Reduce VAD memory usage (#1198)

Co-authored-by: Mahmoud Ashraf <hassouna97.ma@gmail.com>

faster_whisper/vad.py

@@ -260,8 +260,9 @@ def __init__(self, encoder_path, decoder_path):
             ) from e
 
         opts = onnxruntime.SessionOptions()
-        opts.inter_op_num_threads = 0
-        opts.intra_op_num_threads = 0
+        opts.inter_op_num_threads = 1
+        opts.intra_op_num_threads = 1
+        opts.enable_cpu_mem_arena = False
         opts.log_severity_level = 4
 
         self.encoder_session = onnxruntime.InferenceSession(
@@ -301,7 +302,16 @@ def __call__(
 
         batched_audio = batched_audio.reshape(-1, num_samples + context_size_samples)
 
-        encoder_output = self.encoder_session.run(None, {"input": batched_audio})[0]
+        encoder_batch_size = 10000
+        num_segments = batched_audio.shape[0]
+        encoder_outputs = []
+        for i in range(0, num_segments, encoder_batch_size):
+            encoder_output = self.encoder_session.run(
+                None, {"input": batched_audio[i : i + encoder_batch_size]}
+            )[0]
+            encoder_outputs.append(encoder_output)
+
+        encoder_output = np.concatenate(encoder_outputs, axis=0)
         encoder_output = encoder_output.reshape(batch_size, -1, 128)
 
         decoder_outputs = []