import torch
import torchaudio
from typing import Callable, List
import warnings
languages = ['ru', 'en', 'de', 'es']
class OnnxWrapper():
def __init__(self, path, force_onnx_cpu=False):
import numpy as np
global np
import onnxruntime
opts = onnxruntime.SessionOptions()
opts.inter_op_num_threads = 1
opts.intra_op_num_threads = 1
if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers():
self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'], sess_options=opts)
else:
self.session = onnxruntime.InferenceSession(path, sess_options=opts)
self.reset_states()
if '16k' in path:
warnings.warn('This model support only 16000 sampling rate!')
self.sample_rates = [16000]
else:
self.sample_rates = [8000, 16000]
def _validate_input(self, x, sr: int):
if x.dim() == 1:
x = x.unsqueeze(0)
if x.dim() > 2:
raise ValueError(f"Too many dimensions for input audio chunk {x.dim()}")
if sr != 16000 and (sr % 16000 == 0):
step = sr // 16000
x = x[:,::step]
sr = 16000
if sr not in self.sample_rates:
raise ValueError(f"Supported sampling rates: {self.sample_rates} (or multiply of 16000)")
if sr / x.shape[1] > 31.25:
raise ValueError("Input audio chunk is too short")
return x, sr
def reset_states(self, batch_size=1):
self._state = torch.zeros((2, batch_size, 128)).float()
self._context = torch.zeros(0)
self._last_sr = 0
self._last_batch_size = 0
def __call__(self, x, sr: int):
x, sr = self._validate_input(x, sr)
num_samples = 512 if sr == 16000 else 256
if x.shape[-1] != num_samples:
raise ValueError(f"Provided number of samples is {x.shape[-1]} (Supported values: 256 for 8000 sample rate, 512 for 16000)")
batch_size = x.shape[0]
context_size = 64 if sr == 16000 else 32
if not self._last_batch_size:
self.reset_states(batch_size)
if (self._last_sr) and (self._last_sr != sr):
self.reset_states(batch_size)
if (self._last_batch_size) and (self._last_batch_size != batch_size):
self.reset_states(batch_size)
if not len(self._context):
self._context = torch.zeros(batch_size, context_size)
x = torch.cat([self._context, x], dim=1)
if sr in [8000, 16000]:
ort_inputs = {'input': x.numpy(), 'state': self._state.numpy(), 'sr': np.array(sr, dtype='int64')}
ort_outs = self.session.run(None, ort_inputs)
out, state = ort_outs
self._state = torch.from_numpy(state)
else:
raise ValueError()
self._context = x[..., -context_size:]
self._last_sr = sr
self._last_batch_size = batch_size
out = torch.from_numpy(out)
return out
def audio_forward(self, x, sr: int):
outs = []
x, sr = self._validate_input(x, sr)
self.reset_states()
num_samples = 512 if sr == 16000 else 256
if x.shape[1] % num_samples:
pad_num = num_samples - (x.shape[1] % num_samples)
x = torch.nn.functional.pad(x, (0, pad_num), 'constant', value=0.0)
for i in range(0, x.shape[1], num_samples):
wavs_batch = x[:, i:i+num_samples]
out_chunk = self.__call__(wavs_batch, sr)
outs.append(out_chunk)
stacked = torch.cat(outs, dim=1)
return stacked.cpu()
class Validator():
def __init__(self, url, force_onnx_cpu):
self.onnx = True if url.endswith('.onnx') else False
torch.hub.download_url_to_file(url, 'inf.model')
if self.onnx:
import onnxruntime
if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers():
self.model = onnxruntime.InferenceSession('inf.model', providers=['CPUExecutionProvider'])
else:
self.model = onnxruntime.InferenceSession('inf.model')
else:
self.model = init_jit_model(model_path='inf.model')
def __call__(self, inputs: torch.Tensor):
with torch.no_grad():
if self.onnx:
ort_inputs = {'input': inputs.cpu().numpy()}
outs = self.model.run(None, ort_inputs)
outs = [torch.Tensor(x) for x in outs]
else:
outs = self.model(inputs)
return outs
def read_audio(path: str,
sampling_rate: int = 16000):
list_backends = torchaudio.list_audio_backends()
assert len(list_backends) > 0, 'The list of available backends is empty, please install backend manually. \
\n Recommendations: \n \tSox (UNIX OS) \n \tSoundfile (Windows OS, UNIX OS) \n \tffmpeg (Windows OS, UNIX OS)'
try:
effects = [
['channels', '1'],
['rate', str(sampling_rate)]
]
wav, sr = torchaudio.sox_effects.apply_effects_file(path, effects=effects)
except:
wav, sr = torchaudio.load(path)
if wav.size(0) > 1:
wav = wav.mean(dim=0, keepdim=True)
if sr != sampling_rate:
transform = torchaudio.transforms.Resample(orig_freq=sr,
new_freq=sampling_rate)
wav = transform(wav)
sr = sampling_rate
assert sr == sampling_rate
return wav.squeeze(0)
def save_audio(path: str,
tensor: torch.Tensor,
sampling_rate: int = 16000):
torchaudio.save(path, tensor.unsqueeze(0), sampling_rate, bits_per_sample=16)
def init_jit_model(model_path: str,
device=torch.device('cpu')):
model = torch.jit.load(model_path, map_location=device)
model.eval()
return model
def make_visualization(probs, step):
import pandas as pd
pd.DataFrame({'probs': probs},
index=[x * step for x in range(len(probs))]).plot(figsize=(16, 8),
kind='area', ylim=[0, 1.05], xlim=[0, len(probs) * step],
xlabel='seconds',
ylabel='speech probability',
colormap='tab20')
@torch.no_grad()
def get_speech_timestamps(audio: torch.Tensor,
model,
threshold: float = 0.5,
sampling_rate: int = 16000,
min_speech_duration_ms: int = 250,
max_speech_duration_s: float = float('inf'),
min_silence_duration_ms: int = 100,
speech_pad_ms: int = 30,
return_seconds: bool = False,
time_resolution: int = 1,
visualize_probs: bool = False,
progress_tracking_callback: Callable[[float], None] = None,
neg_threshold: float = None,
window_size_samples: int = 512,
min_silence_at_max_speech: float = 98,
use_max_poss_sil_at_max_speech: bool = True):
"""
This method is used for splitting long audios into speech chunks using silero VAD
Parameters
----------
audio: torch.Tensor, one dimensional
One dimensional float torch.Tensor, other types are casted to torch if possible
model: preloaded .jit/.onnx silero VAD model
threshold: float (default - 0.5)
Speech threshold. Silero VAD outputs speech probabilities for each audio chunk, probabilities ABOVE this value are considered as SPEECH.
It is better to tune this parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets.
sampling_rate: int (default - 16000)
Currently silero VAD models support 8000 and 16000 (or multiply of 16000) sample rates
min_speech_duration_ms: int (default - 250 milliseconds)
Final speech chunks shorter min_speech_duration_ms are thrown out
max_speech_duration_s: int (default - inf)
Maximum duration of speech chunks in seconds
Chunks longer than max_speech_duration_s will be split at the timestamp of the last silence that lasts more than 100ms (if any), to prevent agressive cutting.
Otherwise, they will be split aggressively just before max_speech_duration_s.
min_silence_duration_ms: int (default - 100 milliseconds)
In the end of each speech chunk wait for min_silence_duration_ms before separating it
speech_pad_ms: int (default - 30 milliseconds)
Final speech chunks are padded by speech_pad_ms each side
return_seconds: bool (default - False)
whether return timestamps in seconds (default - samples)
time_resolution: bool (default - 1)
time resolution of speech coordinates when requested as seconds
visualize_probs: bool (default - False)
whether draw prob hist or not
progress_tracking_callback: Callable[[float], None] (default - None)
callback function taking progress in percents as an argument
neg_threshold: float (default = threshold - 0.15)
Negative threshold (noise or exit threshold). If model's current state is SPEECH, values BELOW this value are considered as NON-SPEECH.
min_silence_at_max_speech: float (default - 98ms)
Minimum silence duration in ms which is used to avoid abrupt cuts when max_speech_duration_s is reached
use_max_poss_sil_at_max_speech: bool (default - True)
Whether to use the maximum possible silence at max_speech_duration_s or not. If not, the last silence is used.
window_size_samples: int (default - 512 samples)
!!! DEPRECATED, DOES NOTHING !!!
Returns
----------
speeches: list of dicts
list containing ends and beginnings of speech chunks (samples or seconds based on return_seconds)
"""
if not torch.is_tensor(audio):
try:
audio = torch.Tensor(audio)
except:
raise TypeError("Audio cannot be casted to tensor. Cast it manually")
if len(audio.shape) > 1:
for i in range(len(audio.shape)): # trying to squeeze empty dimensions
audio = audio.squeeze(0)
if len(audio.shape) > 1:
raise ValueError("More than one dimension in audio. Are you trying to process audio with 2 channels?")
if sampling_rate > 16000 and (sampling_rate % 16000 == 0):
step = sampling_rate // 16000
sampling_rate = 16000
audio = audio[::step]
warnings.warn('Sampling rate is a multiply of 16000, casting to 16000 manually!')
else:
step = 1
if sampling_rate not in [8000, 16000]:
raise ValueError("Currently silero VAD models support 8000 and 16000 (or multiply of 16000) sample rates")
window_size_samples = 512 if sampling_rate == 16000 else 256
hop_size_samples = int(window_size_samples)
model.reset_states()
min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
speech_pad_samples = sampling_rate * speech_pad_ms / 1000
max_speech_samples = sampling_rate * max_speech_duration_s - window_size_samples - 2 * speech_pad_samples
min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
min_silence_samples_at_max_speech = sampling_rate * min_silence_at_max_speech / 1000
audio_length_samples = len(audio)
speech_probs = []
for current_start_sample in range(0, audio_length_samples, hop_size_samples):
chunk = audio[current_start_sample: current_start_sample + window_size_samples]
if len(chunk) < window_size_samples:
chunk = torch.nn.functional.pad(chunk, (0, int(window_size_samples - len(chunk))))
try:
speech_prob = model(chunk, sampling_rate).item()
except Exception as e:
import ipdb; ipdb.set_trace()
speech_probs.append(speech_prob)
# caculate progress and seng it to callback function
progress = current_start_sample + hop_size_samples
if progress > audio_length_samples:
progress = audio_length_samples
progress_percent = (progress / audio_length_samples) * 100
if progress_tracking_callback:
progress_tracking_callback(progress_percent)
triggered = False
speeches = []
current_speech = {}
if neg_threshold is None:
neg_threshold = max(threshold - 0.15, 0.01)
temp_end = 0 # to save potential segment end (and tolerate some silence)
prev_end = next_start = 0 # to save potential segment limits in case of maximum segment size reached
possible_ends = []
for i, speech_prob in enumerate(speech_probs):
if (speech_prob >= threshold) and temp_end:
if temp_end != 0:
sil_dur = (hop_size_samples * i) - temp_end
if sil_dur > min_silence_samples_at_max_speech:
possible_ends.append((temp_end, sil_dur))
temp_end = 0
if next_start < prev_end:
next_start = hop_size_samples * i
if (speech_prob >= threshold) and not triggered:
triggered = True
current_speech['start'] = hop_size_samples * i
continue
if triggered and (hop_size_samples * i) - current_speech['start'] > max_speech_samples:
if possible_ends:
if use_max_poss_sil_at_max_speech:
prev_end, dur = max(possible_ends, key=lambda x: x[1]) # use the longest possible silence segment in the current speech chunk
else:
prev_end, dur = possible_ends[-1] # use the last possible silence segement
current_speech['end'] = prev_end
speeches.append(current_speech)
current_speech = {}
next_start = prev_end + dur
if next_start < prev_end + hop_size_samples * i: # previously reached silence (< neg_thres) and is still not speech (< thres)
#triggered = False
current_speech['start'] = next_start
else:
triggered = False
#current_speech['start'] = next_start
prev_end = next_start = temp_end = 0
possible_ends = []
else:
current_speech['end'] = hop_size_samples * i
speeches.append(current_speech)
current_speech = {}
prev_end = next_start = temp_end = 0
triggered = False
possible_ends = []
continue
if (speech_prob < neg_threshold) and triggered:
if not temp_end:
temp_end = hop_size_samples * i
# if ((hop_size_samples * i) - temp_end) > min_silence_samples_at_max_speech: # condition to avoid cutting in very short silence
# prev_end = temp_end
if (hop_size_samples * i) - temp_end < min_silence_samples:
continue
else:
current_speech['end'] = temp_end
if (current_speech['end'] - current_speech['start']) > min_speech_samples:
speeches.append(current_speech)
current_speech = {}
prev_end = next_start = temp_end = 0
triggered = False
possible_ends = []
continue
if current_speech and (audio_length_samples - current_speech['start']) > min_speech_samples:
current_speech['end'] = audio_length_samples
speeches.append(current_speech)
for i, speech in enumerate(speeches):
if i == 0:
speech['start'] = int(max(0, speech['start'] - speech_pad_samples))
if i != len(speeches) - 1:
silence_duration = speeches[i+1]['start'] - speech['end']
if silence_duration < 2 * speech_pad_samples:
speech['end'] += int(silence_duration // 2)
speeches[i+1]['start'] = int(max(0, speeches[i+1]['start'] - silence_duration // 2))
else:
speech['end'] = int(min(audio_length_samples, speech['end'] + speech_pad_samples))
speeches[i+1]['start'] = int(max(0, speeches[i+1]['start'] - speech_pad_samples))
else:
speech['end'] = int(min(audio_length_samples, speech['end'] + speech_pad_samples))
if return_seconds:
audio_length_seconds = audio_length_samples / sampling_rate
for speech_dict in speeches:
speech_dict['start'] = max(round(speech_dict['start'] / sampling_rate, time_resolution), 0)
speech_dict['end'] = min(round(speech_dict['end'] / sampling_rate, time_resolution), audio_length_seconds)
elif step > 1:
for speech_dict in speeches:
speech_dict['start'] *= step
speech_dict['end'] *= step
if visualize_probs:
make_visualization(speech_probs, hop_size_samples / sampling_rate)
return speeches
class VADIterator:
def __init__(self,
model,
threshold: float = 0.5,
sampling_rate: int = 16000,
min_silence_duration_ms: int = 100,
speech_pad_ms: int = 30
):
"""
Class for stream imitation
Parameters
----------
model: preloaded .jit/.onnx silero VAD model
threshold: float (default - 0.5)
Speech threshold. Silero VAD outputs speech probabilities for each audio chunk, probabilities ABOVE this value are considered as SPEECH.
It is better to tune this parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets.
sampling_rate: int (default - 16000)
Currently silero VAD models support 8000 and 16000 sample rates
min_silence_duration_ms: int (default - 100 milliseconds)
In the end of each speech chunk wait for min_silence_duration_ms before separating it
speech_pad_ms: int (default - 30 milliseconds)
Final speech chunks are padded by speech_pad_ms each side
"""
self.model = model
self.threshold = threshold
self.sampling_rate = sampling_rate
if sampling_rate not in [8000, 16000]:
raise ValueError('VADIterator does not support sampling rates other than [8000, 16000]')
self.min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
self.speech_pad_samples = sampling_rate * speech_pad_ms / 1000
self.reset_states()
def reset_states(self):
self.model.reset_states()
self.triggered = False
self.temp_end = 0
self.current_sample = 0
@torch.no_grad()
def __call__(self, x, return_seconds=False, time_resolution: int = 1):
"""
x: torch.Tensor
audio chunk (see examples in repo)
return_seconds: bool (default - False)
whether return timestamps in seconds (default - samples)
time_resolution: int (default - 1)
time resolution of speech coordinates when requested as seconds
"""
if not torch.is_tensor(x):
try:
x = torch.Tensor(x)
except:
raise TypeError("Audio cannot be casted to tensor. Cast it manually")
window_size_samples = len(x[0]) if x.dim() == 2 else len(x)
self.current_sample += window_size_samples
speech_prob = self.model(x, self.sampling_rate).item()
if (speech_prob >= self.threshold) and self.temp_end:
self.temp_end = 0
if (speech_prob >= self.threshold) and not self.triggered:
self.triggered = True
speech_start = max(0, self.current_sample - self.speech_pad_samples - window_size_samples)
return {'start': int(speech_start) if not return_seconds else round(speech_start / self.sampling_rate, time_resolution)}
if (speech_prob < self.threshold - 0.15) and self.triggered:
if not self.temp_end:
self.temp_end = self.current_sample
if self.current_sample - self.temp_end < self.min_silence_samples:
return None
else:
speech_end = self.temp_end + self.speech_pad_samples - window_size_samples
self.temp_end = 0
self.triggered = False
return {'end': int(speech_end) if not return_seconds else round(speech_end / self.sampling_rate, time_resolution)}
return None
def collect_chunks(tss: List[dict],
wav: torch.Tensor,
seconds: bool = False,
sampling_rate: int = None) -> torch.Tensor:
"""Collect audio chunks from a longer audio clip
This method extracts audio chunks from an audio clip, using a list of
provided coordinates, and concatenates them together. Coordinates can be
passed either as sample numbers or in seconds, in which case the audio
sampling rate is also needed.
Parameters
----------
tss: List[dict]
Coordinate list of the clips to collect from the audio.
wav: torch.Tensor, one dimensional
One dimensional float torch.Tensor, containing the audio to clip.
seconds: bool (default - False)
Whether input coordinates are passed as seconds or samples.
sampling_rate: int (default - None)
Input audio sampling rate. Required if seconds is True.
Returns
-------
torch.Tensor, one dimensional
One dimensional float torch.Tensor of the concatenated clipped audio
chunks.
Raises
------
ValueError
Raised if sampling_rate is not provided when seconds is True.
"""
if seconds and not sampling_rate:
raise ValueError('sampling_rate must be provided when seconds is True')
chunks = list()
_tss = _seconds_to_samples_tss(tss, sampling_rate) if seconds else tss
for i in _tss:
chunks.append(wav[i['start']:i['end']])
return torch.cat(chunks)
def drop_chunks(tss: List[dict],
wav: torch.Tensor,
seconds: bool = False,
sampling_rate: int = None) -> torch.Tensor:
"""Drop audio chunks from a longer audio clip
This method extracts audio chunks from an audio clip, using a list of
provided coordinates, and drops them. Coordinates can be passed either as
sample numbers or in seconds, in which case the audio sampling rate is also
needed.
Parameters
----------
tss: List[dict]
Coordinate list of the clips to drop from from the audio.
wav: torch.Tensor, one dimensional
One dimensional float torch.Tensor, containing the audio to clip.
seconds: bool (default - False)
Whether input coordinates are passed as seconds or samples.
sampling_rate: int (default - None)
Input audio sampling rate. Required if seconds is True.
Returns
-------
torch.Tensor, one dimensional
One dimensional float torch.Tensor of the input audio minus the dropped
chunks.
Raises
------
ValueError
Raised if sampling_rate is not provided when seconds is True.
"""
if seconds and not sampling_rate:
raise ValueError('sampling_rate must be provided when seconds is True')
chunks = list()
cur_start = 0
_tss = _seconds_to_samples_tss(tss, sampling_rate) if seconds else tss
for i in _tss:
chunks.append((wav[cur_start: i['start']]))
cur_start = i['end']
return torch.cat(chunks)
def _seconds_to_samples_tss(tss: List[dict], sampling_rate: int) -> List[dict]:
"""Convert coordinates expressed in seconds to sample coordinates.
"""
return [{
'start': round(crd['start']) * sampling_rate,
'end': round(crd['end']) * sampling_rate
} for crd in tss]
这个是silero vad 的模型框架吗
最新发布
本文详细介绍了语音信号处理之时域分析，包括端点检测的实现与Python代码示例。通过实例展示了如何使用音量阈值和过零率来区分人类语音与噪音，并提供了频率范围过滤方法和VAD（语音活动检测）的概念。文章还探讨了时域与频域的端点检测方法，旨在帮助读者理解和应用语音信号处理技术。
扫一扫
1291
到【灌水乐园】发言
