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--- |
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language: "en" |
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thumbnail: |
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tags: |
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- embeddings |
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- Commands |
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- Keywords |
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- Keyword Spotting |
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- pytorch |
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- xvectors |
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- TDNN |
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- Command Recognition |
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license: "apache-2.0" |
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datasets: |
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- google speech commands |
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metrics: |
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- Accuracy |
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--- |
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<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> |
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<br/><br/> |
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# Command Recognition with xvector embeddings on Google Speech Commands |
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This repository provides all the necessary tools to perform command recognition with SpeechBrain using a model pretrained on Google Speech Commands. |
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You can download the dataset [here](https://www.tensorflow.org/datasets/catalog/speech_commands) |
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The dataset provides small training, validation, and test sets useful for detecting single keywords in short audio clips. The provided system can recognize the following 12 keywords: |
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``` |
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'yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go', 'unknown', 'silence' |
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``` |
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For a better experience, we encourage you to learn more about |
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[SpeechBrain](https://speechbrain.github.io). The given model performance on the test set is: |
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| Release | Accuracy(%) |
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|:-------------:|:--------------:| |
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| 06-02-21 | 98.14 | |
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## Pipeline description |
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This system is composed of a TDNN model coupled with statistical pooling. A classifier, trained with Categorical Cross-Entropy Loss, is applied on top of that. |
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## Install SpeechBrain |
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First of all, please install SpeechBrain with the following command: |
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``` |
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pip install speechbrain |
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``` |
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Please notice that we encourage you to read our tutorials and learn more about |
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[SpeechBrain](https://speechbrain.github.io). |
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### Perform Command Recognition |
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```python |
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import torchaudio |
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from speechbrain.pretrained import EncoderClassifier |
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classifier = EncoderClassifier.from_hparams(source="speechbrain/google_speech_command_xvector", savedir="pretrained_models/google_speech_command_xvector") |
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out_prob, score, index, text_lab = classifier.classify_file('speechbrain/google_speech_command_xvector/yes.wav') |
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print(text_lab) |
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out_prob, score, index, text_lab = classifier.classify_file('speechbrain/google_speech_command_xvector/stop.wav') |
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print(text_lab) |
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``` |
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### Inference on GPU |
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. |
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### Training |
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The model was trained with SpeechBrain (b7ff9dc4). |
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To train it from scratch follows these steps: |
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1. Clone SpeechBrain: |
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```bash |
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git clone https://github.com/speechbrain/speechbrain/ |
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``` |
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2. Install it: |
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``` |
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cd speechbrain |
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pip install -r requirements.txt |
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pip install -e . |
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``` |
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3. Run Training: |
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``` |
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cd recipes/Google-speech-commands |
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python train.py hparams/xvect.yaml --data_folder=your_data_folder |
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``` |
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You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1BKwtr1mBRICRe56PcQk2sCFq63Lsvdpc?usp=sharing). |
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### Limitations |
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The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. |
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#### Referencing xvectors |
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```@inproceedings{DBLP:conf/odyssey/SnyderGMSPK18, |
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author = {David Snyder and |
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Daniel Garcia{-}Romero and |
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Alan McCree and |
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Gregory Sell and |
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Daniel Povey and |
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Sanjeev Khudanpur}, |
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title = {Spoken Language Recognition using X-vectors}, |
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booktitle = {Odyssey 2018}, |
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pages = {105--111}, |
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year = {2018}, |
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} |
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``` |
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#### Referencing Google Speech Commands |
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```@article{speechcommands, |
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author = { {Warden}, P.}, |
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title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}", |
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journal = {ArXiv e-prints}, |
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archivePrefix = "arXiv", |
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eprint = {1804.03209}, |
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primaryClass = "cs.CL", |
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keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction}, |
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year = 2018, |
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month = apr, |
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url = {https://arxiv.org/abs/1804.03209}, |
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} |
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``` |
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#### Referencing SpeechBrain |
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``` |
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@misc{SB2021, |
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author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, |
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title = {SpeechBrain}, |
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year = {2021}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\\url{https://github.com/speechbrain/speechbrain}}, |
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} |
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``` |
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#### About SpeechBrain |
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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. |
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Website: https://speechbrain.github.io/ |
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GitHub: https://github.com/speechbrain/speechbrain |
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