You will get I will build an automatic speech recognition(ASR) model to transcribe audio
Project details
Accurately transcribe audio to text with a custom ASR model
I help businesses, developers, and content creators convert spoken audio into reliable text using machine learning-based Automatic Speech Recognition (ASR).
This service includes:
✔️ Audio data preprocessing
✔️ Feature extraction (MFCC, spectrograms)
✔️ Training speech recognition models
✔️ Evaluation of accuracy and word error rate (WER)
✔️ Output text files or structured transcripts
✔️ Optional export formats (text, json, csv)
What you will receive:
• Clean, reproducible Python code
• A working speech-to-text model
• Sample transcripts
• Performance report
Business value:
• Automate transcription workflows
• Reduce manual effort
• Improve accessibility
• Support voice interfaces
Ready to transcribe your audio? Send me a sample and let's start!
I help businesses, developers, and content creators convert spoken audio into reliable text using machine learning-based Automatic Speech Recognition (ASR).
This service includes:
✔️ Audio data preprocessing
✔️ Feature extraction (MFCC, spectrograms)
✔️ Training speech recognition models
✔️ Evaluation of accuracy and word error rate (WER)
✔️ Output text files or structured transcripts
✔️ Optional export formats (text, json, csv)
What you will receive:
• Clean, reproducible Python code
• A working speech-to-text model
• Sample transcripts
• Performance report
Business value:
• Automate transcription workflows
• Reduce manual effort
• Improve accessibility
• Support voice interfaces
Ready to transcribe your audio? Send me a sample and let's start!
Machine Learning Tools
ChatGPT, Keras, MLflow, NLTK, NumPy, NVIDIA AI Platform, OpenCV, pandas, Python, PyTorch, scikit-learn, SciPy, TensorFlow, Theano, Word2vecWhat's included
| Service Tiers |
Starter
$20
|
Standard
$50
|
Advanced
$90
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 1 | 3 | 5 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 2 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code |
Frequently asked questions
About Christian Junior
Machine Learning Engineer|Predictive Modeling & Data Analysis (Python)
Yaounde, Cameroon - 1:57 pm local time
Are you looking to turn your data into accurate predictions and actionable insights?
I am a Machine Learning Engineer specialized in predictive modeling and data-driven solutions. I help businesses:
✔️ Build classification and regression models
✔️ Predict customer churn and sales
✔️ Segment customers using clustering
✔️ Improve model accuracy and performance
✔️ Clean, analyze, and structure datasets
Recent Projects
- Telco Churn Prediction (Classification models, feature engineering, evaluation)
- Sales Forecasting using ML techniques
- Customer Segmentation with K-Means
- Deep learning experiments (CNN, RNN, NLP models)
Technical Stack
- Python (Pandas, NumPy, Scikit-learn, PyTorch)
- SQL (MySQL, PostgreSQL)
- Data visualization (Matplotlib, Seaborn, Power BI)
- Model evaluation & optimization
My Approach
- Clear understanding of business goals
- Structured data analysis
- Clean and reproducible code
- Clear interpretation of results
I focus on delivering practical, reliable, and well-documented solutions.
Let’s discuss your project.
Steps for completing your project
After purchasing the project, send requirements so Christian Junior can start the project.
Delivery time starts when Christian Junior receives requirements from you.
Christian Junior works on your project following the steps below.
Revisions may occur after the delivery date.
Preparation of audio data
Collect audio files (WAV, MP3, etc.) and check their quality. Clean up the signal (noise reduction, normalization). Extract acoustic features (MFCC, spectrograms).
Model design and training
Choose a suitable model (RNN, CNN, Transformer, CTC, etc.). Train the model on audio data and transcripts. Evaluate performance using metrics such as WER (Word Error Rate).