init

experiment_cache/cluster/xlsr_se.expresso.speaker_id.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4874b093274ca6d59fd4989784891e2d594c7ef1c2099eaa93bf28fa769c86c8
-size 297681

experiment_cache/cluster/xlsr_se.expresso.style.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:01a55057f9b14bd51c5ac3645e17146ad139e1083b64e82545fadb6f48690cb4
-size 331093

experiment_cache/cluster/xlsr_se.voxceleb1-test-split.speaker_id.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f30902626df185dfa6a9a630de702d5a191698fdc24a75c985693ebcacb6c0a0
-size 184308

experiment_cache/embeddings/xlsr_se.expresso.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fc33ceb69103f5e23f96ab1e2d8beca4ba9e9964a8bbefb26b72b25975e5c196
-size 192054155

experiment_cache/embeddings/xlsr_se.voxceleb1-test-split.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:aa7137389f225ee791db9c46713487b8439afdf91e4c56ad2401ce05751db108
-size 80339549

experiment_cache/tsne/xlsr_se.expresso.speaker_id.npy

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5ead246e98e244fa2522cdd7cac3d51da21f73e37cd244de08863e82d9b92a38
-size 93048

experiment_cache/tsne/xlsr_se.expresso.style.npy

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5ead246e98e244fa2522cdd7cac3d51da21f73e37cd244de08863e82d9b92a38
-size 93048

experiment_cache/tsne/xlsr_se.voxceleb1-test-split.speaker_id.npy

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2a50b70d103968b9cf1a44c5032ef9fc583828e60ace0e83f77d35186785e0e2
-size 39120

experiment_speaker_verification.py

@@ -18,7 +18,7 @@ from model_meta_voice import MetaVoiceEmbedding
 from model_pyannote_embedding import PyannoteEmbedding
 from model_w2v_bert import W2VBERTEmbedding
 from model_clap import CLAPEmbedding, CLAPGeneralEmbedding
-from model_xls import XLSREmbedding
+from model_xls import Wav2VecEmbedding, XLSR300MEmbedding, XLSR1BEmbedding, XLSR2BEmbedding
 from model_hubert import HuBERTBaseEmbedding, HuBERTLargeEmbedding, HuBERTXLEmbedding
 
 
@@ -121,50 +121,65 @@ if __name__ == '__main__':
     # get_embedding(W2VBERTEmbedding, "w2v_bert_se", "asahi417/voxceleb1-test-split", "test")
     # get_embedding(CLAPEmbedding, "clap_se", "asahi417/voxceleb1-test-split", "test")
     # get_embedding(CLAPGeneralEmbedding, "clap_general_se", "asahi417/voxceleb1-test-split", "test")
-    # get_embedding(XLSREmbedding, "xlsr_se", "asahi417/voxceleb1-test-split", "test")
-    get_embedding(HuBERTBaseEmbedding, "hubert_base_se", "asahi417/voxceleb1-test-split", "test")
+    # get_embedding(HuBERTBaseEmbedding, "hubert_base_se", "asahi417/voxceleb1-test-split", "test")
     # get_embedding(HuBERTLargeEmbedding, "hubert_large_se", "asahi417/voxceleb1-test-split", "test")
     # get_embedding(HuBERTXLEmbedding, "hubert_xl_se", "asahi417/voxceleb1-test-split", "test")
+    get_embedding(Wav2VecEmbedding, "wav2vec_se", "asahi417/voxceleb1-test-split", "test")
+    get_embedding(XLSR300MEmbedding, "xlsr_300m_se", "asahi417/voxceleb1-test-split", "test")
+    get_embedding(XLSR1BEmbedding, "xlsr_1b_se", "asahi417/voxceleb1-test-split", "test")
+    get_embedding(XLSR2BEmbedding, "xlsr_2b_se", "asahi417/voxceleb1-test-split", "test")
 
     # get_embedding(MetaVoiceEmbedding, "meta_voice_se", "ylacombe/expresso", "train")
     # get_embedding(PyannoteEmbedding, "pyannote_se", "ylacombe/expresso", "train")
     # get_embedding(W2VBERTEmbedding, "w2v_bert_se", "ylacombe/expresso", "train")
     # get_embedding(CLAPEmbedding, "clap_se", "ylacombe/expresso", "train")
     # get_embedding(CLAPGeneralEmbedding, "clap_general_se", "ylacombe/expresso", "train")
-    # get_embedding(XLSREmbedding, "xlsr_se", "ylacombe/expresso", "train")
-    get_embedding(HuBERTBaseEmbedding, "hubert_base_se", "ylacombe/expresso", "train")
+    # get_embedding(HuBERTBaseEmbedding, "hubert_base_se", "ylacombe/expresso", "train")
     # get_embedding(HuBERTLargeEmbedding, "hubert_large_se", "ylacombe/expresso", "train")
     # get_embedding(HuBERTXLEmbedding, "hubert_xl_se", "ylacombe/expresso", "train")
+    get_embedding(Wav2VecEmbedding, "wav2vec_se", "ylacombe/expresso", "train")
+    get_embedding(XLSR300MEmbedding, "xlsr_300m_se", "ylacombe/expresso", "train")
+    get_embedding(XLSR1BEmbedding, "xlsr_1b_se", "ylacombe/expresso", "train")
+    get_embedding(XLSR2BEmbedding, "xlsr_2b_se", "ylacombe/expresso", "train")
 
     # cluster_embedding("meta_voice_se", "asahi417/voxceleb1-test-split", "speaker_id")
     # cluster_embedding("pyannote_se", "asahi417/voxceleb1-test-split", "speaker_id")
     # cluster_embedding("w2v_bert_se", "asahi417/voxceleb1-test-split", "speaker_id")
     # cluster_embedding("clap_se", "asahi417/voxceleb1-test-split", "speaker_id")
     # cluster_embedding("clap_general_se", "asahi417/voxceleb1-test-split", "speaker_id")
-    # cluster_embedding("xlsr_se", "asahi417/voxceleb1-test-split", "speaker_id")
-    cluster_embedding("hubert_base_se", "asahi417/voxceleb1-test-split", "speaker_id")
+    # cluster_embedding("hubert_base_se", "asahi417/voxceleb1-test-split", "speaker_id")
     # cluster_embedding("hubert_large_se", "asahi417/voxceleb1-test-split", "speaker_id")
     # cluster_embedding("hubert_xl_se", "asahi417/voxceleb1-test-split", "speaker_id")
+    cluster_embedding("wav2vec_se", "asahi417/voxceleb1-test-split", "speaker_id")
+    cluster_embedding("xlsr_300m_se", "asahi417/voxceleb1-test-split", "speaker_id")
+    cluster_embedding("xlsr_1b_se", "asahi417/voxceleb1-test-split", "speaker_id")
+    cluster_embedding("xlsr_2b_se", "asahi417/voxceleb1-test-split", "speaker_id")
 
     # cluster_embedding("meta_voice_se", "ylacombe/expresso", "speaker_id")
     # cluster_embedding("pyannote_se", "ylacombe/expresso", "speaker_id")
     # cluster_embedding("w2v_bert_se", "ylacombe/expresso", "speaker_id")
     # cluster_embedding("clap_se", "ylacombe/expresso", "speaker_id")
     # cluster_embedding("clap_general_se", "ylacombe/expresso", "speaker_id")
-    # cluster_embedding("xlsr_se", "ylacombe/expresso", "speaker_id")
-    cluster_embedding("hubert_base_se", "ylacombe/expresso", "speaker_id")
+    # cluster_embedding("hubert_base_se", "ylacombe/expresso", "speaker_id")
     # cluster_embedding("hubert_large_se", "ylacombe/expresso", "speaker_id")
     # cluster_embedding("hubert_xl_se", "ylacombe/expresso", "speaker_id")
+    cluster_embedding("wav2vec_se", "ylacombe/expresso", "speaker_id")
+    cluster_embedding("xlsr_300m_se", "ylacombe/expresso", "speaker_id")
+    cluster_embedding("xlsr_1b_se", "ylacombe/expresso", "speaker_id")
+    cluster_embedding("xlsr_2b_se", "ylacombe/expresso", "speaker_id")
 
     # cluster_embedding("meta_voice_se", "ylacombe/expresso", "style")
     # cluster_embedding("pyannote_se", "ylacombe/expresso", "style")
     # cluster_embedding("w2v_bert_se", "ylacombe/expresso", "style")
     # cluster_embedding("clap_se", "ylacombe/expresso", "style")
     # cluster_embedding("clap_general_se", "ylacombe/expresso", "style")
-    # cluster_embedding("xlsr_se", "ylacombe/expresso", "style")
-    cluster_embedding("hubert_base_se", "ylacombe/expresso", "style")
+    # cluster_embedding("hubert_base_se", "ylacombe/expresso", "style")
     # cluster_embedding("hubert_large_se", "ylacombe/expresso", "style")
     # cluster_embedding("hubert_xl_se", "ylacombe/expresso", "style")
+    cluster_embedding("wav2vec_se", "ylacombe/expresso", "style")
+    cluster_embedding("xlsr_300m_se", "ylacombe/expresso", "style")
+    cluster_embedding("xlsr_1b_se", "ylacombe/expresso", "style")
+    cluster_embedding("xlsr_2b_se", "ylacombe/expresso", "style")
 
 
 

model_hubert.py

@@ -26,8 +26,7 @@ class HuBERTXLEmbedding:
         inputs = self.processor(wav, sampling_rate=self.processor.sampling_rate, return_tensors="pt")
         with torch.no_grad():
             outputs = self.model(**{k: v.to(self.device) for k, v in inputs.items()})
-        return outputs
-        # return outputs.last_hidden_state.mean(1).cpu().numpy()[0]
+        return outputs.last_hidden_state.mean(1).cpu().numpy()[0]
 
 
 class HuBERTLargeEmbedding(HuBERTXLEmbedding):

model_w2v_bert.py

@@ -11,9 +11,9 @@ from transformers import Wav2Vec2BertModel, AutoFeatureExtractor
 
 
 class W2VBERTEmbedding:
-    def __init__(self):
-        self.processor = AutoFeatureExtractor.from_pretrained("facebook/w2v-bert-2.0")
-        self.model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0")
+    def __init__(self, ckpt: str = "facebook/w2v-bert-2.0"):
+        self.processor = AutoFeatureExtractor.from_pretrained(ckpt)
+        self.model = Wav2Vec2BertModel.from_pretrained(ckpt)
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.model.to(self.device)
         self.model.eval()

model_xls.py

@@ -1,7 +1,6 @@
 """Meta's XLS-R based speaker embedding.
 - feature dimension: 768
-- source: https://huggingface.co/facebook/wav2vec2-large-xlsr-53
-https://huggingface.co/docs/transformers/en/model_doc/wav2vec2#transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput
+- source: https://huggingface.co/docs/transformers/en/model_doc/wav2vec2#transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput
 """
 from typing import Optional
 
@@ -11,10 +10,11 @@ import numpy as np
 from transformers import AutoFeatureExtractor, AutoModelForPreTraining
 
 
-class XLSREmbedding:
-    def __init__(self):
-        self.processor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-large-xlsr-53")
-        self.model = AutoModelForPreTraining.from_pretrained("facebook/wav2vec2-large-xlsr-53")
+class Wav2VecEmbedding:
+
+    def __init__(self, ckpt: str = "facebook/wav2vec2-large-xlsr-53"):
+        self.processor = AutoFeatureExtractor.from_pretrained(ckpt)
+        self.model = AutoModelForPreTraining.from_pretrained(ckpt)
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.model.to(self.device)
         self.model.eval()
@@ -27,3 +27,21 @@ class XLSREmbedding:
         with torch.no_grad():
             outputs = self.model(**{k: v.to(self.device) for k, v in inputs.items()})
         return outputs.projected_states.mean(1).cpu().numpy()[0]
+
+
+class XLSR2BEmbedding(Wav2VecEmbedding):
+
+    def __init__(self):
+        super().__init__("facebook/wav2vec2-xls-r-2b")
+
+
+class XLSR1BEmbedding(Wav2VecEmbedding):
+
+    def __init__(self):
+        super().__init__("facebook/wav2vec2-xls-r-1b")
+
+
+class XLSR300MEmbedding(Wav2VecEmbedding):
+
+    def __init__(self):
+        super().__init__("facebook/wav2vec2-xls-r-300m")

test.py

@@ -3,14 +3,14 @@ from model_clap import CLAPEmbedding
 from model_meta_voice import MetaVoiceEmbedding
 from model_pyannote_embedding import PyannoteEmbedding
 from model_w2v_bert import W2VBERTEmbedding
-from model_xls import XLSREmbedding
+from model_xls import XLSR300MEmbedding
 from model_hubert import HuBERTXLEmbedding
 
 
 def test():
     wav, sr = librosa.load("sample.wav")
     print("XLS-R")
-    model = XLSREmbedding()
+    model = XLSR300MEmbedding()
     v = model.get_speaker_embedding(wav, sr)
     print(v.shape)
     print("CLAP")