bdim404
2fa9c1fcb6
- Rename qwen3-tts-backend → canto-backend - Rename qwen3-tts-frontend → canto-frontend - Remove NovelWriter embedded repo Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
"""
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IndexTTS2 standalone test - following official webui best practices.
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Key differences from our current backend:
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- is_fp16=False (official webui default, we were using is_fp16=True)
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- Tests all emotion modes: none, audio-ref, emo_text, emo_vector
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"""
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import os
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import sys
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import time
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MODEL_DIR = os.path.join(os.path.dirname(__file__), "qwen3-tts-backend", "Qwen", "IndexTTS2")
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CFG_PATH = os.path.join(MODEL_DIR, "config.yaml")
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REF_AUDIO = os.path.join(os.path.dirname(__file__), "test_indextts2_outputs", "ref_audio_id242_test54321.wav")
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OUT_DIR = os.path.join(os.path.dirname(__file__), "test_indextts2_v2")
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os.makedirs(OUT_DIR, exist_ok=True)
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sys.path.insert(0, "/home/bdim/Documents/github/indexTTS2")
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print(f"Model dir: {MODEL_DIR}")
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print(f"Config: {CFG_PATH}")
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print(f"Ref audio: {REF_AUDIO}")
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print(f"Output dir: {OUT_DIR}")
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print()
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print("Loading IndexTTS2 model (is_fp16=False, matching official webui)...")
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t0 = time.time()
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from indextts.infer_indextts2 import IndexTTS2
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tts = IndexTTS2(
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cfg_path=CFG_PATH,
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model_dir=MODEL_DIR,
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is_fp16=False,
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use_cuda_kernel=False,
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use_deepspeed=False,
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)
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print(f"Model loaded in {time.time() - t0:.1f}s\n")
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def run(name, **kwargs):
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out = os.path.join(OUT_DIR, f"{name}.wav")
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print(f"--- [{name}] ---")
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t = time.time()
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tts.infer(
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spk_audio_prompt=REF_AUDIO,
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output_path=out,
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verbose=True,
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**kwargs,
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)
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sz = os.path.getsize(out)
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print(f"Done in {time.time()-t:.1f}s, size={sz} bytes -> {out}\n")
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TEXT = "今天天气真不错，阳光明媚，感觉一切都很美好。"
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# Keyword-mapped emo_vector (bypasses broken QwenEmotion)
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# Uses _emo_text_to_vector() logic from IndexTTS2Backend
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def emo_keywords_to_vector(emo_text):
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EMO_KEYWORDS = [
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['喜', '开心', '快乐', '高兴', '欢乐', '愉快', 'happy', '热情', '兴奋', '愉悦', '激动'],
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['怒', '愤怒', '生气', '恼', 'angry', '气愤', '愤慨'],
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['哀', '悲伤', '难过', '忧郁', '伤心', '悲', 'sad', '感慨', '沉重', '沉痛', '哭'],
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['惧', '恐惧', '害怕', '恐', 'fear', '担心', '紧张'],
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['厌恶', '厌', 'hate', '讨厌', '反感'],
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['低落', '沮丧', '消沉', 'low', '抑郁', '颓废'],
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['惊喜', '惊讶', '意外', 'surprise', '惊', '吃惊', '震惊'],
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['自然', '平静', '中性', '平和', 'neutral', '平淡', '冷静', '稳定'],
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]
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text = emo_text.lower()
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matched = []
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for idx, words in enumerate(EMO_KEYWORDS):
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for word in words:
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if word in text:
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matched.append(idx)
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break
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if not matched:
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return None
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vec = [0.0] * 8
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score = 0.8 if len(matched) == 1 else 0.5
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for idx in matched:
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vec[idx] = score
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return vec
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# Baseline: no emotion
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run("v3_00_no_emotion", text=TEXT)
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# Test each emotion via keyword → vector mapping
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cases = [
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("v3_01_happy", TEXT, "开心愉悦"),
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("v3_02_sad", TEXT, "悲伤难过"),
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("v3_03_angry", TEXT, "愤怒生气"),
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("v3_04_low", TEXT, "低落沮丧"),
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("v3_05_surprise",TEXT, "惊讶意外"),
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("v3_06_calm", TEXT, "平静自然"),
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]
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for name, t, emo in cases:
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vec = emo_keywords_to_vector(emo)
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print(f" emo_text={repr(emo)} → emo_vector={vec}")
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run(name, text=t, emo_vector=vec, emo_alpha=1.0)
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print("All tests complete. Files saved to:", OUT_DIR)
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print("Files:")
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for f in sorted(os.listdir(OUT_DIR)):
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path = os.path.join(OUT_DIR, f)
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print(f" {f} ({os.path.getsize(path)} bytes)")
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