mirror of
https://github.com/snakers4/silero-vad.git
synced 2026-02-04 17:39:22 +08:00
249 lines
8.4 KiB
Python
249 lines
8.4 KiB
Python
import torch
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import torchaudio
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import numpy as np
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from typing import List
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from itertools import repeat
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from collections import deque
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import torch.nn.functional as F
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torchaudio.set_audio_backend("soundfile") # switch backend
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def validate(model,
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inputs: torch.Tensor):
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with torch.no_grad():
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outs = model(inputs)
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return outs
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def read_audio(path: str,
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target_sr: int = 16000):
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assert torchaudio.get_audio_backend() == 'soundfile'
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wav, sr = torchaudio.load(path)
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if wav.size(0) > 1:
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wav = wav.mean(dim=0, keepdim=True)
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if sr != target_sr:
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transform = torchaudio.transforms.Resample(orig_freq=sr,
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new_freq=target_sr)
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wav = transform(wav)
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sr = target_sr
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assert sr == target_sr
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return wav.squeeze(0)
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def save_audio(path: str,
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tensor: torch.Tensor,
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sr: int):
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torchaudio.save(path, tensor, sr)
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def init_jit_model(model_path: str,
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device=torch.device('cpu')):
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torch.set_grad_enabled(False)
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model = torch.jit.load(model_path, map_location=device)
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model.eval()
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return model
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def get_speech_ts(wav: torch.Tensor,
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model,
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trig_sum: float = 0.25,
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neg_trig_sum: float = 0.02,
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num_steps: int = 8,
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batch_size: int = 200,
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run_function=validate):
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num_samples = 4000
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assert num_samples % num_steps == 0
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step = int(num_samples / num_steps) # stride / hop
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outs = []
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to_concat = []
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for i in range(0, len(wav), step):
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chunk = wav[i: i+num_samples]
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if len(chunk) < num_samples:
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chunk = F.pad(chunk, (0, num_samples - len(chunk)))
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to_concat.append(chunk.unsqueeze(0))
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if len(to_concat) >= batch_size:
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chunks = torch.Tensor(torch.cat(to_concat, dim=0))
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out = run_function(model, chunks)[-2]
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outs.append(out)
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to_concat = []
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if to_concat:
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chunks = torch.Tensor(torch.cat(to_concat, dim=0))
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out = run_function(model, chunks)[-2]
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outs.append(out)
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outs = torch.cat(outs, dim=0)
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buffer = deque(maxlen=num_steps) # when max queue len is reached, first element is dropped
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triggered = False
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speeches = []
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current_speech = {}
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speech_probs = outs[:, 1] # this is very misleading
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for i, predict in enumerate(speech_probs): # add name
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buffer.append(predict)
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if (np.mean(buffer) >= trig_sum) and not triggered:
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triggered = True
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current_speech['start'] = step * max(0, i-num_steps)
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if (np.mean(buffer) < neg_trig_sum) and triggered:
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current_speech['end'] = step * i
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if (current_speech['end'] - current_speech['start']) > 10000:
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speeches.append(current_speech)
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current_speech = {}
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triggered = False
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if current_speech:
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current_speech['end'] = len(wav)
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speeches.append(current_speech)
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return speeches
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class VADiterator:
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def __init__(self,
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trig_sum: float = 0.26,
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neg_trig_sum: float = 0.02,
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num_steps: int = 8):
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self.num_samples = 4000
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self.num_steps = num_steps
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assert self.num_samples % num_steps == 0
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self.step = int(self.num_samples / num_steps)
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self.prev = torch.zeros(self.num_samples)
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self.last = False
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self.triggered = False
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self.buffer = deque(maxlen=num_steps)
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self.num_frames = 0
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self.trig_sum = trig_sum
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self.neg_trig_sum = neg_trig_sum
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self.current_name = ''
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def refresh(self):
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self.prev = torch.zeros(self.num_samples)
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self.last = False
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self.triggered = False
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self.buffer = deque(maxlen=self.num_steps)
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self.num_frames = 0
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def prepare_batch(self, wav_chunk, name=None):
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if (name is not None) and (name != self.current_name):
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self.refresh()
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self.current_name = name
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assert len(wav_chunk) <= self.num_samples
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self.num_frames += len(wav_chunk)
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if len(wav_chunk) < self.num_samples:
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wav_chunk = F.pad(wav_chunk, (0, self.num_samples - len(wav_chunk))) # short chunk => eof audio
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self.last = True
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stacked = torch.cat([self.prev, wav_chunk])
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self.prev = wav_chunk
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overlap_chunks = [stacked[i:i+self.num_samples].unsqueeze(0)
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for i in range(self.step, self.num_samples+1, self.step)] # 500 sample step is good enough
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return torch.cat(overlap_chunks, dim=0)
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def state(self, model_out):
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current_speech = {}
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speech_probs = model_out[:, 1] # this is very misleading
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for i, predict in enumerate(speech_probs):
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self.buffer.append(predict)
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if (np.mean(self.buffer) >= self.trig_sum) and not self.triggered:
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self.triggered = True
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current_speech[self.num_frames - (self.num_steps-i) * self.step] = 'start'
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if (np.mean(self.buffer) < self.neg_trig_sum) and self.triggered:
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current_speech[self.num_frames - (self.num_steps-i) * self.step] = 'end'
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self.triggered = False
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if self.triggered and self.last:
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current_speech[self.num_frames] = 'end'
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if self.last:
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self.refresh()
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return current_speech, self.current_name
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def state_generator(model,
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audios: List[str],
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onnx: bool = False,
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trig_sum: float = 0.26,
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neg_trig_sum: float = 0.02,
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num_steps: int = 8,
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audios_in_stream: int = 5,
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run_function=validate):
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VADiters = [VADiterator(trig_sum, neg_trig_sum, num_steps) for i in range(audios_in_stream)]
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for i, current_pieces in enumerate(stream_imitator(audios, audios_in_stream)):
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for_batch = [x.prepare_batch(*y) for x, y in zip(VADiters, current_pieces)]
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batch = torch.cat(for_batch)
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outs = run_function(model, batch)
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vad_outs = np.split(outs[-2].numpy(), audios_in_stream)
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states = []
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for x, y in zip(VADiters, vad_outs):
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cur_st = x.state(y)
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if cur_st[0]:
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states.append(cur_st)
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yield states
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def stream_imitator(audios: List[str],
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audios_in_stream: int):
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audio_iter = iter(audios)
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iterators = []
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num_samples = 4000
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# initial wavs
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for i in range(audios_in_stream):
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next_wav = next(audio_iter)
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wav = read_audio(next_wav)
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wav_chunks = iter([(wav[i:i+num_samples], next_wav) for i in range(0, len(wav), num_samples)])
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iterators.append(wav_chunks)
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print('Done initial Loading')
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good_iters = audios_in_stream
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while True:
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values = []
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for i, it in enumerate(iterators):
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try:
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out, wav_name = next(it)
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except StopIteration:
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try:
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next_wav = next(audio_iter)
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print('Loading next wav: ', next_wav)
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wav = read_audio(next_wav)
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iterators[i] = iter([(wav[i:i+num_samples], next_wav) for i in range(0, len(wav), num_samples)])
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out, wav_name = next(iterators[i])
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except StopIteration:
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good_iters -= 1
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iterators[i] = repeat((torch.zeros(num_samples), 'junk'))
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out, wav_name = next(iterators[i])
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if good_iters == 0:
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return
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values.append((out, wav_name))
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yield values
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def single_audio_stream(model,
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audio: str,
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onnx: bool = False,
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trig_sum: float = 0.26,
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neg_trig_sum: float = 0.02,
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num_steps: int = 8,
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run_function=validate):
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num_samples = 4000
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VADiter = VADiterator(trig_sum, neg_trig_sum, num_steps)
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wav = read_audio(audio)
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wav_chunks = iter([wav[i:i+num_samples] for i in range(0, len(wav), num_samples)])
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for chunk in wav_chunks:
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batch = VADiter.prepare_batch(chunk)
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outs = run_function(model, batch)
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vad_outs = outs[-2] # this is very misleading
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states = []
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state = VADiter.state(vad_outs)
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if state[0]:
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states.append(state[0])
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yield states
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