init

model_hubert.py → model_speaker_embedding.py

@@ -1,18 +1,20 @@
-"""Meta's HuBERT based speaker embedding.
+"""Meta's w2vBERT based speaker embedding.
 - feature dimension: 1024
-- source: https://huggingface.co/facebook/hubert-large-ll60k
+- source: https://huggingface.co/facebook/w2v-bert-2.0
 """
 from typing import Optional
 
 import torch
 import librosa
 import numpy as np
-from transformers import AutoFeatureExtractor, AutoModel
+from transformers import AutoModel, AutoFeatureExtractor
 
 
-class HuBERTXLEmbedding:
-
-    def __init__(self, ckpt: str = "facebook/hubert-xlarge-ll60k"):
+############
+# W2V BERT #
+############
+class W2VBERTEmbedding:
+    def __init__(self, ckpt: str = "facebook/w2v-bert-2.0"):
         self.processor = AutoFeatureExtractor.from_pretrained(ckpt)
         self.model = AutoModel.from_pretrained(ckpt)
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -29,12 +31,45 @@ class HuBERTXLEmbedding:
         return outputs.last_hidden_state.mean(1).cpu().numpy()[0]
 
 
-class HuBERTLargeEmbedding(HuBERTXLEmbedding):
+##########
+# HuBERT #
+##########
+class HuBERTXLEmbedding(W2VBERTEmbedding):
+    def __init__(self):
+        super().__init__("facebook/hubert-xlarge-ll60k")
+
+
+class HuBERTLargeEmbedding(W2VBERTEmbedding):
     def __init__(self):
         super().__init__("facebook/hubert-large-ll60k")
 
 
-class HuBERTBaseEmbedding(HuBERTXLEmbedding):
+class HuBERTBaseEmbedding(W2VBERTEmbedding):
     def __init__(self):
         super().__init__("facebook/hubert-base-ls960")
 
+
+###########
+# wav2vec #
+###########
+class Wav2VecEmbedding(W2VBERTEmbedding):
+    def __init__(self):
+        super().__init__("facebook/wav2vec2-large-xlsr-53")
+
+
+#########
+# XLS-R #
+#########
+class XLSR2BEmbedding(W2VBERTEmbedding):
+    def __init__(self):
+        super().__init__("facebook/wav2vec2-xls-r-2b")
+
+
+class XLSR1BEmbedding(W2VBERTEmbedding):
+    def __init__(self):
+        super().__init__("facebook/wav2vec2-xls-r-1b")
+
+
+class XLSR300MEmbedding(W2VBERTEmbedding):
+    def __init__(self):
+        super().__init__("facebook/wav2vec2-xls-r-300m")

model_w2v_bert.py

@@ -1,28 +0,0 @@
-"""Meta's w2vBERT based speaker embedding.
-- feature dimension: 1024
-- source: https://huggingface.co/facebook/w2v-bert-2.0
-"""
-from typing import Optional
-
-import torch
-import librosa
-import numpy as np
-from transformers import Wav2Vec2BertModel, AutoFeatureExtractor
-
-
-class W2VBERTEmbedding:
-    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()
-
-    def get_speaker_embedding(self, wav: np.ndarray, sampling_rate: Optional[int] = None) -> np.ndarray:
-        # audio file is decoded on the fly
-        if sampling_rate != self.processor.sampling_rate:
-            wav = librosa.resample(wav, orig_sr=sampling_rate, target_sr=self.processor.sampling_rate)
-        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.last_hidden_state.mean(1).cpu().numpy()[0]

model_xls.py

@@ -1,47 +0,0 @@
-"""Meta's XLS-R based speaker embedding.
-- feature dimension: 768
-- source: https://huggingface.co/docs/transformers/en/model_doc/wav2vec2#transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput
-"""
-from typing import Optional
-
-import torch
-import librosa
-import numpy as np
-from transformers import AutoFeatureExtractor, AutoModelForPreTraining
-
-
-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()
-
-    def get_speaker_embedding(self, wav: np.ndarray, sampling_rate: Optional[int] = None) -> np.ndarray:
-        # audio file is decoded on the fly
-        if sampling_rate != self.processor.sampling_rate:
-            wav = librosa.resample(wav, orig_sr=sampling_rate, target_sr=self.processor.sampling_rate)
-        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.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

@@ -2,9 +2,7 @@ import librosa
 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 XLSR300MEmbedding
-from model_hubert import HuBERTXLEmbedding
+from model_speaker_embedding import W2VBERTEmbedding, XLSR300MEmbedding, HuBERTXLEmbedding
 
 
 def test():