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. The model performance on Voxceleb1-test set(Cleaned) is: 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.…
Organization
SpeechBrain
speechbrain
Deep Learning, Speech Technologies
Models in Library5
Datasets in Library0
Models on Hugging Face127
Followers715
Models
This repository provides all the necessary tools for using a HiFIGAN vocoder trained with LibriTTS (with multiple speakers). The sample rate used for the vocoder is 22050 Hz. The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spectrogram. Alternatives to this models are the following: - tts-hifigan-libritts-16kHz (same model trained on the same dataset, but for a sample rate of 16000 Hz) - tts-hifigan-ljspeech (same model trained on LJSpeech for a sample rate of 22050 Hz). Please notice that we encourage you to read our tutorials and learn more about To perform inference on…
This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses more fully connected hidden layers after the embedding layer, and cross-entropy loss was used for training. We observed that this improved the performance of extracted utterance embeddings for downstream tasks. 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 classifyfile if needed. The model can classify a speech utterance according to the language…
This repository provides all the necessary tools to perform emotion recognition with a fine-tuned wav2vec2 (base) model using SpeechBrain. It is trained on IEMOCAP training data. For a better experience, we encourage you to learn more about SpeechBrain. The model performance on IEMOCAP test set is: This system is composed of an wav2vec2 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. The system is trained with recordings sampled at 16kHz (single channel). The code will…
This repository provides all the necessary tools to extract speaker embeddings with a pretrained TDNN model using SpeechBrain. The system is trained on Voxceleb 1+ Voxceleb2 training data. For a better experience, we encourage you to learn more about SpeechBrain. The given model performance on Voxceleb1-test set (Cleaned) is: This system is composed of a TDNN model coupled with statistical pooling. The system is trained with Categorical Cross-Entropy Loss. First of all, please install SpeechBrain with the following command: Please notice that we encourage you to read our tutorials and learn more about The system is trained with recordings sampled at 16kHz (single channel). The code will…