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README.md

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----
-license: apache-2.0
----
+---
+language: "en"
+thumbnail:
+tags:
+- speechbrain
+- embeddings
+- Speaker
+- Verification
+- Identification
+- pytorch
+- ECAPA
+- TDNN
+license: "apache-2.0"
+datasets:
+- voxceleb
+metrics:
+- EER
+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
+---
+
+<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/>
+
+# Speaker Verification with ECAPA-TDNN embeddings on Voxceleb
+
+This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain. 
+The system can be used to extract speaker embeddings as well. 
+It is trained on Voxceleb 1+ Voxceleb2 training data. 
+
+For a better experience, we encourage you to learn more about
+[SpeechBrain](https://speechbrain.github.io). The model performance on Voxceleb1-test set(Cleaned) is:
+
+| Release | EER(%) 
+|:-------------:|:--------------:|
+| 05-03-21 | 0.80 | 
+
+
+## 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.  Speaker Verification is performed using cosine distance between speaker embeddings.
+
+## Install SpeechBrain
+
+First of all, please install SpeechBrain with the following command:
+
+```
+pip install git+https://github.com/speechbrain/speechbrain.git@develop
+```
+
+Please notice that we encourage you to read our tutorials and learn more about
+[SpeechBrain](https://speechbrain.github.io).
+
+### Compute your speaker embeddings
+
+```python
+import torchaudio
+from speechbrain.inference.speaker import EncoderClassifier
+classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb")
+signal, fs =torchaudio.load('tests/samples/ASR/spk1_snt1.wav')
+embeddings = classifier.encode_batch(signal)
+```
+The system is trained with recordings sampled at 16kHz (single channel).
+The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *encode_batch* and *classify_batch*.
+
+### Perform Speaker Verification
+
+```python
+from speechbrain.inference.speaker import SpeakerRecognition
+verification = SpeakerRecognition.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb", savedir="pretrained_models/spkrec-ecapa-voxceleb")
+score, prediction = verification.verify_files("tests/samples/ASR/spk1_snt1.wav", "tests/samples/ASR/spk2_snt1.wav") # Different Speakers
+score, prediction = verification.verify_files("tests/samples/ASR/spk1_snt1.wav", "tests/samples/ASR/spk1_snt2.wav") # Same Speaker
+```
+ The prediction is 1 if the two signals in input are from the same speaker and 0 otherwise.
+
+### 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 (aa018540).
+To train it from scratch follows these steps:
+1. Clone SpeechBrain:
+```bash
+git clone https://github.com/speechbrain/speechbrain/
+```
+2. Install it:
+```
+cd speechbrain
+pip install -r requirements.txt
+pip install -e .
+```
+
+3. Run Training:
+```
+cd  recipes/VoxCeleb/SpeakerRec
+python train_speaker_embeddings.py hparams/train_ecapa_tdnn.yaml --data_folder=your_data_folder
+```
+
+You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1-ahC1xeyPinAHp2oAohL-02smNWO41Cc?usp=sharing).
+
+### Limitations
+The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
+
+#### Referencing ECAPA-TDNN
+```
+@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
+  author    = {Brecht Desplanques and
+               Jenthe Thienpondt and
+               Kris Demuynck},
+  editor    = {Helen Meng and
+               Bo Xu and
+               Thomas Fang Zheng},
+  title     = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation
+               in {TDNN} Based Speaker Verification},
+  booktitle = {Interspeech 2020},
+  pages     = {3830--3834},
+  publisher = {{ISCA}},
+  year      = {2020},
+}
+```
+
+# **Citing SpeechBrain**
+Please, cite SpeechBrain if you use it for your research or business.
+
+```bibtex
+@misc{speechbrain,
+  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
+  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
+  year={2021},
+  eprint={2106.04624},
+  archivePrefix={arXiv},
+  primaryClass={eess.AS},
+  note={arXiv:2106.04624}
+}
+```
+
+# **About SpeechBrain**
+- Website: https://speechbrain.github.io/
+- Code: https://github.com/speechbrain/speechbrain/
+- HuggingFace: https://huggingface.co/speechbrain/

classifier.ckpt

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config.json

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+{
+  "speechbrain_interface": "SpeakerRecognition"
+}

embedding_model.ckpt

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hyperparams.yaml

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+# ############################################################################
+# Model: ECAPA big for Speaker verification
+# ############################################################################
+
+# Feature parameters
+n_mels: 80
+
+# Pretrain folder (HuggingFace)
+pretrained_path: speechbrain/spkrec-ecapa-voxceleb
+
+# Output parameters
+out_n_neurons: 7205
+
+# Model params
+compute_features: !new:speechbrain.lobes.features.Fbank
+    n_mels: !ref <n_mels>
+
+mean_var_norm: !new:speechbrain.processing.features.InputNormalization
+    norm_type: sentence
+    std_norm: False
+
+embedding_model: !new:speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN
+    input_size: !ref <n_mels>
+    channels: [1024, 1024, 1024, 1024, 3072]
+    kernel_sizes: [5, 3, 3, 3, 1]
+    dilations: [1, 2, 3, 4, 1]
+    attention_channels: 128
+    lin_neurons: 192
+
+classifier: !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
+    input_size: 192
+    out_neurons: !ref <out_n_neurons>
+
+mean_var_norm_emb: !new:speechbrain.processing.features.InputNormalization
+    norm_type: global
+    std_norm: False
+
+modules:
+    compute_features: !ref <compute_features>
+    mean_var_norm: !ref <mean_var_norm>
+    embedding_model: !ref <embedding_model>
+    mean_var_norm_emb: !ref <mean_var_norm_emb>
+    classifier: !ref <classifier>
+        
+label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder
+
+        
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+    loadables:
+        embedding_model: !ref <embedding_model>
+        mean_var_norm_emb: !ref <mean_var_norm_emb>
+        classifier: !ref <classifier>
+        label_encoder: !ref <label_encoder>
+    paths:
+        embedding_model: !ref <pretrained_path>/embedding_model.ckpt
+        mean_var_norm_emb: !ref <pretrained_path>/mean_var_norm_emb.ckpt
+        classifier: !ref <pretrained_path>/classifier.ckpt
+        label_encoder: !ref <pretrained_path>/label_encoder.txt
+

mean_var_norm_emb.ckpt

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