def bss_evaluate(model_config, dataset, load_model):
'''
Calculates source separation evaluation metrics of a given separator model on the test set using BSS-Eval
:param model_config: Separation network configuration required to build symbolic computation graph of network
:param dataset: Test dataset
:param load_model: Path to separator model checkpoint containing the network weights
:return: Dict containing evaluation metrics, wav files of predicted drum and acc are written to results directory
'''
# Determine input and output shapes, if we use U-net as separator
track_number = 1
freq_bins = model_config["num_fft"] / 2 + 1 # Make even number of freq bins
disc_input_shape = [1, freq_bins - 1, model_config["num_frames"],
1] # Shape of discriminator input
separator_class = Models.Unet.Unet(model_config["num_layers"])
sep_input_shape, sep_output_shape = separator_class.getUnetPadding(
np.array(disc_input_shape))
separator_func = separator_class.get_output
# Placeholders and input normalisation
input_ph, queue, [mix_context, acc, drums] = Input.get_multitrack_input(
sep_output_shape[1:],
1,
name="input_batch",
input_shape=sep_input_shape[1:])
mix = Input.crop(mix_context, sep_output_shape)
mix_norm, mix_context_norm, acc_norm, drums_norm = Input.norm(mix), Input.norm(mix_context), \
Input.norm(acc), Input.norm(drums)
print("Testing...")
# BUILD MODELS
# Separator
separator_acc_norm, separator_drums_norm = separator_func(mix_context_norm,
reuse=False)
separator_acc, separator_drums = Input.denorm(
separator_acc_norm), Input.denorm(separator_drums_norm)
# Start session and queue input threads
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Load model
# Load pretrained model to continue training, if we are supposed to
restorer = tf.train.Saver(None, write_version=tf.train.SaverDef.V2)
print("Num of variables" + str(len(tf.global_variables())))
restorer.restore(sess, load_model)
print('Pre-trained model restored for testing')
# Initialize total score object
song_scores = list()
for multitrack in dataset[8:15]:
filename = multitrack[0].path
print("Evaluating file: " + filename + "Track number: " + track_number)
if filename.__contains__("DSD100"):
db = "DSD100"
elif filename.__contains__("Kala"):
db = "IKala"
elif filename.__contains__("ccmixter"):
db = "CCMixter"
elif filename.__contains__("MedleyDB"):
db = "MedleyDB"
else:
db = 'musdb18orNIStems'
song_info = {"Title": filename, "Database": db}
# Load mixture and pad it so that output sources have the same length after STFT/ISTFT
mix_audio, mix_sr = librosa.load(multitrack[0].path,
sr=model_config["expected_sr"])
mix_length = len(mix_audio)
# Pad input so that ISTFT later leads to same-length audio
mix_audio_pad = librosa.util.fix_length(
mix_audio, mix_length + model_config["num_fft"] // 2)
mix_mag, mix_ph = Input.audioFileToSpectrogram(mix_audio_pad,
model_config["num_fft"],
model_config["num_hop"])
source_time_frames = mix_mag.shape[1]
# Preallocate source predictions (same shape as input mixture)
acc_pred_mag = np.zeros(mix_mag.shape, np.float32)
drums_pred_mag = np.zeros(mix_mag.shape, np.float32)
input_time_frames = sep_input_shape[2]
output_time_frames = sep_output_shape[2]
# Pad mix spectrogram across time at beg and end so network can make prediction at the beginning and end of signal
pad_time_frames = (input_time_frames - output_time_frames) / 2
mix_mag = np.pad(mix_mag, [(0, 0), (pad_time_frames, pad_time_frames)],
mode="constant",
constant_values=0.0)
# Iterate over mixture magnitudes, fetch network prediction
for source_pos in range(0, source_time_frames, output_time_frames):
# If output patch reaches over the end of source spectrogram, set it so we predict at end of the output, then stop
if source_pos + output_time_frames > source_time_frames:
source_pos = source_time_frames - output_time_frames
# Prepare mixture excerpt by selecting time interval
mix_mag_part = mix_mag[:,
source_pos:source_pos + input_time_frames]
mix_mag_part = Utils.pad_freqs(
mix_mag_part, sep_input_shape[1:3]) # Pad along frequency axis
mix_mag_part = mix_mag_part[np.newaxis, :, :, np.newaxis]
# Fetch network prediction
acc_mag_part, drums_mag_part = sess.run(
[separator_acc, separator_drums],
feed_dict={mix_context: mix_mag_part})
# Save predictions
#source_shape = [1, freq_bins, acc_mag_part.shape[2], 1]
acc_pred_mag[:, source_pos:source_pos +
output_time_frames] = acc_mag_part[0, :-1, :, 0]
drums_pred_mag[:, source_pos:source_pos +
output_time_frames] = drums_mag_part[0, :-1, :, 0]
# Spectrograms to audio, using mixture phase
acc_pred_audio = Input.spectrogramToAudioFile(acc_pred_mag,
model_config["num_fft"],
model_config["num_hop"],
phase=mix_ph,
length=mix_length,
phaseIterations=0)
drums_pred_audio = Input.spectrogramToAudioFile(
drums_pred_mag,
model_config["num_fft"],
model_config["num_hop"],
phase=mix_ph,
length=mix_length,
phaseIterations=0)
# Load original sources
if isinstance(multitrack[1], float):
acc_audio = np.zeros(mix_audio.shape, np.float32)
else:
acc_audio, _ = librosa.load(multitrack[1].path,
sr=model_config["expected_sr"])
if isinstance(multitrack[2], float):
drum_audio = np.zeros(mix_audio.shape, np.float32)
else:
drum_audio, _ = librosa.load(multitrack[2].path,
sr=model_config["expected_sr"])
# Check if any reference source is completely silent, if so, inject some very slight noise to avoid problems during SDR
reference_zero = False
if np.max(np.abs(acc_audio)) == 0.0:
acc_audio += np.random.uniform(-1e-10, 1e-10, size=acc_audio.shape)
reference_zero = True
if np.max(np.abs(drum_audio)) == 0.0:
drum_audio += np.random.uniform(-1e-10,
1e-10,
size=drum_audio.shape)
reference_zero = True
# Evaluate BSS according to MIREX separation method # http://www.music-ir.org/mirex/wiki/2016:Singing_Voice_Separation
#/ np.linalg.norm(acc_audio + drum_audio) # Normalized audio
ref_sources = np.vstack([acc_audio, drum_audio])
#/ np.linalg.norm(acc_pred_audio + drums_pred_audio) # Normalized estimates
pred_sources = np.vstack([acc_pred_audio, drums_pred_audio])
validate(ref_sources, pred_sources)
scores = bss_eval_sources(ref_sources,
pred_sources,
compute_permutation=False)
song_info["SDR"] = scores[0]
song_info["SIR"] = scores[1]
song_info["SAR"] = scores[2]
# Compute reference scores and SNR only if both sources are not silent, since they are undefined otherwise
if not reference_zero:
mix_ref = np.vstack([mix_audio, mix_audio
]) #/ np.linalg.norm(mix_audio + mix_audio)
mix_scores = bss_eval_sources(ref_sources,
mix_ref,
compute_permutation=False)
norm_scores = np.array(scores) - np.array(mix_scores)
# Compute SNR: 10 log_10 ( ||s_target||^2 / ||s_target - alpha * s_estimate||^2 ), scale target for optimal SNR
drums_snr = alpha_snr(drum_audio, drums_pred_audio)
acc_snr = alpha_snr(acc_audio, acc_pred_audio)
drums_ref_snr = alpha_snr(drum_audio, mix_audio)
acc_ref_snr = alpha_snr(acc_audio, mix_audio)
song_info["NSDR"] = norm_scores[0]
song_info["NSIR"] = norm_scores[1]
song_info["SNR"] = np.array([acc_snr, drums_snr])
song_info["NSNR"] = np.array(
[acc_snr - acc_ref_snr, drums_snr - drums_ref_snr])
song_scores.append(song_info)
print(song_info)
dirpath = os.path.join(os.getcwd(), 'results')
try:
fn = dirpath + "tr_" + track_number + "_drums.wav"
librosa.output.write_wav(fn,
drums_pred_audio,
sr=model_config["expected_sr"])
fn = dirpath + "tr_" + track_number + "_acc.wav"
librosa.output.write_wav(fn,
acc_pred_audio,
sr=model_config["expected_sr"])
except Exception as e:
print("Failed to write wav files, error: " + str(e))
track_number = track_number + 1
try:
with open(dirpath + '/' + str(experiment_id) + "BSS_eval.pkl",
"wb") as file:
pickle.dump(song_scores, file)
except Exception as e:
print("Failed to write score files, error: " + str(e))
#Close session, clear computational graph
sess.close()
tf.reset_default_graph()
return song_scores
