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import librosa
import numpy as np
import torch
def singmos_warmup():
predictor = torch.hub.load(
"South-Twilight/SingMOS:v0.2.0", "singing_ssl_mos", trust_repo=True
)
return predictor
def singmos_evaluation(predictor, wav_info, fs):
wav_mos = librosa.resample(wav_info, orig_sr=fs, target_sr=16000)
wav_mos = torch.from_numpy(wav_mos).unsqueeze(0)
len_mos = torch.tensor([wav_mos.shape[1]])
score = predictor(wav_mos, len_mos)
return score
def initialize_audiobox_predictor():
from audiobox_aesthetics.infer import initialize_predictor
predictor = initialize_predictor()
return predictor
def audiobox_aesthetics_evaluation(predictor, audio_path):
score = predictor.forward([{"path": str(audio_path)}])
return score
def score_extract_warmpup():
from basic_pitch.inference import predict
return predict
def score_metric_evaluation(score_extractor, audio_path):
model_output, midi_data, note_events = score_extractor(audio_path)
metrics = {}
assert (
len(midi_data.instruments) == 1
), f"Detected {len(midi_data.instruments)} instruments for {audio_path}"
midi_notes = midi_data.instruments[0].notes
melody = [note.pitch for note in midi_notes]
if len(melody) == 0:
print(f"No notes detected in {audio_path}")
return {}
intervals = [abs(melody[i + 1] - melody[i]) for i in range(len(melody) - 1)]
metrics["pitch_range"] = max(melody) - min(melody)
if len(intervals) > 0:
metrics["interval_mean"] = np.mean(intervals)
metrics["interval_std"] = np.std(intervals)
metrics["interval_large_jump_ratio"] = np.mean([i > 5 for i in intervals])
metrics["dissonance_rate"] = compute_dissonance_rate(intervals)
return metrics
def compute_dissonance_rate(intervals, dissonant_intervals={1, 2, 6, 10, 11}):
dissonant = [i % 12 in dissonant_intervals for i in intervals]
return np.mean(dissonant) if intervals else np.nan
if __name__ == "__main__":
import argparse
from pathlib import Path
parser = argparse.ArgumentParser()
parser.add_argument(
"--wav_path",
type=Path,
help="Path to the wav file",
)
parser.add_argument(
"--results_csv",
type=Path,
help="csv file to save the results",
)
args = parser.parse_args()
args.results_csv.parent.mkdir(parents=True, exist_ok=True)
y, fs = librosa.load(args.wav_path, sr=None)
# warmup
predictor = singmos_warmup()
score_extractor = score_extract_warmpup()
aesthetic_predictor = initialize_audiobox_predictor()
# evaluate the audio
metrics = {}
# singmos evaluation
score = singmos_evaluation(predictor, y, fs)
metrics["singmos"] = score
# score metric evaluation
score_results = score_metric_evaluation(score_extractor, args.wav_path)
metrics.update(score_results)
# audiobox aesthetics evaluation
score_results = audiobox_aesthetics_evaluation(aesthetic_predictor, args.wav_path)
metrics.update(score_results[0])
# save results
with open(args.results_csv, "a") as f:
header = "file," + ",".join(metrics.keys()) + "\n"
if f.tell() == 0:
f.write(header)
else:
with open(args.results_csv, "r") as f2:
file_header = f2.readline()
if file_header != header:
raise ValueError(f"Header mismatch: {file_header} vs {header}")
line = (
",".join([str(args.wav_path)] + [str(v) for v in metrics.values()]) + "\n"
)
f.write(line)
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