Updates

app.py

@@ -6,6 +6,7 @@ import numpy as np
 import pandas as pd
 import torch
 import torchaudio
+from scipy.io import wavfile
 
 # MODEL LOADING and INITIALISATION
 # model = torch.jit.load("snorenetv1_small.ptl")
@@ -51,25 +52,12 @@ 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)
-    # num_segments = len(np_wav)//100000
-    # segments_list = []
-    # for i in range(num_segments):
-    #     start_sample = i * 100000
-    #     end_sample = (i + 1) * 100000
-    #     segment = np_wav[start_sample:end_sample]
-    #     segments_list.append(segment)
-    
-    # wav_tensor = torch.tensor(np_wav, dtype=torch.float32)
-    # segment_samples = int(44100 * 1)
-    # num_segments = len(wav_tensor) // segment_samples
-    # segments_list = []
-    # for i in range(num_segments):
-    #     start_sample = i * segment_samples
-    #     end_sample = (i + 1) * segment_samples
-    #     segment = wav_tensor[start_sample:end_sample]
-    #     segments_list.append(segment)
-    st.write(type(wav_audio_data))
+    output_filename = "audio_data.wav"
+    audio_data_bytes = wav_audio_data.decode('latin-1')
+    np_wav = np.frombuffer(audio_data_bytes, dtype=np.int16)
+    wavfile.write(output_filename, RATE, np_wav)
+    loaded_data = torchaudio.load("audio_data.wav")
+    st.write(loaded_data.shape)
     # PERCENTAGE OF SNORING PLOT