Create app.py

app.py

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+import streamlit as st
+from pocketsphinx import LiveSpeech, get_model_path
+import os
+import sounddevice as sd
+import numpy as np
+import scipy.io.wavfile as wav
+
+# Function to capture audio from the mic
+def record_audio(filename='temp.wav', duration=5, fs=16000):
+    st.text("Recording Audio...")
+    with st.spinner(f'Recording for {duration} seconds...'):
+        myrecording = sd.rec(int(duration * fs), samplerate=fs, channels=1)
+        sd.wait()  # Waits until recording is finished
+        wav.write(filename, fs, myrecording)  # Save as WAV file 
+
+# Get the model path for pocketsphinx
+model_path = get_model_path()
+config = {
+    'verbose': False,
+    'hmm': os.path.join(model_path, 'en-us'),
+    'lm': os.path.join(model_path, 'en-us.lm.bin'),
+    'dict': os.path.join(model_path, 'cmudict-en-us.dict')
+}
+
+# Streamlit UI
+st.title("Simple Speech Recognition with Streamlit and PocketSphinx")
+button = st.button("Press to Speak")
+
+# Store the state of the recording
+if 'recording_done' not in st.session_state:
+    st.session_state.recording_done = False
+
+# When the button is pressed
+if button:
+    # Record the audio
+    record_audio()
+    st.session_state.recording_done = True
+
+# If an audio was recorded, process it with PocketSphinx
+if st.session_state.recording_done:
+    audio = LiveSpeech(**config)
+
+    st.text("Processing Audio...")
+    with st.spinner('Recognizing...'):
+        for phrase in audio:
+            st.write(phrase)
+            break  # We'll stop after the first phrase
+
+    # Reset the state
+    st.session_state.recording_done = False