Update asr_diarizer.py

asr_diarizer.py

@@ -1,12 +1,10 @@
 from typing import List, Optional, Union
 
 import numpy as np
-import torch
-from torchaudio import functional as F
-
 import requests
-
+import torch
 from pyannote.audio import Pipeline
+from torchaudio import functional as F
 from transformers import pipeline
 from transformers.pipelines.audio_utils import ffmpeg_read
 
@@ -19,7 +17,7 @@ class ASRDiarizationPipeline:
     ):
         self.asr_pipeline = asr_pipeline
         self.diarization_pipeline = diarization_pipeline
-        
+
         self.sampling_rate = self.asr_pipeline.feature_extractor.sampling_rate
 
     @classmethod
@@ -138,7 +136,7 @@ class ASRDiarizationPipeline:
             end_timestamps = end_timestamps[upto_idx + 1 :]
 
         return segmented_preds
-    
+
     def preprocess(self, inputs):
         if isinstance(inputs, str):
             if inputs.startswith("http://") or inputs.startswith("https://"):
@@ -169,9 +167,7 @@ class ASRDiarizationPipeline:
             in_sampling_rate = inputs.pop("sampling_rate")
             inputs = _inputs
             if in_sampling_rate != self.sampling_rate:
-                inputs = F.resample(
-                    torch.from_numpy(inputs), in_sampling_rate, self.sampling_rate
-                ).numpy()
+                inputs = F.resample(torch.from_numpy(inputs), in_sampling_rate, self.sampling_rate).numpy()
 
         if not isinstance(inputs, np.ndarray):
             raise ValueError(f"We expect a numpy ndarray as input, got `{type(inputs)}`")