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beamdecode.py
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beamdecode.py
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import _init_path
import torch
import feature
from models.conv import GatedConv
import torch.nn.functional as F
from ctcdecode import CTCBeamDecoder
alpha = 0.8
beta = 0.3
lm_path = "lm/zh_giga.no_cna_cmn.prune01244.klm"
cutoff_top_n = 40
cutoff_prob = 1.0
beam_width = 32
num_processes = 4
blank_index = 0
model = GatedConv.load("pretrained/gated-conv.pth")
model.eval()
decoder = CTCBeamDecoder(
model.vocabulary,
lm_path,
alpha,
beta,
cutoff_top_n,
cutoff_prob,
beam_width,
num_processes,
blank_index,
)
def translate(vocab, out, out_len):
return "".join([vocab[x] for x in out[0:out_len]])
def predict(f):
wav = feature.load_audio(f)
spec = feature.spectrogram(wav)
spec.unsqueeze_(0)
with torch.no_grad():
y = model.cnn(spec)
y = F.softmax(y, 1)
y_len = torch.tensor([y.size(-1)])
y = y.permute(0, 2, 1) # B * T * V
print("decoding")
out, score, offset, out_len = decoder.decode(y, y_len)
return translate(model.vocabulary, out[0][0], out_len[0][0])