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63ef013 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | 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() |