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Reduce VAD memory usage (#1198)

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Co-authored-by: Mahmoud Ashraf <hassouna97.ma@gmail.com>
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2024-12-12 12:23:30 +00:00
committed by GitHub
parent b568faec40
commit 1b24f284c9
1 changed files with 13 additions and 3 deletions
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16
faster_whisper/vad.py
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@@ -260,8 +260,9 @@ class SileroVADModel:
) 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 @@ class SileroVADModel:
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 = []