ref: 4f3761b0199df7024b6e6b2004fc5eb7a6dbb28b
dir: /dnn/torch/testsuite/run_test.py/
import os
import multiprocess as multiprocessing
import random
import subprocess
import argparse
import shutil
import yaml
from utils.files import get_wave_file_list
from utils.warpq import compute_WAPRQ
from utils.pesq import compute_PESQ
from utils.pitch import compute_pitch_error
parser = argparse.ArgumentParser()
parser.add_argument('setup', type=str, help='setup yaml specifying end to end processing with model under test')
parser.add_argument('input_folder', type=str, help='input folder path')
parser.add_argument('output_folder', type=str, help='output folder path')
parser.add_argument('--num-testitems', type=int, help="number of testitems to be processed (default 100)", default=100)
parser.add_argument('--seed', type=int, help='seed for random item selection', default=None)
parser.add_argument('--fs', type=int, help="sampling rate at which input is presented as wave file (defaults to 16000)", default=16000)
parser.add_argument('--num-workers', type=int, help="number of subprocesses to be used (default=4)", default=4)
parser.add_argument('--plc-suffix', type=str, default="_is_lost.txt", help="suffix of plc error pattern file: only relevant if command chain uses PLCFILE (default=_is_lost.txt)")
parser.add_argument('--metrics', type=str, default='warpq', help='comma separated string of metrics, supported: {{"warpq", "pesq"}}, default="warpq"')
parser.add_argument('--verbose', action='store_true', help='enables printouts of all commands run in the pipeline')
def check_for_sox_in_path():
r = subprocess.run("sox -h", shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
return r.returncode == 0
def run_save_sh(command, verbose=False):
if verbose:
print(f"[run_save_sh] running command {command}...")
r = subprocess.run(command, shell=True)
if r.returncode != 0:
raise RuntimeError(f"command '{command}' failed with exit code {r.returncode}")
def run_processing_chain(input_path, output_path, model_commands, fs, metrics={'warpq'}, plc_suffix="_is_lost.txt", verbose=False):
# prepare model input
model_input = output_path + ".resamp.wav"
run_save_sh(f"sox {input_path} -r {fs} {model_input}", verbose=verbose)
plcfile = os.path.splitext(input_path)[0] + plc_suffix
if os.path.isfile(plcfile):
run_save_sh(f"cp {plcfile} {os.path.dirname(output_path)}")
# generate model output
for command in model_commands:
run_save_sh(command.format(INPUT=model_input, OUTPUT=output_path, PLCFILE=plcfile), verbose=verbose)
scores = dict()
cache = dict()
for metric in metrics:
if metric == 'warpq':
# run warpq
score = compute_WAPRQ(input_path, output_path, sr=fs)
elif metric == 'pesq':
# run pesq
score = compute_PESQ(input_path, output_path, fs=fs)
elif metric == 'pitch_error':
if metric in cache:
score = cache[metric]
else:
rval = compute_pitch_error(input_path, output_path, fs=fs)
score = rval[metric]
cache['voicing_error'] = rval['voicing_error']
elif metric == 'voicing_error':
if metric in cache:
score = cache[metric]
else:
rval = compute_pitch_error(input_path, output_path, fs=fs)
score = rval[metric]
cache['pitch_error'] = rval['pitch_error']
else:
ValueError(f'error: unknown metric {metric}')
scores[metric] = score
return (output_path, scores)
def get_output_path(root_folder, input, output_folder):
input_relpath = os.path.relpath(input, root_folder)
os.makedirs(os.path.join(output_folder, 'processing', os.path.dirname(input_relpath)), exist_ok=True)
output_path = os.path.join(output_folder, 'processing', input_relpath + '.output.wav')
return output_path
def add_audio_table(f, html_folder, results, title, metric):
item_folder = os.path.join(html_folder, 'items')
os.makedirs(item_folder, exist_ok=True)
# table with results
f.write(f"""
<div>
<h2> {title} </h2>
<table>
<tr>
<th> Rank </th>
<th> Name </th>
<th> {metric.upper()} </th>
<th> Audio (out) </th>
<th> Audio (orig) </th>
</tr>
""")
for i, r in enumerate(results):
item, score = r
item_name = os.path.basename(item)
new_item_path = os.path.join(item_folder, item_name)
shutil.copyfile(item, new_item_path)
shutil.copyfile(item + '.resamp.wav', os.path.join(item_folder, item_name + '.orig.wav'))
f.write(f"""
<tr>
<td> {i + 1} </td>
<td> {item_name.split('.')[0]} </td>
<td> {score:.3f} </td>
<td>
<audio controls>
<source src="items/{item_name}">
</audio>
</td>
<td>
<audio controls>
<source src="items/{item_name + '.orig.wav'}">
</audio>
</td>
</tr>
""")
# footer
f.write("""
</table>
</div>
""")
def create_html(output_folder, results, title, metric):
html_folder = output_folder
items_folder = os.path.join(html_folder, 'items')
os.makedirs(html_folder, exist_ok=True)
os.makedirs(items_folder, exist_ok=True)
with open(os.path.join(html_folder, 'index.html'), 'w') as f:
# header and title
f.write(f"""
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>{title}</title>
<style>
article {{
align-items: flex-start;
display: flex;
flex-wrap: wrap;
gap: 4em;
}}
html {{
box-sizing: border-box;
font-family: "Amazon Ember", "Source Sans", "Verdana", "Calibri", sans-serif;
padding: 2em;
}}
td {{
padding: 3px 7px;
text-align: center;
}}
td:first-child {{
text-align: end;
}}
th {{
background: #ff9900;
color: #000;
font-size: 1.2em;
padding: 7px 7px;
}}
</style>
</head>
</body>
<h1>{title}</h1>
<article>
""")
# top 20
add_audio_table(f, html_folder, results[:-21: -1], "Top 20", metric)
# 20 around median
N = len(results) // 2
add_audio_table(f, html_folder, results[N + 10 : N - 10: -1], "Median 20", metric)
# flop 20
add_audio_table(f, html_folder, results[:20], "Flop 20", metric)
# footer
f.write("""
</article>
</body>
</html>
""")
metric_sorting_signs = {
'warpq' : -1,
'pesq' : 1,
'pitch_error' : -1,
'voicing_error' : -1
}
def is_valid_result(data, metrics):
if not isinstance(data, dict):
return False
for metric in metrics:
if not metric in data:
return False
return True
def evaluate_results(output_folder, results, metric):
results = sorted(results, key=lambda x : metric_sorting_signs[metric] * x[1])
with open(os.path.join(args.output_folder, f'scores_{metric}.txt'), 'w') as f:
for result in results:
f.write(f"{os.path.relpath(result[0], args.output_folder)} {result[1]}\n")
# some statistics
mean = sum([r[1] for r in results]) / len(results)
top_mean = sum([r[1] for r in results[-20:]]) / 20
bottom_mean = sum([r[1] for r in results[:20]]) / 20
with open(os.path.join(args.output_folder, f'stats_{metric}.txt'), 'w') as f:
f.write(f"mean score: {mean}\n")
f.write(f"bottom mean score: {bottom_mean}\n")
f.write(f"top mean score: {top_mean}\n")
print(f"\nmean score: {mean}")
print(f"bottom mean score: {bottom_mean}")
print(f"top mean score: {top_mean}\n")
# create output html
create_html(os.path.join(output_folder, 'html', metric), results, setup['test'], metric)
if __name__ == "__main__":
args = parser.parse_args()
# check for sox
if not check_for_sox_in_path():
raise RuntimeError("script requires sox")
# prepare output folder
if os.path.exists(args.output_folder):
print("warning: output folder exists")
reply = input('continue? (y/n): ')
while reply not in {'y', 'n'}:
reply = input('continue? (y/n): ')
if reply == 'n':
os._exit()
else:
# start with a clean sleight
shutil.rmtree(args.output_folder)
os.makedirs(args.output_folder, exist_ok=True)
# extract metrics
metrics = args.metrics.split(",")
for metric in metrics:
if not metric in metric_sorting_signs:
print(f"unknown metric {metric}")
args.usage()
# read setup
print(f"loading {args.setup}...")
with open(args.setup, "r") as f:
setup = yaml.load(f.read(), yaml.FullLoader)
model_commands = setup['processing']
print("\nfound the following model commands:")
for command in model_commands:
print(command.format(INPUT='input.wav', OUTPUT='output.wav', PLCFILE='input_is_lost.txt'))
# store setup to output folder
setup['input'] = os.path.abspath(args.input_folder)
setup['output'] = os.path.abspath(args.output_folder)
setup['seed'] = args.seed
with open(os.path.join(args.output_folder, 'setup.yml'), 'w') as f:
yaml.dump(setup, f)
# get input
print(f"\nCollecting audio files from {args.input_folder}...")
file_list = get_wave_file_list(args.input_folder, check_for_features=False)
print(f"...{len(file_list)} files found\n")
# sample from file list
file_list = sorted(file_list)
random.seed(args.seed)
random.shuffle(file_list)
num_testitems = min(args.num_testitems, len(file_list))
file_list = file_list[:num_testitems]
print(f"\nlaunching test on {num_testitems} items...")
# helper function for parallel processing
def func(input_path):
output_path = get_output_path(args.input_folder, input_path, args.output_folder)
try:
rval = run_processing_chain(input_path, output_path, model_commands, args.fs, metrics=metrics, plc_suffix=args.plc_suffix, verbose=args.verbose)
except:
rval = (input_path, -1)
return rval
with multiprocessing.Pool(args.num_workers) as p:
results = p.map(func, file_list)
results_dict = dict()
for name, values in results:
if is_valid_result(values, metrics):
results_dict[name] = values
print(results_dict)
# evaluating results
num_failures = num_testitems - len(results_dict)
print(f"\nprocessing of {num_failures} items failed\n")
for metric in metrics:
print(metric)
evaluate_results(
args.output_folder,
[(name, value[metric]) for name, value in results_dict.items()],
metric
)