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# End-to-end SLU model for Timers and Such

Attention-based RNN sequence-to-sequence model for [Timers and Such](https://arxiv.org/abs/2104.01604) trained on the `train-real` subset. This model checkpoint achieves 86.7% accuracy on `test-real`.

The model uses an ASR model trained on LibriSpeech ([`speechbrain/asr-crdnn-rnnlm-librispeech`](https://huggingface.co/speechbrain/asr-crdnn-rnnlm-librispeech)) to extract features from the input audio, then maps these features to an intent and slot labels using a beam search. 

The dataset has four intents: `SetTimer`, `SetAlarm`, `SimpleMath`, and `UnitConversion`. Try testing the model by saying something like "set a timer for 5 minutes" or "what's 32 degrees Celsius in Fahrenheit?"

You can try the model on the `math.wav` file included here as follows:
```python
from speechbrain.pretrained import EndToEndSLU
slu = EndToEndSLU.from_hparams("speechbrain/slu-timers-and-such-direct-librispeech-asr")
slu.decode_file("speechbrain/slu-timers-and-such-direct-librispeech-asr/math.wav")
```

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 *decode_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *decode_batch* and *decode_batch*.

### Inference on GPU
To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.

### Training
The model was trained with SpeechBrain (d254489a).
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/timers-and-such/direct
python train.py hparams/train.yaml --data_folder=your_data_folder
```

You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/18c2anEv8hx-ZjmEN8AdUA8AZziYIidON?usp=sharing).

### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

#### Referencing SpeechBrain

```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}
}
```


#### Referencing Timers and Such

```
@article{lugosch2021timers,
      title={{Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers}}, 
      author={Lugosch, Loren and Papreja, Piyush and Ravanelli, Mirco and Heba, Abdelwahab and Parcollet, Titouan},
      year={2021},
      journal={NeurIPS Datasets and Benchmarks}
}
```

#### About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.

Website: https://speechbrain.github.io/

GitHub: https://github.com/speechbrain/speechbrain
{"language": "en", "license": "cc0-1.0", "tags": ["Spoken language understanding", "speechbrain"], "datasets": ["Timers-and-Such"], "metrics": ["Accuracy"]}
speechbrain Spoken language understanding en dataset:Timers-and-Such arxiv:2104.01604 arxiv:2106.04624 license:cc0-1.0 region:us
TAGS
#speechbrain #Spoken language understanding #en #dataset-Timers-and-Such #arxiv-2104.01604 #arxiv-2106.04624 #license-cc0-1.0 #region-us 


<iframe src="URL frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>


# End-to-end SLU model for Timers and Such

Attention-based RNN sequence-to-sequence model for Timers and Such trained on the 'train-real' subset. This model checkpoint achieves 86.7% accuracy on 'test-real'.

The model uses an ASR model trained on LibriSpeech ('speechbrain/asr-crdnn-rnnlm-librispeech') to extract features from the input audio, then maps these features to an intent and slot labels using a beam search. 

The dataset has four intents: 'SetTimer', 'SetAlarm', 'SimpleMath', and 'UnitConversion'. Try testing the model by saying something like "set a timer for 5 minutes" or "what's 32 degrees Celsius in Fahrenheit?"

You can try the model on the 'URL' file included here as follows:


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 *decode_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *decode_batch* and *decode_batch*.

### Inference on GPU
To perform inference on the GPU, add  'run_opts={"device":"cuda"}'  when calling the 'from_hparams' method.

### Training
The model was trained with SpeechBrain (d254489a).
To train it from scratch follows these steps:
1. Clone SpeechBrain:

2. Install it:


3. Run Training:


You can find our training results (models, logs, etc) here.

### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

#### Referencing SpeechBrain




#### Referencing Timers and Such



#### About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.

Website: URL

GitHub: URL
# End-to-end SLU model for Timers and Such

Attention-based RNN sequence-to-sequence model for Timers and Such trained on the 'train-real' subset. This model checkpoint achieves 86.7% accuracy on 'test-real'.

The model uses an ASR model trained on LibriSpeech ('speechbrain/asr-crdnn-rnnlm-librispeech') to extract features from the input audio, then maps these features to an intent and slot labels using a beam search. 

The dataset has four intents: 'SetTimer', 'SetAlarm', 'SimpleMath', and 'UnitConversion'. Try testing the model by saying something like "set a timer for 5 minutes" or "what's 32 degrees Celsius in Fahrenheit?"

You can try the model on the 'URL' file included here as follows:


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 *decode_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *decode_batch* and *decode_batch*. ### Inference on GPU
To perform inference on the GPU, add  'run_opts={"device":"cuda"}'  when calling the 'from_hparams' method. ### Training
The model was trained with SpeechBrain (d254489a).
To train it from scratch follows these steps:
1. Clone SpeechBrain:

2. Install it:


3. Run Training:


You can find our training results (models, logs, etc) here. ### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing SpeechBrain #### Referencing Timers and Such #### About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.

Website: URL

GitHub: URL
TAGS
#speechbrain #Spoken language understanding #en #dataset-Timers-and-Such #arxiv-2104.01604 #arxiv-2106.04624 #license-cc0-1.0 #region-us 
 # End-to-end SLU model for Timers and Such

Attention-based RNN sequence-to-sequence model for Timers and Such trained on the 'train-real' subset. This model checkpoint achieves 86.7% accuracy on 'test-real'.

The model uses an ASR model trained on LibriSpeech ('speechbrain/asr-crdnn-rnnlm-librispeech') to extract features from the input audio, then maps these features to an intent and slot labels using a beam search. 

The dataset has four intents: 'SetTimer', 'SetAlarm', 'SimpleMath', and 'UnitConversion'. Try testing the model by saying something like "set a timer for 5 minutes" or "what's 32 degrees Celsius in Fahrenheit?"

You can try the model on the 'URL' file included here as follows:


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 *decode_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *decode_batch* and *decode_batch*. ### Inference on GPU
To perform inference on the GPU, add  'run_opts={"device":"cuda"}'  when calling the 'from_hparams' method. ### Training
The model was trained with SpeechBrain (d254489a).
To train it from scratch follows these steps:
1. Clone SpeechBrain:

2. Install it:


3. Run Training:


You can find our training results (models, logs, etc) here. ### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing SpeechBrain #### Referencing Timers and Such #### About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.

Website: URL

GitHub: URL
passage: TAGS
#speechbrain #Spoken language understanding #en #dataset-Timers-and-Such #arxiv-2104.01604 #arxiv-2106.04624 #license-cc0-1.0 #region-us 
# End-to-end SLU model for Timers and Such

Attention-based RNN sequence-to-sequence model for Timers and Such trained on the 'train-real' subset. This model checkpoint achieves 86.7% accuracy on 'test-real'.

The model uses an ASR model trained on LibriSpeech ('speechbrain/asr-crdnn-rnnlm-librispeech') to extract features from the input audio, then maps these features to an intent and slot labels using a beam search. 

The dataset has four intents: 'SetTimer', 'SetAlarm', 'SimpleMath', and 'UnitConversion'. Try testing the model by saying something like "set a timer for 5 minutes" or "what's 32 degrees Celsius in Fahrenheit?"

You can try the model on the 'URL' file included here as follows:


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 *decode_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *decode_batch* and *decode_batch*.### Inference on GPU
To perform inference on the GPU, add  'run_opts={"device":"cuda"}'  when calling the 'from_hparams' method.### Training
The model was trained with SpeechBrain (d254489a).
To train it from scratch follows these steps:
1. Clone SpeechBrain:

2. Install it:


3. Run Training:


You can find our training results (models, logs, etc) here.### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.#### Referencing SpeechBrain