warmup

app.py

@@ -5,6 +5,7 @@ from transformers.pipelines.audio_utils import ffmpeg_read
 import torch
 import gradio as gr
 import time
+import copy
 import numpy as np
 
 BATCH_SIZE = 16
@@ -98,7 +99,6 @@ def transcribe(inputs):
         return result
 
     pipe._forward = _forward_time
-    print(inputs)
     text = pipe(inputs, batch_size=BATCH_SIZE)["text"]
 
     yield distil_text, distil_runtime, text, runtime
@@ -109,8 +109,8 @@ inputs = np.random.randn(30 * pipe.feature_extractor.sampling_rate)
 inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
 
 for _ in range(N_WARMUP):
-    _ = pipe_forward(inputs.copy(), batch_size=BATCH_SIZE)["text"]
-    _ = distil_pipe_forward(inputs.copy(), batch_size=BATCH_SIZE)["text"]
+    _ = pipe_forward(copy.deepcopy(inputs), batch_size=BATCH_SIZE)["text"]
+    _ = distil_pipe_forward(copy.deepcopy(inputs), batch_size=BATCH_SIZE)["text"]
     print(_)
 print("Models warmed up!")