Update app.py

app.py

@@ -4,19 +4,19 @@ from snac import SNAC
 import soundfile as sf
 import gradio as gr
 
-# Load models
+device = "cpu"
+
 model = AutoModelForCausalLM.from_pretrained(
     "maya-research/maya1",
-    torch_dtype=torch.bfloat16,
-    device_map="auto"
+    dtype=torch.bfloat16,
+    device_map=None
 )
 tokenizer = AutoTokenizer.from_pretrained("maya-research/maya1")
-snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().to("cuda")
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().to(device)
 
-# Main generation function
 def generate_voice(description, text):
     prompt = f'<description="{description}"> {text}'
-    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
     with torch.inference_mode():
         outputs = model.generate(
             **inputs,
@@ -25,28 +25,30 @@ def generate_voice(description, text):
             top_p=0.9,
             do_sample=True
         )
-
     generated_ids = outputs[0, inputs['input_ids'].shape[1]:]
     snac_tokens = [t.item() for t in generated_ids if 128266 <= t <= 156937]
-
     frames = len(snac_tokens) // 7
     codes = [[], [], []]
     for i in range(frames):
-        s = snac_tokens[i*7:(i+1)*7]
-        codes[0].append((s[0]-128266) % 4096)
-        codes[1].extend([(s[1]-128266) % 4096, (s[4]-128266) % 4096])
-        codes[2].extend([(s[2]-128266) % 4096, (s[3]-128266) % 4096,
-                         (s[5]-128266) % 4096, (s[6]-128266) % 4096])
-
-    codes_tensor = [torch.tensor(c, dtype=torch.long, device="cuda").unsqueeze(0) for c in codes]
+        s = snac_tokens[i * 7:(i + 1) * 7]
+        codes[0].append((s[0] - 128266) % 4096)
+        codes[1].extend([(s[1] - 128266) % 4096, (s[4] - 128266) % 4096])
+        codes[2].extend([
+            (s[2] - 128266) % 4096,
+            (s[3] - 128266) % 4096,
+            (s[5] - 128266) % 4096,
+            (s[6] - 128266) % 4096
+        ])
+    codes_tensor = [
+        torch.tensor(c, dtype=torch.long, device=device).unsqueeze(0)
+        for c in codes
+    ]
     with torch.inference_mode():
         audio = snac_model.decoder(snac_model.quantizer.from_codes(codes_tensor))[0, 0].cpu().numpy()
-
     out_path = "output.wav"
     sf.write(out_path, audio, 24000)
     return out_path
 
-# Gradio interface — no preset text, fully user-controlled
 demo = gr.Interface(
     fn=generate_voice,
     inputs=[
@@ -54,8 +56,8 @@ demo = gr.Interface(
         gr.Textbox(label="Text to Speak (type anything you want)")
     ],
     outputs=gr.Audio(label="Generated Speech"),
-    title="🎙️ Maya1 Voice Generator",
-    description="Generate expressive emotional speech using the open-source Maya1 + SNAC pipeline."
+    title="🎙️ Maya1 Voice Generator (CPU-only)",
+    description="Generate expressive emotional speech using Maya1 + SNAC on CPU."
 )
 
 if __name__ == "__main__":