You will get an end-to-end audio machine learning model for speech analysis
Project details
I will build an end-to-end audio machine learning solution for speech emotion recognition, sound classification, or audio-based analysis. This service is ideal for use cases such as call quality analysis, emotion detection, voice-based insights, or custom audio classification tasks.
My workflow includes audio preprocessing, noise handling, feature extraction (such as MFCCs and spectrograms), model development using deep learning or ML-based approaches, evaluation, and final delivery in your preferred output format. I focus on building models that are accurate, reliable, and practical for real-world deployment.
You will receive clean source code, trained models, output files (predictions, scores, or reports), and clear guidance on how to run or integrate the solution into your workflow. My goal is to deliver audio ML systems that are technically strong and easy to use.
My workflow includes audio preprocessing, noise handling, feature extraction (such as MFCCs and spectrograms), model development using deep learning or ML-based approaches, evaluation, and final delivery in your preferred output format. I focus on building models that are accurate, reliable, and practical for real-world deployment.
You will receive clean source code, trained models, output files (predictions, scores, or reports), and clear guidance on how to run or integrate the solution into your workflow. My goal is to deliver audio ML systems that are technically strong and easy to use.
Machine Learning Tools
NumPy, Python, Python Scikit-Learn, PyTorch, scikit-learn, TensorFlowWhat's included
| Service Tiers |
Starter
$150
|
Standard
$350
|
Advanced
$650
|
|---|---|---|---|
| Delivery Time | 4 days | 8 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 4 | 6 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
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ML
Maggie L.
Mar 27, 2026
Applied ML / AI Engineer
About Ratnapriya
Senior Data Scientist | Machine Learning, NLP & Predictive Modeling
Ahmedabad, India - 7:30 am local time
What I deliver:
• Text summarization, sentiment, topic & NLP pipelines (transformers + classical)
• Speech emotion & audio analysis (MFCCs, spectrograms)
• Predictive models & lead scoring (XGBoost, tabular pipelines)
• Clustering/EDA for root-cause analysis and automation workflows
I focus on clear results, reproducible code, and outputs non-technical teams can use. I can start immediately.
Steps for completing your project
After purchasing the project, send requirements so Ratnapriya can start the project.
Delivery time starts when Ratnapriya receives requirements from you.
Ratnapriya works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement & Audio Review
I review your audio files, target labels, output expectations, and success criteria to clearly define the speech or audio ML task.
Audio Preprocessing & Feature Extraction
I clean audio signals, handle noise, segment files if needed, and extract features such as MFCCs or spectrograms.