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# 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()
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