Create app.py

app.py

@@ -0,0 +1,57 @@
+import streamlit as st
+import torch
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from peft import PeftModel
+import numpy as np
+import pyaudio
+
+# Tải mô hình
+@st.cache_resource
+def load_model():
+    base_model_id = "openai/whisper-tiny"
+    adapter_id = "longhoang2112/whisper-turbo-fine-tuning-adapters"
+    
+    processor = WhisperProcessor.from_pretrained(base_model_id)
+    model = WhisperForConditionalGeneration.from_pretrained(base_model_id)
+    try:
+        model = PeftModel.from_pretrained(model, adapter_id)
+        model.set_active_adapters(adapter_id)
+    except:
+        st.warning("Adapter loading failed. Using base model.")
+    
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+    return processor, model, device
+
+processor, model, device = load_model()
+
+# Ghi âm
+def record_audio(duration=5, sample_rate=16000):
+    CHUNK = 1024
+    FORMAT = pyaudio.paFloat32
+    CHANNELS = 1
+    p = pyaudio.PyAudio()
+    stream = p.open(format=FORMAT, channels=CHANNELS, rate=sample_rate, input=True, frames_per_buffer=CHUNK)
+    
+    st.write(f"Đang ghi âm... ({duration} giây)")
+    frames = []
+    for _ in range(0, int(sample_rate / CHUNK * duration)):
+        data = stream.read(CHUNK)
+        frames.append(np.frombuffer(data, dtype=np.float32))
+    
+    stream.stop_stream()
+    stream.close()
+    p.terminate()
+    return np.concatenate(frames), sample_rate
+
+# Giao diện
+st.title("Whisper Turbo với Adapter")
+duration = st.slider("Thời gian ghi âm (giây):", 1, 10, 5)
+
+if st.button("Ghi âm"):
+    audio, sample_rate = record_audio(duration)
+    input_features = processor(audio, sampling_rate=sample_rate, return_tensors="pt").input_features.to(device)
+    with torch.no_grad():
+        predicted_ids = model.generate(input_features)
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+    st.write("**Kết quả:**", transcription)