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https://github.com/pwenker/pronunciation_trainer
This repository/app showcases how a phoneme-based pronunciation trainer (including personalized LLM-based feedback) overcomes the limitations of a grapheme-based approach.
https://github.com/pwenker/pronunciation_trainer
Last synced: 22 days ago
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This repository/app showcases how a phoneme-based pronunciation trainer (including personalized LLM-based feedback) overcomes the limitations of a grapheme-based approach.
- Host: GitHub
- URL: https://github.com/pwenker/pronunciation_trainer
- Owner: pwenker
- Created: 2024-05-10T11:52:34.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-05-11T12:43:33.000Z (9 months ago)
- Last Synced: 2025-01-02T01:06:48.481Z (23 days ago)
- Language: Python
- Homepage: https://pwenker-pronunciation-trainer.hf.space/
- Size: 1.59 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Pronunciation Trainer 🗣️
This repository/app showcases how a [phoneme-based pronunciation trainer](https://github.com/pwenker/pronunciation_trainer/blob/main/docs/phoneme_based_solution.md)
(including personalized LLM-based feedback) overcomes the limitations of a [grapheme-based approach](https://github.com/pwenker/pronunciation_trainer/blob/main/docs/grapheme_based_solution.md)
For convenience, you find a feature comparison overview of the two solutions below:
| Feature | Grapheme-Based Solution | Phoneme-Based Solution |
|-----------------------------------|----------------------------------------------------------|---------------------------------------------------------|
| **Input Type** | Text transcriptions of speech | Audio files and phoneme transcriptions |
| **Feedback Mechanism** | Comparison of grapheme sequences | Comparison of phoneme sequences and advanced LLM-based feedback |
| **Technological Approach** | Simple text comparison using `SequenceMatcher` | Advanced ASR models like Wav2Vec2 for phoneme recognition |
| **Feedback Detail** | Basic similarity score and diff | Detailed phoneme comparison, LLM-based feedback including motivational and corrective elements |
| **Error Sensitivity** | Sensitive to homophones and transcription errors | More accurate in capturing pronunciation nuances |
| **Suprasegmental Features** | Does not capture (stress, intonation) | Potentially captures through phoneme dynamics and advanced evaluation |
| **Personalization** | Limited to error feedback based on text similarity | Advanced personalization considering learner's native language and target language proficiency |
| **Scalability** | Easy to scale with basic text processing tools | Requires more computational resources for ASR and LLM processing |
| **Cost** | Lower, primarily involves basic computational resources | Higher, due to usage of advanced APIs and model processing |
| **Accuracy** | Lower, prone to misinterpretations of homophones | Higher, better at handling diverse pronunciation patterns (but LLM hallucinations) |
| **Feedback Quality** | Basic, often not linguistically rich | Rich, detailed, personalized, and linguistically informed |
| **Potential for Learning** | Limited to recognizing text differences | High, includes phonetic and prosodic feedback, as well as resource and practice recommendations |
## Quickstart 🚀
### 👉 Click here to try out the app directly:
[**Pronunciation Trainer App**](https://pwenker-pronunciation-trainer.hf.space/)
### 🔍 Inspect the code at:
- **GitHub:** [pwenker/pronunciation_trainer](https://github.com/pwenker/pronunciation_trainer)
- **Hugging Face Spaces:** [pwenker/pronunciation_trainer](https://huggingface.co/spaces/pwenker/pronunciation_trainer)
### 📚 Read about the pronunciation trainer:
1. [Grapheme-based Approach](https://github.com/pwenker/pronunciation_trainer/blob/main/docs/grapheme_based_solution.md)
2. [Phoneme-based Approach](https://github.com/pwenker/pronunciation_trainer/blob/main/docs/phoneme_based_solution.md)
## Local Deployment 🏠
### Prerequisites 📋
#### Rye 🌾
[Install `Rye`](https://rye-up.com/guide/installation/#installing-rye)
> Rye is a comprehensive tool designed for Python developers. It simplifies your workflow by managing Python installations and dependencies. Simply install Rye, and it takes care of the rest.
- Create a `.env` file in the `pronunciation_trainer` folder and add the following variable:
#### OPENAI API Key 🔑
```
OPENAI_API_KEY=... # Token for the OpenAI API
```
### Set-Up 🛠️
Clone the repository:
```
git clone [repository-url] # Replace [repository-url] with the actual URL of the repository
```
Navigate to the directory:
```
cd pronunciation_trainer
```
Create a virtual environment in `.venv` and synchronize the repo:
```
rye sync
```
For more details, visit: [Basics - Rye](https://rye-up.com/guide/basics/)
### Start the App 🌟
Launch the app using:
```
rye run python src/pronunciation_trainer/app.py
```
Then, open your browser and visit [http://localhost:7860](http://localhost:7860/) to start practicing!