Update README.md

README.md

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 ---
+language:
+- fr
+thumbnail: null
+tags:
+- response-generation
+- gpt
+- pytorch
+- speechbrain
 license: apache-2.0
+datasets:
+- multiwoz
+metrics:
+- name: Test PPL
+  type: ppl
+  value: ' 17.58'
 ---
+
+<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
+<br/><br/>
+
+# GPT2 trained on MultiWOZ.2.1
+This repository provides all the necessary tools to perform response generation from an end-to-end system within
+SpeechBrain. For a better experience, we encourage you to learn more about
+[SpeechBrain](https://speechbrain.github.io). 
+The performance of the model is the following:
+
+| Release | Test PPL | Test BLEU 4 | GPUs |
+|:-------------:|:--------------:|:--------------:| :--------:|
+| 15.08.23 | 7.61 | 17.58 | 1xV100 32GB |
+
+## Credits
+The model is provided by [vitas.ai](https://www.vitas.ai/).
+
+## Pipeline description
+This dialouge system is composed of 2 different but linked blocks:
+
+- Pretrained GPT Tokenizer that transforms words into subwords.
+- GPT2LMHeadModel to generate the next sentence given the history of the dialogue.
+
+The system is trained with dialogue from MultiWOZ corpus.
+
+
+## Install SpeechBrain
+First of all, please install SpeechBrain with the following command:
+
+```
+pip install speechbrain
+```
+
+Please notice that we encourage you to read our tutorials and learn more about
+[SpeechBrain](https://speechbrain.github.io).
+
+### Generating your Own Dialuge
+
+```python
+from speechbrain.pretrained import ResponseGenerator
+res_gen_model = ResponseGenerator.from_hparams(source="speechbrain/MultiWOZ-GPT-Response_Generation", savedir="pretrained_models/MultiWOZ-GPT-Response_Generation", pymodule_file="custom.py")
+res_gen_model.generate_response()
+```
+
+### Inference on GPU
+
+To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
+
+## Parallel Inference on a Batch
+
+Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model.
+
+### Training
+
+The model was trained with SpeechBrain (986a2175).
+To train it from scratch follows these steps:
+
+1. Clone SpeechBrain:
+
+```bash
+git clone https://github.com/speechbrain/speechbrain/
+```
+
+2. Install it:
+
+```
+cd speechbrain
+pip install -r requirements.txt
+pip install -e .
+```
+
+3. Run Training:
+
+```
+cd recipes/MultiWOZ/response_generation
+pip install -r extra_requirements.txt
+python train_with_gpt.py hparams/train_gpt.yaml --data_folder=your_data_folder
+```
+
+You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/vm8f5iavohr4zz9/AACrkOxXuxsrvJy4Cjpih9bQa?dl=0)
+
+### Limitations
+
+The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
+
+# **About SpeechBrain**
+
+- Website: https://speechbrain.github.io/
+- Code: https://github.com/speechbrain/speechbrain/
+- HuggingFace: https://huggingface.co/speechbrain/
+
+# **Citing SpeechBrain**
+
+Please, cite SpeechBrain if you use it for your research or business.
+
+```bibtex
+@misc{speechbrain,
+  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
+  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
+  year={2021},
+  eprint={2106.04624},
+  archivePrefix={arXiv},
+  primaryClass={eess.AS},
+  note={arXiv:2106.04624}
+}
+```