https://github.com/analyticsinmotion/werx
🐍📦 Easy-to-use Python package for lightning-fast Word Error Rate analysis
https://github.com/analyticsinmotion/werx
asr automatic-speech-recognition levenshtein-distance metrics speech-to-text stt wer werx word-error-rate word-error-rate-calculator
Last synced: 4 months ago
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🐍📦 Easy-to-use Python package for lightning-fast Word Error Rate analysis
- Host: GitHub
- URL: https://github.com/analyticsinmotion/werx
- Owner: analyticsinmotion
- License: apache-2.0
- Created: 2025-05-05T12:08:26.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-19T01:20:59.000Z (5 months ago)
- Last Synced: 2025-05-25T12:50:07.513Z (5 months ago)
- Topics: asr, automatic-speech-recognition, levenshtein-distance, metrics, speech-to-text, stt, wer, werx, word-error-rate, word-error-rate-calculator
- Language: Python
- Homepage:
- Size: 227 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
⚡Lightning fast Word Error Rate Calculations
Meta
## What is WERx?
**WERx** is a high-performance Python package for calculating Word Error Rate (WER), built with Rust for unmatched speed, memory efficiency, and stability. WERx delivers accurate results with exceptional performance, making it ideal for large-scale evaluation tasks.
## 🚀 Why Use WERx?
⚡ **Blazing Fast:** Rust-powered core delivers outstanding performance, optimized for large datasets
🧩 **Robust:** Designed to handle edge cases gracefully, including empty strings and mismatched sequences
📐 **Insightful:** Provides rich word-level error breakdowns, including substitutions, insertions, deletions, and weighted error rates
🛡️ **Production-Ready:** Minimal dependencies, memory-efficient, and engineered for stability
## ⚙️ Installation
You can install WERx either with 'uv' or 'pip'.
### Using uv (recommended):
```bash
uv pip install werx
```
### Using pip:
```bash
pip install werx
```
## ✨ Usage
**Import the WERx package**
*Python Code:*
```python
import werx
```
### Examples:
### 1. Single sentence comparison
*Python Code:*
```python
wer = werx.wer('i love cold pizza', 'i love pizza')
print(wer)
```
*Results Output:*
```
0.25
```
### 2. Corpus level Word Error Rate Calculation
*Python Code:*
```python
ref = ['i love cold pizza','the sugar bear character was popular']
hyp = ['i love pizza','the sugar bare character was popular']
wer = werx.wer(ref, hyp)
print(wer)
```
*Results Output:*
```
0.2
```
### 3. Weighted Word Error Rate Calculation
*Python Code:*
```python
ref = ['i love cold pizza', 'the sugar bear character was popular']
hyp = ['i love pizza', 'the sugar bare character was popular']
# Apply lower weight to insertions and deletions, standard weight for substitutions
wer = werx.weighted_wer(
ref,
hyp,
insertion_weight=0.5,
deletion_weight=0.5,
substitution_weight=1.0
)
print(wer)
```
*Results Output:*
```
0.15
```
### 4. Complete Word Error Rate Breakdown
The `analysis()` function provides a complete breakdown of word error rates, supporting both standard WER and weighted WER calculations.
It delivers detailed, per-sentence metrics—including insertions, deletions, substitutions, and word-level error tracking, with the flexibility to customize error weights.
Results are easily accessible through standard Python objects or can be conveniently converted into Pandas and Polars DataFrames for further analysis and reporting.
#### 4a. Getting Started
*Python Code:*
```python
ref = ["the quick brown fox"]
hyp = ["the quick brown dog"]
results = werx.analysis(ref, hyp)
print("Inserted:", results[0].inserted_words)
print("Deleted:", results[0].deleted_words)
print("Substituted:", results[0].substituted_words)
```
*Results Output:*
```
Inserted Words : []
Deleted Words : []
Substituted Words: [('fox', 'dog')]
```
#### 4b. Converting Analysis Results to a DataFrame
*Note:* To use this module, you must have either `pandas` or `polars` (or both) installed.
*Install Pandas / Polars for DataFrame Conversion*
```python
uv pip install pandas
uv pip install polars
```
*Python Code:*
```python
ref = ["i love cold pizza", "the sugar bear character was popular"]
hyp = ["i love pizza", "the sugar bare character was popular"]
results = werx.analysis(
ref, hyp,
insertion_weight=2,
deletion_weight=2,
substitution_weight=1
)
```
We’ve created a special utility to make working with DataFrames seamless.
Just import the following helper:
```python
import werx
from werx.utils import to_polars, to_pandas
```
You can then easily convert analysis results to get output using **Polars**:
```python
# Convert to Polars DataFrame
df_polars = to_polars(results)
print(df_polars)
```
Alternatively, you can also use **Pandas** depending on your preference:
```python
# Convert to Pandas DataFrame
df_pandas = to_pandas(results)
print(df_pandas)
```
*Results Output:*
| wer | wwer | ld | n_ref | insertions | deletions | substitutions | inserted_words | deleted_words | substituted_words |
|--------|--------|-----|-------|------------|-----------|---------------|----------------|----------------|---------------------|
| 0.25 | 0.50 | 1 | 4 | 0 | 1 | 0 | [] | ['cold'] | [] |
| 0.1667 | 0.1667 | 1 | 6 | 0 | 0 | 1 | [] | [] | [('bear', 'bare')] |
## 📄 License
This project is licensed under the Apache License 2.0.