Create app.py

app.py

@@ -0,0 +1,126 @@
+# app.py
+import gradio as gr
+import torch
+import soundfile as sf
+from pathlib import Path
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel, get_peft_model, LoraConfig, TaskType
+from snac import SNAC
+
+# -----------------------------
+# CONFIG
+# -----------------------------
+MODEL_NAME = "rahul7star/nava1.0"   # Base Maya model
+LORA_NAME  = "rahul7star/nava-audio"  # LoRA adapter
+SEQ_LEN = 2048
+TARGET_SR = 24000
+OUT_ROOT = Path("/tmp/data")
+OUT_ROOT.mkdir(parents=True, exist_ok=True)
+
+# -----------------------------
+# GENERATE AUDIO (LoRA)
+# -----------------------------
+def generate_audio_cpu_lora(text: str):
+    logs = []
+    try:
+        DEVICE_CPU = "cpu"
+
+        # Load tokenizer and base model
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+        base_model = AutoModelForCausalLM.from_pretrained(
+            MODEL_NAME,
+            device_map={"": DEVICE_CPU},
+            torch_dtype=torch.float32,
+            trust_remote_code=True
+        )
+        logs.append("✅ Loaded base Maya model")
+
+        # Load LoRA adapter from HF Hub
+        model = PeftModel.from_pretrained(base_model, LORA_NAME, device_map={"": DEVICE_CPU})
+        model.eval()
+        logs.append(f"✅ Applied LoRA adapter from {LORA_NAME}")
+
+        # Build prompt: just text prompt
+        soh_token = tokenizer.decode([128259])
+        eoh_token = tokenizer.decode([128260])
+        soa_token = tokenizer.decode([128261])
+        sos_token = tokenizer.decode([128257])
+        eot_token = tokenizer.decode([128009])
+        bos_token = tokenizer.bos_token
+        prompt = soh_token + bos_token + text + eot_token + eoh_token + soa_token + sos_token
+
+        inputs = tokenizer(prompt, return_tensors="pt").to(DEVICE_CPU)
+
+        # Generate tokens
+        with torch.inference_mode():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=SEQ_LEN,
+                temperature=0.4,
+                top_p=0.9,
+                repetition_penalty=1.1,
+                do_sample=True,
+                eos_token_id=128258,
+                pad_token_id=tokenizer.pad_token_id
+            )
+        generated_ids = outputs[0, inputs['input_ids'].shape[1]:].tolist()
+        logs.append(f"✅ Generated {len(generated_ids)} token IDs")
+
+        # Extract SNAC codes
+        snac_min, snac_max = 128266, 156937
+        eos_id = 128258
+        try:
+            eos_idx = generated_ids.index(eos_id)
+        except ValueError:
+            eos_idx = len(generated_ids)
+        snac_tokens = [t for t in generated_ids[:eos_idx] if snac_min <= t <= snac_max]
+
+        # Unpack 7-token SNAC frames
+        l1, l2, l3 = [], [], []
+        frames = len(snac_tokens) // 7
+        snac_tokens = snac_tokens[:frames*7]
+        for i in range(frames):
+            slots = snac_tokens[i*7:(i+1)*7]
+            l1.append((slots[0]-128266)%4096)
+            l2.extend([(slots[1]-128266)%4096, (slots[4]-128266)%4096])
+            l3.extend([(slots[2]-128266)%4096, (slots[3]-128266)%4096, (slots[5]-128266)%4096, (slots[6]-128266)%4096])
+        logs.append(f"✅ Unpacked to {len(l1)} L1 frames, {len(l2)} L2 codes, {len(l3)} L3 codes")
+
+        # SNAC decoder
+        snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().to(DEVICE_CPU)
+        codes_tensor = [torch.tensor(level, dtype=torch.long, device=DEVICE_CPU).unsqueeze(0) for level in [l1,l2,l3]]
+        with torch.inference_mode():
+            z_q = snac_model.quantizer.from_codes(codes_tensor)
+            audio = snac_model.decoder(z_q)[0, 0].cpu().numpy()
+        if len(audio) > 2048:
+            audio = audio[2048:]
+
+        audio_path = OUT_ROOT / "tts_output_cpu_lora.wav"
+        sf.write(audio_path, audio, TARGET_SR)
+        logs.append(f"✅ Audio saved: {audio_path}, duration: {len(audio)/TARGET_SR:.2f}s")
+
+        return str(audio_path), "\n".join(logs)
+
+    except Exception as e:
+        import traceback
+        logs.append(f"[❌] CPU LoRA TTS error: {e}\n{traceback.format_exc()}")
+        return None, "\n".join(logs)
+
+# -----------------------------
+# GRADIO UI
+# -----------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("# Maya LoRA TTS (CPU)")
+    input_text = gr.Textbox(label="Enter text", lines=2, placeholder="Type Hindi text here...")
+    run_button = gr.Button("🔊 Generate Audio")
+    audio_output = gr.Audio(label="Generated Audio", type="filepath")
+    logs_output = gr.Textbox(label="Logs", lines=12)
+
+    run_button.click(
+        fn=generate_audio_cpu_lora,
+        inputs=[input_text],
+        outputs=[audio_output, logs_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()