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.gitignore

@@ -0,0 +1,134 @@
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README.md

@@ -1,10 +1,10 @@
 ---
-title: Speaker Anonymization Gan
-emoji: 📊
-colorFrom: gray
-colorTo: yellow
+title: Speaker Anonymization
+emoji: 🐠
+colorFrom: red
+colorTo: indigo
 sdk: gradio
-sdk_version: 3.16.0
+sdk_version: 3.3
 app_file: app.py
 pinned: false
 license: gpl-3.0

anonymization/__init__.py

@@ -0,0 +1,3 @@
+from .demo_pool_anonymizer import DemoPoolAnonymizer
+from .demo_random_anonymizer import DemoRandomAnonymizer
+from .demo_gan_anonymizer import DemoGANAnonymizer

anonymization/demo_gan_anonymizer.py

@@ -0,0 +1,56 @@
+from pathlib import Path
+import torch
+import numpy as np
+from scipy.spatial.distance import cosine
+import json
+
+from .demo_speaker_embeddings import DemoSpeakerEmbeddings
+
+
+class DemoGANAnonymizer:
+
+    def __init__(self, vec_type='xvector', device=None, sim_threshold=0.7):
+        self.vec_type = vec_type
+        self.device = device
+
+        self.sim_threshold = sim_threshold
+
+        self.embedding_extractor = DemoSpeakerEmbeddings(vec_type=self.vec_type, device=self.device)
+
+        self.model_dir = None
+        self.vectors_file = None
+        self.gan_vectors = None
+        self.unused_indices = None
+
+    def load_parameters(self, model_dir: Path):
+        self.model_dir = model_dir
+        with open(model_dir / 'settings.json') as f:
+            settings = json.load(f)
+        self.vec_type = settings.get('vec_type', self.vec_type)
+        self.vectors_file = settings.get('vectors_file', self.vectors_file)
+
+        self.gan_vectors = torch.load(model_dir / self.vectors_file, map_location=self.device)
+        self.unused_indices = self.load_unused_indices()
+
+    def load_unused_indices(self):
+        return torch.load(self.model_dir / f'unused_indices_{self.vectors_file}', map_location='cpu')
+
+    def anonymize_embedding(self, audio, sr):
+        speaker_embedding = self.embedding_extractor.extract_vector_from_audio(wave=audio, sr=sr)
+        anon_vec = self._select_gan_vector(spk_vec=speaker_embedding)
+        return anon_vec
+
+    def _select_gan_vector(self, spk_vec):
+        i = 0
+        limit = 20
+        while i < limit:
+            idx = np.random.choice(self.unused_indices)
+            anon_vec = self.gan_vectors[idx]
+            sim = 1 - cosine(spk_vec.cpu().numpy(), anon_vec.cpu().numpy())
+            if sim < self.sim_threshold:
+                break
+            i += 1
+        self.unused_indices = self.unused_indices[self.unused_indices != idx]
+        if len(self.unused_indices) == 0:
+            self.unused_indices = self.load_unused_indices()
+        return anon_vec

anonymization/demo_pool_anonymizer.py

@@ -0,0 +1,75 @@
+from pathlib import Path
+import numpy as np
+import torch
+import json
+from sklearn.metrics.pairwise import cosine_distances
+
+from .plda_model import PLDAModel
+from .demo_speaker_embeddings import DemoSpeakerEmbeddings
+
+
+class DemoPoolAnonymizer:
+
+    def __init__(self, vec_type='xvector', N=200, N_star=100, distance='plda', proximity='farthest', device=None):
+        # Pool anonymization method based on the primary baseline of the Voice Privacy Challenge 2020.
+        # Given a speaker vector, the N most distant vectors in an external speaker pool are extracted,
+        # and an average of a random subset of N_star vectors is computed and taken as new speaker vector.
+        # Default distance measure is PLDA.
+        self.vec_type = vec_type
+        self.device = device
+
+        self.N = N  # number of most distant vectors to consider
+        self.N_star = N_star  # number of vectors to include in averaged vector
+        self.distance = distance  # distance measure, either 'plda' or 'cosine'
+        self.proximity = proximity  # proximity method, either 'farthest' (distant vectors), 'nearest', or 'closest'
+
+        self.embedding_extractor = DemoSpeakerEmbeddings(vec_type=self.vec_type, device=self.device)
+
+        self.pool_embeddings = None
+        self.plda = None
+
+    def load_parameters(self, model_dir: Path):
+        self._load_settings(model_dir / 'settings.json')
+        self.pool_embeddings = torch.load(model_dir / 'pool_embeddings' / f'speaker_vectors.pt',
+                                          map_location=self.device)
+        if self.distance == 'plda':
+            self.plda = PLDAModel(train_embeddings=None, results_path=model_dir)
+
+    def anonymize_embedding(self, audio, sr):
+        speaker_embedding = self.embedding_extractor.extract_vector_from_audio(wave=audio, sr=sr)
+
+        distances = self._compute_distances(vectors_a=self.pool_embeddings,
+                                            vectors_b=speaker_embedding.unsqueeze(0)).squeeze()
+
+        candidates = self._get_pool_candidates(distances)
+        selected_anon_pool = np.random.choice(candidates, self.N_star, replace=False)
+        anon_vec = torch.mean(self.pool_embeddings[selected_anon_pool], dim=0)
+
+        return anon_vec
+
+    def _compute_distances(self, vectors_a, vectors_b):
+        if self.distance == 'plda':
+            return 1 - self.plda.compute_distance(enrollment_vectors=vectors_a, trial_vectors=vectors_b)
+        elif self.distance == 'cosine':
+            return cosine_distances(X=vectors_a.cpu(), Y=vectors_b.cpu())
+        else:
+            return []
+
+    def _get_pool_candidates(self, distances):
+        if self.proximity == 'farthest':
+            return np.argpartition(distances, -self.N)[-self.N:]
+        elif self.proximity == 'nearest':
+            return np.argpartition(distances, self.N)[:self.N]
+        elif self.proximity == 'center':
+            sorted_distances = np.sort(distances)
+            return sorted_distances[len(sorted_distances)//2:(len(sorted_distances)//2)+self.N]
+
+    def _load_settings(self, filename):
+        with open(filename, 'r') as f:
+            settings = json.load(f)
+
+        self.N = settings['N'] if 'N' in settings else self.N
+        self.N_star = settings['N*'] if 'N*' in settings else self.N_star
+        self.distance = settings['distance'] if 'distance' in settings else self.distance
+        self.proximity = settings['proximity'] if 'proximity' in settings else self.proximity
+        self.vec_type = settings['vec_type'] if 'vec_type' in settings else self.vec_type

anonymization/demo_random_anonymizer.py

@@ -0,0 +1,37 @@
+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

anonymization/demo_speaker_embeddings.py

@@ -0,0 +1,64 @@
+import numpy as np
+import torch
+import torchaudio
+import pyloudnorm as pyln
+from speechbrain.pretrained import EncoderClassifier
+
+from IMSToucan.Preprocessing.AudioPreprocessor import AudioPreprocessor
+
+VALID_VEC_TYPES = {'xvector', 'ecapa', 'ecapa+xvector'}
+
+
+class DemoSpeakerEmbeddings:
+
+    def __init__(self, vec_type='xvector', device=torch.device('cpu')):
+        self.vec_type = vec_type
+        assert self.vec_type in VALID_VEC_TYPES, f'Invalid vec_type {self.vec_type}, must be one of {VALID_VEC_TYPES}'
+        self.device = device
+
+        self.encoders = []
+        if 'ecapa' in self.vec_type:
+            self.encoders.append(EncoderClassifier.from_hparams(source='speechbrain/spkrec-ecapa-voxceleb',
+                                                                savedir='models/speaker_embeddings/spkrec-ecapa-voxceleb',
+                                                                run_opts={'device': self.device}))
+        if 'xvector' in self.vec_type:
+            self.encoders.append(EncoderClassifier.from_hparams(source='speechbrain/spkrec-xvect-voxceleb',
+                                                                savedir='models/speaker_embeddings/spkrec-xvect-voxceleb',
+                                                                run_opts={'device': self.device}))
+
+        self.ap = AudioPreprocessor(input_sr=48000, output_sr=16000, melspec_buckets=80, hop_length=256, n_fft=1024,
+                                    cut_silence=False)
+
+    def extract_vector_from_audio(self, wave, sr):
+        # adapted from IMSToucan/Preprocessing/AudioPreprocessor
+        #norm_wave = self._normalize_wave(wave, sr)
+        norm_wave = self.ap.audio_to_wave_tensor(normalize=True, audio=wave)
+        norm_wave = torch.tensor(np.trim_zeros(norm_wave.numpy()))
+
+        spk_embs = [encoder.encode_batch(wavs=norm_wave.unsqueeze(0)).squeeze() for encoder in self.encoders]
+        if len(spk_embs) == 1:
+            return spk_embs[0]
+        else:
+            return torch.cat(spk_embs, dim=0)
+
+    def _normalize_wave(self, wave, sr):
+        # adapted from IMSToucan/Preprocessing/AudioPreprocessor
+        wave = torch.tensor(wave)
+        print(wave.shape)
+        print(wave)
+        dur = wave.shape[0] / sr
+        wave = wave.squeeze().cpu().numpy()
+
+        # normalize loudness
+        meter = pyln.Meter(sr, block_size=min(dur - 0.0001, abs(dur - 0.1)) if dur < 0.4 else 0.4)
+        loudness = meter.integrated_loudness(wave)
+        loud_normed = pyln.normalize.loudness(wave, loudness, -30.0)
+        peak = np.amax(np.abs(loud_normed))
+        wave = np.divide(loud_normed, peak)
+
+        wave = torch.Tensor(wave).to(self.device)
+
+        if sr != 16000:
+            wave = torchaudio.transforms.Resample(orig_freq=sr, new_freq=16000).to(self.device)(wave)
+
+        return wave.cpu()

anonymization/plda_model.py

@@ -0,0 +1,82 @@
+# This code is based on the descriptions in https://github.com/speechbrain/speechbrain/blob/develop/speechbrain/processing/PLDA_LDA.py
+from pathlib import Path
+from speechbrain.processing.PLDA_LDA import PLDA, StatObject_SB, Ndx, fast_PLDA_scoring
+import numpy as np
+import torch
+
+
+class PLDAModel:
+
+    def __init__(self, train_embeddings, results_path: Path=None):
+        self.mean, self.F, self.Sigma = None, None, None
+
+        files_exist = False
+        if results_path and results_path.exists():
+            files_exist = self.load_parameters(results_path)
+        if not files_exist:
+            self._train_plda(train_embeddings)
+            self.save_parameters(results_path)
+
+    def compute_distance(self, enrollment_vectors, trial_vectors):
+        enrol_vecs = enrollment_vectors.cpu().numpy()
+        en_sets, en_s, en_stat0 = self._get_vector_stats(enrol_vecs, sg_tag='en')
+        en_stat = StatObject_SB(modelset=en_sets, segset=en_sets, start=en_s, stop=en_s, stat0=en_stat0,
+                                stat1=enrol_vecs)
+
+        trial_vecs = trial_vectors.cpu().numpy()
+        te_sets, te_s, te_stat0 = self._get_vector_stats(trial_vecs, sg_tag='te')
+        te_stat = StatObject_SB(modelset=te_sets, segset=te_sets, start=te_s, stop=te_s, stat0=te_stat0,
+                                stat1=trial_vecs)
+
+        ndx = Ndx(models=en_sets, testsegs=te_sets)
+        scores_plda = fast_PLDA_scoring(en_stat, te_stat, ndx, self.mean, self.F, self.Sigma)
+        return scores_plda.scoremat
+
+    def save_parameters(self, filename):
+        filename.parent.mkdir(parents=True, exist_ok=True)
+        np.save(filename / 'plda_mean.npy', self.mean)
+        np.save(filename / 'plda_F.npy', self.F)
+        np.save(filename / 'plda_Sigma.npy', self.Sigma)
+
+    def load_parameters(self, dir_path):
+        existing_files = [x.name for x in dir_path.glob('*')]
+        files_exist = True
+        if 'plda_mean.npy' in existing_files:
+            self.mean = np.load(dir_path / 'plda_mean.npy')
+        else:
+            files_exist = False
+
+        if 'plda_F.npy' in existing_files:
+            self.F = np.load(dir_path / 'plda_F.npy')
+        else:
+            files_exist = False
+
+        if 'plda_Sigma.npy' in existing_files:
+            self.Sigma = np.load(dir_path / 'plda_Sigma.npy')
+        else:
+            files_exist = False
+        return files_exist
+
+    def _train_plda(self, train_embeddings):
+        vectors = train_embeddings.speaker_vectors.to(torch.float64)
+        speakers = train_embeddings.speakers
+
+        modelset = np.array([f'md{speaker}' for speaker in speakers], dtype="|O")
+        segset, s, stat0 = self._get_vector_stats(vectors, sg_tag='sg')
+
+        xvectors_stat = StatObject_SB(modelset=modelset, segset=segset, start=s, stop=s, stat0=stat0,
+                                      stat1=vectors.cpu().numpy())
+
+        plda = PLDA(rank_f=100)
+        plda.plda(xvectors_stat)
+
+        self.mean = plda.mean
+        self.F = plda.F
+        self.Sigma = plda.Sigma
+
+    def _get_vector_stats(self, vectors, sg_tag='sg'):
+        N, dim = vectors.shape
+        segset = np.array([f'{sg_tag}{i}' for i in range(N)], dtype="|O")
+        s = np.array([None] * N)
+        stat0 = np.array([[1.0]] * N)
+        return segset, s, stat0

app.py

@@ -0,0 +1,119 @@
+import os
+import gradio as gr
+import numpy as np
+import torch
+from pathlib import Path
+
+os.system("pip uninstall -y gradio")
+os.system("pip install gradio==3.2")
+
+from demo_inference.demo_tts import DemoTTS
+from demo_inference.demo_asr import DemoASR
+from demo_inference.demo_anonymization import DemoAnonymizer
+
+
+def pcm2float(sig, dtype='float32'):
+    """
+    https://gist.github.com/HudsonHuang/fbdf8e9af7993fe2a91620d3fb86a182
+    """
+    sig = np.asarray(sig)
+    if sig.dtype.kind not in 'iu':
+        raise TypeError("'sig' must be an array of integers")
+    dtype = np.dtype(dtype)
+    if dtype.kind != 'f':
+        raise TypeError("'dtype' must be a floating point type")
+
+    i = np.iinfo(sig.dtype)
+    abs_max = 2 ** (i.bits - 1)
+    offset = i.min + abs_max
+    return (sig.astype(dtype) - offset) / abs_max
+
+
+def float2pcm(sig, dtype='int16'):
+    """
+    https://gist.github.com/HudsonHuang/fbdf8e9af7993fe2a91620d3fb86a182
+    """
+    sig = np.asarray(sig)
+    if sig.dtype.kind != 'f':
+        raise TypeError("'sig' must be a float array")
+    dtype = np.dtype(dtype)
+    if dtype.kind not in 'iu':
+        raise TypeError("'dtype' must be an integer type")
+    i = np.iinfo(dtype)
+    abs_max = 2 ** (i.bits - 1)
+    offset = i.min + abs_max
+    return (sig * abs_max + offset).clip(i.min, i.max).astype(dtype)
+
+
+class VPInterface:
+
+    def __init__(self):
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+        self.path_to_tts_models = Path('models', 'tts')
+        self.path_to_asr_model = Path('models', 'asr')
+        self.path_to_anon_model = Path('models', 'anonymization')
+
+        self.synthesis_model = DemoTTS(model_paths=self.path_to_tts_models, device=self.device)
+        self.asr_model = DemoASR(model_path=self.path_to_asr_model, device=self.device)
+        self.anon_model = DemoAnonymizer(model_path=self.path_to_anon_model, model_tag='gan', device=self.device)
+
+    def read(self, recording, anon_model_tag):
+        sr, audio = recording
+        audio = pcm2float(audio)
+
+        self._check_models(anon_model_tag)
+
+        text_is_phonemes = True
+        text = self.asr_model.recognize_speech(audio, sr)
+        speaker_embedding = self.anon_model.anonymize_embedding(audio, sr)
+        syn_audio = self.synthesis_model.read_text(transcription=text, speaker_embedding=speaker_embedding,
+                                                   text_is_phonemes=text_is_phonemes)
+
+        return 48000, float2pcm(syn_audio.cpu().numpy())
+
+    def _check_models(self, anon_model_tag):
+        if anon_model_tag != self.anon_model.model_tag:
+            self.anon_model = DemoAnonymizer(model_path=self.path_to_anon_model, model_tag=anon_model_tag,
+                                             device=self.device)
+
+
+model = VPInterface()
+
+article = """
+This demo allows you to anonymize your input speech by defining different anonymization models. If 
+you want to know more about each model, please read the paper linked above. Every time you click the *submit* button, 
+you should receive a new voice.
+
+Note that for *pool* anonymization in this demo, we are using a different scaling approach (
+sklearn.preprocessing.StandardScaler instead of sklearn.preprocessing.MinMaxScaler) because we are processing only 
+one sample at a time and would otherwise always end up with the same voice.
+
+This demo is still work in progress, so please be lenient with possible low quality and errors. Also, be aware that 
+this Huggingface space runs on CPU which makes the demo quite slow.
+
+For more information about this system, visit our Github page: [https://github.com/DigitalPhonetics/speaker-anonymization](https://github.com/DigitalPhonetics/speaker-anonymization/tree/gan_embeddings)
+"""
+
+description = """
+## Test demo corresponding to the models in our paper [Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy](https://arxiv.org/abs/2210.07002)
+"""
+
+css = """
+.gr-button-primary {background-color: green !important, border-color: green}
+"""
+
+iface = gr.Interface(fn=model.read,
+                     inputs=[gr.inputs.Audio(source='microphone', type='numpy', label='Say a sentence in English.'),
+                             gr.inputs.Dropdown(['gan', 'pool', 'random'], type='value', default='gan',
+                                                label='Anonymization')
+                             ],
+                     outputs=gr.outputs.Audio(type='numpy', label=None),
+                     layout='vertical',
+                     title='IMS Speaker Anonymization',
+                     description=description,
+                     theme='default',
+                     allow_flagging='never',
+                     article=article,
+                     allow_screenshot=False)
+iface.launch(enable_queue=True)

demo_inference/demo_anonymization.py

@@ -0,0 +1,79 @@
+import json
+import torch
+import numpy as np
+from sklearn.preprocessing import minmax_scale, StandardScaler
+
+from anonymization import DemoPoolAnonymizer, DemoRandomAnonymizer, DemoGANAnonymizer
+
+TAGS_TO_MODELS = {
+    'pool': 'pool_minmax_ecapa+xvector',
+    'random': 'random_in-scale_ecapa+xvector',
+    'gan': 'gan'
+}
+
+ANON_MODELS = {
+    'pool': DemoPoolAnonymizer,
+    'random': DemoRandomAnonymizer,
+    'gan': DemoGANAnonymizer
+}
+
+
+class DemoAnonymizer:
+
+    def __init__(self, model_path, model_tag, device):
+        self.device = device
+        self.scaling = None
+        self.std_scaler = None
+        self.model_tag = model_tag
+
+        self.dim_ranges = self._load_dim_ranges(model_path / TAGS_TO_MODELS[model_tag])
+        self.anonymizer = self._load_anonymizer(model_path / TAGS_TO_MODELS[model_tag])
+
+    def anonymize_embedding(self, audio, sr):
+
+        anon_embedding = self.anonymizer.anonymize_embedding(audio, sr)
+        if self.dim_ranges:
+            anon_embedding = self._scale_embedding(anon_embedding)
+        return anon_embedding
+
+    def _load_dim_ranges(self, model_dir):
+        if (model_dir / 'stats_per_dim.json').exists():
+            with open(model_dir / 'stats_per_dim.json') as f:
+                dim_ranges = json.load(f)
+                return [(v['min'], v['max']) for k, v in sorted(dim_ranges.items(), key=lambda x: int(x[0]))]
+
+    def _load_anonymizer(self, model_dir):
+        model_name = model_dir.name.lower()
+
+        if 'pool' in model_name:
+            model_type = 'pool'
+        elif 'gan' in model_name:
+            model_type = 'gan'
+        else:
+            model_type = 'random'
+
+        print(f'Model type of anonymizer: {model_type}')
+
+        model = ANON_MODELS[model_type](device=self.device, vec_type='ecapa+xvector')
+        model.load_parameters(model_dir)
+
+        if 'minmax' in model_name:
+            self.scaling = 'std'
+            self.std_scaler = StandardScaler()
+            self.std_scaler.fit(model.pool_embeddings.cpu().numpy())
+
+        return model
+
+    def _scale_embedding(self, vector):
+        if self.scaling == 'minmax':
+            vector = vector.cpu().numpy()
+            scaled_dims = []
+            for i in range(len(self.dim_ranges)):
+                scaled_dims.append(minmax_scale(np.array([vector[i]]), self.dim_ranges[i])[0])
+
+            vector = torch.tensor(scaled_dims).to(self.device)
+        elif self.scaling == 'std':
+            vector = vector.unsqueeze(0).cpu().numpy()
+            vector = torch.tensor(self.std_scaler.transform(vector)[0])
+
+        return vector

demo_inference/demo_asr.py

@@ -0,0 +1,31 @@
+from espnet2.bin.asr_inference import Speech2Text
+import resampy
+from espnet_model_zoo.downloader import ModelDownloader
+
+
+
+class DemoASR:
+
+    def __init__(self, model_path, device):
+        model_file = 'asr_improved_tts-phn_en.zip'
+
+        d = ModelDownloader()
+
+        self.speech2text = Speech2Text(
+            **d.download_and_unpack(str(model_path / model_file)),
+            device=str(device),
+            minlenratio=0.0,
+            maxlenratio=0.0,
+            ctc_weight=0.4,
+            beam_size=15,
+            batch_size=1,
+            nbest=1
+        )
+
+    def recognize_speech(self, audio, sr):
+        if len(audio.shape) == 2:
+            audio = audio.T[0]
+        speech = resampy.resample(audio, sr, 16000)
+        nbests = self.speech2text(speech)
+        text, *_ = nbests[0]
+        return text

demo_inference/demo_tts.py

@@ -0,0 +1,16 @@
+from IMSToucan.InferenceInterfaces.AnonFastSpeech2 import AnonFastSpeech2
+
+
+class DemoTTS:
+
+    def __init__(self, model_paths, device):
+        self.device = device
+        fastspeech_path = model_paths / 'FastSpeech2_Multi' / 'trained_on_ground_truth_phonemes.pt'
+        hifigan_path = model_paths / 'HiFiGAN_combined' / 'best.pt'
+        self.model = AnonFastSpeech2(device=self.device, path_to_hifigan_model=hifigan_path,
+                                     path_to_fastspeech_model=fastspeech_path)
+
+    def read_text(self, transcription, speaker_embedding, text_is_phonemes=False):
+        self.model.default_utterance_embedding = speaker_embedding.to(self.device)
+        wav = self.model(text=transcription, text_is_phonemes=text_is_phonemes)
+        return wav

models/anonymization/gan/settings.json

@@ -0,0 +1,4 @@
+{
+	"vec_type": "ecapa+xvector",
+	"vectors_file": "wgan_generated_speaker_embeddings.pt"
+}

models/anonymization/gan/unused_indices_wgan_generated_speaker_embeddings.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9a3a87651ca8e2aca6162d0a9cc5c4f4c041049802084b7305a1de2112a3401
+size 40747

models/anonymization/gan/wgan_generated_speaker_embeddings.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:443f7e9a21c4bee5c179229a42f74afa7b4e50bec8e19bdd0e269d6b6e2389eb
+size 14080747

models/anonymization/pool_minmax_ecapa+xvector/plda_F.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d670104eb168655894bed5572342cd727d70eb5ce6d643518cac497cb540adf3
+size 563328

models/anonymization/pool_minmax_ecapa+xvector/plda_Sigma.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:02d05f288b9feb54da267f1d2f9d9834f9f7b00d33b8d5abbca0748e4ae7efbd
+size 3965056

models/anonymization/pool_minmax_ecapa+xvector/plda_mean.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3837bb4b16227db7dfa9cfb66b4dcd6b39f53e3c5089f773a5b30766f4f12935
+size 5760

models/anonymization/pool_minmax_ecapa+xvector/pool_embeddings/speaker_vectors.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb3ed65c30653831be6ba590f56a1c9677588a9b5c31653230f1dbe311d87f1d
+size 3267307

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models/anonymization/pool_minmax_ecapa+xvector/settings.json

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models/anonymization/random_in-scale_ecapa+xvector/settings.json

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models/anonymization/random_in-scale_ecapa+xvector/stats_per_dim.json

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