mirror of
https://github.com/snakers4/silero-vad.git
synced 2026-02-04 17:39:22 +08:00
626 lines
22 KiB
Python
626 lines
22 KiB
Python
import torch
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import torchaudio
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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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languages = ['ru', 'en', 'de', 'es']
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class IterativeMedianMeter():
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def __init__(self):
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self.reset()
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def reset(self):
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self.median = 0
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self.counts = {}
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for i in range(0, 101, 1):
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self.counts[i / 100] = 0
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self.total_values = 0
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def __call__(self, val):
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self.total_values += 1
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rounded = round(abs(val), 2)
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self.counts[rounded] += 1
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bin_sum = 0
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for j in self.counts:
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bin_sum += self.counts[j]
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if bin_sum >= self.total_values / 2:
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self.median = j
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break
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return self.median
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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 = 16000):
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torchaudio.save(path, tensor.unsqueeze(0), 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 = 0.07,
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num_steps: int = 8,
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batch_size: int = 200,
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num_samples_per_window: int = 4000,
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min_speech_samples: int = 10000, #samples
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min_silence_samples: int = 500,
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run_function=validate,
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visualize_probs=False):
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num_samples = num_samples_per_window
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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)
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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)
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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) # maxlen reached => first element dropped
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triggered = False
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speeches = []
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current_speech = {}
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if visualize_probs:
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import pandas as pd
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smoothed_probs = []
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speech_probs = outs[:, 1] # this is very misleading
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temp_end = 0
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for i, predict in enumerate(speech_probs): # add name
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buffer.append(predict)
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smoothed_prob = (sum(buffer) / len(buffer))
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if visualize_probs:
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smoothed_probs.append(float(smoothed_prob))
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if (smoothed_prob >= trig_sum) and temp_end:
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temp_end=0
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if (smoothed_prob >= 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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continue
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if (smoothed_prob < neg_trig_sum) and triggered:
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if not temp_end:
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temp_end = step * i
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if step * i - temp_end < min_silence_samples:
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continue
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else:
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current_speech['end'] = temp_end
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if (current_speech['end'] - current_speech['start']) > min_speech_samples:
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speeches.append(current_speech)
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temp_end = 0
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current_speech = {}
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triggered = False
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continue
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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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if visualize_probs:
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pd.DataFrame({'probs':smoothed_probs}).plot(figsize=(16,8))
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return speeches
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def get_speech_ts_adaptive(wav: torch.Tensor,
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model,
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batch_size: int = 200,
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step: int = 500,
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num_samples_per_window: int = 4000, # Number of samples per audio chunk to feed to NN (4000 for 16k SR, 2000 for 8k SR is optimal)
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min_speech_samples: int = 10000, # samples
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min_silence_samples: int = 4000,
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speech_pad_samples: int = 2000,
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run_function=validate,
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visualize_probs=False,
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device='cpu'):
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"""
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This function is used for splitting long audios into speech chunks using silero VAD
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Attention! All default sample rate values are optimal for 16000 sample rate model, if you are using 8000 sample rate model optimal values are half as much!
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Parameters
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----------
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batch_size: int
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batch size to feed to silero VAD (default - 200)
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step: int
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step size in samples, (default - 500)
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num_samples_per_window: int
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window size in samples (chunk length in samples to feed to NN, default - 4000)
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min_speech_samples: int
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if speech duration is shorter than this value, do not consider it speech (default - 10000)
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min_silence_samples: int
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number of samples to wait before considering as the end of speech (default - 4000)
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speech_pad_samples: int
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widen speech by this amount of samples each side (default - 2000)
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run_function: function
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function to use for the model call
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visualize_probs: bool
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whether draw prob hist or not (default: False)
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device: string
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torch device to use for the model call (default - "cpu")
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Returns
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----------
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speeches: list
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list containing ends and beginnings of speech chunks (in samples)
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"""
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if visualize_probs:
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import pandas as pd
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num_samples = num_samples_per_window
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num_steps = int(num_samples / step)
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assert min_silence_samples >= step
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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)).to(device)
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out = run_function(model, chunks)
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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)).to(device)
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out = run_function(model, chunks)
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outs.append(out)
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outs = torch.cat(outs, dim=0).cpu()
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buffer = deque(maxlen=num_steps)
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triggered = False
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speeches = []
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smoothed_probs = []
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current_speech = {}
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speech_probs = outs[:, 1] # 0 index for silence probs, 1 index for speech probs
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median_probs = speech_probs.median()
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trig_sum = 0.89 * median_probs + 0.08 # 0.08 when median is zero, 0.97 when median is 1
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temp_end = 0
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for i, predict in enumerate(speech_probs):
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buffer.append(predict)
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smoothed_prob = max(buffer)
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if visualize_probs:
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smoothed_probs.append(float(smoothed_prob))
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if (smoothed_prob >= trig_sum) and temp_end:
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temp_end = 0
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if (smoothed_prob >= 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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continue
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if (smoothed_prob < trig_sum) and triggered:
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if not temp_end:
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temp_end = step * i
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if step * i - temp_end < min_silence_samples:
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continue
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else:
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current_speech['end'] = temp_end
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if (current_speech['end'] - current_speech['start']) > min_speech_samples:
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speeches.append(current_speech)
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temp_end = 0
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current_speech = {}
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triggered = False
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continue
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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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if visualize_probs:
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pd.DataFrame({'probs': smoothed_probs}).plot(figsize=(16, 8))
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for i, ts in enumerate(speeches):
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if i == 0:
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ts['start'] = max(0, ts['start'] - speech_pad_samples)
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if i != len(speeches) - 1:
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silence_duration = speeches[i+1]['start'] - ts['end']
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if silence_duration < 2 * speech_pad_samples:
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ts['end'] += silence_duration // 2
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speeches[i+1]['start'] = max(0, speeches[i+1]['start'] - silence_duration // 2)
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else:
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ts['end'] += speech_pad_samples
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else:
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ts['end'] = min(len(wav), ts['end'] + speech_pad_samples)
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return speeches
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def get_number_ts(wav: torch.Tensor,
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model,
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model_stride=8,
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hop_length=160,
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sample_rate=16000,
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run_function=validate):
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wav = torch.unsqueeze(wav, dim=0)
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perframe_logits = run_function(model, wav)[0]
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perframe_preds = torch.argmax(torch.softmax(perframe_logits, dim=1), dim=1).squeeze() # (1, num_frames_strided)
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extended_preds = []
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for i in perframe_preds:
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extended_preds.extend([i.item()] * model_stride)
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# len(extended_preds) is *num_frames_real*; for each frame of audio we know if it has a number in it.
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triggered = False
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timings = []
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cur_timing = {}
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for i, pred in enumerate(extended_preds):
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if pred == 1:
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if not triggered:
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cur_timing['start'] = int((i * hop_length) / (sample_rate / 1000))
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triggered = True
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elif pred == 0:
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if triggered:
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cur_timing['end'] = int((i * hop_length) / (sample_rate / 1000))
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timings.append(cur_timing)
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cur_timing = {}
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triggered = False
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if cur_timing:
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cur_timing['end'] = int(len(wav) / (sample_rate / 1000))
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timings.append(cur_timing)
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return timings
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def get_language(wav: torch.Tensor,
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model,
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run_function=validate):
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wav = torch.unsqueeze(wav, dim=0)
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lang_logits = run_function(model, wav)[2]
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lang_pred = torch.argmax(torch.softmax(lang_logits, dim=1), dim=1).item() # from 0 to len(languages) - 1
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assert lang_pred < len(languages)
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return languages[lang_pred]
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def get_language_and_group(wav: torch.Tensor,
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model,
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lang_dict: dict,
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lang_group_dict: dict,
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top_n=1,
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run_function=validate):
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wav = torch.unsqueeze(wav, dim=0)
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lang_logits, lang_group_logits = run_function(model, wav)
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softm = torch.softmax(lang_logits, dim=1).squeeze()
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softm_group = torch.softmax(lang_group_logits, dim=1).squeeze()
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srtd = torch.argsort(softm, descending=True)
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srtd_group = torch.argsort(softm_group, descending=True)
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outs = []
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outs_group = []
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for i in range(top_n):
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prob = round(softm[srtd[i]].item(), 2)
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prob_group = round(softm_group[srtd_group[i]].item(), 2)
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outs.append((lang_dict[str(srtd[i].item())], prob))
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outs_group.append((lang_group_dict[str(srtd_group[i].item())], prob_group))
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return outs, outs_group
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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 = 0.07,
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num_steps: int = 8,
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num_samples_per_window: int = 4000):
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self.num_samples = num_samples_per_window
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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) # 500 samples is good enough
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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)]
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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 ((sum(self.buffer) / len(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 ((sum(self.buffer) / len(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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class VADiteratorAdaptive:
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def __init__(self,
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trig_sum: float = 0.26,
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neg_trig_sum: float = 0.06,
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step: int = 500,
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num_samples_per_window: int = 4000,
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speech_pad_samples: int = 1000,
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accum_period: int = 50):
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"""
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This class is used for streaming silero VAD usage
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Parameters
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----------
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trig_sum: float
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trigger value for speech probability, probs above this value are considered speech, switch to TRIGGERED state (default - 0.26)
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neg_trig_sum: float
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in triggered state probabilites below this value are considered nonspeech, switch to NONTRIGGERED state (default - 0.06)
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step: int
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step size in samples, (default - 500)
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num_samples_per_window: int
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window size in samples (chunk length in samples to feed to NN, default - 4000)
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speech_pad_samples: int
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widen speech by this amount of samples each side (default - 1000)
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accum_period: int
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number of chunks / iterations to wait before switching from constant (initial) trig and neg_trig coeffs to adaptive median coeffs (default - 50)
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"""
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self.num_samples = num_samples_per_window
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self.num_steps = int(num_samples_per_window / step)
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self.step = step
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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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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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self.median_meter = IterativeMedianMeter()
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self.median = 0
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self.total_steps = 0
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self.accum_period = accum_period
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self.speech_pad_samples = speech_pad_samples
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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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self.median_meter.reset()
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self.median = 0
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self.total_steps = 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)
|
|
for i in range(self.step, self.num_samples+1, self.step)]
|
|
return torch.cat(overlap_chunks, dim=0)
|
|
|
|
def state(self, model_out):
|
|
current_speech = {}
|
|
speech_probs = model_out[:, 1] # 0 index for silence probs, 1 index for speech probs
|
|
for i, predict in enumerate(speech_probs):
|
|
self.median = self.median_meter(predict.item())
|
|
if self.total_steps < self.accum_period:
|
|
trig_sum = self.trig_sum
|
|
neg_trig_sum = self.neg_trig_sum
|
|
else:
|
|
trig_sum = 0.89 * self.median + 0.08 # 0.08 when median is zero, 0.97 when median is 1
|
|
neg_trig_sum = 0.6 * self.median
|
|
self.total_steps += 1
|
|
self.buffer.append(predict)
|
|
smoothed_prob = max(self.buffer)
|
|
if (smoothed_prob >= trig_sum) and not self.triggered:
|
|
self.triggered = True
|
|
current_speech[max(0, self.num_frames - (self.num_steps-i) * self.step - self.speech_pad_samples)] = 'start'
|
|
if (smoothed_prob < neg_trig_sum) and self.triggered:
|
|
current_speech[self.num_frames - (self.num_steps-i) * self.step + self.speech_pad_samples] = 'end'
|
|
self.triggered = False
|
|
if self.triggered and self.last:
|
|
current_speech[self.num_frames] = 'end'
|
|
if self.last:
|
|
self.refresh()
|
|
return current_speech, self.current_name
|
|
|
|
|
|
def state_generator(model,
|
|
audios: List[str],
|
|
onnx: bool = False,
|
|
trig_sum: float = 0.26,
|
|
neg_trig_sum: float = 0.07,
|
|
num_steps: int = 8,
|
|
num_samples_per_window: int = 4000,
|
|
audios_in_stream: int = 2,
|
|
run_function=validate):
|
|
VADiters = [VADiterator(trig_sum, neg_trig_sum, num_steps, num_samples_per_window) for i in range(audios_in_stream)]
|
|
for i, current_pieces in enumerate(stream_imitator(audios, audios_in_stream, num_samples_per_window)):
|
|
for_batch = [x.prepare_batch(*y) for x, y in zip(VADiters, current_pieces)]
|
|
batch = torch.cat(for_batch)
|
|
|
|
outs = run_function(model, batch)
|
|
vad_outs = torch.split(outs, num_steps)
|
|
|
|
states = []
|
|
for x, y in zip(VADiters, vad_outs):
|
|
cur_st = x.state(y)
|
|
if cur_st[0]:
|
|
states.append(cur_st)
|
|
yield states
|
|
|
|
|
|
def stream_imitator(audios: List[str],
|
|
audios_in_stream: int,
|
|
num_samples_per_window: int = 4000):
|
|
audio_iter = iter(audios)
|
|
iterators = []
|
|
num_samples = num_samples_per_window
|
|
# initial wavs
|
|
for i in range(audios_in_stream):
|
|
next_wav = next(audio_iter)
|
|
wav = read_audio(next_wav)
|
|
wav_chunks = iter([(wav[i:i+num_samples], next_wav) for i in range(0, len(wav), num_samples)])
|
|
iterators.append(wav_chunks)
|
|
print('Done initial Loading')
|
|
good_iters = audios_in_stream
|
|
while True:
|
|
values = []
|
|
for i, it in enumerate(iterators):
|
|
try:
|
|
out, wav_name = next(it)
|
|
except StopIteration:
|
|
try:
|
|
next_wav = next(audio_iter)
|
|
print('Loading next wav: ', next_wav)
|
|
wav = read_audio(next_wav)
|
|
iterators[i] = iter([(wav[i:i+num_samples], next_wav) for i in range(0, len(wav), num_samples)])
|
|
out, wav_name = next(iterators[i])
|
|
except StopIteration:
|
|
good_iters -= 1
|
|
iterators[i] = repeat((torch.zeros(num_samples), 'junk'))
|
|
out, wav_name = next(iterators[i])
|
|
if good_iters == 0:
|
|
return
|
|
values.append((out, wav_name))
|
|
yield values
|
|
|
|
|
|
def single_audio_stream(model,
|
|
audio: torch.Tensor,
|
|
num_samples_per_window:int = 4000,
|
|
run_function=validate,
|
|
iterator_type='basic',
|
|
**kwargs):
|
|
|
|
num_samples = num_samples_per_window
|
|
if iterator_type == 'basic':
|
|
VADiter = VADiterator(num_samples_per_window=num_samples_per_window, **kwargs)
|
|
elif iterator_type == 'adaptive':
|
|
VADiter = VADiteratorAdaptive(num_samples_per_window=num_samples_per_window, **kwargs)
|
|
|
|
wav = read_audio(audio)
|
|
wav_chunks = iter([wav[i:i+num_samples] for i in range(0, len(wav), num_samples)])
|
|
for chunk in wav_chunks:
|
|
batch = VADiter.prepare_batch(chunk)
|
|
|
|
outs = run_function(model, batch)
|
|
|
|
states = []
|
|
state = VADiter.state(outs)
|
|
if state[0]:
|
|
states.append(state[0])
|
|
yield states
|
|
|
|
|
|
def collect_chunks(tss: List[dict],
|
|
wav: torch.Tensor):
|
|
chunks = []
|
|
for i in tss:
|
|
chunks.append(wav[i['start']: i['end']])
|
|
return torch.cat(chunks)
|
|
|
|
|
|
def drop_chunks(tss: List[dict],
|
|
wav: torch.Tensor):
|
|
chunks = []
|
|
cur_start = 0
|
|
for i in tss:
|
|
chunks.append((wav[cur_start: i['start']]))
|
|
cur_start = i['end']
|
|
return torch.cat(chunks)
|