def predict_method(
self,
data: List[List[str]],
max_seq_len: int = 128,
batch_size: int = 1,
use_gpu: bool = False
):
"""
Run predict method as a service.
Serving as a task which is specified from serving config.
Tasks supported:
1. seq-cls: sequence classification;
2. token-cls: sequence labeling;
3. None: embedding.
Args:
data (obj:`List(List(str))`): The processed data whose each element is the list of a single text or a pair of texts.
max_seq_len (:obj:`int`, `optional`, defaults to 128):
If set to a number, will limit the total sequence returned so that it has a maximum length.
batch_size(obj:`int`, defaults to 1): The number of batch.
use_gpu(obj:`bool`, defaults to `False`): Whether to use gpu to run or not.
Returns:
results(obj:`list`): All the predictions labels.
"""
if self.task in self._tasks_supported: # cls service
if self.label_map:
# compatible with json decoding label_map
self.label_map = {int(k): v for k, v in self.label_map.items()}
results = self.predict(data, max_seq_len, batch_size, use_gpu)
if self.task == 'token-cls':
# remove labels of [CLS] token and pad tokens
results = [
token_labels[1:len(data[i][0])+1] for i, token_labels in enumerate(results)
]
return results
elif self.task is None: # embedding service
token_results, sentence_results = self.get_embedding(data, max_seq_len, batch_size, use_gpu)
token_results = [
token_embeddings[1:len(data[i][0])+1] for i, token_embeddings in enumerate(token_results)
]
return token_results, sentence_results
else: # unknown service
logger.error(
f'Unknown task {self.task}, current tasks supported:\n'
'1. seq-cls: sequence classification service;\n'
'2. token-cls: sequence labeling service;\n'
'3. None: embedding service'
)
return
def extract_melspectrogram(y,
sample_rate: int = 32000,
window_size: int = 1024,
hop_size: int = 320,
mel_bins: int = 64,
fmin: int = 50,
fmax: int = 14000,
window: str = 'hann',
center: bool = True,
pad_mode: str = 'reflect',
ref: float = 1.0,
amin: float = 1e-10,
top_db: float = None):
'''
Extract Mel Spectrogram from a waveform.
'''
try:
import librosa
except Exception:
logger.error(
'Failed to import librosa. Please check that librosa and numba are correctly installed.'
)
raise
s = librosa.stft(y,
n_fft=window_size,
hop_length=hop_size,
win_length=window_size,
window=window,
center=center,
pad_mode=pad_mode)
power = np.abs(s)**2
melW = librosa.filters.mel(sr=sample_rate,
n_fft=window_size,
n_mels=mel_bins,
fmin=fmin,
fmax=fmax)
mel = np.matmul(melW, power)
db = librosa.power_to_db(mel, ref=ref, amin=amin, top_db=None)
db = db.transpose()
return db
def __init__(self, extract_embedding: bool = True, checkpoint: str = None):
super(CNN14, self).__init__()
self.bn0 = nn.BatchNorm2D(64)
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
self.fc1 = nn.Linear(2048, self.emb_size)
self.fc_audioset = nn.Linear(self.emb_size, 527)
if checkpoint is not None and os.path.isfile(checkpoint):
state_dict = paddle.load(checkpoint)
self.set_state_dict(state_dict)
logger.info(
f'Loaded CNN14 pretrained parameters from: {checkpoint}')
else:
logger.error(
'No valid checkpoints for CNN14. Start training from scratch.')
self.extract_embedding = extract_embedding
