Upload app.py

app.py

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+# Load model directly
+import streamlit as st
+import torch
+import numpy as np
+import librosa
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq, pipeline
+from io import BytesIO
+
+# Configure Streamlit page settings
+st.set_page_config(
+    page_title="Transcribe with Whisper",
+    page_icon=":rocket:",
+    layout="centered"
+)
+
+st.title("🎙️ Whisper Audio Transcriber")
+st.divider()
+
+# Set up device and data type
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Load the Whisper model and processor
+with st.spinner("🚀 Loading Whisper model... please wait!"):
+    model_name = "openai/whisper-large-v3"
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_name, 
+        torch_dtype=torch_dtype, 
+        low_cpu_mem_usage=True, 
+        use_safetensors=True
+    )
+    model.to(device)
+    processor = AutoProcessor.from_pretrained(model_name)
+
+# Initialize ASR pipeline
+asr_pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+st.markdown("Upload your audio files, and let the Whisper model transcribe them instantly. 🚀")
+
+# File uploader for audio files
+uploaded_files = st.file_uploader("📂 Select audio files to transcribe", type=["wav","mp3"], accept_multiple_files=True)
+
+# Transcription button and result display
+if uploaded_files:
+    if st.button("✍️ Transcribe"):
+        results = []
+
+        for idx, audio_file in enumerate(uploaded_files):
+            try:
+                # Read audio file
+                audio_data, sr = librosa.load(BytesIO(audio_file.read()), sr=16000)
+
+                # Run ASR pipeline
+                result = asr_pipe(audio_data)
+                transcription = result['text']
+                results.append((audio_file.name, transcription))
+            except Exception as e:
+                st.error(f"Error processing '{audio_file.name}':{e}")
+
+        # Display results
+        st.subheader("Transcriptions")
+        for filename, transcription in results:
+            st.text_area(f"📂 **{filename}**:", value=transcription)
+else:
+    st.info("📤 Please upload audio files to start transcription.")