deepgeek/silero-vad
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snakers41
2020-12-11 13:57:22 +00:00
parent cef1644a5f
commit 5412e5a769
2 changed files with 24 additions and 19 deletions
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2
silero-vad.ipynb
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@@ -301,7 +301,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.8.3" "version": "3.7.7"
}, },
"toc": { "toc": {
"base_numbering": 1, "base_numbering": 1,

19
utils.py
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@@ -73,11 +73,15 @@ def init_jit_model(model_path,
return model return model
def get_speech_ts(wav, model, extractor, trig_sum=0.25, neg_trig_sum=0.01, num_steps=8, batch_size=200): def get_speech_ts(wav, model, extractor,
trig_sum=0.25, neg_trig_sum=0.01,
num_steps=8, batch_size=200):
assert 4000 % num_steps == 0 assert 4000 % num_steps == 0
step = int(4000 / num_steps) step = int(4000 / num_steps) # stride / hop
outs = [] outs = []
to_concat = [] to_concat = []
for i in range(0, len(wav), step): for i in range(0, len(wav), step):
chunk = wav[i: i+4000] chunk = wav[i: i+4000]
if len(chunk) < 4000: if len(chunk) < 4000:
@@ -98,11 +102,12 @@ def get_speech_ts(wav, model, extractor, trig_sum=0.25, neg_trig_sum=0.01, num_s
outs = torch.cat(outs, dim=0) outs = torch.cat(outs, dim=0)
buffer = deque(maxlen=num_steps) buffer = deque(maxlen=num_steps) # when max queue len is reach, first element is dropped
triggered = False triggered = False
speeches = [] speeches = []
current_speech = {} current_speech = {}
for i, predict in enumerate(outs[:, 1]):
for i, predict in enumerate(outs[:, 1]): # add name
buffer.append(predict) buffer.append(predict)
if (np.mean(buffer) >= trig_sum) and not triggered: if (np.mean(buffer) >= trig_sum) and not triggered:
triggered = True triggered = True
@@ -150,7 +155,9 @@ class STFTExtractor(nn.Module):
class VADiterator: class VADiterator:
def __init__(self, trig_sum=0.26, neg_trig_sum=0.01, num_steps=8): def __init__(self,
trig_sum=0.26, neg_trig_sum=0.01,
num_steps=8):
self.num_steps = num_steps self.num_steps = num_steps
assert 4000 % num_steps == 0 assert 4000 % num_steps == 0
self.step = int(4000 / num_steps) self.step = int(4000 / num_steps)
@@ -203,7 +210,6 @@ class VADiterator:
return current_speech, self.current_name return current_speech, self.current_name
def state_generator(model, audios, extractor, onnx=False, trig_sum=0.26, neg_trig_sum=0.01, num_steps=8, audios_in_stream=5): def state_generator(model, audios, extractor, onnx=False, trig_sum=0.26, neg_trig_sum=0.01, num_steps=8, audios_in_stream=5):
VADiters = [VADiterator(trig_sum, neg_trig_sum, num_steps) for i in range(audios_in_stream)] VADiters = [VADiterator(trig_sum, neg_trig_sum, num_steps) for i in range(audios_in_stream)]
for i, current_pieces in enumerate(stream_imitator(audios, audios_in_stream)): for i, current_pieces in enumerate(stream_imitator(audios, audios_in_stream)):
@@ -259,4 +265,3 @@ def stream_imitator(stereo, audios_in_stream):
values.append((out, wav_name)) values.append((out, wav_name))
yield values yield values