readme: ---
  language: "en"
  thumbnail:
  tags:
  - speechbrain
  - VAD
  - SAD
  - Voice Activity Detection
  - Speech Activity Detection
  - Speaker Diarization
  - pytorch
  - CRDNN
  - LibriSpeech
  - LibryParty
  datasets:
  - Urbansound8k
  metrics:
  - Accuracy
  
  ---
  
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  <br/><br/>
  
  # Voice Activity Detection with a (small) CRDNN model trained on Libriparty
  
  This repository provides all the necessary tools to perform voice activity detection with SpeechBrain using a model pretrained on Libriparty.
  
  The pre-trained system can process short and long speech recordings and outputs the segments where speech activity is detected. 
  The output of the system looks like this:
  
  ```
  segment_001  0.00  2.57 NON_SPEECH
  segment_002  2.57  8.20 SPEECH
  segment_003  8.20  9.10 NON_SPEECH
  segment_004  9.10  10.93 SPEECH
  segment_005  10.93  12.00 NON_SPEECH
  segment_006  12.00  14.40 SPEECH
  segment_007  14.40  15.00 NON_SPEECH
  segment_008  15.00  17.70 SPEECH
  ```
  
  The system expects input recordings sampled at 16kHz (single channel).
  If your signal has a different sample rate, resample it (e.g., using torchaudio or sox) before using the interface.
  
  For a better experience, we encourage you to learn more about
  [SpeechBrain](https://speechbrain.github.io).
  
  # Results
  The model performance on the LibriParty test set is:
  
  | Release | hyperparams file | Test Precision | Test Recall | Test F-Score | Model link | GPUs |
  |:-------------:|:---------------------------:| -----:| -----:| --------:| :-----------:|  :-----------:|
  | 2021-09-09 | train.yaml |  0.9518 | 0.9437 | 0.9477 | [Model](https://drive.google.com/drive/folders/1YLYGuiyuTH0D7fXOOp6cMddfQoM74o-Y?usp=sharing) | 1xV100 16GB
  
  
  ## Pipeline description
  This system is composed of a CRDNN that outputs posteriors probabilities with a value close to one for speech frames and close to zero for non-speech segments. 
  A threshold is applied on top of the posteriors to detect candidate speech boundaries. 
  
  Depending on the active options, these boundaries can be post-processed  (e.g, merging close segments, removing short segments, etc) to further improve the performance. See more details below.
  
  ## Install SpeechBrain
  
  ```
  pip install speechbrain
  ```
  
  Please notice that we encourage you to read our tutorials and learn more about
  [SpeechBrain](https://speechbrain.github.io).
  
  ### Perform Voice Activity Detection
  
  ```
  from speechbrain.pretrained import VAD
  
  VAD = VAD.from_hparams(source="speechbrain/vad-crdnn-libriparty", savedir="pretrained_models/vad-crdnn-libriparty")
  boundaries = VAD.get_speech_segments("speechbrain/vad-crdnn-libriparty/example_vad.wav")
  
  # Print the output
  VAD.save_boundaries(boundaries)
  ```
  The output is a tensor that contains the beginning/end second of each
  detected speech segment. You can save the boundaries on a file with:
  
  ```
  VAD.save_boundaries(boundaries, save_path='VAD_file.txt')
  ```
  
  Sometimes it is useful to jointly visualize the VAD output with the input signal itself. This is helpful to quickly figure out if the VAD is doing or not a good job.  
  
  To do it:
  
  ```
  import torchaudio
  upsampled_boundaries = VAD.upsample_boundaries(boundaries, 'pretrained_model_checkpoints/example_vad.wav')    
  torchaudio.save('vad_final.wav', upsampled_boundaries.cpu(), 16000) 
  ```  
  
  This creates a "VAD signal" with the same dimensionality as the original signal. 
  
  You can now open *vad_final.wav* and *pretrained_model_checkpoints/example_vad.wav* with software like audacity to visualize them jointly. 
  
  
  ### VAD pipeline details
  The pipeline for detecting the speech segments is the following:
  1. Compute posteriors probabilities at the frame level.
  2. Apply a threshold on the posterior probability.
  3. Derive candidate speech segments on top of that.
  4. Apply energy VAD within each candidate segment (optional). This might break down long sentences into short one based on the energy content.
  5. Merge segments that are too close.
  6. Remove segments that are too short.
  7. Double-check speech segments (optional). This could is a final check to make sure the detected segments are actually speech ones.
  
  We designed the VAD such that you can have access to all of these steps (this might help to debug):
  
  
  ```python
  from speechbrain.pretrained import VAD
  VAD = VAD.from_hparams(source="speechbrain/vad-crdnn-libriparty", savedir="pretrained_models/vad-crdnn-libriparty")
  
  # 1- Let's compute frame-level posteriors first
  audio_file = 'pretrained_model_checkpoints/example_vad.wav'
  prob_chunks = VAD.get_speech_prob_file(audio_file)
  
  # 2- Let's apply a threshold on top of the posteriors
  prob_th = VAD.apply_threshold(prob_chunks).float()
  
  # 3- Let's now derive the candidate speech segments
  boundaries = VAD.get_boundaries(prob_th)
  
  # 4- Apply energy VAD within each candidate speech segment (optional)
  
  boundaries = VAD.energy_VAD(audio_