Fabian
91 lines
3.3 KiB
Python
91 lines
3.3 KiB
Python
#!/usr/bin/env python3
|
|
"""Deep engine init diagnostic — catch every failure point."""
|
|
import os, sys, traceback, logging
|
|
from pathlib import Path
|
|
|
|
# Configure ALL loggers to console
|
|
logging.basicConfig(level=logging.DEBUG, stream=sys.stderr, format='%(name)s %(levelname)s %(message)s')
|
|
|
|
MODEL_DIR = "/models/qwen3-tts"
|
|
parent = os.path.dirname(os.path.abspath(MODEL_DIR))
|
|
basename = os.path.basename(MODEL_DIR)
|
|
os.chdir(parent)
|
|
print(f"cwd={Path.cwd()}, basename={basename}", flush=True)
|
|
|
|
project_root = Path.cwd()
|
|
model_dir = project_root / basename
|
|
print(f"model_dir={model_dir}", flush=True)
|
|
|
|
# Step 1: AssetsManager
|
|
print("\n=== Step 1: AssetsManager ===", flush=True)
|
|
try:
|
|
from qwen3_tts_gguf.inference.assets import AssetsManager
|
|
assets = AssetsManager(str(model_dir))
|
|
print(f" OK: assets={assets}", flush=True)
|
|
except Exception as e:
|
|
print(f" FAIL: {e}", flush=True)
|
|
traceback.print_exc()
|
|
|
|
# Step 2: Tokenizer
|
|
print("\n=== Step 2: Tokenizer ===", flush=True)
|
|
try:
|
|
from tokenizers import Tokenizer
|
|
tok = Tokenizer.from_file(str(model_dir / "tokenizer.json"))
|
|
print(f" OK: tokenizer loaded", flush=True)
|
|
except Exception as e:
|
|
print(f" FAIL: {e}", flush=True)
|
|
traceback.print_exc()
|
|
|
|
# Step 3: CodecEncoder
|
|
print("\n=== Step 3: CodecEncoder ===", flush=True)
|
|
try:
|
|
from qwen3_tts_gguf.inference.encoder import CodecEncoder
|
|
codec = CodecEncoder(str(model_dir / "qwen3_tts_codec_encoder.fp16.onnx"))
|
|
print(f" OK: codec_encoder={codec}", flush=True)
|
|
except Exception as e:
|
|
print(f" FAIL: {e}", flush=True)
|
|
traceback.print_exc()
|
|
|
|
# Step 4: SpeakerEncoder
|
|
print("\n=== Step 4: SpeakerEncoder ===", flush=True)
|
|
try:
|
|
from qwen3_tts_gguf.inference.encoder import SpeakerEncoder
|
|
spk = SpeakerEncoder(str(model_dir / "qwen3_tts_speaker_encoder.fp16.onnx"))
|
|
print(f" OK: speaker_encoder={spk}", flush=True)
|
|
except Exception as e:
|
|
print(f" FAIL: {e}", flush=True)
|
|
traceback.print_exc()
|
|
|
|
# Step 5: DecoderProxy
|
|
print("\n=== Step 5: DecoderProxy ===", flush=True)
|
|
try:
|
|
from qwen3_tts_gguf.inference.proxy import DecoderProxy
|
|
decoder = DecoderProxy(str(model_dir / "qwen3_tts_decoder.fp16.onnx"), onnx_provider="CPUExecutionProvider", chunk_size=12)
|
|
print(f" OK: decoder={decoder}", flush=True)
|
|
print(" Waiting for decoder ready (timeout=10)...", flush=True)
|
|
is_ready = decoder.wait_until_ready(timeout=10)
|
|
print(f" decoder ready={is_ready}", flush=True)
|
|
if hasattr(decoder, 'ready_states'):
|
|
print(f" ready_states={decoder.ready_states}", flush=True)
|
|
except Exception as e:
|
|
print(f" FAIL: {e}", flush=True)
|
|
traceback.print_exc()
|
|
|
|
# Step 6: LlamaModel
|
|
print("\n=== Step 6: LlamaModel (GGUF) ===", flush=True)
|
|
try:
|
|
from qwen3_tts_gguf.inference import llama
|
|
t_path = (model_dir / "qwen3_tts_talker.q5_k.gguf").relative_to(project_root).as_posix()
|
|
p_path = (model_dir / "qwen3_tts_predictor.q8_0.gguf").relative_to(project_root).as_posix()
|
|
print(f" talker_path={t_path}", flush=True)
|
|
print(f" predictor_path={p_path}", flush=True)
|
|
talker = llama.LlamaModel(t_path, n_gpu_layers=-1)
|
|
print(f" OK: talker={talker}", flush=True)
|
|
predictor = llama.LlamaModel(p_path, n_gpu_layers=-1)
|
|
print(f" OK: predictor={predictor}", flush=True)
|
|
except Exception as e:
|
|
print(f" FAIL: {e}", flush=True)
|
|
traceback.print_exc()
|
|
|
|
print("\n=== DONE ===", flush=True)
|