Gilbert-FR-Source is the foundational baseline model for the Gilbert research project, a comprehensive initiative focused on developing state-of-the-art automatic speech recognition (ASR) systems optimized for French language applications. This model serves as the frozen reference point for all subsequent research, fine-tuning, and development work within the Gilbert ecosystem.

Important Notice on Intellectual Property:

This baseline model (MEscriva/gilbert-fr-source) is distributed under the MIT License, allowing research and commercial use.
All derivative models, fine-tuned variants, and specialized models developed from this baseline as part of the Gilbert project are the exclusive intellectual property of Lexia France.
While this baseline can be used freely under MIT terms, any models built upon it for the Gilbert project are proprietary and subject to separate licensing terms.

Research Context

The Gilbert project is a systematic research and development effort aimed at creating highly specialized ASR systems for:

Professional meeting transcription (hybrid and remote meetings)
Long-form multi-speaker discourse (30-120 minute sessions)
Institutional environments (education, public sector, healthcare)
Constrained audio conditions (telephony, VoIP, low signal-to-noise ratio)
Sociolinguistic diversity (African, Canadian, Belgian, and other French accents)

This baseline model provides the controlled starting point for all experimental work, ensuring reproducibility and enabling fair comparison across different research directions.

Model Details

Architecture

Base Model: OpenAI Whisper Large V3
Fine-tuning: Optimized for French language performance
Framework: Compatible with Hugging Face Transformers, OpenAI Whisper, CTranslate2, ONNX Runtime, and MLX
Model Size: ~3.2 GB (full precision)

Key Characteristics

Language: French (primary), with multilingual capabilities
Context Length: Long-form audio support (up to 30 minutes per segment)
Output: Text transcription with word-level timestamps
Performance: Optimized for French speech recognition accuracy

Intended Use

Research and Development

This model is intended for:

Research Baseline: Use as a reference point for ASR research and experimentation
Comparative Studies: Benchmark against this baseline when evaluating new architectures or training strategies
Fine-tuning Foundation: Use as a starting point for domain-specific fine-tuning (subject to Gilbert project IP terms)
Educational Purposes: Learning and understanding ASR model behavior

Production Use

While this baseline model can be used directly, production deployments should use specialized Gilbert models that are optimized for specific use cases and domains. Contact the Gilbert team for production-grade models.

Performance Benchmarks

Reference Results

The following WER (Word Error Rate) scores serve as baseline reference for future Gilbert model development:

Dataset	WER	Notes
MLS (FR)	3.98%	Multilingual LibriSpeech French
Common Voice FR (v13.0)	7.28%	Diverse French speech
VoxPopuli (FR)	8.91%	European Parliament speeches
Fleurs (FR)	4.84%	FLORES evaluation
African Accented French	4.20%	Regional accent evaluation

Note: These results represent the upper bound before targeted fine-tuning. Future Gilbert variants will be evaluated against these baselines to measure improvement.

Usage

Installation

pip install transformers torch torchaudio librosa soundfile

Basic Usage with Transformers

python

from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
import torch

model_id = "MEscriva/gilbert-fr-source"
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if device == "cuda" else torch.float32

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id,
    torch_dtype=torch_dtype,
    low_cpu_mem_usage=True
)
model.to(device)

# Process audio
audio_path = "your_audio.wav"
inputs = processor(audio_path, return_tensors="pt", sampling_rate=16000)
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.no_grad():
    generated_ids = model.generate(
        inputs["input_features"],
        language="fr",
        task="transcribe"
    )

transcription = processor.batch_decode(
    generated_ids,
    skip_special_tokens=True
)[0]

Usage with OpenAI Whisper

python

import whisper

# Load the model
model = whisper.load_model("large-v3")

# Transcribe French audio
result = model.transcribe(
    "audio.wav",
    language="fr",
    task="transcribe"
)

print(result["text"])

Research Methodology

Baseline Purpose

This model serves as:

Frozen Reference: Weights remain unchanged to ensure consistent baseline comparisons
Reproducibility Anchor: All experiments reference this exact checkpoint
Version Control: Future Gilbert models explicitly reference this baseline version for traceability

Evaluation Standards

WER Calculation: Standard normalization (lowercasing, punctuation removal)
Metrics: Word Error Rate (WER), Character Error Rate (CER), BLEU score
Advanced Metrics: Speaker-attributed WER (SA-WER), long-context stability (internal research)

Versioning

Current Version: 0.1 (Research Baseline)
Future Versions: All Gilbert model variants will reference this baseline version

Limitations

This baseline model inherits known limitations from Whisper and the underlying training data:

Overlapping Speech: Sensitivity to simultaneous speakers
Long-form Decoding: Occasional hallucinations in very long audio segments
Domain Shift: Suboptimal performance on spontaneous dialogue without fine-tuning
Accent Distribution: Potential biases related to accent representation in training data
Telephony Bandwidth: Suboptimal performance on narrowband (8 kHz) audio without adaptation

Understanding and quantifying these limitations is a core objective of the Gilbert research roadmap.

Future Research Directions

The following specialized models will be developed as independent checkpoints from this baseline:

Planned Gilbert Models

Gilbert-FR-Longform-v1
- Optimized for long meetings (30-120 minutes)
- Multi-speaker interaction handling
- Discourse-level context stability
Gilbert-FR-Accents-v1
- Robustness to regional and international French accents
- African, Canadian, Belgian accent optimization
Gilbert-FR-Telephone-v1
- Optimized for 8 kHz VoIP/call-center speech
- Narrowband audio adaptation
Gilbert-Multilingual-v1
- Extended cross-lingual performance
- Optimized French anchors with multilingual support

All future Gilbert models are the exclusive intellectual property of Lexia France and will include detailed evaluation reports adhering to research reproducibility standards.

Intellectual Property and Licensing

License for This Baseline

This baseline model (MEscriva/gilbert-fr-source) is distributed under the MIT License, allowing:

✅ Commercial use
✅ Modification
✅ Distribution
✅ Private use
✅ Patent use

See the LICENSE file for full terms.

Intellectual Property Notice

Important: While this baseline model is available under MIT License:

All derivative models, fine-tuned variants, and specialized models developed as part of the Gilbert project are the exclusive intellectual property of Lexia France.
Use of this baseline for Gilbert project development implies acceptance of these IP terms.
Commercial use of Gilbert project derivatives requires separate licensing agreements.

For licensing inquiries regarding Gilbert project models, contact: mathis@lexiapro.fr

Citation

If you use this baseline model in your research, please cite:

bibtex

@software{gilbert_fr_source_2024,
  title={Gilbert-FR-Source: Research Baseline for French Automatic Speech Recognition},
  author={MEscriva and Lexia France},
  year={2024},
  url={https://huggingface.co/MEscriva/gilbert-fr-source},
  version={0.1},
  note={Research baseline for the Gilbert project}
}

Acknowledgments

This baseline model is based on:

OpenAI Whisper Large V3 (MIT License)
bofenghuang/whisper-large-v3-french (French fine-tuning)

We acknowledge the contributions of the open-source community and the original Whisper research team.

Contact

For research collaboration, evaluation access, or technical inquiries:

Website: https://gilbert-assistant.fr
Email: mathis@lexiapro.fr
Repository: https://huggingface.co/MEscriva/gilbert-fr-source

Changelog

Version 0.1 (2024-12-19)

Initial research baseline release
Based on Whisper Large V3 with French optimization
Established as frozen reference point for Gilbert project
Documentation of baseline performance metrics