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| import json | |
| import torch | |
| import numpy as np | |
| from .demo_speaker_embeddings import DemoSpeakerEmbeddings | |
| class DemoRandomAnonymizer: | |
| def __init__(self, device, vec_type='xvector', in_scale=False): | |
| self.device = device | |
| self.vec_type = vec_type | |
| self.in_scale = in_scale | |
| self.dim_ranges = None | |
| self.embedding_extractor = DemoSpeakerEmbeddings(vec_type=self.vec_type, device=self.device) | |
| def load_parameters(self, model_dir): | |
| with open(model_dir / 'settings.json') as f: | |
| settings = json.load(f) | |
| self.vec_type = settings['vec_type'] if 'vec_type' in settings else self.vec_type | |
| self.in_scale = settings.get('in_scale', self.in_scale) | |
| if self.in_scale: | |
| with open(model_dir / 'stats_per_dim.json') as f: | |
| dim_ranges = json.load(f) | |
| self.dim_ranges = [(v['min'], v['max']) for k, v in sorted(dim_ranges.items(), key=lambda x: int(x[0]))] | |
| def anonymize_embedding(self, audio, sr): | |
| speaker_embedding = torch.tensor(self.embedding_extractor.extract_vector_from_audio(wave=audio, sr=sr)) | |
| if self.dim_ranges: | |
| anon_vec = torch.tensor([np.random.uniform(*dim_range) for dim_range in self.dim_ranges]).to(self.device) | |
| else: | |
| mask = torch.zeros(speaker_embedding.shape[0]).float().random_(-40, 40).to(self.device) | |
| anon_vec = speaker_embedding * mask | |
| return anon_vec | |