update

app.py

@@ -4,10 +4,11 @@ import matplotlib.pyplot as plt
 import sounddevice as sd
 import numpy as np
 import pandas as pd
-import torch
-import torchaudio
+# import torch
+# import torchaudio
+import wave
+import io
 from scipy.io import wavfile
-
 # MODEL LOADING and INITIALISATION
 # model = torch.jit.load("snorenetv1_small.ptl")
 # model.eval()
@@ -52,9 +53,27 @@ with st.expander('About this App'):
 
 wav_audio_data = st_audiorec()
 if wav_audio_data is not None:
-    np_wav = np.frombuffer(wav_audio_data, dtype=np.int16)
-    # input_tensor = torch.tensor(np_wav[:RATE])
-    # st.write(input_tensor.shape)
+    
+    data = np.frombuffer(wav_audio_data, dtype=np.int16)
+    st.write(len(data))
+    duration = len(data)//100000
+    num_of_samples = len(data)
+    sample_rate = num_of_samples // duration
+    # data = np.array(wav_audio_data, dtype=float)
+    max_abs_value = np.max(np.abs(data))
+    np_array = (data/max_abs_value) * 32767
+    scaled_data = np_array.astype(np.int16).tobytes()
+    with io.BytesIO() as fp, wave.open(fp, mode="wb") as waveobj:
+        waveobj.setnchannels(1)
+        waveobj.setframerate(sample_rate)
+        waveobj.setsampwidth(2)
+        waveobj.setcomptype("NONE", "NONE")
+        waveobj.writeframes(scaled_data)
+        wav_make = fp.getvalue()
+    
+    with open("output.wav", 'wb') as wav_file:
+        wav_file.write(wav_make)
+    st.audio("output.wav" , format="audio/wav")
     # st.audio(loaded_waveform, "audio/wav")
     # PERCENTAGE OF SNORING PLOT