This tutorial will guide you through creating a serverless function using Runpod’s Python SDK that simulates a text-to-speech (TTS) process. We’ll use a streaming handler to stream results incrementally, demonstrating how to handle long-running tasks efficiently in a serverless environment. A streaming handler in the Runpod’s Python SDK is a special type of function that allows you to iterate over a sequence of values lazily. Instead of returning a single value and exiting, a streaming handler yields multiple values, one at a time, pausing the function’s state between each yield. This is particularly useful for handling large data streams or long-running tasks, as it allows the function to produce and return results incrementally, rather than waiting until the entire process is complete.

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Setting up your Serverless Function

Let’s break down the process of creating our TTS simulator into steps.

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Import required libraries

First, import the necessary libraries: 
import runpod
import time
import re
import json
import sys

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Create the TTS Simulator

Define a function that simulates the text-to-speech process: 
def text_to_speech_simulator(text, chunk_size=5, delay=0.5):
    words = re.findall(r'\w+', text)
    
    for i in range(0, len(words), chunk_size):
        chunk = words[i:i+chunk_size]
        audio_chunk = f"Audio chunk {i//chunk_size + 1}: {' '.join(chunk)}"
        time.sleep(delay)  # Simulate processing time
        yield audio_chunk
This function:
  1. Splits the input text into words
  2. Processes the words in chunks
  3. Simulates a delay for each chunk
  4. Yields each “audio chunk” as it’s processed

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Create the Streaming Handler

Now, let’s create the main handler function: 
def streaming_handler(job):
    job_input = job['input']
    text = job_input.get('text', "Welcome to Runpod's text-to-speech simulator!")
    chunk_size = job_input.get('chunk_size', 5)
    delay = job_input.get('delay', 0.5)
    
    print(f"TTS Simulator | Starting job {job['id']}")
    print(f"Processing text: {text}")
    
    for audio_chunk in text_to_speech_simulator(text, chunk_size, delay):
        yield {"status": "processing", "chunk": audio_chunk}
    
    yield {"status": "completed", "message": "Text-to-speech conversion completed"}
This handler:
  1. Extracts parameters from the job input
  2. Logs the start of the job
  3. Calls the TTS simulator and yields each chunk as it’s processed using a streaming handler
  4. Yields a completion message when finished

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Set up the main function

Finally, set up the main execution block: 
if __name__ == "__main__":
    if "--test_input" in sys.argv:
        # Code for local testing (see full example)
    else:
        runpod.serverless.start({"handler": streaming_handler, "return_aggregate_stream": True})
This block allows for both local testing and deployment as a Runpod serverless function.

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Complete code example

Here’s the full code for our serverless TTS simulator using a streaming handler: 
import runpod
import time
import re
import json
import sys

def text_to_speech_simulator(text, chunk_size=5, delay=0.5):
    words = re.findall(r'\w+', text)
    
    for i in range(0, len(words), chunk_size):
        chunk = words[i:i+chunk_size]
        audio_chunk = f"Audio chunk {i//chunk_size + 1}: {' '.join(chunk)}"
        time.sleep(delay)  # Simulate processing time
        yield audio_chunk

def streaming_handler(job):
    job_input = job['input']
    text = job_input.get('text', "Welcome to Runpod's text-to-speech simulator!")
    chunk_size = job_input.get('chunk_size', 5)
    delay = job_input.get('delay', 0.5)
    
    print(f"TTS Simulator | Starting job {job['id']}")
    print(f"Processing text: {text}")
    
    for audio_chunk in text_to_speech_simulator(text, chunk_size, delay):
        yield {"status": "processing", "chunk": audio_chunk}
    
    yield {"status": "completed", "message": "Text-to-speech conversion completed"}

if __name__ == "__main__":
    if "--test_input" in sys.argv:
        test_input_index = sys.argv.index("--test_input")
        if test_input_index + 1 < len(sys.argv):
            test_input_json = sys.argv[test_input_index + 1]
            try:
                job = json.loads(test_input_json)
                gen = streaming_handler(job)
                for item in gen:
                    print(json.dumps(item))
            except json.JSONDecodeError:
                print("Error: Invalid JSON in test_input")
        else:
            print("Error: --test_input requires a JSON string argument")
    else:
        runpod.serverless.start({"handler": streaming_handler, "return_aggregate_stream": True})

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Testing your Serverless Function

To test your function locally, use this command: 
python your_script.py --test_input '
{
  "input": {
    "text": "This is a test of the Runpod text-to-speech simulator. It processes text in chunks and simulates audio generation.",
    "chunk_size": 4,
    "delay": 1
  },
  "id": "local_test"
}'

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Understanding the output

When you run the test, you’ll see output similar to this: 
{"status": "processing", "chunk": "Audio chunk 1: This is a test"}
{"status": "processing", "chunk": "Audio chunk 2: of the Runpod"}
{"status": "processing", "chunk": "Audio chunk 3: text to speech"}
{"status": "processing", "chunk": "Audio chunk 4: simulator It processes"}
{"status": "processing", "chunk": "Audio chunk 5: text in chunks"}
{"status": "processing", "chunk": "Audio chunk 6: and simulates audio"}
{"status": "processing", "chunk": "Audio chunk 7: generation"}
{"status": "completed", "message": "Text-to-speech conversion completed"}
This output demonstrates:
  1. The incremental processing of text chunks
  2. Real-time status updates for each chunk
  3. A completion message when the entire text is processed

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Conclusion

You’ve now created a serverless function using Runpod’s Python SDK that simulates a streaming text-to-speech process. This example showcases how to handle long-running tasks and stream results incrementally in a serverless environment. To further enhance this application, consider:
  • Implementing a real text-to-speech model
  • Adding error handling for various input types
  • Exploring Runpod’s documentation for advanced features like GPU acceleration for audio processing
Runpod’s serverless library provides a powerful foundation for building scalable, efficient applications that can process and stream data in real-time without the need to manage infrastructure.