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Diffstat (limited to 'vad.py')
| -rw-r--r-- | vad.py | 313 |
1 files changed, 313 insertions, 0 deletions
@@ -0,0 +1,313 @@ +# MIT License +# +# Copyright (c) 2023 Guillaume Klein +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import bisect +import functools +import os +import warnings + +from typing import List, NamedTuple, Optional + +import numpy as np + + +# The code below is adapted from https://github.com/snakers4/silero-vad. +class VadOptions(NamedTuple): + """VAD options. + + Attributes: + threshold: 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. + min_speech_duration_ms: Final speech chunks shorter min_speech_duration_ms are thrown out. + max_speech_duration_s: 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 aggressive cutting. Otherwise, they will be + split aggressively just before max_speech_duration_s. + min_silence_duration_ms: In the end of each speech chunk wait for min_silence_duration_ms + before separating it + window_size_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. + Values other than these may affect model performance!! + speech_pad_ms: Final speech chunks are padded by speech_pad_ms each side + """ + + threshold: float = 0.5 + min_speech_duration_ms: int = 250 + max_speech_duration_s: float = float("inf") + min_silence_duration_ms: int = 2000 + window_size_samples: int = 1024 + speech_pad_ms: int = 400 + + +def get_speech_timestamps( + audio: np.ndarray, + vad_options: Optional[VadOptions] = None, + **kwargs, +) -> List[dict]: + """This method is used for splitting long audios into speech chunks using silero VAD. + + Args: + audio: One dimensional float array. + vad_options: Options for VAD processing. + kwargs: VAD options passed as keyword arguments for backward compatibility. + + Returns: + List of dicts containing begin and end samples of each speech chunk. + """ + if vad_options is None: + vad_options = VadOptions(**kwargs) + + threshold = vad_options.threshold + min_speech_duration_ms = vad_options.min_speech_duration_ms + max_speech_duration_s = vad_options.max_speech_duration_s + min_silence_duration_ms = vad_options.min_silence_duration_ms + window_size_samples = vad_options.window_size_samples + speech_pad_ms = vad_options.speech_pad_ms + + if window_size_samples not in [512, 1024, 1536]: + warnings.warn( + "Unusual window_size_samples! Supported window_size_samples:\n" + " - [512, 1024, 1536] for 16000 sampling_rate" + ) + + sampling_rate = 16000 + 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) + + model = get_vad_model() + state = model.get_initial_state(batch_size=1) + + 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 = np.pad(chunk, (0, int(window_size_samples - len(chunk)))) + speech_prob, state = model(chunk, state, sampling_rate) + speech_probs.append(speech_prob) + + triggered = False + speeches = [] + current_speech = {} + neg_threshold = threshold - 0.15 + + # to save potential segment end (and tolerate some silence) + temp_end = 0 + # to save potential segment limits in case of maximum segment size reached + prev_end = next_start = 0 + + 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 = {} + # previously reached silence (< neg_thres) and is still not speech (< thres) + if next_start < prev_end: + 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 + # condition to avoid cutting in very short silence + if (window_size_samples * i) - temp_end > min_silence_samples_at_max_speech: + 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) + ) + + return speeches + + +def collect_chunks(audio: np.ndarray, chunks: List[dict]) -> np.ndarray: + """Collects and concatenates audio chunks.""" + if not chunks: + return np.array([], dtype=np.float32) + + return np.concatenate([audio[chunk["start"] : chunk["end"]] for chunk in chunks]) + + +class SpeechTimestampsMap: + """Helper class to restore original speech timestamps.""" + + def __init__(self, chunks: List[dict], sampling_rate: int, time_precision: int = 2): + self.sampling_rate = sampling_rate + self.time_precision = time_precision + self.chunk_end_sample = [] + self.total_silence_before = [] + + previous_end = 0 + silent_samples = 0 + + for chunk in chunks: + silent_samples += chunk["start"] - previous_end + previous_end = chunk["end"] + + self.chunk_end_sample.append(chunk["end"] - silent_samples) + self.total_silence_before.append(silent_samples / sampling_rate) + + def get_original_time( + self, + time: float, + chunk_index: Optional[int] = None, + ) -> float: + if chunk_index is None: + chunk_index = self.get_chunk_index(time) + + total_silence_before = self.total_silence_before[chunk_index] + return round(total_silence_before + time, self.time_precision) + + def get_chunk_index(self, time: float) -> int: + sample = int(time * self.sampling_rate) + return min( + bisect.bisect(self.chunk_end_sample, sample), + len(self.chunk_end_sample) - 1, + ) + + +@functools.lru_cache +def get_vad_model(): + """Returns the VAD model instance.""" + abspath = os.path.abspath(__file__) + my_dir = os.path.dirname(abspath) + path = os.path.join(my_dir, "Models/silero_vad.onnx") + return SileroVADModel(path) + + +class SileroVADModel: + def __init__(self, path): + try: + import onnxruntime + except ImportError as e: + raise RuntimeError( + "Applying the VAD filter requires the onnxruntime package" + ) from e + + opts = onnxruntime.SessionOptions() + opts.inter_op_num_threads = 1 + opts.intra_op_num_threads = 1 + opts.log_severity_level = 4 + + self.session = onnxruntime.InferenceSession( + path, + providers=["CPUExecutionProvider"], + sess_options=opts, + ) + + def get_initial_state(self, batch_size: int): + h = np.zeros((2, batch_size, 64), dtype=np.float32) + c = np.zeros((2, batch_size, 64), dtype=np.float32) + return h, c + + def __call__(self, x, state, sr: int): + if len(x.shape) == 1: + x = np.expand_dims(x, 0) + if len(x.shape) > 2: + raise ValueError( + f"Too many dimensions for input audio chunk {len(x.shape)}" + ) + if sr / x.shape[1] > 31.25: + raise ValueError("Input audio chunk is too short") + + h, c = state + + ort_inputs = { + "input": x, + "h": h, + "c": c, + "sr": np.array(sr, dtype="int64"), + } + + out, h, c = self.session.run(None, ort_inputs) + state = (h, c) + + return out, state |