Kuptimi audio

Binjakët mund të analizojnë të dhënat audio dhe të gjenerojnë përgjigje me tekst.

Python 

from google import genai
import base64

client = genai.Client()

uploaded_file = client.files.upload(file="path/to/sample.mp3")

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "text", "text": "Describe this audio clip"},
        {
            "type": "audio",
            "uri": uploaded_file.uri,
            "mime_type": uploaded_file.mime_type
        }
    ]
)
print(interaction.output_text)

JavaScript 

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const uploadedFile = await client.files.upload({
    file: "path/to/sample.mp3",
    config: { mime_type: "audio/mp3" }
});

const interaction = await client.interactions.create({
    model: "gemini-3.8-flash",
    input: [
        {type: "text", text: "Describe this audio clip"},
        {
            type: "audio",
            uri: uploadedFile.uri,
            mime_type: uploadedFile.mimeType
        }
    ]
});
console.log(interaction.output_text);

Java 

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.AudioContent;
import com.google.genai.gaos.models.interactions.AudioContentMimeType;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.File;
import com.google.genai.types.UploadFileConfig;
import java.util.Arrays;
import java.util.List;

Client client = new Client();

File uploadedFile =
    client.files.upload(
        "path/to/sample.mp3", UploadFileConfig.builder().mimeType("audio/mp3").build());

Content textContent = TextContent.builder().text("Describe this audio clip").build();
Content audioContent =
    AudioContent.builder()
        .uri(uploadedFile.uri().get())
        .mimeType(AudioContentMimeType.of(uploadedFile.mimeType().get()))
        .build();

List<Content> contents = Arrays.asList(textContent, audioContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

Shko 

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    uploadedFile, err := client.Files.UploadFromPath(ctx, "path/to/sample.mp3", &genai.UploadFileConfig{
        MIMEType: "audio/mp3",
    })
    if err != nil {
        log.Fatal(err)
    }

    contents := []interactions.Content{
        interactions.NewContent(interactions.TextContent{
            Text: "Describe this audio clip",
        }),
        interactions.NewContent(interactions.AudioContent{
            URI:      genai.Ptr(uploadedFile.URI),
            MimeType: interactions.AudioContentMimeType(uploadedFile.MIMEType).ToPointer(),
        }),
    }

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput(contents),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }

    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

PUSHTIM 

# First upload the file, then use the URI:
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.8-flash",
    "input": [
      {"type": "text", "text": "Describe this audio clip"},
      {
        "type": "audio",
        "uri": "YOUR_FILE_URI",
        "mime_type": "audio/mp3"
      }
    ]
  }'

Përmbledhje

Gemini mund të analizojë dhe kuptojë të dhënat audio dhe të gjenerojë përgjigje me tekst, duke mundësuar raste përdorimi si:

  • Përshkruani, përmbledhni ose përgjigjuni pyetjeve rreth përmbajtjes audio
  • Transkriptim dhe përkthim (nga të folurit në tekst)
  • Diarizimi i folësit (identifikimi i folësve të ndryshëm)
  • Zbulimi i emocioneve në të folur dhe muzikë
  • Analizimi i segmenteve specifike me vula kohore

Për ndërveprimet me zë dhe video në kohë reale, shihni Live API . Për modele të dedikuara të të folurit në tekst me mbështetje për transkriptim në kohë reale, përdorni Google Cloud Speech-to-Text API .

Transkripto të folurit në tekst

Ky shembull tregon se si të transkriptohet, përkthehet dhe përmblidhet fjalimi me vula kohore, ditarizim të folësit dhe zbulim të emocioneve duke përdorur rezultate të strukturuara .

Python 

from google import genai

client = genai.Client()

YOUTUBE_URL = "https://www.youtube.com/watch?v=ku-N-eS1lgM"

prompt = """
  Process the audio file and generate a detailed transcription.

  Requirements:
  1. Identify distinct speakers (e.g., Speaker 1, Speaker 2).
  2. Provide accurate timestamps for each segment (Format: MM:SS).
  3. Detect the primary language of each segment.
  4. If not English, provide the English translation.
  5. Identify the primary emotion: Happy, Sad, Angry, or Neutral.
  6. Provide a brief summary at the beginning.
"""

response_schema = {
    "type": "object",
    "properties": {
        "summary": {"type": "string"},
        "segments": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "speaker": {"type": "string"},
                    "timestamp": {"type": "string"},
                    "content": {"type": "string"},
                    "language": {"type": "string"},
                    "emotion": {
                        "type": "string",
                        "enum": ["happy", "sad", "angry", "neutral"]
                    }
                },
                "required": ["speaker", "timestamp", "content", "emotion"]
            }
        }
    },
    "required": ["summary", "segments"]
}

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "video", "uri": YOUTUBE_URL, "mime_type": "video/mp4"},
        {"type": "text", "text": prompt}
    ],
    response_format=response_schema,
)

print(interaction.output_text)

JavaScript 

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const YOUTUBE_URL = "https://www.youtube.com/watch?v=ku-N-eS1lgM";

const prompt = `
  Process the audio file and generate a detailed transcription.

  Requirements:
  1. Identify distinct speakers (e.g., Speaker 1, Speaker 2).
  2. Provide accurate timestamps for each segment (Format: MM:SS).
  3. Detect the primary language of each segment.
  4. If not English, provide the English translation.
  5. Identify the primary emotion: Happy, Sad, Angry, or Neutral.
  6. Provide a brief summary at the beginning.
`;

const responseSchema = {
    type: "object",
    properties: {
        summary: { type: "string" },
        segments: {
            type: "array",
            items: {
                type: "object",
                properties: {
                    speaker: { type: "string" },
                    timestamp: { type: "string" },
                    content: { type: "string" },
                    language: { type: "string" },
                    emotion: {
                        type: "string",
                        enum: ["happy", "sad", "angry", "neutral"]
                    }
                },
                required: ["speaker", "timestamp", "content", "emotion"]
            }
        }
    },
    required: ["summary", "segments"]
};

const interaction = await client.interactions.create({
    model: "gemini-3.8-flash",
    input: [
        { type: "video", uri: YOUTUBE_URL, mime_type: "video/mp4" },
        { type: "text", text: prompt }
    ],
    response_format: responseSchema,
});

console.log(JSON.parse(interaction.output_text));

Java 

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.ResponseFormat;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.interactions.VideoContent;
import com.google.genai.gaos.models.interactions.VideoContentMimeType;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;
import java.util.List;
import java.util.HashMap;
import java.util.Map;

Client client = new Client();

String youtubeUrl = "https://www.youtube.com/watch?v=ku-N-eS1lgM";

String prompt =
    "Process the audio file and generate a detailed transcription.\n\n"
        + "Requirements:\n"
        + "1. Identify distinct speakers (e.g., Speaker 1, Speaker 2).\n"
        + "2. Provide accurate timestamps for each segment (Format: MM:SS).\n"
        + "3. Detect the primary language of each segment.\n"
        + "4. If not English, provide the English translation.\n"
        + "5. Identify the primary emotion: Happy, Sad, Angry, or Neutral.\n"
        + "6. Provide a brief summary at the beginning.";

Map<String, Object> emotionProp = new HashMap<>();
emotionProp.put("type", "string");
emotionProp.put("enum", Arrays.asList("happy", "sad", "angry", "neutral"));

Map<String, Object> stringType = new HashMap<>();
stringType.put("type", "string");

Map<String, Object> segmentProps = new HashMap<>();
segmentProps.put("speaker", stringType);
segmentProps.put("timestamp", stringType);
segmentProps.put("content", stringType);
segmentProps.put("language", stringType);
segmentProps.put("emotion", emotionProp);

Map<String, Object> segmentItem = new HashMap<>();
segmentItem.put("type", "object");
segmentItem.put("properties", segmentProps);
segmentItem.put("required", Arrays.asList("speaker", "timestamp", "content", "emotion"));

Map<String, Object> segmentsProp = new HashMap<>();
segmentsProp.put("type", "array");
segmentsProp.put("items", segmentItem);

Map<String, Object> properties = new HashMap<>();
properties.put("summary", stringType);
properties.put("segments", segmentsProp);

Map<String, Object> responseSchema = new HashMap<>();
responseSchema.put("type", "object");
responseSchema.put("properties", properties);
responseSchema.put("required", Arrays.asList("summary", "segments"));

Content videoContent =
    VideoContent.builder()
        .uri(youtubeUrl)
        .mimeType(VideoContentMimeType.VIDEO_MP4)
        .build();
Content textContent = TextContent.builder().text(prompt).build();

List<Content> contents = Arrays.asList(videoContent, textContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .responseFormat(
            CreateModelInteractionResponseFormat.of(ResponseFormat.of(responseSchema)))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

Shko 

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    youtubeURL := "https://www.youtube.com/watch?v=ku-N-eS1lgM"

    prompt := "Process the audio file and generate a detailed transcription.\n\n" +
        "Requirements:\n" +
        "1. Identify distinct speakers (e.g., Speaker 1, Speaker 2).\n" +
        "2. Provide accurate timestamps for each segment (Format: MM:SS).\n" +
        "3. Detect the primary language of each segment.\n" +
        "4. If not English, provide the English translation.\n" +
        "5. Identify the primary emotion: Happy, Sad, Angry, or Neutral.\n" +
        "6. Provide a brief summary at the beginning."

    responseSchema := map[string]any{
        "type": "object",
        "properties": map[string]any{
            "summary": map[string]any{"type": "string"},
            "segments": map[string]any{
                "type": "array",
                "items": map[string]any{
                    "type": "object",
                    "properties": map[string]any{
                        "speaker":   map[string]any{"type": "string"},
                        "timestamp": map[string]any{"type": "string"},
                        "content":   map[string]any{"type": "string"},
                        "language":  map[string]any{"type": "string"},
                        "emotion": map[string]any{
                            "type": "string",
                            "enum": []string{"happy", "sad", "angry", "neutral"},
                        },
                    },
                    "required": []string{"speaker", "timestamp", "content", "emotion"},
                },
            },
        },
        "required": []string{"summary", "segments"},
    }

    contents := []interactions.Content{
        interactions.NewContent(interactions.VideoContent{
            URI:      genai.Ptr(youtubeURL),
            MimeType: interactions.VideoContentMimeTypeVideoMp4.ToPointer(),
        }),
        interactions.NewContent(interactions.TextContent{
            Text: prompt,
        }),
    }

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput(contents),
            ResponseFormat: genai.Ptr(interactions.NewCreateModelInteractionResponseFormat(
                interactions.NewResponseFormat(responseSchema),
            )),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }

    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

PUSHTIM 

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.8-flash",
    "input": [
      {
        "type": "video",
        "uri": "https://www.youtube.com/watch?v=ku-N-eS1lgM",
        "mime_type": "video/mp4"
      },
      {
        "type": "text",
        "text": "Transcribe with speaker diarization and emotion detection."
      }
    ],
    "response_format": {
        "type": "object",
        "properties": {
          "summary": {"type": "string"},
          "segments": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "speaker": {"type": "string"},
                "timestamp": {"type": "string"},
                "content": {"type": "string"},
                "emotion": {"type": "string", "enum": ["happy", "sad", "angry", "neutral"]}
              }
            }
          }
        }
      }
  }'

Një aplikacion shumëgjuhësh për transkriptim audio Gemini

Hyrje audioje

Ju mund të jepni të dhëna audio në mënyrat e mëposhtme:

Ngarko një skedar audio

Përdorni API-n e Skedarëve për skedarë më të mëdhenj se 20 MB.

Python 

from google import genai

client = genai.Client()

uploaded_file = client.files.upload(file="path/to/sample.mp3")

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "text", "text": "Describe this audio clip"},
        {
            "type": "audio",
            "uri": uploaded_file.uri,
            "mime_type": uploaded_file.mime_type
        }
    ]
)
print(interaction.output_text)

JavaScript 

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const uploadedFile = await client.files.upload({
    file: "path/to/sample.mp3",
    config: { mimeType: "audio/mp3" }
});

const interaction = await client.interactions.create({
    model: "gemini-3.8-flash",
    input: [
        {type: "text", text: "Describe this audio clip"},
        {
            type: "audio",
            uri: uploadedFile.uri,
            mime_type: uploadedFile.mimeType
        }
    ]
});
console.log(interaction.output_text);

Java 

// Upload an audio file using the Files API (recommended for files > 20 MB)
import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.AudioContent;
import com.google.genai.gaos.models.interactions.AudioContentMimeType;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.File;
import com.google.genai.types.UploadFileConfig;
import java.util.Arrays;
import java.util.List;

Client client = new Client();

File uploadedFile =
    client.files.upload(
        "path/to/sample.mp3", UploadFileConfig.builder().mimeType("audio/mp3").build());

Content textContent = TextContent.builder().text("Describe this audio clip").build();
Content audioContent =
    AudioContent.builder()
        .uri(uploadedFile.uri().get())
        .mimeType(AudioContentMimeType.of(uploadedFile.mimeType().get()))
        .build();

List<Content> contents = Arrays.asList(textContent, audioContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

Shko 

// Upload an audio file using the Files API (recommended for files > 20 MB)
package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    uploadedFile, err := client.Files.UploadFromPath(ctx, "path/to/sample.mp3", &genai.UploadFileConfig{
        MIMEType: "audio/mp3",
    })
    if err != nil {
        log.Fatal(err)
    }

    contents := []interactions.Content{
        interactions.NewContent(interactions.TextContent{
            Text: "Describe this audio clip",
        }),
        interactions.NewContent(interactions.AudioContent{
            URI:      genai.Ptr(uploadedFile.URI),
            MimeType: interactions.AudioContentMimeType(uploadedFile.MIMEType).ToPointer(),
        }),
    }

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput(contents),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }

    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

PUSHTIM 

# First upload the file using the Files API, then use the URI:
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.8-flash",
    "input": [
      {"type": "text", "text": "Describe this audio clip"},
      {
        "type": "audio",
        "uri": "YOUR_FILE_URI",
        "mime_type": "audio/mp3"
      }
    ]
  }'

Kaloni të dhënat audio në linjë

Për skedarë të vegjël audio nën madhësinë totale të kërkesës 20MB:

Python 

from google import genai
import base64

client = genai.Client()

with open('path/to/small-sample.mp3', 'rb') as f:
    audio_bytes = f.read()

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "text", "text": "Describe this audio clip"},
        {
            "type": "audio",
            "data": base64.b64encode(audio_bytes).decode('utf-8'),
            "mime_type": "audio/mp3"
        }
    ]
)
print(interaction.output_text)

JavaScript 

import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const client = new GoogleGenAI({});

const audioData = fs.readFileSync("path/to/small-sample.mp3", {
    encoding: "base64"
});

const interaction = await client.interactions.create({
    model: "gemini-3.8-flash",
    input: [
        {type: "text", text: "Describe this audio clip"},
        {
            type: "audio",
            data: audioData,
            mime_type: "audio/mp3"
        }
    ]
});
console.log(interaction.output_text);

Java 

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.AudioContent;
import com.google.genai.gaos.models.interactions.AudioContentMimeType;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.Arrays;
import java.util.Base64;
import java.util.List;

Client client = new Client();

byte[] audioBytes = Files.readAllBytes(Paths.get("path/to/small-sample.mp3"));
String base64Audio = Base64.getEncoder().encodeToString(audioBytes);

Content textContent = TextContent.builder().text("Describe this audio clip").build();
Content audioContent =
    AudioContent.builder()
        .data(base64Audio)
        .mimeType(AudioContentMimeType.AUDIO_MP3)
        .build();

List<Content> contents = Arrays.asList(textContent, audioContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

Shko 

package main

import (
    "context"
    "encoding/base64"
    "fmt"
    "log"
    "os"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    audioBytes, err := os.ReadFile("path/to/small-sample.mp3")
    if err != nil {
        log.Fatal(err)
    }
    base64Audio := base64.StdEncoding.EncodeToString(audioBytes)

    contents := []interactions.Content{
        interactions.NewContent(interactions.TextContent{
            Text: "Describe this audio clip",
        }),
        interactions.NewContent(interactions.AudioContent{
            Data:     genai.Ptr(base64Audio),
            MimeType: interactions.AudioContentMimeTypeAudioMp3.ToPointer(),
        }),
    }

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput(contents),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }

    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

PUSHTIM 

AUDIO_PATH="path/to/sample.mp3"

if [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  B64FLAGS="--input"
else
  B64FLAGS="-w0"
fi

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.8-flash",
    "input": [
      {"type": "text", "text": "Describe this audio clip"},
      {
        "type": "audio",
        "data": "'$(base64 $B64FLAGS $AUDIO_PATH)'",
        "mime_type": "audio/mp3"
      }
    ]
  }'

Shënime mbi të dhënat audio të integruara: * Madhësia maksimale e kërkesës është gjithsej 20 MB (duke përfshirë kërkesat dhe të gjitha skedarët) * Për ripërdorim, ngarkoni skedarin në vend të tij

Merr një transkript

Për të marrë një transkript, kërkojeni atë në njoftim:

Python 

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "text", "text": "Generate a transcript of the speech."},
        {
            "type": "audio",
            "uri": uploaded_file.uri,
            "mime_type": uploaded_file.mime_type
        }
    ]
)
print(interaction.output_text)

JavaScript 

const interaction = await client.interactions.create({
    model: "gemini-3.8-flash",
    input: [
        { type: "text", text: "Generate a transcript of the speech." },
        {
            type: "audio",
            uri: uploadedFile.uri,
            mime_type: uploadedFile.mimeType
        }
    ]
});
console.log(interaction.output_text);

Java 

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.AudioContent;
import com.google.genai.gaos.models.interactions.AudioContentMimeType;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.File;
import com.google.genai.types.UploadFileConfig;
import java.util.Arrays;
import java.util.List;

Client client = new Client();

File uploadedFile =
    client.files.upload(
        "path/to/sample.mp3", UploadFileConfig.builder().mimeType("audio/mp3").build());

Content textContent = TextContent.builder().text("Generate a transcript of the speech.").build();
Content audioContent =
    AudioContent.builder()
        .uri(uploadedFile.uri().get())
        .mimeType(AudioContentMimeType.of(uploadedFile.mimeType().get()))
        .build();

List<Content> contents = Arrays.asList(textContent, audioContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

Shko 

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    uploadedFile, err := client.Files.UploadFromPath(ctx, "path/to/sample.mp3", &genai.UploadFileConfig{
        MIMEType: "audio/mp3",
    })
    if err != nil {
        log.Fatal(err)
    }

    contents := []interactions.Content{
        interactions.NewContent(interactions.TextContent{
            Text: "Generate a transcript of the speech.",
        }),
        interactions.NewContent(interactions.AudioContent{
            URI:      genai.Ptr(uploadedFile.URI),
            MimeType: interactions.AudioContentMimeType(uploadedFile.MIMEType).ToPointer(),
        }),
    }

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput(contents),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }

    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

Referojuni vulave kohore

Përdorni formatin MM:SS për t'iu referuar seksioneve specifike:

Python 

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "text", "text": "Provide a transcript from 02:30 to 03:29."},
        {
            "type": "audio",
            "uri": uploaded_file.uri,
            "mime_type": uploaded_file.mime_type
        }
    ]
)

JavaScript 

const interaction = await client.interactions.create({
    model: "gemini-3.8-flash",
    input: [
        { type: "text", text: "Provide a transcript from 02:30 to 03:29." },
        { type: "audio", uri: uploadedFile.uri, mime_type: "audio/mp3" }
    ]
});

Java 

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.AudioContent;
import com.google.genai.gaos.models.interactions.AudioContentMimeType;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.File;
import com.google.genai.types.UploadFileConfig;
import java.util.Arrays;
import java.util.List;

Client client = new Client();

File uploadedFile =
    client.files.upload(
        "path/to/sample.mp3", UploadFileConfig.builder().mimeType("audio/mp3").build());

Content textContent =
    TextContent.builder().text("Provide a transcript from 02:30 to 03:29.").build();
Content audioContent =
    AudioContent.builder()
        .uri(uploadedFile.uri().get())
        .mimeType(AudioContentMimeType.of(uploadedFile.mimeType().get()))
        .build();

List<Content> contents = Arrays.asList(textContent, audioContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

Shko 

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    uploadedFile, err := client.Files.UploadFromPath(ctx, "path/to/sample.mp3", &genai.UploadFileConfig{
        MIMEType: "audio/mp3",
    })
    if err != nil {
        log.Fatal(err)
    }

    contents := []interactions.Content{
        interactions.NewContent(interactions.TextContent{
            Text: "Provide a transcript from 02:30 to 03:29.",
        }),
        interactions.NewContent(interactions.AudioContent{
            URI:      genai.Ptr(uploadedFile.URI),
            MimeType: interactions.AudioContentMimeType(uploadedFile.MIMEType).ToPointer(),
        }),
    }

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput(contents),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }

    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

Numëroni shenjat

Numëroni tokenët në një skedar audio:

Python 

response = client.models.count_tokens(
    model="gemini-3.8-flash",
    contents=[uploaded_file]
)
print(response)

JavaScript 

const response = await client.models.countTokens({
    model: "gemini-3.8-flash",
    contents: [
        { fileData: { fileUri: uploadedFile.uri, mimeType: uploadedFile.mimeType } }
    ]
});
console.log(response.totalTokens);

Java 

import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.CountTokensResponse;
import com.google.genai.types.File;
import com.google.genai.types.Part;
import com.google.genai.types.UploadFileConfig;
import java.util.Arrays;

Client client = new Client();

File uploadedFile =
    client.files.upload(
        "path/to/sample.mp3", UploadFileConfig.builder().mimeType("audio/mp3").build());

CountTokensResponse response =
    client.models.countTokens(
        "gemini-3.8-flash",
        Arrays.asList(
            Content.fromParts(
                Part.fromUri(uploadedFile.uri().get(), uploadedFile.mimeType().get()))),
        null);

System.out.println(response);

Shko 

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    uploadedFile, err := client.Files.UploadFromPath(ctx, "path/to/sample.mp3", &genai.UploadFileConfig{
        MIMEType: "audio/mp3",
    })
    if err != nil {
        log.Fatal(err)
    }

    response, err := client.Models.CountTokens(
        ctx,
        "gemini-3.8-flash",
        []*genai.Content{
            genai.NewContentFromURI(uploadedFile.URI, uploadedFile.MIMEType, genai.RoleUser),
        },
        nil,
    )
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(response.TotalTokens)
}

Formatet audio të mbështetura

Gemini mbështet llojet e mëposhtme të formateve audio MIME:

  • WAV - audio/wav
  • MP3 - audio/mp3
  • AIFF - audio/aiff
  • AAC - audio/aac
  • OGG - audio/ogg
  • FLAC - audio/flac
  • MPEG - audio/mpeg
  • M4A - audio/m4a
  • L16 - audio/l16
  • Opus - audio/opus
  • ALAW - audio/alaw
  • MULAW - audio/mulaw
  • WebM - audio/webm

Për listën e plotë të llojeve MIME dhe skemave të parametrave të mbështetura, shihni referencën e Interactions API .

Detajet teknike rreth audios

  • Tokenat : 32 tokena për sekondë audio (1 minutë = 1,920 tokena)
  • Jo-folës : Binjakët i kuptojnë tingujt jo-folës (kënga e zogjve, sirenat, etj.)
  • Gjatësia maksimale : 9.5 orë audio për çdo mesazh
  • Rezolucioni : I reduktuar në 16 Kbps
  • Kanalet : Audio shumëkanalëshe e kombinuar në një kanal të vetëm

Çfarë vjen më pas