SmartHearingAids-data / copy_removeall_samples.py
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"""
Copy only the "remove all distractors" samples from eval_outputs_removeonly_old
into eval_outputs_removeall_old for each model.
A sample qualifies if:
- command_type is "remove_only"
- target_sources is ["speech"]
- ALL distractors listed in metadata are named in the user_input command
"""
import json
import os
import shutil
BASE = "/home/kthakka2/scratchmelhila1/karan/EMMA2_text_conditioning_contextual/experiments"
MODELS = [
"TSDL_old_mixtures",
"no_TSDL_old_mixtures",
"combined_v1",
"frequencyweighted_v1",
"multiscale_v1",
]
for model in MODELS:
src_dir = os.path.join(BASE, model, "eval_outputs_removeonly_old", "outputs")
dst_dir = os.path.join(BASE, model, "eval_outputs_removeall_old", "outputs")
if not os.path.exists(src_dir):
print(f"SKIP {model}: {src_dir} not found")
continue
os.makedirs(dst_dir, exist_ok=True)
total = 0
copied = 0
for sample_dir in sorted(os.listdir(src_dir)):
sample_path = os.path.join(src_dir, sample_dir)
meta_path = os.path.join(sample_path, "metadata.json")
if not os.path.isdir(sample_path) or not os.path.exists(meta_path):
continue
total += 1
with open(meta_path) as f:
meta = json.load(f)
cmd = meta.get("command_variant", {})
user_input = cmd.get("user_input", "") or ""
target_sources = cmd.get("target_sources", [])
distractors = meta.get("distractors", [])
if target_sources != ["speech"]:
continue
if not distractors:
continue
# Check all distractors are named in the command
all_removed = all(
dist.replace("_", " ") in user_input for dist in distractors
)
if not all_removed:
continue
# Copy the sample directory
dst_sample = os.path.join(dst_dir, sample_dir)
if os.path.exists(dst_sample):
shutil.rmtree(dst_sample)
shutil.copytree(sample_path, dst_sample)
copied += 1
# Also copy the summary JSON if it exists
for summary_file in ["results_summary.json", "eval_results.json"]:
src_summary = os.path.join(BASE, model, "eval_outputs_removeonly_old", summary_file)
if os.path.exists(src_summary):
shutil.copy2(src_summary, os.path.join(BASE, model, "eval_outputs_removeall_old", summary_file))
print(f"{model}: copied {copied}/{total} samples to eval_outputs_removeall_old")