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Running on Zero
Running on Zero
| from __future__ import annotations | |
| from typing import Dict, List | |
| import numpy as np | |
| import torch | |
| import torchaudio | |
| def load_audio_mono(path: str, target_sr: int = 16000): | |
| wav, sr = torchaudio.load(path) | |
| wav = wav.mean(dim=0) | |
| if sr != target_sr: | |
| wav = torchaudio.functional.resample(wav, sr, target_sr) | |
| sr = target_sr | |
| return wav, sr | |
| def audio_quality_report(path: str, target_sr: int = 16000) -> Dict: | |
| wav, sr = load_audio_mono(path, target_sr) | |
| x = wav.detach().cpu().numpy().astype(float) | |
| if len(x) == 0: | |
| return {"warnings": ["Audio appears empty."], "metrics": {"duration_sec": 0.0}} | |
| duration = len(x) / sr | |
| rms = float(np.sqrt(np.mean(x ** 2)) + 1e-12) | |
| peak = float(np.max(np.abs(x)) + 1e-12) | |
| silence_ratio = float(np.mean(np.abs(x) < 1e-4)) | |
| clipping_ratio = float(np.mean(np.abs(x) > 0.98)) | |
| warnings: List[str] = [] | |
| if duration < 1.0: | |
| warnings.append("Audio is very short; prediction may be unreliable.") | |
| if rms < 0.005: | |
| warnings.append("Audio volume is very low.") | |
| if clipping_ratio > 0.01: | |
| warnings.append("Audio may be clipped or distorted.") | |
| if silence_ratio > 0.70: | |
| warnings.append("Audio contains a lot of silence.") | |
| if peak < 0.02: | |
| warnings.append("Audio peak level is very low.") | |
| return { | |
| "warnings": warnings, | |
| "metrics": { | |
| "sample_rate": sr, | |
| "duration_sec": duration, | |
| "rms": rms, | |
| "peak": peak, | |
| "silence_ratio": silence_ratio, | |
| "clipping_ratio": clipping_ratio, | |
| }, | |
| } | |