Create app.py

app.py

@@ -0,0 +1,77 @@
+import streamlit as st
+import torch
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+import torchaudio
+import os
+
+def load_models():
+    st.session_state.transcription_pipe = pipeline(
+        task="automatic-speech-recognition",
+        model="alvanlii/whisper-small-cantonese",
+        chunk_length_s=60,
+        device="cuda" if torch.cuda.is_available() else "cpu"
+    )
+    st.session_state.transcription_pipe.model.config.forced_decoder_ids = st.session_state.transcription_pipe.tokenizer.get_decoder_prompt_ids(language="zh", task="transcribe")
+    
+    st.session_state.translation_tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
+    st.session_state.translation_model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
+    
+    st.session_state.summary_pipe = pipeline("text-summarization", model="facebook/bart-large-cnn")
+    
+    st.session_state.rating_pipe = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
+
+def transcribe_audio(audio_path):
+    pipe = st.session_state.transcription_pipe
+    return pipe(audio_path)["text"]
+
+def translate_text(text):
+    tokenizer = st.session_state.translation_tokenizer
+    model = st.session_state.translation_model
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model.generate(inputs["input_ids"], max_length=1000, num_beams=5)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+def summarize_text(text):
+    return st.session_state.summary_pipe(text)[0]['summary_text']
+
+def rate_quality(text):
+    result = st.session_state.rating_pipe(text)[0]
+    label_map = {"LABEL_0": "Poor", "LABEL_1": "Average", "LABEL_2": "Good"}
+    return label_map.get(result["label"], "Unknown")
+
+def main():
+    st.title("Audio Processing & Conversation Quality Rating")
+    
+    if "transcription_pipe" not in st.session_state:
+        with st.spinner("Loading models..."):
+            load_models()
+    
+    uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "m4a"])
+    
+    if uploaded_file is not None:
+        with st.spinner("Processing audio..."):
+            file_path = "temp_audio.wav"
+            with open(file_path, "wb") as f:
+                f.write(uploaded_file.read())
+            
+            transcript = transcribe_audio(file_path)
+            translation = translate_text(transcript)
+            summary = summarize_text(translation)
+            rating = rate_quality(translation)
+            
+            os.remove(file_path)
+        
+        st.subheader("Transcription")
+        st.write(transcript)
+        
+        st.subheader("Translation (English)")
+        st.write(translation)
+        
+        st.subheader("Summary")
+        st.write(summary)
+        
+        st.subheader("Conversation Quality Rating")
+        st.write(rating)
+
+if __name__ == "__main__":
+    main()