将长音频文件转写为文字

本页面演示了如何使用 Speech-to-Text API 和异步语音识别将长音频文件（时长超过一分钟）转写为文字。

异步语音识别简介

批量语音识别会启动一项长时间运行的音频处理操作。使用异步语音识别转写超过 60 秒的音频。对于较短的音频，同步语音识别更为简单快捷。异步语音识别的上限为 480 分钟（8 小时）。

批量语音识别功能只能转写 Cloud Storage 中存储的音频。转写输出可以在响应中以内嵌方式提供（对于单文件批量识别请求），也可以写入 Cloud Storage。

批量识别请求会返回一个 Operation，其中包含正在进行的请求识别处理的相关信息。您可以轮询操作，以了解操作何时完成以及转写功能何时可用。

准备工作

1. 登录您的 Google Cloud 账号。如果您是 Google Cloud 新手，请创建一个账号来评估我们的产品在实际场景中的表现。新客户还可获享 $300 赠金，用于运行、测试和部署工作负载。

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. 确保您的 Google Cloud 项目已启用结算功能。

4. Enable the Speech-to-Text APIs.

   Enable the APIs

5. Make sure that you have the following role or roles on the project: Cloud Speech Administrator

   Check for the roles

   1. In the Google Cloud console, go to the IAM page.

      Go to IAM
   2. Select the project.

   3. In the Principal column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator.

   4. For all rows that specify or include you, check the Role colunn to see whether the list of roles includes the required roles.

   Grant the roles

   1. In the Google Cloud console, go to the IAM page.

      前往 IAM
   2. 选择项目。
   3. 点击 授予访问权限。

   4. 在新的主账号字段中，输入您的用户标识符。 这通常是 Google 账号的电子邮件地址。

   5. 在选择角色列表中，选择一个角色。
   6. 如需授予其他角色，请点击 添加其他角色，然后添加其他各个角色。
   7. 点击保存。
   8. Install the Google Cloud CLI.

   9. To initialize the gcloud CLI, run the following command:

      gcloud init

      Note: If you installed the gcloud CLI previously, make sure you have the latest version by running gcloud components update.

   10. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

       Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

       Go to project selector

   11. 确保您的 Google Cloud 项目已启用结算功能。

   12. Enable the Speech-to-Text APIs.

       Enable the APIs

   13. Make sure that you have the following role or roles on the project: Cloud Speech Administrator

       Check for the roles

       1. In the Google Cloud console, go to the IAM page.

          Go to IAM
       2. Select the project.

       3. In the Principal column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator.

       4. For all rows that specify or include you, check the Role colunn to see whether the list of roles includes the required roles.

       Grant the roles

       1. In the Google Cloud console, go to the IAM page.

          前往 IAM
       2. 选择项目。
       3. 点击 授予访问权限。

       4. 在新的主账号字段中，输入您的用户标识符。 这通常是 Google 账号的电子邮件地址。

       5. 在选择角色列表中，选择一个角色。
       6. 如需授予其他角色，请点击 添加其他角色，然后添加其他各个角色。
       7. 点击保存。
       8. Install the Google Cloud CLI.

       9. To initialize the gcloud CLI, run the following command:

          gcloud init

          Note: If you installed the gcloud CLI previously, make sure you have the latest version by running gcloud components update.

       客户端库可以使用应用默认凭据轻松进行 Google API 身份验证，并向这些 API 发送请求。借助应用默认凭据，您可以在本地测试应用并部署它，无需更改底层代码。有关详情，请参阅使用客户端库进行身份验证。

       10. If you're using a local shell, then create local authentication credentials for your user account:

           gcloud auth application-default login

           You don't need to do this if you're using Cloud Shell.

       此外，请确保您已安装客户端库。

       启用对 Cloud Storage 的访问权限

       Speech-to-Text 使用服务账号访问 Cloud Storage 中的文件。默认情况下，服务账号可以访问同一项目中的 Cloud Storage 文件。

       服务账号电子邮件地址如下所示：

       [e-mail]
       

       如需转写另一个项目中的 Cloud Storage 文件，您可以向此服务账号授予另一个项目中的 Speech-to-Text Service Agent 角色：

       gcloud projects add-iam-policy-binding PROJECT_ID \
           --member=serviceAccount:[e-mail] \
           --role=roles/speech.serviceAgent

       如需详细了解项目 IAM 政策，请参阅管理对项目、文件夹和组织的访问权限

       您还可以通过向服务账号授予对特定 Cloud Storage 存储桶的权限，为服务账号授予更精细的访问权限：

       gcloud storage buckets add-iam-policy-binding gs://BUCKET_NAME \
           --member=serviceAccount:[e-mail] \
           --role=roles/storage.admin

       如需详细了解如何管理对 Cloud Storage 的访问权限，请参阅 Cloud Storage 文档中的创建和管理访问控制列表。

       使用内嵌结果执行批量识别

       以下示例演示了如何对 Cloud Storage 中的音频文件执行批量语音识别，并从响应中读取内嵌的转写结果：

       Python

       import os
       
       from google.cloud.speech_v2 import SpeechClient
       from google.cloud.speech_v2.types import cloud_speech
       
       PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
       
       
       def transcribe_batch_gcs_input_inline_output_v2(
           audio_uri: str,
       ) -> cloud_speech.BatchRecognizeResults:
           """Transcribes audio from a Google Cloud Storage URI using the Google Cloud Speech-to-Text API.
               The transcription results are returned inline in the response.
           Args:
               audio_uri (str): The Google Cloud Storage URI of the input audio file.
                   E.g., gs://[BUCKET]/[FILE]
           Returns:
               cloud_speech.BatchRecognizeResults: The response containing the transcription results.
           """
           # Instantiates a client
           client = SpeechClient()
       
           config = cloud_speech.RecognitionConfig(
               auto_decoding_config=cloud_speech.AutoDetectDecodingConfig(),
               language_codes=["en-US"],
               model="long",
           )
       
           file_metadata = cloud_speech.BatchRecognizeFileMetadata(uri=audio_uri)
       
           request = cloud_speech.BatchRecognizeRequest(
               recognizer=f"projects/{PROJECT_ID}/locations/global/recognizers/_",
               config=config,
               files=[file_metadata],
               recognition_output_config=cloud_speech.RecognitionOutputConfig(
                   inline_response_config=cloud_speech.InlineOutputConfig(),
               ),
           )
       
           # Transcribes the audio into text
           operation = client.batch_recognize(request=request)
       
           print("Waiting for operation to complete...")
           response = operation.result(timeout=120)
       
           for result in response.results[audio_uri].transcript.results:
               print(f"Transcript: {result.alternatives[0].transcript}")
       
           return response.results[audio_uri].transcript
       
       

       执行批量识别并将结果写入 Cloud Storage

       以下示例演示了如何对 Cloud Storage 中的音频文件执行批量语音识别，并从 Cloud Storage 的输出文件中读取转写结果。请注意，写入 Cloud Storage 的文件是 JSON 格式的 BatchRecognizeResults 消息：

       Python

       import os
       
       import re
       
       from google.cloud import storage
       from google.cloud.speech_v2 import SpeechClient
       from google.cloud.speech_v2.types import cloud_speech
       
       PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
       
       
       def transcribe_batch_gcs_input_gcs_output_v2(
           audio_uri: str,
           gcs_output_path: str,
       ) -> cloud_speech.BatchRecognizeResults:
           """Transcribes audio from a Google Cloud Storage URI using the Google Cloud Speech-to-Text API.
           The transcription results are stored in another Google Cloud Storage bucket.
           Args:
               audio_uri (str): The Google Cloud Storage URI of the input audio file.
                   E.g., gs://[BUCKET]/[FILE]
               gcs_output_path (str): The Google Cloud Storage bucket URI where the output transcript will be stored.
                   E.g., gs://[BUCKET]
           Returns:
               cloud_speech.BatchRecognizeResults: The response containing the URI of the transcription results.
           """
           # Instantiates a client
           client = SpeechClient()
       
           config = cloud_speech.RecognitionConfig(
               auto_decoding_config=cloud_speech.AutoDetectDecodingConfig(),
               language_codes=["en-US"],
               model="long",
           )
       
           file_metadata = cloud_speech.BatchRecognizeFileMetadata(uri=audio_uri)
       
           request = cloud_speech.BatchRecognizeRequest(
               recognizer=f"projects/{PROJECT_ID}/locations/global/recognizers/_",
               config=config,
               files=[file_metadata],
               recognition_output_config=cloud_speech.RecognitionOutputConfig(
                   gcs_output_config=cloud_speech.GcsOutputConfig(
                       uri=gcs_output_path,
                   ),
               ),
           )
       
           # Transcribes the audio into text
           operation = client.batch_recognize(request=request)
       
           print("Waiting for operation to complete...")
           response = operation.result(timeout=120)
       
           file_results = response.results[audio_uri]
       
           print(f"Operation finished. Fetching results from {file_results.uri}...")
           output_bucket, output_object = re.match(
               r"gs://([^/]+)/(.*)", file_results.uri
           ).group(1, 2)
       
           # Instantiates a Cloud Storage client
           storage_client = storage.Client()
       
           # Fetch results from Cloud Storage
           bucket = storage_client.bucket(output_bucket)
           blob = bucket.blob(output_object)
           results_bytes = blob.download_as_bytes()
           batch_recognize_results = cloud_speech.BatchRecognizeResults.from_json(
               results_bytes, ignore_unknown_fields=True
           )
       
           for result in batch_recognize_results.results:
               print(f"Transcript: {result.alternatives[0].transcript}")
       
           return batch_recognize_results
       
       

       对多个文件执行批量识别

       以下示例演示了如何对 Cloud Storage 中的多个音频文件执行批量语音识别，并从 Cloud Storage 的输出文件中读取转写结果：

       Python

       import os
       import re
       from typing import List
       
       from google.cloud import storage
       from google.cloud.speech_v2 import SpeechClient
       from google.cloud.speech_v2.types import cloud_speech
       
       PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
       
       
       def transcribe_batch_multiple_files_v2(
           audio_uris: List[str],
           gcs_output_path: str,
       ) -> cloud_speech.BatchRecognizeResponse:
           """Transcribes audio from multiple Google Cloud Storage URIs using the Google Cloud Speech-to-Text API.
           The transcription results are stored in another Google Cloud Storage bucket.
           Args:
               audio_uris (List[str]): The list of Google Cloud Storage URIs of the input audio files.
                   E.g., ["gs://[BUCKET]/[FILE]", "gs://[BUCKET]/[FILE]"]
               gcs_output_path (str): The Google Cloud Storage bucket URI where the output transcript will be stored.
                   E.g., gs://[BUCKET]
           Returns:
               cloud_speech.BatchRecognizeResponse: The response containing the URIs of the transcription results.
           """
           # Instantiates a client
           client = SpeechClient()
       
           config = cloud_speech.RecognitionConfig(
               auto_decoding_config=cloud_speech.AutoDetectDecodingConfig(),
               language_codes=["en-US"],
               model="long",
           )
       
           files = [cloud_speech.BatchRecognizeFileMetadata(uri=uri) for uri in audio_uris]
       
           request = cloud_speech.BatchRecognizeRequest(
               recognizer=f"projects/{PROJECT_ID}/locations/global/recognizers/_",
               config=config,
               files=files,
               recognition_output_config=cloud_speech.RecognitionOutputConfig(
                   gcs_output_config=cloud_speech.GcsOutputConfig(
                       uri=gcs_output_path,
                   ),
               ),
           )
       
           # Transcribes the audio into text
           operation = client.batch_recognize(request=request)
       
           print("Waiting for operation to complete...")
           response = operation.result(timeout=120)
       
           print("Operation finished. Fetching results from:")
           for uri in audio_uris:
               file_results = response.results[uri]
               print(f"  {file_results.uri}...")
               output_bucket, output_object = re.match(
                   r"gs://([^/]+)/(.*)", file_results.uri
               ).group(1, 2)
       
               # Instantiates a Cloud Storage client
               storage_client = storage.Client()
       
               # Fetch results from Cloud Storage
               bucket = storage_client.bucket(output_bucket)
               blob = bucket.blob(output_object)
               results_bytes = blob.download_as_bytes()
               batch_recognize_results = cloud_speech.BatchRecognizeResults.from_json(
                   results_bytes, ignore_unknown_fields=True
               )
       
               for result in batch_recognize_results.results:
                   print(f"     Transcript: {result.alternatives[0].transcript}")
       
           return response
       
       

       对批量识别启用动态批处理

       动态批处理可降低转写费用，但延迟时间较长此功能仅适用于批量识别。

       以下示例演示了如何在启用动态批处理的情况下对 Cloud Storage 中的音频文件执行批量识别：

       Python

       import os
       
       from google.cloud.speech_v2 import SpeechClient
       from google.cloud.speech_v2.types import cloud_speech
       
       PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
       
       
       def transcribe_batch_dynamic_batching_v2(
           audio_uri: str,
       ) -> cloud_speech.BatchRecognizeResults:
           """Transcribes audio from a Google Cloud Storage URI using dynamic batching.
           Args:
               audio_uri (str): The Cloud Storage URI of the input audio.
               E.g., gs://[BUCKET]/[FILE]
           Returns:
               cloud_speech.BatchRecognizeResults: The response containing the transcription results.
           """
           # Instantiates a client
           client = SpeechClient()
       
           config = cloud_speech.RecognitionConfig(
               auto_decoding_config=cloud_speech.AutoDetectDecodingConfig(),
               language_codes=["en-US"],
               model="long",
           )
       
           file_metadata = cloud_speech.BatchRecognizeFileMetadata(uri=audio_uri)
       
           request = cloud_speech.BatchRecognizeRequest(
               recognizer=f"projects/{PROJECT_ID}/locations/global/recognizers/_",
               config=config,
               files=[file_metadata],
               recognition_output_config=cloud_speech.RecognitionOutputConfig(
                   inline_response_config=cloud_speech.InlineOutputConfig(),
               ),
               processing_strategy=cloud_speech.BatchRecognizeRequest.ProcessingStrategy.DYNAMIC_BATCHING,
           )
       
           # Transcribes the audio into text
           operation = client.batch_recognize(request=request)
       
           print("Waiting for operation to complete...")
           response = operation.result(timeout=120)
       
           for result in response.results[audio_uri].transcript.results:
               print(f"Transcript: {result.alternatives[0].transcript}")
       
           return response.results[audio_uri].transcript
       
       

       替换每个文件的识别功能

       默认情况下，批量识别功能对批量识别请求中的每个文件使用相同的识别配置。如果不同的文件需要不同的配置或功能，您可以使用 [BatchRecognizeFileMetadata][batch-file-metadata-grpc] 消息中的 config 字段为每个文件替换配置。如需查看覆盖识别功能的示例，请参阅识别器文档。

       清理

       为避免因本页中使用的资源导致您的 Google Cloud 账号产生费用，请按照以下步骤操作。

       1. Optional: Revoke the authentication credentials that you created, and delete the local credential file.

          gcloud auth application-default revoke

       2. Optional: Revoke credentials from the gcloud CLI.

          gcloud auth revoke

       控制台

       注意：删除项目会产生以下影响：

       • 项目中的所有内容都会被删除。如果您将现有项目用于本文档中的任务，则删除该项目后，还将删除您已在该项目中完成的任何其他工作。
       • 自定义项目 ID 丢失。创建此项目时，您可能创建了要在将来使用的自定义项目 ID。要保留使用该项目 ID 的网址（例如 appspot.com 网址），请删除项目内的所选资源，而不是删除整个项目。

       如果您打算探索多个架构、教程或快速入门，则重复使用项目可以帮助您避免超出项目配额限制。

       In the Google Cloud console, go to the Manage resources page.

       Go to Manage resources

       In the project list, select the project that you want to delete, and then click Delete.

       In the dialog, type the project ID, and then click Shut down to delete the project.

       gcloud

       注意：删除项目会产生以下影响：

       • 项目中的所有内容都会被删除。如果您将现有项目用于本文档中的任务，则删除该项目后，还将删除您已在该项目中完成的任何其他工作。
       • 自定义项目 ID 丢失。创建此项目时，您可能创建了要在将来使用的自定义项目 ID。要保留使用该项目 ID 的网址（例如 appspot.com 网址），请删除项目内的所选资源，而不是删除整个项目。

       如果您打算探索多个架构、教程或快速入门，则重复使用项目可以帮助您避免超出项目配额限制。

       Delete a Google Cloud project:

       gcloud projects delete PROJECT_ID

       后续步骤

       • 请查看参考文档了解批量识别。
       • 了解如何转写流式传输音频。
       • 练习转写短音频文件。
       • 使用 Chirp 转写音频文件。
       • 如需了解关于最佳性能、准确度和其他方面的提示，请参阅最佳实践文档。