readme: ---
  language: "en"
  thumbnail: 
  tags:
  - speechbrain
  - embeddings
  - Speaker
  - Verification
  - Identification
  - pytorch
  - ECAPA-TDNN
  license: "apache-2.0"
  datasets:
  - voxceleb
  metrics:
  - EER
  - Accuracy
  inference: true
  widget:
  - example_title: VoxCeleb Speaker id10003
    src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav
  - example_title: VoxCeleb Speaker id10004
    src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav
  ---
  
  # Speaker Identification with ECAPA-TDNN embeddings on Voxceleb
  
  This repository provides a pretrained ECAPA-TDNN model using SpeechBrain. The system can be used to extract speaker embeddings as well. Since we can't find any resource that has SpeechBrain or HuggingFace compatible checkpoints that has only been trained on VoxCeleb2 development data, so we decide to pre-train an ECAPA-TDNN system from scratch.
  
  # Pipeline description
  
  This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. 
  
  We use FBank (16kHz, 25ms frame length, 10ms hop length, 80 filter-bank channels) as the input features. It was trained using initial learning rate of 0.001 and batch size of 512 with cyclical learning rate policy (CLR) for 20 epochs on 4 A100 GPUs. We employ additive noises and reverberation from [MUSAN](http://www.openslr.org/17/) and [RIR](http://www.openslr.org/28/) datasets to enrich the supervised information. The pre-training progress takes approximately ten days for the ECAPA-TDNN model.
  
  # Performance
  
  **VoxCeleb1-O** is the original verification test set from VoxCeleb1 consisting of 40 speakers. All speakers with names starting with "E" are reserved for testing. **VoxCeleb1-E** uses the entire VoxCeleb1 dataset, covering 1251 speakers. **VoxCeleb1-H** is a hard version of evaluation set consisting of 552536 pairs with 1190 speakers with the same nationality and gender. There are 18 nationality-gender combinations each with at least 5 individuals. 
  
  | Splits | Backend | S-norm | EER(%) | minDCF(0.01) |
  |:-------------:|:--------------:|:--------------:|:--------------:|:--------------:|
  | VoxCeleb1-O | cosine | no | 1.29 | 0.13 |
  | VoxCeleb1-O | cosine | yes | 1.19 | 0.11 |
  | VoxCeleb1-E | cosine | no | 1.42 | 0.16  |
  | VoxCeleb1-E | cosine | yes | 1.31 | 0.14  |
  | VoxCeleb1-H | cosine | no | 2.66 | 0.26  |
  | VoxCeleb1-H | cosine | yes | 2.48 | 0.23  |
  
   - VoxCeleb1-O: includes 37611 test pairs with 40 speakers.
   - VoxCeleb1-E: includes 579818 test pairs with 1251 speakers.
   - VoxCeleb1-H: includes 550894 test pairs with 1190 speakers.
  
  # Compute the speaker embeddings
  
  The system is trained with recordings sampled at 16kHz (single channel).
  
  ```python
  import torch
  import torchaudio
  from speechbrain.pretrained.interfaces import Pretrained
  from speechbrain.pretrained import EncoderClassifier
  
  
  class Encoder(Pretrained):
  
      MODULES_NEEDED = [
          "compute_features",
          "mean_var_norm",
          "embedding_model"
      ]
  
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
  
      def encode_batch(self, wavs, wav_lens=None, normalize=False):
          # Manage single waveforms in input
          if len(wavs.shape) == 1:
              wavs = wavs.unsqueeze(0)
  
          # Assign full length if wav_lens is not assigned
          if wav_lens is None:
              wav_lens = torch.ones(wavs.shape[0], device=self.device)
  
          # Storing waveform in the specified device
          wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
          wavs = wavs.float()
  
          # Computing features and embeddings
          feats = self.mods.compute_features(wavs)
          feats = self.mods.mean_var_norm(feats, wav_lens)
          embeddings = self.mods.embedding_model(feats, wav_lens)
          if normalize:
              embeddings = self.hparams.mean_var_norm_emb(
                  embeddings,
                  torch.ones(embeddings.shape[0], device=self.device)
              )
          return embeddings
  
  
  classifier = Encoder.from_hparams(
      source="yangwang825/ecapa-tdnn-vox2"
  )
  signal, fs = torchaudio.load('spk1_snt1.wav')
  embeddings = classifier.encode_batch(signal)
  >>> torch.Size([1, 1, 192])
  ```
  
  We will release our training results (models, logs, etc) shortly.
  
  # References
  
  1. Ravanelli et al., SpeechBrain: A General-Purpose Speech Toolkit, 2021
  2. Desplanques et al., ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification, 2020