def load_data(from_path=None, ckpt_path=None, data_path=None, save_path=None):
if from_path is None:
if ckpt_path is None:
raise Exception('No checkpoint path provided')
from resemblyzer import preprocess_wav, VoiceEncoder
from tqdm import tqdm
device = torch.device('cuda')
encoder = VoiceEncoder(device=device, loss_device=device)
encoder.load_ckpt(ckpt_path, device=device)
encoder.eval()
wav_fpaths = list(Path(data_path).glob("**/*.flac"))
# Preprocess and save encoded utterance and label to list
X = []
y = []
for wav_fpath in tqdm(wav_fpaths):
wav = preprocess_wav(wav_fpath)
X.append(encoder.embed_utterance(wav).cpu().numpy())
y.append(wav_fpath.parent.parent.stem)
# Save for testing
if save_path is not None:
np.save(Path(save_path, 'embeds.npy'), X)
np.save(Path(save_path, 'labels.npy'), y)
else:
raise Exception('No save_path provided')
else:
X = np.load(Path(from_path, 'embeds.npy'), allow_pickle=True)
y = np.load(Path(from_path, 'labels.npy'), allow_pickle=True)
return X, y
class Predictor():
def __init__(self,
clf_ckpt_path='exp/clv/mlp/mlp_best_val_loss.pt',
enc_ckpt_path='ckpt/pretrained.pt',
device=torch.device('cuda'),
num_class=381,
verbose=False):
start = timer()
self.encoder = VoiceEncoder(device=device, loss_device=device)
self.encoder.load_ckpt(enc_ckpt_path, device)
self.encoder.eval()
self.classifier = MLP(num_class=num_class)
self.classifier.load_ckpt(clf_ckpt_path, device)
self.classifier.eval()
if verbose:
print(
f'Encoder and classifier models loaded successfully in {timer() - start}s'
)
def preprocess(self, f):
"""
Applies preprocessing operations to a waveform either on disk or in memory such that
The waveform will be resampled to match the data hyperparameters.
:param f: either a filepath to an audio file or the waveform as a numpy array of floats.
"""
return preprocess_wav(f)
def predict(self, audio, topk=2):
"""
Predict top_k classes with highest probabilities.
:param audio: preprocessed waveform.
:param topk: Keep topk classes with highest probabilities.
"""
embed = self.encoder.embed_utterance(audio)
inp = embed.unsqueeze(dim=0)
top_probs, top_classes = self.classifier.predict(inp, topk=topk)
return top_probs, top_classes
