README.md

---
dataset_info:
  pretty_name: LibriSpeech Evaluation Annotations
  tags:
    - automatic-speech-recognition
    - asr
    - evaluation
    - librispeech
  task_categories:
    - automatic-speech-recognition
  language:
    - en
  license: cc-by-4.0
---

# 📚 LibriSpeech Evaluation Annotations Dataset



### 📖 **Dataset Description**

This dataset contains **evaluation hypotheses and reference transcripts** for the [LibriSpeech ASR Corpus](https://www.openslr.org/12).  
It is designed for benchmarking Automatic Speech Recognition (ASR) models such as [OpenAI Whisper](https://github.com/openai/whisper) and [Faster-Whisper](https://github.com/guillaumekln/faster-whisper).

---

### 📦 **Dataset Highlights**
- 📚 Based on the official [LibriSpeech ASR Corpus](https://www.openslr.org/12/).
- 📈 Provides standardized **hypotheses** and **reference transcripts** for ASR evaluation.
- 📂 Includes pre-generated `.trn` files for multiple ASR models and versions.
- ✅ Ideal for benchmarking **Word Error Rate (WER)** and comparing ASR model performance.

---

### 📚 **Supported Tasks**
- ✔️ **Automatic Speech Recognition (ASR) Evaluation**
- ✔️ **Benchmarking Word Error Rate (WER)**
- ✔️ **Model Comparison Across Dataset Splits**

---

### 🌍 Languages

- English (`en`)

---

### 📂 Dataset Structure

```
librispeech-eval/
├── generate_csv.py
├── dataset.py
├── all_splits.csv 
├── test-clean/
│   ├── test-clean.ref.trn
│   └── test-clean.hyp.whisper-base-v20240930.trn
├── test-other/
│   ├── test-other.ref.trn
│   └── test-other.hyp.whisper-base-v20240930.trn
├── dev-clean/
│   ├── dev-clean.ref.trn
│   └── dev-clean.hyp.whisper-base-v20240930.trn
└── dev-other/
    ├── dev-other.ref.trn
    └── dev-other.hyp.whisper-base-v20240930.trn

```

---

### 📖 Usage Example

```python
from datasets import load_dataset
import werpy
import werx

# 📥 Load the consolidated CSV from the Hugging Face Hub
dataset = load_dataset(
    "analyticsinmotion/librispeech-eval",
    data_files="all_splits.csv",
    split="train"
)

# 📄 Specify which split and model/version to evaluate
split = "test-clean"
model_name = "whisper-base"
model_version = "v20240930"

# 📚 Filter references and hypotheses for the chosen split/model/version
filtered = dataset.filter(
    lambda x: x["split"] == split and
              x["model_name"] == model_name and
              x["model_version"] == model_version
)

references = [row["reference"] for row in filtered]
hypotheses = [row["hypothesis"] for row in filtered]

# ✅ Normalize using werpy
normalized_refs = [werpy.normalize(ref) for ref in references]
normalized_hyps = [werpy.normalize(hyp) for hyp in hypotheses]

# 📈 Compute WER directly using werx
final_wer = werx.wer(normalized_refs, normalized_hyps)

print(f"{model_name} WER (normalized) on {split}: {final_wer:.2%}")

```

#### 🏃 Example Output

```
README.md: 100%
5.66k/5.66k [00:00<00:00, 839kB/s]
all_splits.csv: 100%
2.65M/2.65M [00:01<00:00, 2.50MB/s]
Generating train split: 
11126/0 [00:00<00:00, 145686.84 examples/s]
Filter: 100%
11126/11126 [00:00<00:00, 123119.41 examples/s]
whisper-base WER (normalized) on test-clean: 5.96%
```

---

### 📄 Generating the Consolidated CSV

You can generate or update `all_splits.csv` at any time using the included script:

```bash
python generate_csv.py
```

- This script automatically scans available dataset splits and hypothesis files.

- It will generate a consolidated CSV file at librispeech-eval/all_splits.csv.

- The CSV makes it easier to load and analyze the dataset programmatically.

---

### 📄 CSV Columns (`all_splits.csv`)

| Column         | Description                                |
|----------------|--------------------------------------------|
| `split`        | Dataset split (e.g., `test-clean`, `test-other`) |
| `hypothesis`   | Predicted transcript                      |
| `reference`    | Ground truth transcript                   |
| `model_name`   | ASR model name (e.g., `whisper-base`)     |
| `model_version`| ASR model version (e.g., `v20240930`)     |

---

### 📅 Dataset Splits

| Split Name    | Type            | Data Characteristics      | Samples | Duration (Hours) | Suitable For                                |
|----------------|-----------------|---------------------------|---------|------------------|----------------------------------------------|
| `test-clean`  | Test Set        | Clean, high-quality audio | 2,620   | 5.4              | Evaluating model **performance** under ideal conditions |
| `test-other`  | Test Set        | Noisy, challenging audio  | 2,939   | 5.1              | Evaluating model **robustness** to challenging/noisy environments |
| `dev-clean`   | Validation Set  | Clean, high-quality audio | 2,703   | 5.4              | **Hyperparameter tuning** and validation under ideal conditions |
| `dev-other`   | Validation Set  | Noisy, challenging audio  | 2,864   | 5.3              | **Stress-testing** during validation under difficult conditions |


---

### 📄 License

This dataset is licensed under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).

- You are free to **share** and **adapt** the data, provided appropriate credit is given.
- The original audio and official transcripts remain under the [LibriSpeech License](https://www.openslr.org/12/).

---

### 📢 Citation

If you use this dataset, please cite the original LibriSpeech paper:

```
@inproceedings{panayotov2015librispeech,
  title={Librispeech: An ASR corpus based on public domain audio books},
  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
  booktitle={ICASSP},
  pages={5206--5210},
  year={2015},
  organization={IEEE}
}
```