add

.gitignore

@@ -0,0 +1,139 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# PyCharm
+.idea/
+
+# my ignore
+pretrain_models/
+deprecated/
+preprocess/
+**/.DS_Store
+**/._.DS_Store

speak_detect/snakers4_silero-vad_master/__init__.py

@@ -0,0 +1,12 @@
+from importlib.metadata import version
+try:
+    __version__ = version(__name__)
+except:
+    pass
+
+from silero_vad.model import load_silero_vad
+from silero_vad.utils_vad import (get_speech_timestamps,
+                                  save_audio,
+                                  read_audio,
+                                  VADIterator,
+                                  collect_chunks)

speak_detect/snakers4_silero-vad_master/data/silero_vad.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2623a2953f6ff3d2c1e61740c6cdb7168133479b267dfef114a4a3cc5bdd788f
+size 2327524

speak_detect/snakers4_silero-vad_master/hubconf.py

@@ -0,0 +1,51 @@
+dependencies = ['torch', 'torchaudio']
+import torch
+import os
+import sys
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
+from utils_vad import (init_jit_model,
+                                  get_speech_timestamps,
+                                  save_audio,
+                                  read_audio,
+                                  VADIterator,
+                                  collect_chunks,
+                                  drop_chunks,
+                                  OnnxWrapper)
+
+
+def versiontuple(v):
+    splitted = v.split('+')[0].split(".")
+    version_list = []
+    for i in splitted:
+        try:
+            version_list.append(int(i))
+        except:
+            version_list.append(0)
+    return tuple(version_list)
+
+
+def silero_vad(onnx=False, force_onnx_cpu=False):
+    """Silero Voice Activity Detector
+    Returns a model with a set of utils
+    Please see https://github.com/snakers4/silero-vad for usage examples
+    """
+
+    if not onnx:
+        installed_version = torch.__version__
+        supported_version = '1.12.0'
+        if versiontuple(installed_version) < versiontuple(supported_version):
+            raise Exception(f'Please install torch {supported_version} or greater ({installed_version} installed)')
+
+    model_dir = os.path.join(os.path.dirname(__file__), 'data')
+    if onnx:
+        model = OnnxWrapper(os.path.join(model_dir, 'silero_vad.onnx'), force_onnx_cpu)
+    else:
+        model = init_jit_model(os.path.join(model_dir, 'silero_vad.jit'))
+    utils = (get_speech_timestamps,
+             save_audio,
+             read_audio,
+             VADIterator,
+             collect_chunks,
+             drop_chunks)
+
+    return model, utils

speak_detect/snakers4_silero-vad_master/model.py

@@ -0,0 +1,25 @@
+from .utils_vad import init_jit_model, OnnxWrapper
+import torch
+torch.set_num_threads(1)
+
+def load_silero_vad(onnx=False):
+    model_name = 'silero_vad.onnx' if onnx else 'silero_vad.jit'
+    package_path = "silero_vad.data"
+    
+    try:
+        import importlib_resources as impresources
+        model_file_path = str(impresources.files(package_path).joinpath(model_name))
+    except:
+        from importlib import resources as impresources
+        try:
+            with impresources.path(package_path, model_name) as f:
+                model_file_path = f
+        except:
+            model_file_path = str(impresources.files(package_path).joinpath(model_name))
+
+    if onnx:
+        model = OnnxWrapper(model_file_path, force_onnx_cpu=True)
+    else:
+        model = init_jit_model(model_file_path)
+    
+    return model

speak_detect/snakers4_silero-vad_master/utils_vad.py

@@ -0,0 +1,489 @@
+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()
+        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,
+                          visualize_probs: bool = False,
+                          progress_tracking_callback: Callable[[float], None] = None,
+                          window_size_samples: int = 512,):
+
+    """
+    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)
+
+    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
+
+    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
+
+    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
+
+
+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):
+        """
+        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 = 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, 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 - 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, 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)