Amazon Transcribe Streaming Python API: Event-Driven Processing of Audio After Stream Ends

Amazon Transcribe Streaming Python API: Event-Driven Processing of Audio After Stream Ends

In the world of data science and machine learning, audio processing is a significant field. Many services like Amazon Transcribe provide the ability to convert speech into text. Today, we’ll focus on how to use the Amazon Transcribe Python API to process audio only after the stream ends.

What is Amazon Transcribe?

Amazon Transcribe is an automatic speech recognition (ASR) service that converts speech into text. It can be used for transcribing customer service calls, automating subtitling, and more. It offers both batch processing and real-time streaming options.

Streaming vs Batch Processing

Streaming and batch processing are two different approaches to data processing. In batch processing, all data is collected before it is processed. Streaming, on the other hand, processes data in real time as it arrives.

For applications like live captions, real-time processing is necessary. But what if we want to process the audio only after the stream ends, similar to batch processing? This is where event-driven processing comes in.

Event-Driven Processing of Audio Streams with Amazon Transcribe

Event-driven processing is an approach where computations are triggered by events such as user actions, sensor outputs, or messages from other programs.

In the context of Amazon Transcribe, one such event is the end of an audio stream. We can use this event to trigger our audio processing.

Let’s explore how to implement this using the Amazon Transcribe Streaming Python API.

Setting Up the Environment

First, we need to install the Amazon Transcribe Python API. Use pip to install the amazon-transcribe package.

pip install amazon-transcribe

Ensure that you have configured your AWS credentials correctly, either through the AWS CLI or by setting the AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_SESSION_TOKEN environment variables.

Implementing the Event Handler

We’ll use the StreamingClient class to create a transcribe stream, and the start_stream_transcription method to start the transcription.

import asyncio
from amazon_transcribe.client import TranscribeStreamingClient

async def transcribe_audio():
    client = TranscribeStreamingClient(region="us-west-2")
    stream = await client.start_stream_transcription(
        language_code="en-US",
        media_sample_rate_hz=16000,
        media_encoding="pcm",
    )
    …

Next, we’ll create our event handler. In this case, we’ll define a function that will be called when the StreamEnd event is emitted.

    async def process_after_stream_end():
        print("Stream has ended, beginning processing...")
        # Your audio processing code here

We then subscribe our function to the StreamEnd event.

    stream.stream_end.subscribe(process_after_stream_end)

Finally, we send our audio data to the stream and wait for the stream to end.

    async with open('audio_file.wav', 'rb') as file:
        await stream.input_stream.send_audio_event(audio_chunk=file.read())
    
    await stream.input_stream.send_end_stream()
    await process_after_stream_end()

That’s it! With this setup, your audio processing code will be triggered only after the audio stream has ended.

Conclusion

In this post, we explored how to use the Amazon Transcribe Python API to process audio only after the stream ends. This event-driven approach provides the benefits of batch processing while still using a streaming API. This can be particularly useful for applications where real-time processing is not necessary, and the full context of the audio is needed for processing.

Remember, this is a simple example. In a production environment, you may need to handle errors, retry failed requests, and manage large audio files. However, this should give you a solid foundation for using Amazon Transcribe’s streaming API with event-driven processing.

Happy coding!

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