Fabian
73 lines
3.0 KiB
Python
73 lines
3.0 KiB
Python
"""Diagnose qwen3-tts engine init - run inside container"""
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import sys, os, time, traceback
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os.chdir("/models")
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sys.path.insert(0, "/opt/qwen3-tts")
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os.environ["PYTHONUNBUFFERED"] = "1"
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print("=== Step 1: Assets + Tokenizer ===", flush=True)
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try:
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from qwen3_tts_gguf.inference.assets import AssetsManager
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from tokenizers import Tokenizer
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assets = AssetsManager("qwen3-tts")
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tok = Tokenizer.from_file("/models/qwen3-tts/tokenizer.json")
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print(f" OK: assets loaded, vocab_size={tok.get_vocab_size()}", flush=True)
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except Exception as e:
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print(f" FAIL: {e}", flush=True)
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traceback.print_exc()
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print("\n=== Step 2: Codec + Speaker Encoders ===", flush=True)
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try:
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from qwen3_tts_gguf.inference.encoder import CodecEncoder, SpeakerEncoder
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ce = CodecEncoder("/models/qwen3-tts/qwen3_tts_codec_encoder.fp16.onnx")
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se = SpeakerEncoder("/models/qwen3-tts/qwen3_tts_speaker_encoder.fp16.onnx")
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print(f" OK: codec_encoder + speaker_encoder loaded", flush=True)
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except Exception as e:
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print(f" FAIL: {e}", flush=True)
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traceback.print_exc()
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print("\n=== Step 3: DecoderProxy ===", flush=True)
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try:
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from qwen3_tts_gguf.inference.decoder import DecoderProxy
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t0 = time.time()
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dec = DecoderProxy("/models/qwen3-tts/qwen3_tts_decoder.fp16.onnx", onnx_provider="CPUExecutionProvider", chunk_size=2048)
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print(f" DecoderProxy created in {time.time()-t0:.2f}s", flush=True)
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print(f" Waiting for ready (20s timeout)...", flush=True)
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ready = dec.wait_until_ready(timeout=20)
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print(f" ready={ready}, states={getattr(dec, 'ready_states', 'N/A')}", flush=True)
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except Exception as e:
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print(f" FAIL: {e}", flush=True)
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traceback.print_exc()
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print("\n=== Step 4: GGUF / llama.cpp ===", flush=True)
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try:
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from qwen3_tts_gguf.inference import llama
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print(f" llama module loaded: {dir(llama)}", flush=True)
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t_path = "qwen3-tts/qwen3_tts_talker.q5_k.gguf"
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p_path = "qwen3-tts/qwen3_tts_predictor.q8_0.gguf"
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print(f" Loading talker from {t_path}...", flush=True)
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t0 = time.time()
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talker = llama.LlamaModel(t_path, n_gpu_layers=-1)
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print(f" Talker loaded in {time.time()-t0:.2f}s", flush=True)
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print(f" Loading predictor from {p_path}...", flush=True)
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t0 = time.time()
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predictor = llama.LlamaModel(p_path, n_gpu_layers=-1)
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print(f" Predictor loaded in {time.time()-t0:.2f}s", flush=True)
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except Exception as e:
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print(f" FAIL: {e}", flush=True)
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traceback.print_exc()
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print("\n=== Step 5: Full TTSEngine ===", flush=True)
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try:
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from qwen3_tts_gguf.inference import TTSEngine
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t0 = time.time()
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engine = TTSEngine(model_dir="qwen3-tts", onnx_provider="CPUExecutionProvider")
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print(f" Engine created in {time.time()-t0:.2f}s, ready={engine.ready}", flush=True)
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if engine.ready:
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stream = engine.create_stream(n_ctx=2048)
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print(f" stream={stream}", flush=True)
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except Exception as e:
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print(f" FAIL: {e}", flush=True)
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traceback.print_exc()
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print("\n=== DONE ===", flush=True)
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