Create app.py

app.py

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+import streamlit as st
+import sounddevice as sd
+import numpy as np
+import torch
+from transformers import pipeline
+
+# Load the pipelines
+asr_pipe = pipeline("automatic-speech-recognition", model="alvanlii/whisper-small-cantonese")
+translation_pipe = pipeline("translation", model="raptorkwok/cantonese-chinese-translation")
+tts_pipe = pipeline("text-to-speech", model="myshell-ai/MeloTTS-Chinese")
+
+# Function to record audio
+def record_audio(duration=5, fs=16000):
+    st.write("Recording...")
+    audio = sd.rec(int(duration * fs), samplerate=fs, channels=1, dtype='float32')
+    sd.wait()
+    st.write("Recording complete.")
+    return audio.flatten()
+
+# Function to play audio
+def play_audio(audio, fs=16000):
+    sd.play(audio, fs)
+    sd.wait()
+
+# Streamlit UI
+st.title("Cantonese to Chinese Translator")
+st.write("Click the button below to record your Cantonese speech.")
+
+if st.button("Record"):
+    audio = record_audio()
+
+    # Recognize Cantonese speech
+    audio_input = torch.tensor(audio)
+    result = asr_pipe(audio_input)
+    cantonese_text = result['text']
+    st.write(f"Cantonese Text: {cantonese_text}")
+
+    # Translate Cantonese to Chinese
+    chinese_text = translation_pipe(cantonese_text)[0]['translation_text']
+    st.write(f"Chinese Text: {chinese_text}")
+
+    # Convert Chinese text to speech
+    tts_output = tts_pipe(chinese_text)
+
+    # Play back the Chinese output
+    st.write("Playing back the Chinese translation...")
+    play_audio(tts_output['audio'])
+
+# Run the app using the command:
+# streamlit run app.py