选择转录模型

本页面介绍了如何将特定的机器学习模型用于发送到 Speech-to-Text 的音频转录请求。

转录模型

Speech-to-Text 会将输入与多个机器学习模型中的一个进行比较,以检测音频剪辑中的字词。每个模型都通过分析数百万个示例(在此是指大量实际的人物说话录音)进行过训练。

Speech-to-Text 具有众多专业模型,这些模型均通过特定来源(例如电话或视频)的音频进行过训练。由于这一训练过程,用这些专门的模型处理相似类型的音频数据时,可以得到更好的结果。

例如,Speech-to-Text 具有一个经过训练的转录模型,用于识别在电话中录制的语音。当 Speech-to-Text 使用 phone_call 模型转录电话音频时,所生成的转录结果会由于使用 defaultcommand_and_searchvideo 模型时的结果。

下表显示了可用于 Speech-to-Text 的转录模型。

模型名称 说明
command_and_search 最适合简短话语或单字话语,例如语音指令或语音搜索。
phone_call 最适合来自电话通话的音频(通常以 8khz 的采样率录制)。
video

最适合来自视频的音频或有多人讲话的音频。理想情况是,这类音频以 16khz 或更高的采样率录制。

这是一个高于标准价格的高级模型。如需了解详情,请参阅价格页面

default 最适合那些不适合其他音频模型的音频,例如时间较长的音频或口述。理想情况下,音频为高保真度格式,以 16000 Hz 或更高的采样率录制。

选择用于音频转录的模型

如需指定用于音频转录的特定模型,您必须将请求的 RecognitionConfig 参数的 model 字段设置为允许值之一:videophone_callcommand_and_searchdefault。Speech-to-Text 的模型选择功能支持以下所有语音识别方法:speech:recognizespeech:longrunningrecognize流式

对本地音频文件执行转录

协议

如需了解完整的详细信息,请参阅 [speech:recognize] API 端点。

如需执行同步语音识别,请发出 POST 请求并提供相应的请求正文。以下示例展示了一个使用 curl 发出的 POST 请求。该示例使用通过 Google Cloud Cloud SDK 为项目设置的服务帐号的访问令牌。如需了解有关安装 Cloud SDK、建立项目和服务帐号以及获取访问令牌的说明,请参阅快速入门

curl -s -H "Content-Type: application/json" \
    -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
    https://speech.googleapis.com/v1/speech:recognize \
    --data '{
    "config": {
        "encoding": "LINEAR16",
        "sampleRateHertz": 16000,
        "languageCode": "en-US",
        "model": "video"
    },
    "audio": {
        "uri": "gs://cloud-samples-tests/speech/Google_Gnome.wav"
    }
}'

如需详细了解如何配置请求正文,请参阅 RecognitionConfig 参考文档。

如果请求成功,服务器将返回一个 200 OK HTTP 状态代码以及 JSON 格式的响应。

{
  "results": [
    {
      "alternatives": [
        {
          "transcript": "OK Google stream stranger things from
            Netflix to my TV okay stranger things from
            Netflix playing on TV from the people that brought you
            Google home comes the next evolution of the smart home
            and it's just outside your window me Google know hi
            how can I help okay no what's the weather like outside
            the weather outside is sunny and 76 degrees he's right
            okay no turn on the hose I'm holding sure okay no I'm can
            I eat this lemon tree leaf yes what about this Daisy yes
            but I wouldn't recommend it but I could eat it okay
            Nomad milk to my shopping list I'm sorry that sounds like
            an indoor request I keep doing that sorry you do keep
            doing that okay no is this compost really we're all
            compost if you think about it pretty much everything is
            made up of organic matter and will return",
          "confidence": 0.9251011
        }
      ]
    }
  ]
}

Go


func modelSelection(w io.Writer, path string) error {
	ctx := context.Background()

	client, err := speech.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %v", err)
	}
	defer client.Close()

	// path = "../testdata/Google_Gnome.wav"
	data, err := ioutil.ReadFile(path)
	if err != nil {
		return fmt.Errorf("ReadFile: %v", err)
	}

	req := &speechpb.RecognizeRequest{
		Config: &speechpb.RecognitionConfig{
			Encoding:        speechpb.RecognitionConfig_LINEAR16,
			SampleRateHertz: 16000,
			LanguageCode:    "en-US",
			Model:           "video",
		},
		Audio: &speechpb.RecognitionAudio{
			AudioSource: &speechpb.RecognitionAudio_Content{Content: data},
		},
	}

	resp, err := client.Recognize(ctx, req)
	if err != nil {
		return fmt.Errorf("Recognize: %v", err)
	}

	for i, result := range resp.Results {
		fmt.Fprintf(w, "%s\n", strings.Repeat("-", 20))
		fmt.Fprintf(w, "Result %d\n", i+1)
		for j, alternative := range result.Alternatives {
			fmt.Fprintf(w, "Alternative %d: %s\n", j+1, alternative.Transcript)
		}
	}
	return nil
}

Java

/**
 * Performs transcription of the given audio file synchronously with the selected model.
 *
 * @param fileName the path to a audio file to transcribe
 */
public static void transcribeModelSelection(String fileName) throws Exception {
  Path path = Paths.get(fileName);
  byte[] content = Files.readAllBytes(path);

  try (SpeechClient speech = SpeechClient.create()) {
    // Configure request with video media type
    RecognitionConfig recConfig =
        RecognitionConfig.newBuilder()
            // encoding may either be omitted or must match the value in the file header
            .setEncoding(AudioEncoding.LINEAR16)
            .setLanguageCode("en-US")
            // sample rate hertz may be either be omitted or must match the value in the file
            // header
            .setSampleRateHertz(16000)
            .setModel("video")
            .build();

    RecognitionAudio recognitionAudio =
        RecognitionAudio.newBuilder().setContent(ByteString.copyFrom(content)).build();

    RecognizeResponse recognizeResponse = speech.recognize(recConfig, recognitionAudio);
    // Just print the first result here.
    SpeechRecognitionResult result = recognizeResponse.getResultsList().get(0);
    // There can be several alternative transcripts for a given chunk of speech. Just use the
    // first (most likely) one here.
    SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);
    System.out.printf("Transcript : %s\n", alternative.getTranscript());
  }
}

Node.js

// Imports the Google Cloud client library for Beta API
/**
 * TODO(developer): Update client library import to use new
 * version of API when desired features become available
 */
const speech = require('@google-cloud/speech').v1p1beta1;
const fs = require('fs');

// Creates a client
const client = new speech.SpeechClient();

/**
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const filename = 'Local path to audio file, e.g. /path/to/audio.raw';
// const model = 'Model to use, e.g. phone_call, video, default';
// const encoding = 'Encoding of the audio file, e.g. LINEAR16';
// const sampleRateHertz = 16000;
// const languageCode = 'BCP-47 language code, e.g. en-US';

const config = {
  encoding: encoding,
  sampleRateHertz: sampleRateHertz,
  languageCode: languageCode,
  model: model,
};
const audio = {
  content: fs.readFileSync(filename).toString('base64'),
};

const request = {
  config: config,
  audio: audio,
};

// Detects speech in the audio file
const [response] = await client.recognize(request);
const transcription = response.results
  .map(result => result.alternatives[0].transcript)
  .join('\n');
console.log('Transcription: ', transcription);

Python

def transcribe_model_selection(speech_file, model):
    """Transcribe the given audio file synchronously with
    the selected model."""
    from google.cloud import speech

    client = speech.SpeechClient()

    with open(speech_file, "rb") as audio_file:
        content = audio_file.read()

    audio = speech.RecognitionAudio(content=content)

    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=16000,
        language_code="en-US",
        model=model,
    )

    response = client.recognize(config=config, audio=audio)

    for i, result in enumerate(response.results):
        alternative = result.alternatives[0]
        print("-" * 20)
        print("First alternative of result {}".format(i))
        print(u"Transcript: {}".format(alternative.transcript))

其他语言

C#:请按照客户端库页面上的 C# 设置说明操作,然后访问 .NET 的 Speech-to-Text 参考文档

PHP:请按照客户端库页面上的 PHP 设置说明 操作,然后访问 PHP 的 Speech-to-Text 参考文档

Ruby:请按照客户端库页面上的 Ruby 设置说明操作,然后访问 Ruby 的 Speech-to-Text 参考文档

对 Google Cloud Storage 音频文件执行转录

Java

/**
 * Performs transcription of the remote audio file asynchronously with the selected model.
 *
 * @param gcsUri the path to the remote audio file to transcribe.
 */
public static void transcribeModelSelectionGcs(String gcsUri) throws Exception {
  try (SpeechClient speech = SpeechClient.create()) {

    // Configure request with video media type
    RecognitionConfig config =
        RecognitionConfig.newBuilder()
            // encoding may either be omitted or must match the value in the file header
            .setEncoding(AudioEncoding.LINEAR16)
            .setLanguageCode("en-US")
            // sample rate hertz may be either be omitted or must match the value in the file
            // header
            .setSampleRateHertz(16000)
            .setModel("video")
            .build();

    RecognitionAudio audio = RecognitionAudio.newBuilder().setUri(gcsUri).build();

    // Use non-blocking call for getting file transcription
    OperationFuture<LongRunningRecognizeResponse, LongRunningRecognizeMetadata> response =
        speech.longRunningRecognizeAsync(config, audio);

    while (!response.isDone()) {
      System.out.println("Waiting for response...");
      Thread.sleep(10000);
    }

    List<SpeechRecognitionResult> results = response.get().getResultsList();

    // Just print the first result here.
    SpeechRecognitionResult result = results.get(0);
    // There can be several alternative transcripts for a given chunk of speech. Just use the
    // first (most likely) one here.
    SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);
    System.out.printf("Transcript : %s\n", alternative.getTranscript());
  }
}

Node.js

// Imports the Google Cloud client library for Beta API
/**
 * TODO(developer): Update client library import to use new
 * version of API when desired features become available
 */
const speech = require('@google-cloud/speech').v1p1beta1;

// Creates a client
const client = new speech.SpeechClient();

/**
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const gcsUri = 'gs://my-bucket/audio.raw';
// const model = 'Model to use, e.g. phone_call, video, default';
// const encoding = 'Encoding of the audio file, e.g. LINEAR16';
// const sampleRateHertz = 16000;
// const languageCode = 'BCP-47 language code, e.g. en-US';

const config = {
  encoding: encoding,
  sampleRateHertz: sampleRateHertz,
  languageCode: languageCode,
  model: model,
};
const audio = {
  uri: gcsUri,
};

const request = {
  config: config,
  audio: audio,
};

// Detects speech in the audio file.
const [response] = await client.recognize(request);
const transcription = response.results
  .map(result => result.alternatives[0].transcript)
  .join('\n');
console.log('Transcription: ', transcription);

其他语言

C#:请按照客户端库页面上的 C# 设置说明操作,然后访问 .NET 的 Speech-to-Text 参考文档

PHP:请按照客户端库页面上的 PHP 设置说明 操作,然后访问 PHP 的 Speech-to-Text 参考文档

Ruby:请按照客户端库页面上的 Ruby 设置说明操作,然后访问 Ruby 的 Speech-to-Text 参考文档