---
language:
- en
thumbnail: null
tags:
- response-generation
- gpt
- pytorch
- speechbrain
license: apache-2.0
datasets:
- multiwoz
metrics:
- name: Test PPL
  type: ppl
  value: ' 4.01'
---

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<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 |  4.01 | 2.54e-04 | 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:

```
git clone https://github.com/speechbrain/speechbrain
cd speechbrain
pip install -r requirements.txt
pip install -e .
```

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")
print("Hi,How could I help you today?", end="\n")
while True:
  turn = input()
  response = res_gen_model.generate_response(turn)
  print(response, end="\n")
```

### 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}
}
```