Create app.py

app.py

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+import streamlit as st
+import sounddevice as sd
+import tempfile
+from scipy.io.wavfile import write
+import whisper
+import numpy as np
+if 'recording' not in st.session_state:
+    st.session_state.recording = False
+
+def start_recording():
+    st.session_state.recording = True
+    st.write("Recording started... Click 'Stop' to end.")
+    return sd.rec(int(10 * 44100), samplerate=44100, channels=1, dtype='float64', blocking=False)
+
+def stop_recording(recording):
+    st.session_state.recording = False
+    sd.stop()
+    st.write("Converting speech to text...")
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.wav')
+    write(temp_file.name, 44100, recording)
+    return temp_file.name
+
+def transcribe_audio(file_path):
+    model = whisper.load_model("base")
+    result = model.transcribe(file_path)
+    return result['text']
+
+# Streamlit UI
+st.title("🗣️Brise")
+
+if st.button('Start Recording') and not st.session_state.recording:
+    st.session_state.audio_data = start_recording()
+
+if st.button('Stop Recording') and st.session_state.recording:
+    file_path = stop_recording(st.session_state.audio_data)
+    transcription = transcribe_audio(file_path)
+    st.text_area("Transcription", value=transcription, height=200)