mlx-whisper openai
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Install an MLX backend">
    On macOS, the dependency step installs the standard MLX package. On Linux, install exactly one backend for your hardware before running: `uv pip install -U "mlx[cpu]"`, `uv pip install -U "mlx[cuda12]"`, or `uv pip install -U "mlx[cuda13]"`.
  </Step>

  <Step title="Install ffmpeg">
    Install `ffmpeg` using the macOS or Ubuntu command in the source header.
  </Step>

  <Step title="Add an audio file">
    For a standalone file, replace the `agno_root_dir` assignment with `agno_root_dir = Path(__file__).parent.resolve()`. Save the code as `mlx_transcribe_tools.py`, then add the audio file to `storage/audio` beside the script.
  </Step>

  <Step title="Run the example">
    Save the code above as `mlx_transcribe_tools.py`, then run:

    ```bash theme={null}
    python mlx_transcribe_tools.py
    ```
  </Step>
</Steps>

Full source: [cookbook/91\_tools/mlx\_transcribe\_tools.py](https://github.com/agno-agi/agno/blob/main/cookbook/91_tools/mlx_transcribe_tools.py)