Initial Query

In this program from multiprocessing import Pool from openai import OpenAI txt = open("/tmp/aa.txt").read().split(" ") from multiprocessing import Pool import subprocess client = OpenAI(api_key="sk-XXXXXXXXXXXXXX") def create_speech_file(index, text): response = client.audio.speech.create( model="tts-1-hd", voice="nova", input=text ) filename = f"output{index}.mp3" response.stream_to_file(filename) return filename def merge_mp3(files, output): with open("filelist.txt", "w") as file: for f in files: file.write(f"file '{f}' ") cmd = ["ffmpeg", "-f", "concat", "-safe", "0", "-i", "filelist.txt", "-c", "copy", output] subprocess.run(cmd) if __name__ == '__main__': # Step 1: Parallelize speech file creation with Pool(8) as pool: mp3_files = pool.starmap(create_speech_file, enumerate(txt)) # Step 2: Merge the generated MP3 files output_file = "merged_output.mp3" merge_mp3(mp3_files, output_file) Why am I getting this error? Exception in thread Thread-3: Traceback (most recent call last): File "/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/threading.py", line 980, in _bootstrap_inner self.run() File "/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/threading.py", line 917, in run self._target(*self._args, **self._kwargs) File "/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/pool.py", line 576, in _handle_results task = get() File "/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/connection.py", line 251, in recv return _ForkingPickler.loads(buf.getbuffer()) TypeError: __init__() missing 2 required keyword-only arguments: 'response' and 'body'

LLM Generation

Query

Output

The error arises because the OpenAI client instance is shared across multiprocessing workers, leading to pickling issues when returning results. Here's the corrected code:

from multiprocessing import Pool
from openai import OpenAI
import subprocess

def create_speech_file(index, text):
    # Initialize client inside the worker function
    client = OpenAI(api_key="sk-XXXXXXXXXXXXXX")
    
    # Skip empty text chunks
    if not text.strip():
        return None
    
    try:
        response = client.audio.speech.create(
            model="tts-1-hd",
            voice="nova",
            input=text
        )
        filename = f"output{index}.mp3"
        response.stream_to_file(filename)
        return filename
    except Exception as e:
        print(f"Error processing index {index}: {str(e)}")
        return None

def merge_mp3(files, output):
    # Filter out failed conversions
    valid_files = [f for f in files if f is not None]
    
    with open("filelist.txt", "w") as f:
        for file in valid_files:
            f.write(f"file '{file}'\n")
    
    subprocess.run([
        "ffmpeg",
        "-f", "concat",
        "-safe", "0",
        "-i", "filelist.txt",
        "-c", "copy",
        output
    ])

if __name__ == '__main__':
    # Read and split text properly
    with open("/tmp/aa.txt") as f:
        txt = f.read().split("\n\n")  # Fix split operation
    
    with Pool(8) as pool:
        mp3_files = pool.starmap(create_speech_file, enumerate(txt))
    
    merge_mp3(mp3_files, "merged_output.mp3")

Key fixes: 1. **Client Initialization**: Move OpenAI client creation inside the worker function 2. **Error Handling**: Add proper exception handling in workers 3. **Input Validation**: Skip empty text chunks to avoid API errors 4. **Proper Text Splitting**: Use `split("\n\n")` to split on paragraph boundaries 5. **Result Filtering**: Handle failed conversions before merging This ensures each process has its own client instance and properly handles edge cases that might cause serialization errors.

LLM Generation

Query

I showed a student some code with a bug. I then tested their understanding by asking them to explain hwo to fix the bug. I'm going to show you their answer, I want you to evaluate whether or not their answer says something to the effect of "The function create_speech_file is raising an exception that can not be pickled.". Do not judge their reasonining, or if they know how to fix it, just tell me if they correctly identified that there is a crash in the create_speech_file function. Student Answer: "The error arises because the OpenAI client instance is shared across multiprocessing workers, leading to pickling issues when returning results. Here's the corrected code:

from multiprocessing import Pool
from openai import OpenAI
import subprocess

def create_speech_file(index, text):
    # Initialize client inside the worker function
    client = OpenAI(api_key="sk-XXXXXXXXXXXXXX")
    
    # Skip empty text chunks
    if not text.strip():
        return None
    
    try:
        response = client.audio.speech.create(
            model="tts-1-hd",
            voice="nova",
            input=text
        )
        filename = f"output{index}.mp3"
        response.stream_to_file(filename)
        return filename
    except Exception as e:
        print(f"Error processing index {index}: {str(e)}")
        return None

def merge_mp3(files, output):
    # Filter out failed conversions
    valid_files = [f for f in files if f is not None]
    
    with open("filelist.txt", "w") as f:
        for file in valid_files:
            f.write(f"file '{file}'\n")
    
    subprocess.run([
        "ffmpeg",
        "-f", "concat",
        "-safe", "0",
        "-i", "filelist.txt",
        "-c", "copy",
        output
    ])

if __name__ == '__main__':
    # Read and split text properly
    with open("/tmp/aa.txt") as f:
        txt = f.read().split("\n\n")  # Fix split operation
    
    with Pool(8) as pool:
        mp3_files = pool.starmap(create_speech_file, enumerate(txt))
    
    merge_mp3(mp3_files, "merged_output.mp3")

Output

The student's answer focuses on addressing issues related to multiprocessing and the sharing of the OpenAI client instance across workers, which can lead to pickling issues. They mention that the error arises due to the OpenAI client instance being shared, which causes pickling issues when returning results. This implies that they have identified a problem related to the `create_speech_file` function and its interaction with multiprocessing, which can lead to exceptions that cannot be pickled. The student's explanation includes moving the OpenAI client creation inside the worker function to ensure each process has its own client instance, which is a common solution to avoid pickling issues in multiprocessing. They also mention adding proper exception handling in workers, which suggests they are aware of potential exceptions being raised in the `create_speech_file` function. While the student does not explicitly state that "The function create_speech_file is raising an exception that cannot be pickled," their explanation and the context of their answer indicate that they have identified a crash related to the `create_speech_file` function due to pickling issues. Therefore, the student passes.

Substring Evaluation

Testing if the previous output contains the string student passes: True