Create README.md

README.md

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+---
+license: cc-by-4.0
+language:
+- en
+- nl
+- de
+- pl
+metrics:
+- wer
+base_model:
+- nvidia/stt_en_fastconformer_ctc_large
+pipeline_tag: automatic-speech-recognition
+library_name: nemo
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- nemo
+- fastconformer
+- mixture-of-experts
+- multilingual
+- child-speech
+- adult-speech
+- inclusive-ai
+
+---
+
+# Inclusive ASR MoE
+
+This repository contains the final NVIDIA NeMo automatic speech recognition checkpoints developed by **Amelia Sasin** for the MSc thesis:
+
+> **Mixture-of-Experts for Age-Inclusive ASR: Reducing the Adult-Child Performance Gap in Multilingual Speech Recognition**
+> Delft University of Technology, 2026.
+
+The models investigate inclusive and robust speech recognition for adult and child speech using dense FastConformer and sparse Mixture-of-Experts architectures.
+
+Source code, training configurations, preprocessing scripts, evaluation scripts, and reproducibility instructions are available in the GitHub repository:
+
+`a-sasin/inclusive-asr-moe`
+
+## Base model
+
+All models were initialized directly or indirectly from:
+
+`nvidia/stt_en_fastconformer_ctc_large`
+
+The released checkpoints include further training, child-speech adaptation, multilingual training, and Mixture-of-Experts modifications performed by Amelia Sasin.
+
+These models are independent research derivatives and are not endorsed by NVIDIA.
+
+## Available checkpoints
+
+| Checkpoint                              | Language scope                 | Training population | Architecture                 |
+| --------------------------------------- | ------------------------------ | ------------------- | ---------------------------- |
+| `en_adult_fastconformer.nemo`           | English                        | Adult               | Dense FastConformer          |
+| `en_adult_moe.nemo`                     | English                        | Adult               | Mixture of Experts           |
+| `en_child_fastconformer.nemo`           | English                        | Child               | Dense FastConformer          |
+| `en_child_moe_lb_off.nemo`              | English                        | Child               | MoE, load balancing disabled |
+| `en_child_moe_lb_on.nemo`               | English                        | Child               | MoE, load balancing enabled  |
+| `multilingual_adult_fastconformer.nemo` | English, Dutch, German, Polish | Adult               | Dense FastConformer          |
+| `multilingual_adult_moe.nemo`           | English, Dutch, German, Polish | Adult               | Mixture of Experts           |
+| `multilingual_child_fastconformer.nemo` | English, Dutch, German, Polish | Child               | Dense FastConformer          |
+| `multilingual_child_moe_lb_off.nemo`    | English, Dutch, German, Polish | Child               | MoE, load balancing disabled |
+| `multilingual_child_moe_lb_on.nemo`     | English, Dutch, German, Polish | Child               | MoE, load balancing enabled  |
+
+## Downloading a checkpoint
+
+```python
+from huggingface_hub import hf_hub_download
+
+checkpoint_path = hf_hub_download(
+    repo_id="TUDelft/inclusive-asr-moe",
+    filename="en_adult_fastconformer.nemo",
+)
+```
+
+## Loading a checkpoint
+
+```python
+from nemo.collections.asr.models import ASRModel
+
+model = ASRModel.restore_from(
+    restore_path=checkpoint_path,
+)
+
+model.eval()
+transcriptions = model.transcribe(["example.wav"])
+print(transcriptions)
+```
+
+## Required NeMo fork
+
+The Mixture-of-Experts checkpoints require the custom NeMo fork developed for this work:
+
+`a-sasin/NeMo`
+
+This fork contains the sparse Mixture-of-Experts implementation used to train and restore the released MoE checkpoints.
+
+Install it from source:
+
+```bash
+git clone https://github.com/a-sasin/NeMo.git
+cd NeMo
+pip install -e ".[asr]"
+```
+
+The dense FastConformer checkpoints may work with standard NVIDIA NeMo, but using this fork is recommended for consistency with the training environment.
+
+### Reproducible installation
+
+For exact reproducibility, check out the same NeMo commit used during training:
+
+```bash
+git clone https://github.com/a-sasin/NeMo.git
+cd NeMo
+git checkout <NEMO_COMMIT_HASH>
+pip install -e ".[asr]"
+```
+
+
+The Mixture-of-Experts checkpoints may require the custom NeMo implementation documented in the accompanying GitHub repository.
+
+## Data
+
+The training and evaluation datasets, preprocessing procedures, manifest formats, and access instructions are documented in the accompanying GitHub repository and thesis.
+
+Raw speech datasets are not included in this model repository. Users are responsible for obtaining the datasets from their original providers and complying with their respective licenses and access conditions.
+
+## Intended use
+
+The checkpoints are intended for research involving:
+
+* Automatic speech recognition
+* Child and adult speech recognition
+* Multilingual ASR
+* Inclusive speech technology
+* Mixture-of-Experts architectures
+* Expert routing and load-balancing analysis
+
+## Limitations
+
+Recognition performance may vary depending on:
+
+* Speaker age
+* Accent and dialect
+* Language
+* Speech characteristics
+* Recording conditions
+* Background noise
+* Microphone quality
+* Speaking style
+* Domain-specific vocabulary
+
+The terms “adult” and “child” describe the training and evaluation data. These models are not age-classification systems.
+
+The models should not be treated as error-free or used without additional validation in medical, legal, emergency, educational-assessment, surveillance, or other high-stakes applications.
+
+## License
+
+### Model checkpoints
+
+The model checkpoints are released under the **Creative Commons Attribution 4.0 International license**.
+
+The checkpoints derive from NVIDIA’s `stt_en_fastconformer_ctc_large`, which is also distributed under CC BY 4.0.
+
+When redistributing or adapting these checkpoints, retain attribution to:
+
+* NVIDIA Corporation for the original checkpoint
+* Amelia Sasin for the subsequent training, architecture modifications, experiments, and released checkpoints
+
+### Source code
+
+Original source code authored for the thesis is licensed separately under the MIT License in the accompanying GitHub repository.
+
+Copyright © 2025 Amelia Sasin.
+
+## Citation
+
+```bibtex
+@mastersthesis{sasin2026inclusiveasr,
+  author = {Sasin, Amelia},
+  title = {Mixture-of-Experts for Age-Inclusive ASR: Reducing the Adult-Child Performance Gap in Multilingual Speech Recognition},
+  school = {Delft University of Technology},
+  year = {2026}
+}
+```
+---