Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+import torch
+from transformers import pipeline
+import soundfile as sf
+import tempfile
+import shutil
+import os
+import librosa
+import time
+
+
+def resample_to_16k(audio, orig_sr):
+    y_resampled = librosa.resample(y=audio, orig_sr=orig_sr, target_sr = 16000)
+    return y_resampled
+
+def transcribe(audio,):
+    sr,y = audio
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+    y_resampled = resample_to_16k(y, sr)
+    
+    
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio:
+        temp_audio_path = temp_audio.name
+        sf.write(temp_audio_path, y_resampled, 16000)
+
+    command = f"""main.exe -m 'I:\\ASR\\Whisper CPP\\SubGen\\whisper_blas_bin_v1_3_0\\models\\ggml-model-whisper-small.en.bin' -osrt -f '{temp_audio_path}' -nt"""
+    
+    start_time = time.time()
+    result = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+    end_time = time.time()
+    print("Output",result.stdout)
+    print("Error",result.stderr)
+    transcription = result.stdout
+    print(transcription)
+    
+    print("--------------------------")
+    print(f"Execution time: {end_time - start_time} seconds")
+    return transcription