Fabio
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114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
#!/usr/bin/env python3
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"""Soprano TTS Python warm bench — runs the official Python reference and collects timing.
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Usage:
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python3 tests/soprano_tts/soprano_python_warm_bench.py --model models/Soprano-1.1-80M --out-dir build/logs/warmbench/soprano_tts_py
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import struct
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import sys
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import time
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from pathlib import Path
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REPO_ROOT = Path(__file__).resolve().parents[2]
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def parse_wav_duration(path: str) -> float:
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with open(path, "rb") as f:
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data = f.read()
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sr = struct.unpack("<I", data[24:28])[0]
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pos = data.find(b"data")
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if pos < 0:
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return 0.0
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data_size = struct.unpack("<I", data[pos - 4 : pos])[0]
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fmt = struct.unpack("<H", data[20:22])[0]
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bytes_per_sample = 4 if fmt == 3 else 2
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return data_size / bytes_per_sample / sr if sr > 0 else 0.0
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def load_cases(path: str) -> dict[str, list[str]]:
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catalog: dict[str, list[str]] = {}
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current_section = ""
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with open(path, "r", encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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if line.startswith("[") and line.endswith("]"):
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current_section = line[1:-1]
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elif current_section:
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catalog.setdefault(current_section, []).append(line)
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return catalog
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def main():
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ap = argparse.ArgumentParser(description=__doc__)
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ap.add_argument("--model", default="models/Soprano-1.1-80M")
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ap.add_argument("--cases", default="tests/soprano_tts/soprano_warm_bench_cases.txt")
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ap.add_argument("--out-dir", default="build/logs/warmbench/soprano_tts_py")
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ap.add_argument("--json", action="store_true")
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args = ap.parse_args()
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model_path = os.path.join(REPO_ROOT, args.model)
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cases_path = os.path.join(REPO_ROOT, args.cases)
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out_dir = os.path.join(REPO_ROOT, args.out_dir)
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os.makedirs(out_dir, exist_ok=True)
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from soprano import SopranoTTS
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print("=== Soprano Python WarmBench ===")
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print(f"Model: {model_path}")
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print(f"Output: {out_dir}")
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# Load model
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t0 = time.time()
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model = SopranoTTS(backend="auto", device="cpu", model_path=model_path)
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load_time = time.time() - t0
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print(f"Load: {load_time:.2f}s")
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# Load cases
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catalog = load_cases(cases_path)
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# Warmup
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print("\nWarmup...")
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model.infer("At sunrise the studio monitors clicked on, and the first calibration phrase rolled across the room with steady timing.")
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print("Warmup complete\n")
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# Run cases
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results = []
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for section, texts in catalog.items():
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print(f"Case: {section} ({len(texts)} texts)")
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for i, text in enumerate(texts):
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case_name = f"{section}_{i}"
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out_path = os.path.join(out_dir, f"{case_name}.wav")
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t0 = time.time()
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model.infer(text, out_path)
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infer_time = time.time() - t0
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audio_dur = parse_wav_duration(out_path)
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rtf = infer_time / audio_dur if audio_dur > 0 else 0
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results.append({
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"name": case_name,
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"infer_time_s": round(infer_time, 3),
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"audio_duration_s": round(audio_dur, 3),
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"rtf": round(rtf, 4),
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})
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print(f" {case_name}: {infer_time*1000:.0f} ms infer, {audio_dur:.3f} s audio, RTF={rtf:.4f}")
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# Summary
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print("\n" + "=" * 60)
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print(f"{'Name':<20} {'Infer (s)':<12} {'Audio (s)':<12} {'RTF':<10}")
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print("-" * 54)
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for r in results:
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print(f"{r['name']:<20} {r['infer_time_s']:<12.3f} {r['audio_duration_s']:<12.3f} {r['rtf']:<10.4f}")
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if args.json:
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print(json.dumps({"load_time_s": round(load_time, 3), "results": results}, indent=2))
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if __name__ == "__main__":
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main()
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