From Concept to Completion: How ChatGPT 4o & 4 Helped Me Create My Synthwave Album

[SynthEchos]

Retro Future Pulse: Echoes of a New Dawn | Cyberpunk Synthwave OST for Coding, Gaming & Night Drives

Introduction: AI as a Music Production Assistant

As a YouTube music creator, I’ve explored EDM and Synthwave styles, but for my first full-length album, I wanted a structured workflow to optimize genre selection, track conceptualization, and sequencing.

I used ChatGPT 4o and GPT-4, which helped at various stages:
Genre selection based on branding & engagement trends
Track conceptualization using AI-generated prompts
Audio analysis (tempo, loudness, duration) for track sequencing
Finalizing track order & titles for a cohesive listening experience

Key Takeaway: ChatGPT 4o was great for creative brainstorming, but GPT-4 handled technical audio analysis better due to system constraints in 4o’s environment.

Here’s how ChatGPT guided me through the process—and where it hit roadblocks:

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Step 1: Choosing a Music Genre with ChatGPT 4o

Before composing, I had two genre options:
Instrumental EDM (Progressive House, Chill House, Techno, Trance)
Synthwave/Retro (Chillwave, Cyberpunk, Retrowave, Dark Synth)

ChatGPT 4o’s Genre Insights:
Synthwave/Retro was the best fit for my channel’s futuristic branding
Less competition than mainstream EDM but high engagement potential
Strong crossover appeal with focus music, gaming, and sci-fi aesthetics

Final Choice: Synthwave/Retro Album:

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Step 2: Generating Track Concepts with ChatGPT 4o

Once I locked in the genre, I used ChatGPT 4o to generate track prompts, which I then fed into Suno.com, an AI music generator.

Examples of ChatGPT 4o’s Synthwave Track Prompts:
Neon Echoes – “Cyberpunk synthwave meets dreamy futurism. Neon bass, lush pads, and shimmering arps flow over a 100 BPM groove with rhythmic layers, swells, and subtle drops for focus, gaming, and sci-fi immersion.”
Data Surge – “Cyberpunk synthwave with driving bass, sharp arps, and pulsing 110 BPM beats. Layered synths and evolving textures build momentum, perfect for coding, gaming, and immersive night drives.”

These prompts helped shape the atmosphere, tempo, and sonic characteristics of each track:

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Step 3: Audio Analysis Challenges with ChatGPT 4o

With ten completed tracks, I wanted to analyze tempo, loudness, and energy levels to ensure a well-structured album. ChatGPT 4o initially claimed it could process audio files, but technical limitations prevented this.

ChatGPT 4o Error: Library Compatibility Conflict
When I uploaded my .mp3 and .wav files, ChatGPT 4o attempted to use librosa, but ran into an outdated NumPy reference:

ChatGPT 4o’s Attempted Audio Analysis Code (Failed)

# Re-load necessary libraries after execution state reset
import librosa
import numpy as np
import pandas as pd
import ace_tools as tools

# Re-define uploaded file paths
file_paths = [
    "/mnt/data/Retro-Future Pulse (A).mp3",
    "/mnt/data/Retro-Future Pulse (B).mp3",
    "/mnt/data/Retro-Future Pulse (C).mp3",
    "/mnt/data/Retro-Future Pulse (D).mp3",
    "/mnt/data/Retro-Future Pulse (E).mp3",
    "/mnt/data/Retro-Future Pulse (F).mp3",
    "/mnt/data/Retro-Future Pulse (G).mp3",
    "/mnt/data/Retro-Future Pulse (H).mp3",
    "/mnt/data/Retro-Future Pulse (I).mp3",
    "/mnt/data/Retro-Future Pulse (J).mp3",
]

# Dictionary to store analysis results
track_analysis = {}

# Process each audio file
for file_path in file_paths:
    try:
        # Load audio file
        y, sr = librosa.load(file_path, sr=None)
        
        # Extract key features
        tempo, _ = librosa.beat.beat_track(y=y, sr=sr)  # Tempo detection
        spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=y, sr=sr))  # Brightness measure
        rms_energy = np.mean(librosa.feature.rms(y=y))  # Overall energy level
        duration = librosa.get_duration(y=y, sr=sr)  # Duration of the track
        
        # Store analysis
        track_analysis[file_path] = {
            "Tempo (BPM)": tempo,
            "Spectral Centroid": spectral_centroid,
            "RMS Energy": rms_energy,
            "Duration (seconds)": duration
        }
        
    except Exception as e:
        track_analysis[file_path] = {"Error": str(e)}

# Display the analysis results
df_analysis = pd.DataFrame.from_dict(track_analysis, orient='index')
tools.display_dataframe_to_user(name="Track Analysis", dataframe=df_analysis)

Error Message:

File ~/.local/lib/python3.11/site-packages/numpy/__init__.py:284, in __getattr__(attr)
    281     from .testing import Tester
    282     return Tester
--> 284 raise AttributeError("module {!r} has no attribute "
    285                      "{!r}".format(__name__, attr))

AttributeError: module 'numpy' has no attribute 'complex'

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Analysis: Why Did ChatGPT 4o Fail?

After reviewing the code and error message, my best understanding is:

1. ChatGPT 4o’s environment used an outdated or incompatible version of librosa, which still referenced np.complex.
2. np.complex was deprecated in NumPy 1.20+, and attempting to call it caused librosa to fail.
3. Since librosa depends on NumPy and SciPy, the outdated function broke the analysis.
4. This is a system-level conflict inside ChatGPT 4o’s execution environment—not a user-side issue.

However, if anyone in the community sees another possible explanation, I’d love to hear it!

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Step 4: Successful Audio Analysis with GPT-4

Since ChatGPT 4o failed, I switched to GPT-4, which successfully analyzed my .wav files using pydub.

GPT-4 Workaround Code (Success)

# Re-initializing the required variables correctly and extracting basic audio properties using PyDub

# Re-load the audio file using PyDub
audio = AudioSegment.from_file(audio_path)

# Duration of the audio in seconds
duration_seconds = len(audio) / 1000.0

# Channels and frame rate
channels = audio.channels
frame_rate = audio.frame_rate

# Getting loudness and frame width
loudness = audio.dBFS
frame_width = audio.frame_width

duration_seconds, channels, frame_rate, loudness, frame_width

Key Audio Attributes Identified by GPT-4:

• Duration: 139.64 sec
• Loudness: -16 dBFS
• Tempo: 100 BPM
• Channels: Stereo
• Sample Rate: 48,000 Hz

Why This Worked:

• pydub doesn’t rely on NumPy or librosa, avoiding the outdated function.
• ChatGPT 4’s execution environment must have been different from 4o’s, preventing the library issue.
  
  

Using ChatGPT 4 to Help Draft Track Titles and Sequence

Using ChatGPT 4o to Improve Track Titles and Validate Sequence

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Final Thoughts: ChatGPT 4o vs GPT-4 for Music Production

ChatGPT 4o Strengths:
Great for creative brainstorming (genre selection, music prompts)
Helped improve track titles and validate track sequencing based on technical audio data provided

ChatGPT 4o Limitations:
Failed at direct audio analysis due to outdated libraries
Could not process MP3/WAV files

ChatGPT 4 Strengths:
Accurately analyzed tempo, loudness, and duration
Structured track sequencing based on data

Key Takeaway: ChatGPT 4o excels in creative tasks, while GPT-4 is better for technical audio analysis.

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Community Discussion: Have You Faced Similar AI Challenges?

Have you experienced audio processing errors with ChatGPT tools? If so, were you able to work around them?
How do you approach audio analysis and sequencing in your music production workflow?

Would love to hear your thoughts!