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import json
import random
from concurrent.futures import ThreadPoolExecutor
from datetime import datetime
import librosa
import pandas as pd
from audio import (
assign_outputs_to_refs_by_corr,
loudness_normalize,
make_union_voiced_mask,
)
from config import *
from distortions import apply_adv_distortions, apply_distortions
from metrics import (
compute_pm,
compute_ps,
diffusion_map_torch,
pm_ci_components_full,
ps_ci_components_full,
)
from models import embed_batch, load_model
from utils import *
def compute_mapss_measures(
models,
mixtures,
*,
systems=None,
algos=None,
experiment_id=None,
layer=DEFAULT_LAYER,
add_ci=DEFAULT_ADD_CI,
alpha=DEFAULT_ALPHA,
seed=42,
on_missing="skip",
verbose=False,
max_gpus=None,
):
gpu_distributor = GPUWorkDistributor(max_gpus)
ngpu = get_gpu_count(max_gpus)
if on_missing not in {"skip", "error"}:
raise ValueError("on_missing must be 'skip' or 'error'.")
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
canon_mix = canonicalize_mixtures(mixtures, systems=systems)
mixture_entries = []
for m in canon_mix:
entries = []
for i, refp in enumerate(m.refs):
sid = m.speaker_ids[i]
entries.append(
{"id": sid, "ref": Path(refp), "mixture": m.mixture_id, "outs": {}}
)
mixture_entries.append(entries)
for m, mix_entries in zip(canon_mix, mixture_entries):
for algo, out_list in (m.systems or {}).items():
mapping = assign_outputs_to_refs_by_corr(
[e["ref"] for e in mix_entries], out_list
)
for idx, e in enumerate(mix_entries):
j = mapping[idx]
if j is not None:
e["outs"][algo] = out_list[j]
if algos is None:
algos_to_run = sorted(
{algo for m in canon_mix for algo in (m.systems or {}).keys()}
)
else:
algos_to_run = list(algos)
exp_id = experiment_id or datetime.now().strftime("%Y%m%d_%H%M%S")
exp_root = os.path.join(RESULTS_ROOT, f"experiment_{exp_id}")
os.makedirs(exp_root, exist_ok=True)
params = {
"models": models,
"layer": layer,
"add_ci": add_ci,
"alpha": alpha,
"seed": seed,
"batch_size": BATCH_SIZE,
"ngpu": ngpu,
"max_gpus": max_gpus,
}
with open(os.path.join(exp_root, "params.json"), "w") as f:
json.dump(params, f, indent=2)
canon_struct = [
{
"mixture_id": m.mixture_id,
"references": [str(p) for p in m.refs],
"systems": {
a: [str(p) for p in outs] for a, outs in (m.systems or {}).items()
},
"speaker_ids": m.speaker_ids,
}
for m in canon_mix
]
with open(os.path.join(exp_root, "manifest_canonical.json"), "w") as f:
json.dump(canon_struct, f, indent=2)
print(f"Starting experiment {exp_id} with {ngpu} GPUs")
print(f"Results will be saved to: {exp_root}")
clear_gpu_memory()
get_gpu_memory_info(verbose)
flat_entries = [e for mix in mixture_entries for e in mix]
all_refs = {}
if verbose:
print("Loading reference signals...")
for e in flat_entries:
wav, _ = librosa.load(str(e["ref"]), sr=SR)
all_refs[e["id"]] = torch.from_numpy(loudness_normalize(wav))
if verbose:
print("Computing voiced masks...")
win = int(ENERGY_WIN_MS * SR / 1000)
hop = int(ENERGY_HOP_MS * SR / 1000)
voiced_mask_mix = []
for i, mix in enumerate(mixture_entries):
if verbose:
print(f" Computing mask for mixture {i + 1}/{len(mixture_entries)}")
if ngpu > 0:
with torch.cuda.device(0):
refs_for_mix = [all_refs[e["id"]].cuda() for e in mix]
mask = make_union_voiced_mask(refs_for_mix, win, hop)
voiced_mask_mix.append(mask.cpu())
# Explicitly delete GPU tensors
for ref in refs_for_mix:
del ref
torch.cuda.empty_cache()
else:
refs_for_mix = [all_refs[e["id"]].cpu() for e in mix]
mask = make_union_voiced_mask(refs_for_mix, win, hop)
voiced_mask_mix.append(mask.cpu())
ordered_speakers = [e["id"] for e in flat_entries]
for algo_idx, algo in enumerate(algos_to_run):
if verbose:
print(f"\nProcessing Algorithm {algo_idx + 1}/{len(algos_to_run)}: {algo}")
algo_dir = os.path.join(exp_root, algo)
os.makedirs(algo_dir, exist_ok=True)
all_outs = {}
missing = []
for mix_idx, mix in enumerate(mixture_entries):
for e in mix:
assigned_path = e.get("outs", {}).get(algo)
if assigned_path is None:
missing.append((e["mixture"], e["id"]))
continue
wav, _ = librosa.load(str(assigned_path), sr=SR)
all_outs[e["id"]] = torch.from_numpy(loudness_normalize(wav))
if missing:
msg = f"[{algo}] missing outputs for {len(missing)} speaker(s)"
if on_missing == "error":
raise FileNotFoundError(msg)
else:
if verbose:
warnings.warn(msg + " Skipping those speakers.")
if not all_outs:
if verbose:
warnings.warn(f"[{algo}] No outputs provided. Skipping algorithm.")
continue
ps_ts = {m: {s: [] for s in ordered_speakers} for m in models}
pm_ts = {m: {s: [] for s in ordered_speakers} for m in models}
ps_bias_ts = {m: {s: [] for s in ordered_speakers} for m in models}
ps_prob_ts = {m: {s: [] for s in ordered_speakers} for m in models}
pm_bias_ts = {m: {s: [] for s in ordered_speakers} for m in models}
pm_prob_ts = {m: {s: [] for s in ordered_speakers} for m in models}
for model_idx, mname in enumerate(models):
if verbose:
print(f" Processing Model {model_idx + 1}/{len(models)}: {mname}")
for metric_type in ["PS", "PM"]:
clear_gpu_memory()
gc.collect()
model_wrapper, layer_eff = load_model(mname, layer, max_gpus)
get_gpu_memory_info(verbose)
embs_by_mix = {}
labels_by_mix = {}
for k, mix in enumerate(mixture_entries):
speakers_this_mix = [e for e in mix if e["id"] in all_outs]
if not speakers_this_mix:
continue
if verbose:
print(
f"Processing mixture {k + 1}/{len(mixture_entries)} for {metric_type}"
)
all_signals_mix = []
all_masks_mix = []
all_labels_mix = []
for e in speakers_this_mix:
s = e["id"]
if metric_type == "PS":
dists = [
loudness_normalize(d)
for d in apply_distortions(all_refs[s].numpy(), "all")
]
else:
dists = [
loudness_normalize(d)
for d in apply_adv_distortions(
all_refs[s].numpy(), "all"
)
]
sigs = [all_refs[s].numpy(), all_outs[s].numpy()] + dists
lbls = ["ref", "out"] + [f"d{i}" for i in range(len(dists))]
masks = [voiced_mask_mix[k]] * len(sigs)
all_signals_mix.extend(sigs)
all_masks_mix.extend(masks)
all_labels_mix.extend([f"{s}-{l}" for l in lbls])
try:
# Process in smaller batches
batch_size = min(2, BATCH_SIZE)
embeddings_list = []
for i in range(0, len(all_signals_mix), batch_size):
batch_sigs = all_signals_mix[i:i + batch_size]
batch_masks = all_masks_mix[i:i + batch_size]
batch_embs = embed_batch(
batch_sigs,
batch_masks,
model_wrapper,
layer_eff,
use_mlm=False,
)
if batch_embs.numel() > 0:
embeddings_list.append(batch_embs.cpu())
torch.cuda.empty_cache()
if embeddings_list:
embeddings = torch.cat(embeddings_list, dim=0)
embs_by_mix[k] = embeddings
labels_by_mix[k] = all_labels_mix
except Exception as ex:
if verbose:
print(f" ERROR processing mixture {k + 1}: {ex}")
continue
finally:
# Always clean up after processing a mixture
del all_signals_mix, all_masks_mix
if 'embeddings_list' in locals():
del embeddings_list
clear_gpu_memory()
gc.collect()
if verbose:
print(f" Computing {metric_type} scores for {mname}...")
# Process mixtures with their stored embeddings and labels
with ThreadPoolExecutor(
max_workers=min(2, ngpu if ngpu > 0 else 1)
) as executor:
for k in range(len(mixture_entries)):
if k not in embs_by_mix:
continue
E, L, D = embs_by_mix[k].shape
if L == 0:
if verbose:
print(f" WARNING: mixture {k + 1} produced 0 frames after masking; skipping.")
continue
# Get the labels for this mixture
labels_for_mix = labels_by_mix[k]
def process_frame(f, embeddings_mix, labels_mix):
try:
frame_emb = embeddings_mix[:, f, :].detach().cpu().numpy()
if add_ci:
coords_d, coords_c, eigvals, k_sub_gauss = (
gpu_distributor.execute_on_gpu(
diffusion_map_torch,
frame_emb,
labels_mix,
alpha=alpha,
eig_solver="full",
return_eigs=True,
return_complement=True,
return_cval=add_ci,
)
)
else:
coords_d = gpu_distributor.execute_on_gpu(
diffusion_map_torch,
frame_emb,
labels_mix,
alpha=alpha,
eig_solver="full",
return_eigs=False,
return_complement=False,
return_cval=False,
)
coords_c = None
eigvals = None
k_sub_gauss = 1
if metric_type == "PS":
score = compute_ps(
coords_d, labels_mix, max_gpus
)
bias = prob = None
if add_ci:
bias, prob = ps_ci_components_full(
coords_d,
coords_c,
eigvals,
labels_mix,
delta=DEFAULT_DELTA_CI,
)
return f, "PS", score, bias, prob
else:
score = compute_pm(
coords_d, labels_mix, "gamma", max_gpus
)
bias = prob = None
if add_ci:
bias, prob = pm_ci_components_full(
coords_d,
coords_c,
eigvals,
labels_mix,
delta=DEFAULT_DELTA_CI,
K=k_sub_gauss,
)
return f, "PM", score, bias, prob
except Exception as ex:
if verbose:
print(f" ERROR frame {f + 1}: {ex}")
return None
futures = [
executor.submit(process_frame, f, embs_by_mix[k], labels_for_mix)
for f in range(L)
]
for fut in futures:
result = fut.result()
if result is None:
continue
f, metric, score, bias, prob = result
if metric == "PS":
for sp in score:
ps_ts[mname][sp].append(score[sp])
if add_ci and bias is not None:
ps_bias_ts[mname][sp].append(bias[sp])
ps_prob_ts[mname][sp].append(prob[sp])
else:
for sp in score:
pm_ts[mname][sp].append(score[sp])
if add_ci and bias is not None:
pm_bias_ts[mname][sp].append(bias[sp])
pm_prob_ts[mname][sp].append(prob[sp])
# Clean up after processing all mixtures for this metric
del embs_by_mix, labels_by_mix
clear_gpu_memory()
gc.collect()
del model_wrapper
clear_gpu_memory()
gc.collect()
if verbose:
print(f" Saving results for {algo}...")
for m in models:
def _pad(vec, n):
return vec + [np.nan] * (n - len(vec))
max_len = 0
for s in ordered_speakers:
max_len = max(max_len, len(ps_ts[m][s]), len(pm_ts[m][s]))
pd.DataFrame(
{s: _pad(ps_ts[m][s], max_len) for s in ordered_speakers}
).to_csv(os.path.join(algo_dir, f"ps_scores_{m}.csv"), index=False)
pd.DataFrame(
{s: _pad(pm_ts[m][s], max_len) for s in ordered_speakers}
).to_csv(os.path.join(algo_dir, f"pm_scores_{m}.csv"), index=False)
if add_ci:
ci_cols = {}
for s in ordered_speakers:
ci_cols[f"{s}_ps_bias"] = _pad(ps_bias_ts[m][s], max_len)
ci_cols[f"{s}_ps_prob"] = _pad(ps_prob_ts[m][s], max_len)
ci_cols[f"{s}_pm_bias"] = _pad(pm_bias_ts[m][s], max_len)
ci_cols[f"{s}_pm_prob"] = _pad(pm_prob_ts[m][s], max_len)
pd.DataFrame(ci_cols).to_csv(
os.path.join(algo_dir, f"ci_{m}.csv"), index=False
)
del all_outs
clear_gpu_memory()
gc.collect()
print(f"\nEXPERIMENT COMPLETED")
print(f"Results saved to: {exp_root}")
del all_refs, voiced_mask_mix
# Import and call the cleanup function
from models import cleanup_all_models
cleanup_all_models()
clear_gpu_memory()
get_gpu_memory_info(verbose)
gc.collect()
return exp_root |