deepgeek/silero-vad
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del numpy dependency

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This commit is contained in:
adamnsandle
2020-12-15 13:02:17 +00:00
parent 40dd30780f
commit f9f8919623
2 changed files with 7 additions and 8 deletions
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2
hubconf.py
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@@ -1,4 +1,4 @@
dependencies = ['torch', 'torchaudio', 'numpy']
dependencies = ['torch', 'torchaudio']
import torch
from utils import (init_jit_model,
get_speech_ts,

13
utils.py
View File

@@ -1,6 +1,5 @@
import torch
import torchaudio
import numpy as np
from typing import List
from itertools import repeat
from collections import deque
@@ -90,10 +89,10 @@ def get_speech_ts(wav: torch.Tensor,
speech_probs = outs[:, 1] # this is very misleading
for i, predict in enumerate(speech_probs): # add name
buffer.append(predict)
if (np.mean(buffer) >= trig_sum) and not triggered:
if ((sum(buffer) / len(buffer))>= trig_sum) and not triggered:
triggered = True
current_speech['start'] = step * max(0, i-num_steps)
if (np.mean(buffer) < neg_trig_sum) and triggered:
if ((sum(buffer) / len(buffer)) < neg_trig_sum) and triggered:
current_speech['end'] = step * i
if (current_speech['end'] - current_speech['start']) > 10000:
speeches.append(current_speech)
@@ -152,10 +151,10 @@ class VADiterator:
speech_probs = model_out[:, 1] # this is very misleading
for i, predict in enumerate(speech_probs):
self.buffer.append(predict)
if (np.mean(self.buffer) >= self.trig_sum) and not self.triggered:
if ((sum(self.buffer) / len(self.buffer)) >= self.trig_sum) and not self.triggered:
self.triggered = True
current_speech[self.num_frames - (self.num_steps-i) * self.step] = 'start'
if (np.mean(self.buffer) < self.neg_trig_sum) and self.triggered:
if ((sum(self.buffer) / len(self.buffer)) < self.neg_trig_sum) and self.triggered:
current_speech[self.num_frames - (self.num_steps-i) * self.step] = 'end'
self.triggered = False
if self.triggered and self.last:
@@ -171,7 +170,7 @@ def state_generator(model,
trig_sum: float = 0.26,
neg_trig_sum: float = 0.02,
num_steps: int = 8,
audios_in_stream: int = 5,
audios_in_stream: int = 2,
run_function=validate):
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)):
@@ -179,7 +178,7 @@ def state_generator(model,
batch = torch.cat(for_batch)
outs = run_function(model, batch)
vad_outs = np.split(outs[-2].numpy(), audios_in_stream)
vad_outs = torch.split(outs[-2], num_steps)
states = []
for x, y in zip(VADiters, vad_outs):