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| # Source: https://github.com/snakers4/silero-vad | |
| # | |
| # Copyright (c) 2024 snakers4 | |
| # | |
| # This code is from a MIT-licensed repository. The full license text is available at the root of the source repository. | |
| # | |
| # Note: This code has been modified to fit the context of this repository. | |
| import librosa | |
| import torch | |
| import numpy as np | |
| VAD_THRESHOLD = 20 | |
| SAMPLING_RATE = 16000 | |
| class SileroVAD: | |
| """ | |
| Voice Activity Detection (VAD) using Silero-VAD. | |
| """ | |
| def __init__(self, local=False, model="silero_vad", device=torch.device("cpu")): | |
| """ | |
| Initialize the VAD object. | |
| Args: | |
| local (bool, optional): Whether to load the model locally. Defaults to False. | |
| model (str, optional): The VAD model name to load. Defaults to "silero_vad". | |
| device (torch.device, optional): The device to run the model on. Defaults to 'cpu'. | |
| Returns: | |
| None | |
| Raises: | |
| RuntimeError: If loading the model fails. | |
| """ | |
| try: | |
| vad_model, utils = torch.hub.load( | |
| repo_or_dir="snakers4/silero-vad" if not local else "vad/silero-vad", | |
| model=model, | |
| force_reload=False, | |
| onnx=True, | |
| source="github" if not local else "local", | |
| ) | |
| self.vad_model = vad_model | |
| (get_speech_timestamps, _, _, _, _) = utils | |
| self.get_speech_timestamps = get_speech_timestamps | |
| except Exception as e: | |
| raise RuntimeError(f"Failed to load VAD model: {e}") | |
| def segment_speech(self, audio_segment, start_time, end_time, sampling_rate): | |
| """ | |
| Segment speech from an audio segment and return a list of timestamps. | |
| Args: | |
| audio_segment (np.ndarray): The audio segment to be segmented. | |
| start_time (int): The start time of the audio segment in frames. | |
| end_time (int): The end time of the audio segment in frames. | |
| sampling_rate (int): The sampling rate of the audio segment. | |
| Returns: | |
| list: A list of timestamps, each containing the start and end times of speech segments in frames. | |
| Raises: | |
| ValueError: If the audio segment is invalid. | |
| """ | |
| if audio_segment is None or not isinstance(audio_segment, (np.ndarray, list)): | |
| raise ValueError("Invalid audio segment") | |
| speech_timestamps = self.get_speech_timestamps( | |
| audio_segment, self.vad_model, sampling_rate=sampling_rate | |
| ) | |
| adjusted_timestamps = [ | |
| (ts["start"] + start_time, ts["end"] + start_time) | |
| for ts in speech_timestamps | |
| ] | |
| if not adjusted_timestamps: | |
| return [] | |
| intervals = [ | |
| end[0] - start[1] | |
| for start, end in zip(adjusted_timestamps[:-1], adjusted_timestamps[1:]) | |
| ] | |
| segments = [] | |
| def split_timestamps(start_index, end_index): | |
| if ( | |
| start_index == end_index | |
| or adjusted_timestamps[end_index][1] | |
| - adjusted_timestamps[start_index][0] | |
| < 20 * sampling_rate | |
| ): | |
| segments.append([start_index, end_index]) | |
| else: | |
| if not intervals[start_index:end_index]: | |
| return | |
| max_interval_index = intervals[start_index:end_index].index( | |
| max(intervals[start_index:end_index]) | |
| ) | |
| split_index = start_index + max_interval_index | |
| split_timestamps(start_index, split_index) | |
| split_timestamps(split_index + 1, end_index) | |
| split_timestamps(0, len(adjusted_timestamps) - 1) | |
| merged_timestamps = [ | |
| [adjusted_timestamps[start][0], adjusted_timestamps[end][1]] | |
| for start, end in segments | |
| ] | |
| return merged_timestamps | |
| def vad(self, speakerdia, audio): | |
| """ | |
| Process the audio based on the given speaker diarization dataframe. | |
| Args: | |
| speakerdia (pd.DataFrame): The diarization dataframe containing start, end, and speaker info. | |
| audio (dict): A dictionary containing the audio waveform and sample rate. | |
| Returns: | |
| list: A list of dictionaries containing processed audio segments with start, end, and speaker. | |
| """ | |
| sampling_rate = audio["sample_rate"] | |
| audio_data = audio["waveform"] | |
| out = [] | |
| last_end = 0 | |
| speakers_seen = set() | |
| count_id = 0 | |
| for index, row in speakerdia.iterrows(): | |
| start = float(row["start"]) | |
| end = float(row["end"]) | |
| if end <= last_end: | |
| continue | |
| last_end = end | |
| start_frame = int(start * sampling_rate) | |
| end_frame = int(end * sampling_rate) | |
| if row["speaker"] not in speakers_seen: | |
| speakers_seen.add(row["speaker"]) | |
| if end - start <= VAD_THRESHOLD: | |
| out.append( | |
| { | |
| "index": str(count_id).zfill(5), | |
| "start": start, # in seconds | |
| "end": end, | |
| "speaker": row["speaker"], # same for all | |
| } | |
| ) | |
| count_id += 1 | |
| continue | |
| temp_audio = audio_data[start_frame:end_frame] | |
| # resample from 24k to 16k | |
| temp_audio_resampled = librosa.resample( | |
| temp_audio, orig_sr=sampling_rate, target_sr=SAMPLING_RATE | |
| ) | |
| for start_frame_sub, end_frame_sub in self.segment_speech( | |
| temp_audio_resampled, | |
| int(start * SAMPLING_RATE), | |
| int(end * SAMPLING_RATE), | |
| SAMPLING_RATE, | |
| ): | |
| out.append( | |
| { | |
| "index": str(count_id).zfill(5), | |
| "start": start_frame_sub / SAMPLING_RATE, # in seconds | |
| "end": end_frame_sub / SAMPLING_RATE, | |
| "speaker": row["speaker"], # same for all | |
| } | |
| ) | |
| count_id += 1 | |
| return out | |