README.md

---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- audio-classification
language:
- fr
tags:
- speech
- education
- french
- whisper
- asr
- speech-recognition
- fine-tuning
size_categories:
- 1K<n<10K
---

# French Education Speech - Transcribed Dataset

High-quality French educational speech dataset transcribed with OpenAI Whisper API, prepared for training automatic speech recognition (ASR) models.

## Dataset Summary

This dataset contains 3,933 transcribed audio segments from the French educational domain, totaling approximately 12.82 hours of audio. All transcriptions were performed using OpenAI Whisper API (optimized Whisper-1 model) to ensure maximum accuracy, especially for educational terminology and acronyms.

- **Total segments**: 3,933 (3,720 train + 213 validation)
- **Total duration**: 12.82 hours (12.12h train + 0.70h validation)
- **Average segment duration**: 11.7 seconds
- **Language**: French
- **Domain**: Education (conferences, podcasts, courses, interviews)
- **Transcription quality**: High-precision commercial API (OpenAI Whisper)

## Dataset Structure

### Splits

- **train**: 3,720 segments (12.12 hours)
- **validation**: 213 segments (0.70 hours)

### Features

Each example contains:

- `id` (string): Unique segment identifier
- `audio` (Audio): Audio file at 16kHz sampling rate
- `text` (string): Transcribed text
- `duration` (float32): Duration in seconds
- `category` (string): Segment category (conferences, podcasts, cours, interviews)
- `quality` (string): Audio quality (clean, medium)
- `source` (string): Original source
- `speaker_role` (string): Speaker role (teacher, student, etc.)
- `domain` (string): Educational domain

## Dataset Creation Methodology

### Source Dataset

The dataset is based on `MEscriva/french-education-speech`, which contains 13,711 audio segments (12,988 train + 723 validation) totaling 16.57 hours of audio from the French educational domain.

### Transcription Process

#### 1. Model Selection

After evaluating multiple transcription options, OpenAI Whisper API was selected for transcription:

- **Reason**: Highest precision available, optimized version of Whisper
- **Advantages**: 
  - Superior accuracy for French language
  - Excellent handling of educational terminology and acronyms
  - Commercial-grade reliability
  - Better performance than open-source Whisper models

#### 2. Quality Filtering

To ensure transcription quality and minimize hallucinations, a minimum duration filter was applied:

- **Filter**: Segments >= 4.0 seconds
- **Rationale**: Shorter segments (< 4s) showed higher hallucination rates (e.g., YouTube-style end-of-video subtitles)
- **Result**: 3,990 segments >= 4.0s selected from original 13,711 segments

#### 3. Transcription Execution

The transcription process was executed systematically:

- **Tool**: Custom Python script (`transcribe_premium.py`)
- **API**: OpenAI Whisper API (model: whisper-1)
- **Language**: French (fr)
- **Process**:
  - Automatic resumption: Script could be stopped and resumed without data loss
  - Periodic saving: Every 50 transcriptions to prevent data loss
  - Error handling: Robust error handling for API failures
  - Progress tracking: Real-time progress monitoring

#### 4. Quality Control

##### Hallucination Detection

A systematic hallucination detection system was implemented:

- **Detection keywords**: Common YouTube-style phrases ("Sous-titres réalisés", "Merci d'avoir regardé", "n'oubliez pas de vous abonner", etc.)
- **Monitoring**: Real-time detection during transcription
- **Logging**: All detected hallucinations logged for analysis
- **Rate**: 0.93% hallucination rate detected (37 out of 3,970 segments)

##### Hallucination Removal

All detected hallucinations were removed from the final dataset:

- **Removed**: 35 hallucinations from train set, 2 from validation set
- **Final count**: 3,720 train segments, 213 validation segments
- **Quality assurance**: Manual verification confirmed removal of all hallucinated content

### Data Cleaning Pipeline

1. **Duration filtering**: Segments < 4.0s excluded
2. **Transcription**: OpenAI Whisper API transcription
3. **Hallucination detection**: Automated keyword-based detection
4. **Hallucination removal**: All detected hallucinations removed
5. **Validation**: Final dataset verified for quality

### Statistics

#### Original Dataset
- Total segments: 13,711
- Segments >= 4.0s: 3,990 (29.1%)
- Total duration: 16.57 hours

#### Final Dataset
- Total segments: 3,933 (28.7% of original)
- Segments >= 4.0s: 3,933 (100% of filtered)
- Total duration: 12.82 hours (77.4% of original duration)
- Hallucination rate: 0.93% (removed)

#### Quality Metrics
- Average segment duration: 11.7 seconds
- Average transcription length: 159 characters
- Audio quality distribution: 47.4% clean, 52.6% medium, 0% noisy
- Category distribution: 67.4% conferences, 30.0% podcasts, 2.6% courses

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("MEscriva/french-education-speech-transcribed")

# Access train and validation splits
train = dataset['train']
validation = dataset['validation']

# Example usage
print(train[0])
# {
#     'id': 'f7bc61a3091c0886646b4f80a388114f',
#     'audio': {'path': '...', 'array': [...], 'sampling_rate': 16000},
#     'text': "d'accessibilité.",
#     'duration': 4.95,
#     'category': 'conferences',
#     'quality': 'clean',
#     ...
# }
```

### Training an ASR Model

```python
from datasets import load_dataset

dataset = load_dataset("MEscriva/french-education-speech-transcribed")

# Use with transformers or other ASR training frameworks
# The dataset is ready for fine-tuning Whisper or other ASR models
```

## Dataset Characteristics

### Audio Quality
- **Sampling rate**: 16kHz
- **Format**: WAV
- **Quality**: 47.4% clean, 52.6% medium quality
- **No noisy segments**: All segments are clean or medium quality

### Content Distribution
- **Conferences**: 67.4% (primary domain)
- **Podcasts**: 30.0%
- **Courses**: 2.6%
- **Interviews**: <0.1%

### Speaker Roles
- Teachers, students, and educational professionals
- Various educational contexts and domains

## Limitations and Considerations

1. **Duration filter**: Only segments >= 4.0s are included. Shorter segments were excluded to minimize hallucinations.

2. **Domain specificity**: The dataset is focused on educational content. Performance may vary for other domains.

3. **Hallucination removal**: While hallucination rate is low (0.93%), some false positives may have been removed. Manual verification confirmed high quality.

4. **Audio paths**: Original audio files must be accessible. The dataset references local file paths that may need adjustment for different environments.

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{french_education_speech_transcribed_2024,
  title={French Education Speech - Transcribed Dataset},
  author={MEscriva},
  year={2024},
  url={https://huggingface.co/datasets/MEscriva/french-education-speech-transcribed},
  note={Transcribed with OpenAI Whisper API}
}
```

## Acknowledgments

- Source dataset: `MEscriva/french-education-speech`
- Transcription: OpenAI Whisper API
- Quality assurance: Systematic hallucination detection and removal

## License

CC-BY-4.0 (Creative Commons Attribution 4.0 International)

This dataset is derived from `MEscriva/french-education-speech` which uses CC-BY-4.0 license. The transcriptions are original work created using OpenAI Whisper API, but the audio content follows the same licensing terms as the source dataset.

## Contact

For questions or issues, please open an issue on the Hugging Face dataset repository.