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app.py

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+import gradio as gr
+import torch
+import re
+from dataclasses import dataclass
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from neucodec import NeuCodec
+
+@dataclass
+class Config:
+    model_name = "StepSharp/urdu-tts"
+    device_map = "auto"
+    max_new_tokens = 2048
+    temperature = 0.8
+    top_p = 0.95
+    repetition_penalty = 1.1
+
+
+class UrduTTS:
+    def __init__(self):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            Config.model_name
+        )
+
+        self.model = AutoModelForCausalLM.from_pretrained(
+            Config.model_name,
+            torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+            device_map=Config.device_map,
+        )
+
+        self.codec = NeuCodec.from_pretrained(
+            "neuphonic/neucodec"
+        ).eval().to(self.device)
+
+        vocab = self.tokenizer.get_vocab()
+        self.speech_end = vocab["<|im_end|>"]
+
+    def synthesize(self, text, description):
+
+        speaker = "OutteTTS-urdu-dataset_audio_uat_speaker"
+
+        prompt = (
+            f"<|im_start|>{speaker}: {text}"
+            f"<|description|>{description}"
+            f"<|speech_start|>"
+        )
+
+        inputs = self.tokenizer(
+            prompt,
+            return_tensors="pt"
+        )
+
+        input_ids = inputs.input_ids.to(self.device)
+
+        output = self.model.generate(
+            input_ids=input_ids,
+            max_new_tokens=2048,
+            do_sample=True,
+            temperature=0.8,
+            top_p=0.95,
+            repetition_penalty=1.1,
+            eos_token_id=self.speech_end,
+        )
+
+        decoded = self.tokenizer.decode(
+            output[0],
+            skip_special_tokens=False
+        )
+
+        audio_tokens = re.findall(
+            r"<\|s_(\d+)\|>",
+            decoded
+        )
+
+        audio_tokens = [int(x) for x in audio_tokens]
+
+        codes = (
+            torch.tensor(audio_tokens)
+            .unsqueeze(0)
+            .unsqueeze(0)
+            .to(self.device)
+        )
+
+        with torch.inference_mode():
+            waveform = self.codec.decode_code(codes)
+
+        audio = waveform[0, 0].cpu().numpy()
+
+        return 24000, audio
+
+# model load
+tts = UrduTTS()
+
+
+def generate_audio(text, description):
+    return tts.synthesize(text, description)
+    # return None
+
+
+with gr.Blocks(title="Urdu TTS") as demo:
+
+    gr.Markdown(
+        """
+        # Urdu Text-to-Speech
+        Enter Urdu text and generate speech.
+        """
+    )
+
+    text = gr.Textbox(
+        label="Urdu Text",
+        lines=4,
+        placeholder="اردو متن درج کریں"
+    )
+
+    description = gr.Textbox(
+        label="Voice Description",
+        value="A male Urdu speaker with a calm and clear tone."
+    )
+
+    btn = gr.Button("Generate Speech")
+
+    output = gr.Audio(
+        label="Generated Audio"
+    )
+
+    btn.click(
+        fn=generate_audio,
+        inputs=[text, description],
+        outputs=output
+    )
+
+    gr.Examples(
+        examples=[
+            ["میری عمر اس وقت 26 سال ہے اور اگلا سال 2025 ہوگا۔"],
+            ["براہ کرم چند لمحے انتظار کریں۔"],
+            ["محکمۂ موسمیات کے مطابق درجۂ حرارت ٤٦٫٨ ڈگری سینٹی گریڈ تک پہنچ سکتا ہے، لہٰذا شہری غیرضروری سفر سے گریز کریں۔"],
+            ["بین الاقوامی خلائی تحقیقاتی ادارے نے اعلان کیا کہ سیٹلائٹ “PakSat-X2”"],
+            ["اگر temperature 42.7 ڈگری سینٹی گریڈ سے تجاوز کر جائے تو server automatically shutdown ہو جائے گا۔"]
+        ],
+        inputs=text
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+torch
+transformers
+accelerate
+sentencepiece
+neucodec