[pre-commit.ci] auto fixes from pre-commit.com hooks

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emo_gen.py

@@ -43,8 +43,8 @@ def __init__(self, config):
         self.init_weights()
 
     def forward(
-            self,
-            input_values,
+        self,
+        input_values,
     ):
         outputs = self.wav2vec2(input_values)
         hidden_states = outputs[0]
@@ -55,24 +55,24 @@ def forward(
 
 
 # load model from hub
-device = 'cuda'
-model_name = './emotion/wav2vec2-large-robust-12-ft-emotion-msp-dim'
+device = "cuda"
+model_name = "./emotion/wav2vec2-large-robust-12-ft-emotion-msp-dim"
 processor = Wav2Vec2Processor.from_pretrained(model_name)
 model = EmotionModel.from_pretrained(model_name).to(device)
 
 
 def process_func(
-        x: np.ndarray,
-        sampling_rate: int,
-        embeddings: bool = False,
+    x: np.ndarray,
+    sampling_rate: int,
+    embeddings: bool = False,
 ) -> np.ndarray:
     r"""Predict emotions or extract embeddings from raw audio signal."""
 
     # run through processor to normalize signal
     # always returns a batch, so we just get the first entry
     # then we put it on the device
     y = processor(x, sampling_rate=sampling_rate)
-    y = y['input_values'][0]
+    y = y["input_values"][0]
     y = torch.from_numpy(y).to(device)
 
     # run through model
@@ -83,18 +83,22 @@ def process_func(
     y = y.detach().cpu().numpy()
 
     return y
-
+
+
 rootpath = "/home/ubuntu/CVAEJETS/dataset/nene"
 embs = []
 wavnames = []
+
+
 def extract_dir(path):
     rootpath = path
     for idx, wavname in enumerate(os.listdir(rootpath)):
-        wav, sr =librosa.load(f"{rootpath}/{wavname}", 16000)
+        wav, sr = librosa.load(f"{rootpath}/{wavname}", 16000)
         emb = process_func(np.expand_dims(wav, 0), sr, embeddings=True)
         embs.append(emb)
         wavnames.append(wavname)
         np.save(f"{rootpath}/{wavname}.emo.npy", emb.squeeze(0))
         print(idx, wavname)
 
+
 extract_dir(rootpath)