Add application file

app.py

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+import torch
+import gradio as gr
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+# Set up GPU if available
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Load Whisper model
+model_id = "openai/whisper-large-v3"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+).to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+# Initialize Whisper ASR pipeline
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+# Function to transcribe audio
+def transcribe(audio_file):
+    if not audio_file:
+        return "Error: No audio provided."
+
+    # Run ASR pipeline on the WAV file
+    result = pipe(audio_file)
+    return result["text"]
+
+# Create Gradio UI with WAV format
+demo = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath", label="Record or Upload WAV Audio"),
+    outputs=gr.Textbox(),
+    title="Whisper ASR (Speech-to-Text)",
+    description="Transcribe spoken words into text using OpenAI Whisper Large V3. Supports WAV format.",
+    live=True,
+)
+
+# Launch Gradio app
+demo.launch()