app update

app.py

@@ -1,12 +1,5 @@
 import streamlit as st
-import websockets
-import asyncio
-import base64
-import json
-import pyaudio
-import os
-from pathlib import Path
-import numpy as np
+from st_audiorec import st_audiorec
 
 # Session state
 if 'text' not in st.session_state:
@@ -17,19 +10,11 @@ if 'text' not in st.session_state:
 st.sidebar.header('Audio Parameters')
 
 FRAMES_PER_BUFFER = int(st.sidebar.text_input('Frames per buffer', 3200))
-FORMAT = pyaudio.paInt16
+FORMAT = 'audio/wav'
 CHANNELS = 1
 RATE = int(st.sidebar.text_input('Rate', 16000))
-p = pyaudio.PyAudio()
 
 # Open an audio stream
-stream = p.open(
-    format=FORMAT,
-    channels=CHANNELS,
-    rate=RATE,
-    input=True,
-    frames_per_buffer=FRAMES_PER_BUFFER
-)
 
 monitoring = False
 audio_data = []
@@ -58,19 +43,19 @@ col3.button('Stop', on_click=stop_monitoring)
 record_button = col3.button('Monitor')
 
 while st.session_state.run:
-    audio_chunk = stream.read(FRAMES_PER_BUFFER)
-    audio_data.append(np.frombuffer(audio_chunk, dtype=np.int16))
-
-    if monitoring and len(audio_chunk) >= RATE // FRAMES_PER_BUFFER:
-        audio_data_chunk = np.concatenate(audio_chunk[:RATE//FRAMES_PER_BUFFER])
-        print("PROCESSING BY MODEL GOES HERE")
-        # if model is not None:
-        #     input_tensor = torch.tensor(audio_data_chunk)
-        #     with torch.no_grad():
-        #         output = model(input_tensor)
+    wav_audio_data = st_audiorec()
+    if wav_audio_data is not None:
+        st.audio(wav_audio_data, format=FORMAT, sample_rate=16000)
+
+    # audio_chunk = stream.read(FRAMES_PER_BUFFER)
+    # audio_data.append(np.frombuffer(audio_chunk, dtype=np.int16))
+
+    # if monitoring and len(audio_chunk) >= RATE // FRAMES_PER_BUFFER:
+    #     audio_data_chunk = np.concatenate(audio_chunk[:RATE//FRAMES_PER_BUFFER])
+    #     print("PROCESSING BY MODEL GOES HERE")
+    #     # if model is not None:
+    #     #     input_tensor = torch.tensor(audio_data_chunk)
+    #     #     with torch.no_grad():
+    #     #         output = model(input_tensor)
         
-        audio_chunk = audio_chunk[1:]
-
-stream.stop_stream()
-stream.close()
-p.terminate()
+    #     audio_chunk = audio_chunk[1:]