Davids048andClaude Opus 5.5
f571621ae7
Every example calls VideoGenerator.generate with a nested request instead of generate_video keywords, and passes settings outside the from_pretrained convenience keywords through VideoGenerator.from_config at their typed paths. Each request and generator config equals what the keyword call built. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
"""Generate one LTX2 video and score its audio track.
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LTX2 produces video + audio and muxes both into the output mp4. The
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eval suite reads the audio track straight out of that mp4 (librosa
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decodes via ffmpeg under the hood), so we just point the audio
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metrics at the same path.
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Only the reference-free audio metrics run here — they're the ones
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that make sense for a single one-shot generation:
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* ``audio.clap_score`` — CLAP text↔audio cosine similarity
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* ``audio.audiobox_aesthetics`` — AudioBox 4-axis quality score
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The other audio metrics need extra inputs we don't have for a
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one-shot run: ``audio.frechet_distance`` and ``audio.kl_divergence``
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need a reference audio, ``audio.wer`` needs a ground-truth transcript.
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Install: ``uv pip install -e .[eval-audio]`` covers both metrics here
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(and the rest of the audio suite).
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"""
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import torch
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from fastvideo import VideoGenerator
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from fastvideo.eval import create_evaluator
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PROMPT = ("A warm sunny backyard. The camera starts in a tight cinematic close-up "
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"of a woman and a man in their 30s, facing each other with serious "
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"expressions. The woman, emotional and dramatic, says softly, \"That's "
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"it... Dad's lost it. And we've lost Dad.\" The man exhales, slightly "
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"annoyed: \"Stop being so dramatic, Jess.\" A beat. He glances aside, "
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"then mutters defensively, \"He's just having fun.\"")
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METRICS = [
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"audio.clap_score",
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"audio.audiobox_aesthetics",
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]
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def main() -> None:
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generator = VideoGenerator.from_pretrained(
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"Davids048/LTX2-Base-Diffusers",
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num_gpus=1,
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)
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output_path = "outputs_video/ltx2_audio_eval/output.mp4"
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generator.generate({
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"prompt": PROMPT,
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"sampling": {
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"num_frames": 121,
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"height": 1088,
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"width": 1920,
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},
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"output": {
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"output_path": output_path,
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"save_video": True,
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},
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})
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generator.shutdown()
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torch.cuda.empty_cache()
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print(f"\n[eval] building evaluator: {METRICS}")
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evaluator = create_evaluator(metrics=METRICS)
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results = evaluator.evaluate(audio=output_path, text_prompt=PROMPT)
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print("\n=== Audio scores ===")
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for name in METRICS:
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r = results[name]
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if r.score is None:
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print(f" {name}: SKIPPED ({r.details.get('skipped', 'no score')})")
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else:
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print(f" {name}: {r.score:.4f}")
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if r.details:
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for k, v in r.details.items():
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print(f" {k}: {v}")
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if __name__ == "__main__":
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main()
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