使用 Gemini 1.5 Pro 生成音频文件摘要

此示例展示了如何使用音频文件总结播客。此示例仅适用于 Gemini 1.5 Pro。

深入探索

如需查看包含此代码示例的详细文档，请参阅以下内容：

• 发送多模态提示请求

代码示例

C#

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 C# 设置说明执行操作。如需了解详情，请参阅 Vertex AI C# API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。

using Google.Cloud.AIPlatform.V1;
using System;
using System.Threading.Tasks;

public class AudioInputSummarization
{
    public async Task<string> SummarizeAudio(
        string projectId = "your-project-id",
        string location = "us-central1",
        string publisher = "google",
        string model = "gemini-1.5-pro-preview-0409")
    {
        var predictionServiceClient = new PredictionServiceClientBuilder
        {
            Endpoint = $"{location}-aiplatform.googleapis.com"
        }.Build();

        string prompt = @"Please provide a summary for the audio.
Provide chapter titles with timestamps, be concise and short, no need to provide chapter summaries.
Do not make up any information that is not part of the audio and do not be verbose.";

        var generateContentRequest = new GenerateContentRequest
        {
            Model = $"projects/{projectId}/locations/{location}/publishers/{publisher}/models/{model}",
            Contents =
            {
                new Content
                {
                    Role = "USER",
                    Parts =
                    {
                        new Part { Text = prompt },
                        new Part { FileData = new() { MimeType = "audio/mp3", FileUri = "gs://cloud-samples-data/generative-ai/audio/pixel.mp3" } }
                    }
                }
            }
        };

        GenerateContentResponse response = await predictionServiceClient.GenerateContentAsync(generateContentRequest);

        string responseText = response.Candidates[0].Content.Parts[0].Text;
        Console.WriteLine(responseText);

        return responseText;
    }
}

Go

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Go 设置说明执行操作。如需了解详情，请参阅 Vertex AI Go API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。

import (
	"context"
	"errors"
	"fmt"
	"io"
	"mime"
	"path/filepath"

	"cloud.google.com/go/vertexai/genai"
)

// audioPrompt is a sample prompt type consisting of one audio asset, and a text question.
type audioPrompt struct {
	// audio is a Google Cloud Storage path starting with "gs://"
	audio string
	// question asked to the model
	question string
}

// summarizeAudio shows how to send an audio asset and a text question to a model, writing the response to the
// provided io.Writer.
func summarizeAudio(w io.Writer, prompt audioPrompt, projectID, location, modelName string) error {
	// prompt := audioPrompt{
	// 	audio: "gs://cloud-samples-data/generative-ai/audio/pixel.mp3",
	// 	question: `
	// 		Please provide a summary for the audio.
	// 		Provide chapter titles with timestamps, be concise and short, no need to provide chapter summaries.
	// 		Do not make up any information that is not part of the audio and do not be verbose.
	// 	`,
	// }
	// location := "us-central1"
	// modelName := "gemini-1.5-pro-preview-0409"
	ctx := context.Background()

	client, err := genai.NewClient(ctx, projectID, location)
	if err != nil {
		return fmt.Errorf("unable to create client: %w", err)
	}
	defer client.Close()

	model := client.GenerativeModel(modelName)
	model.SetTemperature(0.4)

	// Given an audio file URL, prepare audio file as genai.Part
	part := genai.FileData{
		MIMEType: mime.TypeByExtension(filepath.Ext(prompt.audio)),
		FileURI:  prompt.audio,
	}

	res, err := model.GenerateContent(ctx, part, genai.Text(prompt.question))
	if err != nil {
		return fmt.Errorf("unable to generate contents: %w", err)
	}

	if len(res.Candidates) == 0 ||
		len(res.Candidates[0].Content.Parts) == 0 {
		return errors.New("empty response from model")
	}

	fmt.Fprintf(w, "generated summary:\n%s\n", res.Candidates[0].Content.Parts[0])
	return nil
}

Java

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。

import com.google.cloud.vertexai.VertexAI;
import com.google.cloud.vertexai.api.GenerateContentResponse;
import com.google.cloud.vertexai.generativeai.ContentMaker;
import com.google.cloud.vertexai.generativeai.GenerativeModel;
import com.google.cloud.vertexai.generativeai.PartMaker;
import com.google.cloud.vertexai.generativeai.ResponseHandler;
import java.io.IOException;

public class AudioInputSummarization {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-google-cloud-project-id";
    String location = "us-central1";
    String modelName = "gemini-1.5-pro-preview-0409";

    summarizeAudio(projectId, location, modelName);
  }

  // Analyzes the given audio input.
  public static String summarizeAudio(String projectId, String location, String modelName)
      throws IOException {
    // Initialize client that will be used to send requests. This client only needs
    // to be created once, and can be reused for multiple requests.
    try (VertexAI vertexAI = new VertexAI(projectId, location)) {
      String audioUri = "gs://cloud-samples-data/generative-ai/audio/pixel.mp3";

      GenerativeModel model = new GenerativeModel(modelName, vertexAI);
      GenerateContentResponse response = model.generateContent(
          ContentMaker.fromMultiModalData(
              "Please provide a summary for the audio.\n"
                  + "Provide chapter titles with timestamps, be concise and short, "
                  + "no need to provide chapter summaries.\n"
                  + "Do not make up any information that is not part of the audio "
                  + "and do not be verbose.",
              PartMaker.fromMimeTypeAndData("audio/mp3", audioUri)
          ));

      String output = ResponseHandler.getText(response);
      System.out.println(output);

      return output;
    }
  }
}

Node.js

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。

const {VertexAI} = require('@google-cloud/vertexai');

/**
 * TODO(developer): Update these variables before running the sample.
 */
async function summarize_audio(projectId = 'PROJECT_ID') {
  const vertexAI = new VertexAI({project: projectId, location: 'us-central1'});

  const generativeModel = vertexAI.getGenerativeModel({
    model: 'gemini-1.5-pro-preview-0409',
  });

  const filePart = {
    file_data: {
      file_uri: 'gs://cloud-samples-data/generative-ai/audio/pixel.mp3',
      mime_type: 'audio/mpeg',
    },
  };
  const textPart = {
    text: `
    Please provide a summary for the audio.
    Provide chapter titles with timestamps, be concise and short, no need to provide chapter summaries.
    Do not make up any information that is not part of the audio and do not be verbose.`,
  };

  const request = {
    contents: [{role: 'user', parts: [filePart, textPart]}],
  };

  const resp = await generativeModel.generateContent(request);
  const contentResponse = await resp.response;
  console.log(JSON.stringify(contentResponse));
}

Python

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。

  import vertexai
  from vertexai.generative_models import GenerativeModel, Part

  # TODO(developer): Update and un-comment below lines
  # project_id = "PROJECT_ID"

  vertexai.init(project=project_id, location="us-central1")

  model = GenerativeModel(model_name="gemini-1.5-flash-preview-0514")

  prompt = """
  Please provide a summary for the audio.
  Provide chapter titles, be concise and short, no need to provide chapter summaries.
  Do not make up any information that is not part of the audio and do not be verbose.
"""

  audio_file_uri = "gs://cloud-samples-data/generative-ai/audio/pixel.mp3"
  audio_file = Part.from_uri(audio_file_uri, mime_type="audio/mpeg")

  contents = [audio_file, prompt]

  response = model.generate_content(contents)
  print(response.text)

后续步骤

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