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app.py

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+import gradio as gr
+import ffmpeg
+import io
+import tempfile
+import srt
+import datetime
+from transformers import WhisperForConditionalGeneration, WhisperProcessor, pipeline, MT5ForConditionalGeneration, MT5Tokenizer
+
+# مدل‌ها
+asr_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base.en")
+asr_processor = WhisperProcessor.from_pretrained("openai/whisper-base.en")
+translator = pipeline("text2text-generation", model="persiannlp/mt5-small-parsinlu-translation_en_fa", tokenizer="persiannlp/mt5-small-parsinlu-translation_en_fa")
+
+def process(video):
+    # استخراج فایل صوتی
+    ytmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+    (
+        ffmpeg
+        .input(video)
+        .output(ytmp.name, format="wav", ac=1, ar="16000")
+        .run(quiet=True, overwrite_output=True)
+    )
+    # ASR
+    input_feat = asr_processor(ytmp.name, return_tensors="pt", sampling_rate=16000)
+    out = asr_model.generate(**input_feat)
+    segments = asr_processor.batch_decode(out, skip_special_tokens=True)  # متن کامل
+
+    # برای زمان‌بندی دستی، تقسیم به جملات
+    subs = []
+    text = segments[0]
+    lines = [l for l in text.split('.') if l.strip()]
+    for i, line in enumerate(lines):
+        start = datetime.timedelta(seconds=i*5)
+        end = datetime.timedelta(seconds=(i+1)*5)
+        # ترجمه
+        tr = translator(line.strip(), max_length=128)[0]['generated_text']
+        subs.append(srt.Subtitle(index=i+1, start=start, end=end, content=tr))
+
+    srt_data = srt.compose(subs)
+    return video, srt_data
+
+demo = gr.Interface(
+    fn=process,
+    inputs=gr.Video(source="upload", format="mp4"),
+    outputs=[gr.Video(), gr.Textbox(label="Subtitles (SRT)")],
+    title="Offline English→Persian Subtitle Maker",
+    description="ویدیو را آپلود کنید؛ زیرنویس فارسی تولید می‌شود."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers
+gradio
+ffmpeg-python
+srt
+torch