Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- speech
+- automatic-speech-recognition
+- whisper
+- multilingual
+- fine-tuned
+- mlc-slm
+- speaker-diarization
+- meeting-transcription
+- DiCoW
+- BUT-FIT
 pipeline_tag: automatic-speech-recognition
 license: apache-2.0
+datasets:
+- microsoft/NOTSOFAR
+- edinburghcstr/ami
 ---
 
-# Model Card for Model ID
+# DiCoW\_v3\_MLC — BUT-FIT Model for MLC-SLM Challenge
 
-This repository contains the model described in the paper [BUT System for the MLC-SLM Challenge](https://huggingface.co/papers/2506.13414).
+This repository contains the **DiCoW\_v3\_MLC** model developed by [BUT Speech@FIT](https://github.com/BUTSpeechFIT) for the [MLC-SLM Challenge](https://www.nexdata.ai/competition/mlc-slm). 
+Diarization-Conditioned Whisper (DiCoW) is a novel approach to target-speaker ASR that leverages speaker diarization outputs as conditioning information.
 
-Code: https://github.com/mubingshen/MLC-SLM-Baseline
+The model is described in detail in the following papers:
 
-<!-- Provide a quick summary of what the model is/does. -->
+* 📰 **Journal paper (main DiCoW paper):** [DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech Recognition](https://authors.elsevier.com/a/1lI9m_K8BYumVY)
+* 📰 **ICASSP paper (initial DiCoW experiments):** [Target Speaker ASR with Whisper](https://ieeexplore.ieee.org/document/10887683)
+* 📰 **MLC-SLM Challenge submission paper:** [BUT System for the MLC-SLM Challenge](https://www.arxiv.org/abs/2506.13414)
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** apache-2.0
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+## Model Summary
 
-## Evaluation
+The model is based on **Whisper large-v3-turbo**, initially trained on:
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+* **NOTSOFAR-1**
+* **AMI** Meeting Corpus
+* **Libri2Mix** dataset
 
-### Testing Data, Factors & Metrics
+It is then fine-tuned on the **MLC-SLM dataset** as part of the MLC-SLM Challenge.
 
-#### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+## Model Details
 
-[More Information Needed]
+* **Developed by:** BUT Speech\@FIT, Brno University of Technology
+* **Model type:** Whisper large-v3-turbo + DiCoW composition
+* **Language(s):** Multilingual (primarily English, but supports multiple languages)
+* **License:** apache-2.0
+* **Fine-tuned from:** openai/whisper-large-v3-turbo
+* **Challenge:** MLC-SLM (Multilingual Conversational Speech Language Model)
+
+## Model Sources
+
+* **Training Code:** [TS-ASR-Whisper GitHub](https://github.com/BUTSpeechFIT/TS-ASR-Whisper)
+* **Inference Code & DiCoW framework:** [DiCoW GitHub](https://github.com/BUTSpeechFIT/DiCoW)
+
+
+## Getting Started
+
+```python
+from transformers import AutoModelForSpeechSeq2Seq
+
+MODEL_NAME = "BUT-FIT/DiCoW_v3_MLC"
+dicow = AutoModelForSpeechSeq2Seq.from_pretrained(MODEL_NAME, trust_remote_code=True)
+```
+
+For detailed inference and full pipelines, refer to:
+👉 [DiCoW GitHub inference repo](https://github.com/BUTSpeechFIT/DiCoW)
+
+
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@article{POLOK2026101841,
+    title = {DiCoW: Diarization-conditioned Whisper for target speaker automatic speech recognition},
+    journal = {Computer Speech & Language},
+    volume = {95},
+    pages = {101841},
+    year = {2026},
+    issn = {0885-2308},
+    doi = {https://doi.org/10.1016/j.csl.2025.101841},
+    url = {https://www.sciencedirect.com/science/article/pii/S088523082500066X},
+    author = {Alexander Polok and Dominik Klement and Martin Kocour and Jiangyu Han and Federico Landini and Bolaji Yusuf and Matthew Wiesner and Sanjeev Khudanpur and Jan Černocký and Lukáš Burget},
+    keywords = {Diarization-conditioned Whisper, Target-speaker ASR, Speaker diarization, Long-form ASR, Whisper adaptation},
+}
+
+@INPROCEEDINGS{10887683,
+    author={Polok, Alexander and Klement, Dominik and Wiesner, Matthew and Khudanpur, Sanjeev and Černocký, Jan and Burget, Lukáš},
+    booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
+    title={Target Speaker ASR with Whisper}, 
+    year={2025},
+    volume={},
+    number={},
+    pages={1-5},
+    keywords={Transforms;Signal processing;Transformers;Acoustics;Speech processing;target-speaker ASR;diarization conditioning;multi-speaker ASR;Whisper},
+    doi={10.1109/ICASSP49660.2025.10887683}
+}
+
+@misc{polok2025mlcslmchallenge,
+    title={BUT System for the MLC-SLM Challenge}, 
+    author={Alexander Polok and Jiangyu Han and Dominik Klement and Samuele Cornell and Jan Černocký and Lukáš Burget},
+    year={2025},
+    eprint={2506.13414},
+    archivePrefix={arXiv},
+    primaryClass={eess.AS},
+    url={https://arxiv.org/abs/2506.13414}, 
+}
+```
+
+## Contact
+
+For questions or collaborations:
+**BUT Speech\@FIT, Brno University of Technology**
+GitHub: [BUTSpeechFIT](https://github.com/BUTSpeechFIT)