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---
language:
- en
library_name: nemo
datasets:
- SLURP
thumbnail: null
tags:
- spoken-language-understanding
- speech-intent-classification
- speech-slot-filling
- SLURP
- Conformer
- Transformer
- pytorch
- NeMo
license: cc-by-4.0
model-index:
- name: slu_conformer_transformer_large_slurp
results:
- task:
name: Speech Slot Filling
type: slot-filling
dataset:
name: SLURP
type: slurp
split: test
metrics:
- name: F1
type: f1
value: 82.27
- task:
name: Speech Intent Classification
type: intent-classification
dataset:
name: SLURP
type: slurp
split: test
metrics:
- name: Intent Accuracy
type: acc
value: 90.14
---
# NeMo End-to-End Speech Intent Classification and Slot Filling
## Model Overview
This model performs joint intent classification and slot filling, directly from audio input. The model treats the problem as an audio-to-text problem, where the output text is the flattened string representation of the semantics annotation. The model is trained on the SLURP dataset [1].
## Model Architecture
The model is has an encoder-decoder architecture, where the encoder is a Conformer-Large model [2], and the decoder is a three-layer Transformer Decoder [3]. We use the Conformer encoder pretrained on NeMo ASR-Set (details [here](https://ngc.nvidia.com/models/nvidia:nemo:stt_en_conformer_ctc_large)), while the decoder is trained from scratch. A start-of-sentence (BOS) and an end-of-sentence (EOS) tokens are added to each sentence. The model is trained end-to-end by minimizing the negative log-likelihood loss with label smoothing and teacher forcing. During inference, the prediction is generated by beam search, where a BOS token is used to trigger the generation process.
## Training
The NeMo toolkit [4] was used for training the models for around 100 epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/slu/slurp/run_slurp_train.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/slu/slurp/configs/conformer_transformer_large_bpe.yaml).
The tokenizers for these models were built using the semantics annotations of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). We use a vocabulary size of 58, including the BOS, EOS and padding tokens.
### Datasets
The model is trained on the combined real and synthetic training sets of the SLURP dataset.
## Performance
| | | | | **Intent (Scenario_Action)** | | **Entity** | | | **SLURP Metrics** | |
|-------|--------------------------------------------------|----------------|--------------------------|------------------------------|---------------|------------|--------|--------------|-------------------|---------------------|
|**Version**| **Model** | **Params (M)** | **Pretrained** | **Accuracy** | **Precision** | **Recall** | **F1** | **Precsion** | **Recall** | **F1** |
|1.13.0| Conformer-Transformer-Large | 127 | NeMo ASR-Set 3.0 | 90.14 | 78.95 | 74.93 | 76.89 | 84.31 | 80.33 | 82.27 |
|Baseline| Conformer-Transformer-Large | 127 | None | 72.56 | 43.19 | 43.5 | 43.34 | 53.59 | 53.92 | 53.76 |
NoDuring inference, we use beam size of 32, and a temperature of 1.25.
## How to Use this Model
The model is available for use in the NeMo toolkit [3], and can be used on another dataset with the same annotation format.
### Automatically load the model from NGC
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.SLUIntentSlotBPEModel.from_pretrained(model_name="slu_conformer_transformer_large_slurp")
```
### Predict intents and slots with this model
```shell
python [NEMO_GIT_FOLDER]/examples/slu/speech_intent_slot/eval_utils/inference.py \
pretrained_name="slu_conformer_transformer_slurp" \
audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" \
sequence_generator.type="<'beam' OR 'greedy' FOR BEAM/GREEDY SEARCH>" \
sequence_generator.beam_size="<SIZE OF BEAM>" \
sequence_generator.temperature="<TEMPERATURE FOR BEAM SEARCH>"
```
### Input
This model accepts 16000 Hz Mono-channel Audio (wav files) as input.
### Output
This model provides the intent and slot annotaions as a string for a given audio sample.
## Limitations
Since this model was trained on only the SLURP dataset [1], the performance of this model might degrade on other datasets.
## References
[1] [SLURP: A Spoken Language Understanding Resource Package](https://arxiv.org/abs/2011.13205)
[2] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
[3] [Attention Is All You Need](https://arxiv.org/abs/1706.03762?context=cs)
[4] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
## Licence
License to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).
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