README.md

---
language:
- de
thumbnail: null
tags:
- automatic-speech-recognition
- transducer
- Attention
- pytorch
- speechbrain
license: apache-2.0
datasets:
- common_voice
metrics:
- name: Test WER
  type: wer
  value: ' 15.25'
---

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<br/><br/>

# Transducer trained on CommonVoice 14.0 German (No LM)
This repository provides all the necessary tools to perform automatic speech
recognition 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 CER | Test WER | GPUs |
|:-------------:|:--------------:|:--------------:| :--------:|
| 15.08.23 | 5.43 | 15.25 | 1xV100 32GB |

## Credits
The model is provided by [vitas.ai](https://www.vitas.ai/).

## Pipeline description
This ASR system is composed of 2 different but linked blocks:

- Tokenizer (unigram) that transforms words into subword units and trained with
the train transcriptions (train.tsv) of CommonVoice (en).
- Transducers augment CTC by adding an autoregressive predictor and a join network.

The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.

## 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).

### Transcribing your own audio files (in German)

```python
from speechbrain.pretrained import EncoderDecoderASR
asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-transducer-commonvoice-14-de", savedir="pretrained_models/asr-transducer-commonvoice-14-de")
asr_model.transcribe_file("speechbrain/asr-transducer-commonvoice-14-de/example-de.wav")
```

### 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/CommonVoice/ASR/transducer
python train.py hparams/train_de.yaml --data_folder=your_data_folder
```

You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/jfge6ixbtoje64t/AADeAgL5un0A8uEjPSM84ex8a?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}
}
```