Audio Tuning

This page provides prerequisites and detailed instructions for fine-tuning Gemini on audio data using supervised learning.

Use cases

Tuning audio models enhances their performance by tailoring them to specific needs. This can involve improving speech recognition for different accents, fine-tuning music genre classification, optimizing sound event detection, customizing audio generation, adapting to noisy environments, improving audio quality, and personalizing audio experiences. Here are some common audio tuning use cases:

  • Enhanced voice assistants:

    • Voice food ordering: Develop voice-activated systems for seamless food ordering and delivery.

  • Audio content analysis:

    • Automated transcription: Generate highly accurate transcripts, even in noisy environments.
    • Audio summarization: Summarize key points from podcasts or audiobooks.
    • Music classification: Categorize music based on genre, mood, or other characteristics.

  • Accessibility and assistive technologies:

    • Real-time captioning: Provide live captions for events or video calls.
    • Voice-controlled applications: Develop applications controlled entirely by voice.
    • Language learning: Create tools that provide personalized feedback on pronunciation.

Limitations

  • Maximum audio length per example: 10 minutes.
  • Maximum audio files per example: 1.
  • Maximum audio file size: 20MB.

To learn more about audio sample requirements, see the Audio understanding (speech only) page.

Dataset format

The following is an example of an audio dataset example.

To see the generic format example, see Dataset example for Gemini 1.5 pro and Gemini 1.5 flash.

{
  "contents": [
    {
      "role": "user",
      "parts": [
        {
          "fileData": {
            "mimeType": "audio/mpeg",
            "fileUri": "gs://cloud-samples-data/generative-ai/audio/pixel.mp3"
            }
        },
        {
          "text": "Please summarize the conversation in one sentence."
        }
      ]
    }, 
    {
      "role": "model",
      "parts": [
        {
          "text": "The podcast episode features two product managers for Pixel devices discussing the new features coming to Pixel phones and watches."
        }
      ]
    }
  ]
}

What's next