Datasets:

MEscriva
/

french-education-speech

	---
	license: mit
	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

	MIT License

	## Contact

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