Comparison of AWS Transcribe and LocalStack Transcribe Outputs

Oct 13, 2025 by thesweetdesigns.com 82 views

Introduction

LocalStack's Transcribe service is a powerful tool for speech-to-text processing, allowing developers to test and debug their applications in a local environment. However, one of the limitations of LocalStack's Transcribe service is its partial implementation of speaker diarization. In this article, we will explore the current state of speaker diarization in LocalStack's Transcribe service and propose an improved implementation that matches the output of the real AWS Transcribe API.

Current State of Speaker Diarization in LocalStack's Transcribe

Currently, LocalStack's Transcribe service only provides a basic implementation of speaker diarization, lacking the detailed results provided by the real AWS Transcribe API. When processing the same audio file, the real AWS Transcribe API returns detailed results, including speaker labels and audio segments, while LocalStack's response only includes basic job information without the additional diarization details.

Comparison of AWS Transcribe and LocalStack Transcribe Outputs

To illustrate the difference between the two services, let's compare the outputs of the same audio file using the real AWS Transcribe API and LocalStack's Transcribe service.

AWS Transcribe Output

{
  "TranscriptionJob": {
    "TranscriptionJobName": "MyTestJob",
    "TranscriptionJobStatus": "COMPLETED",
    "LanguageCode": "es-US",
    "MediaFormat": "wav",
    "Media": {
      "MediaFileUri": "s3://my-audio-bucket/test-audio.wav"
    },
    "Transcript": {
      "TranscriptFileUri": "https://s3.us-east-1.amazonaws.com/my-audio-bucket/MyTestJob.json"
    },
    "Results": {
      "transcripts": [
        { "transcript": "Hola. Hola. Hola. Hola. Hola, Quipen aquí me" }
      ],
      "speaker_labels": {
        "segments": [
          {
            "start_time": "0.349",
            "end_time": "1.33",
            "speaker_label": "spk_0",
            "items": [
              {"start_time": "0.509", "end_time": "0.81"},
              {"start_time": "0.819", "end_time": "1.09"}
            ]
          },
          {
            "start_time": "1.909",
            "end_time": "4.51",
            "speaker_label": "spk_1",
            "items": [
              {"start_time": "1.94", "end_time": "2.329"},
              {"start_time": "2.339", "end_time": "2.569"}
            ]
          }
        ],
        "speakers": 2
      },
      "audio_segments": [
        {
          "id": 0,
          "transcript": "Hola. Hola.",
          "start_time": "0.349",
          "end_time": "1.33",
          "speaker_label": "spk_0"
        },
        {
          "id": 1,
          "transcript": "Hola. Hola. Hola. Hola.",
          "start_time": "1.909",
          "end_time": "4.51",
          "speaker_label": "spk_1"
        }
      ]
    }
  }
}

LocalStack Transcribe Output

{
  "TranscriptionJob": {
    "TranscriptionJobName": "MyTestJob",
    "TranscriptionJobStatus": "COMPLETED",
    "LanguageCode": "en-US",
    "MediaFormat": "wav",
    "Media": {
      "MediaFileUri": "s3://test-audio-bucket/test-audio.wav"
    },
    "Transcript": {
      "TranscriptFileUri": "http://s3.localhost.localstack.cloud:4566/test-audio-bucket/MyTestJob.json?..."
    }
    // Note: The detailed "Results" section, including "speaker_labels" and "audio_segments", is missing.
  }
}

As shown, the real AWS output provides detailed speaker segmentation with multiple speakers, timing information, and grouped audio segments, while LocalStack's response only returns the basic job information without the additional diarization details.

Improved Implementation

To improve the speaker diarization support in LocalStack's Transcribe service, we suggest an improved integration with the underlying Vosk library. The goal would be to extract and format speaker diarization data to match AWS Transcribe's output, including full speaker_labels and audio_segments. This enhancement would allow LocalStack to more closely replicate production behavior.

Benefits of Improved Speaker Diarization Support

The improved speaker diarization support in LocalStack's Transcribe service would provide several benefits, including:

More accurate results: By extracting and formatting speaker diarization data to match AWS Transcribe's output, LocalStack's Transcribe service would provide more accurate results, including detailed speaker segmentation and timing information.
Improved debugging: With more accurate results, developers would be able to debug their applications more effectively, identifying issues related to speaker diarization and audio processing.
Enhanced testing: The improved speaker diarization support would enable developers to test their applications more thoroughly, ensuring that they function correctly in a local environment.

Conclusion

Introduction

In our previous article, we discussed the current state of speaker diarization in LocalStack's Transcribe service and proposed an improved implementation that matches the output of the real AWS Transcribe API. In this article, we will answer some frequently asked questions (FAQs) related to the improved speaker diarization support in LocalStack's Transcribe service.

Q: What is speaker diarization, and why is it important?

A: Speaker diarization is the process of identifying and segmenting speech from multiple speakers in an audio recording. It is an important feature in speech recognition and analysis applications, as it enables the accurate identification of speakers and their corresponding speech segments.

Q: What are the benefits of improved speaker diarization support in LocalStack's Transcribe service?

A: The improved speaker diarization support in LocalStack's Transcribe service provides several benefits, including:

More accurate results: By extracting and formatting speaker diarization data to match AWS Transcribe's output, LocalStack's Transcribe service would provide more accurate results, including detailed speaker segmentation and timing information.
Improved debugging: With more accurate results, developers would be able to debug their applications more effectively, identifying issues related to speaker diarization and audio processing.
Enhanced testing: The improved speaker diarization support would enable developers to test their applications more thoroughly, ensuring that they function correctly in a local environment.

Q: How would the improved speaker diarization support in LocalStack's Transcribe service be implemented?

A: The improved speaker diarization support in LocalStack's Transcribe service would be implemented by improving the integration with the underlying Vosk library. The goal would be to extract and format speaker diarization data to match AWS Transcribe's output, including full speaker_labels and audio_segments.

Q: What are the technical requirements for implementing the improved speaker diarization support in LocalStack's Transcribe service?

A: The technical requirements for implementing the improved speaker diarization support in LocalStack's Transcribe service include:

Vosk library integration: The Vosk library would need to be integrated with LocalStack's Transcribe service to extract and format speaker diarization data.
Speaker diarization algorithm: A speaker diarization algorithm would need to be implemented to identify and segment speech from multiple speakers in an audio recording.
Audio processing: Audio processing capabilities would be required to handle audio recordings and extract relevant features for speaker diarization.

Q: How would the improved speaker diarization support in LocalStack's Transcribe service be tested and validated?

A: The improved speaker diarization support in LocalStack's Transcribe service would be tested and validated through a combination of unit tests, integration tests, and end-to-end tests. The tests would focus on verifying the accuracy and reliability of the speaker diarization results, as well as the overall performance and scalability of the service.

Q: What are the potential challenges and limitations of implementing the improved speaker diarization support in LocalStack's Transcribe service?

A: The potential challenges and limitations of implementing the improved speaker diarization support in LocalStack's Transcribe service include:

Complexity: The implementation of speaker diarization requires complex algorithms and techniques, which can be challenging to implement and optimize.
Computational resources: The processing of audio recordings and extraction of speaker diarization features can be computationally intensive, requiring significant resources and infrastructure.
Data quality: The accuracy of speaker diarization results can be affected by the quality of the audio recordings, which can be noisy, distorted, or otherwise degraded.

Conclusion

In conclusion, the improved speaker diarization support in LocalStack's Transcribe service would provide several benefits, including more accurate results, improved debugging, and enhanced testing capabilities. However, the implementation of this feature would require careful consideration of the technical requirements, potential challenges, and limitations. By addressing these challenges and limitations, developers can create a more accurate and reliable speaker diarization service that meets the needs of their applications.