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import torch
import torchaudio
from typing import Callable, List
import torch.nn.functional as F
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()
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._h = np.zeros((2, batch_size, 64)).astype('float32')
self._c = np.zeros((2, batch_size, 64)).astype('float32')
self._last_sr = 0
self._last_batch_size = 0
def __call__(self, x, sr: int):
x, sr = self._validate_input(x, sr)
batch_size = x.shape[0]
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 sr in [8000, 16000]:
ort_inputs = {'input': x.numpy(), 'h': self._h, 'c': self._c, 'sr': np.array(sr, dtype='int64')}
ort_outs = self.session.run(None, ort_inputs)
out, self._h, self._c = ort_outs
else:
raise ValueError()
self._last_sr = sr
self._last_batch_size = batch_size
out = torch.tensor(out)
return out
def audio_forward(self, x, sr: int, num_samples: int = 512):
outs = []
x, sr = self._validate_input(x, sr)
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)
self.reset_states(x.shape[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):
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')):
torch.set_grad_enabled(False)
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')
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,
window_size_samples: int = 512,
speech_pad_ms: int = 30,
return_seconds: bool = False,
visualize_probs: bool = False,
progress_tracking_callback: Callable[[float], None] = None):
"""
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 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_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
window_size_samples: int (default - 1536 samples)
Audio chunks of window_size_samples size are fed to the silero VAD model.
WARNING! Silero VAD models were trained using 512, 1024, 1536 samples for 16000 sample rate and 256, 512, 768 samples for 8000 sample rate.
Values other than these may affect model perfomance!!
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)
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
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 == 8000 and window_size_samples > 768:
warnings.warn('window_size_samples is too big for 8000 sampling_rate! Better set window_size_samples to 256, 512 or 768 for 8000 sample rate!')
if window_size_samples not in [256, 512, 768, 1024, 1536]:
warnings.warn('Unusual window_size_samples! Supported window_size_samples:\n - [512, 1024, 1536] for 16000 sampling_rate\n - [256, 512, 768] for 8000 sampling_rate')
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 * 98 / 1000
audio_length_samples = len(audio)
speech_probs = []
for current_start_sample in range(0, audio_length_samples, window_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))))
speech_prob = model(chunk, sampling_rate).item()
speech_probs.append(speech_prob)
# caculate progress and seng it to callback function
progress = current_start_sample + window_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 = {}
neg_threshold = threshold - 0.15
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
for i, speech_prob in enumerate(speech_probs):
if (speech_prob >= threshold) and temp_end:
temp_end = 0
if next_start < prev_end:
next_start = window_size_samples * i
if (speech_prob >= threshold) and not triggered:
triggered = True
current_speech['start'] = window_size_samples * i
continue
if triggered and (window_size_samples * i) - current_speech['start'] > max_speech_samples:
if prev_end:
current_speech['end'] = prev_end
speeches.append(current_speech)
current_speech = {}
if next_start < prev_end: # previously reached silence (< neg_thres) and is still not speech (< thres)
triggered = False
else:
current_speech['start'] = next_start
prev_end = next_start = temp_end = 0
else:
current_speech['end'] = window_size_samples * i
speeches.append(current_speech)
current_speech = {}
prev_end = next_start = temp_end = 0
triggered = False
continue
if (speech_prob < neg_threshold) and triggered:
if not temp_end:
temp_end = window_size_samples * i
if ((window_size_samples * i) - temp_end) > min_silence_samples_at_max_speech : # condition to avoid cutting in very short silence
prev_end = temp_end
if (window_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
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:
for speech_dict in speeches:
speech_dict['start'] = round(speech_dict['start'] / sampling_rate, 1)
speech_dict['end'] = round(speech_dict['end'] / sampling_rate, 1)
elif step > 1:
for speech_dict in speeches:
speech_dict['start'] *= step
speech_dict['end'] *= step
if visualize_probs:
make_visualization(speech_probs, window_size_samples / sampling_rate)
return speeches
def get_number_ts(wav: torch.Tensor,
model,
model_stride=8,
hop_length=160,
sample_rate=16000):
wav = torch.unsqueeze(wav, dim=0)
perframe_logits = model(wav)[0]
perframe_preds = torch.argmax(torch.softmax(perframe_logits, dim=1), dim=1).squeeze() # (1, num_frames_strided)
extended_preds = []
for i in perframe_preds:
extended_preds.extend([i.item()] * model_stride)
# len(extended_preds) is *num_frames_real*; for each frame of audio we know if it has a number in it.
triggered = False
timings = []
cur_timing = {}
for i, pred in enumerate(extended_preds):
if pred == 1:
if not triggered:
cur_timing['start'] = int((i * hop_length) / (sample_rate / 1000))
triggered = True
elif pred == 0:
if triggered:
cur_timing['end'] = int((i * hop_length) / (sample_rate / 1000))
timings.append(cur_timing)
cur_timing = {}
triggered = False
if cur_timing:
cur_timing['end'] = int(len(wav) / (sample_rate / 1000))
timings.append(cur_timing)
return timings
def get_language(wav: torch.Tensor,
model):
wav = torch.unsqueeze(wav, dim=0)
lang_logits = model(wav)[2]
lang_pred = torch.argmax(torch.softmax(lang_logits, dim=1), dim=1).item() # from 0 to len(languages) - 1
assert lang_pred < len(languages)
return languages[lang_pred]
def get_language_and_group(wav: torch.Tensor,
model,
lang_dict: dict,
lang_group_dict: dict,
top_n=1):
wav = torch.unsqueeze(wav, dim=0)
lang_logits, lang_group_logits = model(wav)
softm = torch.softmax(lang_logits, dim=1).squeeze()
softm_group = torch.softmax(lang_group_logits, dim=1).squeeze()
srtd = torch.argsort(softm, descending=True)
srtd_group = torch.argsort(softm_group, descending=True)
outs = []
outs_group = []
for i in range(top_n):
prob = round(softm[srtd[i]].item(), 2)
prob_group = round(softm_group[srtd_group[i]].item(), 2)
outs.append((lang_dict[str(srtd[i].item())], prob))
outs_group.append((lang_group_dict[str(srtd_group[i].item())], prob_group))
return outs, outs_group
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 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
def __call__(self, x, return_seconds=False):
"""
x: torch.Tensor
audio chunk (see examples in repo)
return_seconds: bool (default - False)
whether return timestamps in seconds (default - samples)
"""
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 = self.current_sample - self.speech_pad_samples
return {'start': int(speech_start) if not return_seconds else round(speech_start / self.sampling_rate, 1)}
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
self.temp_end = 0
self.triggered = False
return {'end': int(speech_end) if not return_seconds else round(speech_end / self.sampling_rate, 1)}
return None
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)
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