Update README.md

README.md

@@ -5,7 +5,7 @@ tags:
 - Spoken language understanding
 license: "CC0"
 datasets:
-- Timers and Such
+- Fluent Speech Commands
 metrics:
 - Accuracy
 
@@ -14,26 +14,29 @@ metrics:
 <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/>
 
+# Fluent Speech Commands
+The dataset contains real recordings that define a simple spoken language understanding task. You can download it from [here](https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/). 
+The Fluent Speech Commands dataset contains 30,043 utterances from 97 speakers. It is recorded as 16 kHz single-channel .wav files each containing a single utterance used for controlling smart-home appliances or virtual assistant, for example, “put on the music” or “turn up the heat in the kitchen”. Each audio is labeled with three slots: action, object, and location. A slot takes on one of the multiple values: for instance, the “location” slot can take on the values “none”, “kitchen”, “bedroom”, or “washroom”. We refer to the combination of slot values as the intent of the utterance. For each intent, there are multiple possible wordings: for example, the intent {action: “activate”, object: “lights”, location: “none”} can be expressed as “turn on the lights”, “switch the lights on”, “lights on”, etc. The dataset has a total of 248 phrasing mapping to 31 unique intents.
 
-# 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`.
-
+# End-to-end SLU model for Fluent Speech Commands
+Attention-based RNN sequence-to-sequence model for the [Fluent Speech Commands](https://arxiv.org/pdf/1904.03670.pdf) dataset. 
+This model checkpoint achieves 99.6% accuracy on the test set.
 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:
+You can try the model on the `example_fsc.wav` file included here as follows:
 ```
-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")
+>>> from speechbrain.pretrained import EndToEndSLU
+>>> slu = EndToEndSLU.from_hparams("/network/tmp1/ravanelm/slu-direct-fluent-speech-commands-librispeech-asr")
+>>> # Text: "Please, turn on the light of the bedroom"
+>>> slu.decode_file("/network/tmp1/ravanelm/slu-direct-fluent-speech-commands-librispeech-asr/example_fsc.wav")
+'{"action:" "activate"| "object": "lights"| "location": "bedroom"}'
 ```
 ### 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).
+The model was trained with SpeechBrain (f1f421b3).
 To train it from scratch follows these steps:
 1. Clone SpeechBrain:
 ```bash
@@ -48,7 +51,7 @@ pip install -e .
 
 3. Run Training:
 ```
-cd  recipes/timers-and-such/direct
+cd recipes/fluent-speech-commands
 python train.py hparams/train.yaml --data_folder=your_data_folder
 ```
 
@@ -66,20 +69,25 @@ title = {SpeechBrain},
 year = {2021},
 publisher = {GitHub},
 journal = {GitHub repository},
-howpublished = {\url{https://github.com/speechbrain/speechbrain}},
+howpublished = {\\url{https://github.com/speechbrain/speechbrain}},
 }
 ```
 
-#### Referencing Timers and Such
+#### Referencing Fluent Speech Commands
 
 ```
-@misc{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},
-      eprint={2104.01604},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
+@inproceedings{fluent,
+  author    = {Loren Lugosch and
+               Mirco Ravanelli and
+               Patrick Ignoto and
+               Vikrant Singh Tomar and
+               Yoshua Bengio},
+  editor    = {Gernot Kubin and
+               Zdravko Kacic},
+  title     = {Speech Model Pre-Training for End-to-End Spoken Language Understanding},
+  booktitle = {Proc. of Interspeech},
+  pages     = {814--818},
+  year      = {2019},
 }
 ```