Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import torch
+
+# Detect device and dtype for efficiency/memory
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Load all 4 models (with chunking for long audio)
+pipe1 = pipeline(
+    "automatic-speech-recognition",
+    model="IJyad/whisper-large-v3-Tarteel",
+    torch_dtype=dtype,
+    device=device,
+    chunk_length_s=30,
+)
+pipe2 = pipeline(
+    "automatic-speech-recognition",
+    model="deepdml/whisper-medium-ar-quran-mix-norm",
+    torch_dtype=dtype,
+    device=device,
+    chunk_length_s=30,
+)
+pipe3 = pipeline(
+    "automatic-speech-recognition",
+    model="naazimsnh02/whisper-large-v3-turbo-ar-quran",
+    torch_dtype=dtype,
+    device=device,
+    chunk_length_s=30,
+)
+pipe4 = pipeline(
+    "automatic-speech-recognition",
+    model="Habib-HF/tarbiyah-ai-whisper-medium-merged",
+    torch_dtype=dtype,
+    device=device,
+    chunk_length_s=30,
+)
+
+def transcribe(audio):
+    if audio is None:
+        return "No audio", "No audio", "No audio", "No audio"
+    
+    # Force Arabic language for consistency (these models are Arabic/Quran specialized)
+    kwargs = {"language": "arabic", "task": "transcribe"}
+    
+    text1 = pipe1(audio, generate_kwargs=kwargs)["text"]
+    text2 = pipe2(audio, generate_kwargs=kwargs)["text"]
+    text3 = pipe3(audio, generate_kwargs=kwargs)["text"]
+    text4 = pipe4(audio, generate_kwargs=kwargs)["text"]
+    
+    return text1, text2, text3, text4
+
+with gr.Blocks(title="Quran Whisper Models Comparison") as demo:
+    gr.Markdown("""
+    # Quran ASR Models Comparison
+    
+    Upload or record a short Quranic recitation and compare transcriptions side-by-side.
+    
+    Models:
+    - IJyad/whisper-large-v3-Tarteel (large-v3, high accuracy)
+    - deepdml/whisper-medium-ar-quran-mix-norm (medium)
+    - naazimsnh02/whisper-large-v3-turbo-ar-quran (turbo, fast & accurate)
+    - Habib-HF/tarbiyah-ai-whisper-medium-merged (medium, merged general + Quran)
+    """)
+    
+    audio_input = gr.Audio(
+        sources=["microphone", "upload"],
+        type="filepath",
+        label="Record from mic or upload audio file (WAV/MP3, preferably Quran recitation)"
+    )
+    
+    btn = gr.Button("Transcribe with all 4 models")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### IJyad/whisper-large-v3-Tarteel")
+            out1 = gr.Textbox(label="Transcription", lines=6, rtl=True)
+        with gr.Column():
+            gr.Markdown("### deepdml/whisper-medium-ar-quran-mix-norm")
+            out2 = gr.Textbox(label="Transcription", lines=6, rtl=True)
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### naazimsnh02/whisper-large-v3-turbo-ar-quran")
+            out3 = gr.Textbox(label="Transcription", lines=6, rtl=True)
+        with gr.Column():
+            gr.Markdown("### Habib-HF/tarbiyah-ai-whisper-medium-merged")
+            out4 = gr.Textbox(label="Transcription", lines=6, rtl=True)
+    
+    btn.click(transcribe, inputs=audio_input, outputs=[out1, out2, out3, out4])
+    
+    gr.Markdown("""
+    **Notes:**
+    - Best for short Quran recitations (mic recordings are usually <30s).
+    - Transcriptions are plain Arabic text (no tashkeel/diacritics in most cases).
+    - GPU highly recommended — CPU will be slow.
+    - These models are Quran-specialized; general Arabic speech may not work well.
+    """)
+
+demo.queue()  # Helps with concurrent users
+demo.launch()