| |
| """End-to-end demo: audio file(s) -> all 61 VoiceClap attribute scores. |
| |
| python example_inference.py examples/clip_a.mp3 examples/clip_b.mp3 |
| """ |
| import sys |
|
|
| from voiceclap_heads import AttributeScorer |
|
|
| GROUPS = { |
| "speaker / voice": ["Gender", "Age", "High-Pitched_vs._Low-Pitched", "Monotone_vs._Expressive", |
| "Soft_vs._Harsh", "Warm_vs._Cold", "Confident_vs._Hesitant", |
| "Submissive_vs._Dominant", "Serious_vs._Humorous", |
| "Vulnerable_vs._Emotionally_Detached", "talking_speed", "duration"], |
| "recording quality": ["Recording_Quality", "Background_Noise", "Authenticity", |
| "score_overall_quality", "score_speech_quality", |
| "score_background_quality", "score_content_enjoyment"], |
| "core affect": ["Valence", "Arousal"], |
| } |
|
|
|
|
| def main(paths): |
| scorer = AttributeScorer() |
| print(f"loaded {len(scorer.dims)} heads on {scorer.device}\n") |
|
|
| for p in paths: |
| s = scorer.score(p) |
| print("=" * 78) |
| print(p) |
| print("=" * 78) |
| shown = set() |
| for title, dims in GROUPS.items(): |
| print(f"-- {title}") |
| for d in dims: |
| print(f" {d:<38s} {s[d]:7.3f}") |
| shown.add(d) |
| rest = sorted((k for k in s if k not in shown), key=lambda k: -s[k]) |
| print("-- top 8 emotion dimensions") |
| for d in rest[:8]: |
| print(f" {d:<38s} {s[d]:7.3f}") |
| print() |
|
|
| |
| if len(paths) > 1: |
| batch = scorer.score(paths, dims=["Valence", "Arousal", "Gender", "Age"], batch_size=32) |
| print("batched:", {k: [round(float(x), 3) for x in v] for k, v in batch.items()}) |
|
|
|
|
| if __name__ == "__main__": |
| main(sys.argv[1:] or ["examples/clip_a.mp3", "examples/clip_b.mp3"]) |
|
|
