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Diffstat (limited to 'Scripts/vad.py')
| -rw-r--r-- | Scripts/vad.py | 315 |
1 files changed, 0 insertions, 315 deletions
diff --git a/Scripts/vad.py b/Scripts/vad.py deleted file mode 100644 index 25f0ad0..0000000 --- a/Scripts/vad.py +++ /dev/null @@ -1,315 +0,0 @@ -# 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) - parent_dir = os.path.dirname(my_dir) - - path = os.path.join(parent_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 |