Model Overview
Speaker Identification is a broad research area that solves two major tasks: speaker identification (who is speaking?) and speaker verification (is the speaker who they claim to be?). In this work, we focus on far-field, text-independent speaker recognition when the identity of the speaker is based on how the speech is spoken, not necessarily on what is being said. Typically such SR systems operate on unconstrained speech utterances, which are converted into vectors of fixed length, called speaker embeddings. Speaker embeddings are also used in automatic speech recognition (ASR) and speech synthesis.
This model is trained end-to-end using angular softmax loss for speaker verification purposes and for extracting speaker embeddings
Model Architecture
SpeakerNet models consists of 1D Depth-wise separable convolutional layers. These encoded information is then pooled by statistical means based on mean and variance as described in paper [1]
Training
These models were trained on a composite dataset comprising of several thousand hours of speech, compiled from various publicly available sources. The NeMo toolkit [2] was used for training this model over few hundred epochs on multiple GPUs.
Datasets
The following datasets are used for training
- Voxceleb 1 Dev data (1211 speakers)
- Voxceleb 2 Dev data (5994 speakers)
- Musan music and Noise Augmentation Data
Performance
This speakernet-M model which is based on Quartznet Encoder structure with 5M parameters achieves 1.93% EER on voxceleb clean test trial file
How to use this model
For training and extracting embeddings detailed step by step, procedure has provided in Speaker Verification notebook. and Embeddings extraction script
Embedding Extraction
For a single audio file, one can also extract embeddings inline using
import nemo.collections.asr as nemo_asr
speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name="speakerverification_speakernet")
embs = speaker_model.get_embedding('audio_path')
Speaker Verification
Speaker Verification is a task of verifying if two utterances are from the same speaker or not. We provide a helper function to verify the audio files and return True if two provided audio files are from the same speaker, False otherwise. The audio files should be 16KHz mono channel wav files.
import nemo.collections.asr as nemo_asr
speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='titanet_large')
decision = speaker_model.verify_speakers('path/to/one/audio_file','path/to/other/audio_file')
Input
This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
Output
This model provides embeddings of size 256 from a speaker for a given audio sample.
Limitations
This model is trained on non-telephonic speech from voxceleb datasets, hence may not work as well for telephonic speech. If it happens consider finetuning for that speech domain.
References
[1] SpeakerNet: 1D Depth-wise Separable Convolutional Network for Text-Independent Speaker Recognition and Verification
[2] NVIDIA NeMo Toolkit
Licence
License to use this model is covered by the license of the NeMo Toolkit [2]. By downloading the public and release version of the model, you accept the terms and conditions of this license.