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import numpy as np
import librosa
def process_audio(audio, sr=16000, silence_thresh=-60, min_silence_len=250):
"""
Splits an audio signal into segments using a fixed frame size and hop size.
Parameters:
- audio (np.ndarray): The audio signal to split.
- sr (int): The sample rate of the input audio (default is 16000).
- silence_thresh (int): Silence threshold (default =-60dB)
- min_silence_len (int): Minimum silence duration (default 250ms).
Returns:
- list of np.ndarray: A list of audio segments.
- np.ndarray: The intervals where the audio was split.
"""
frame_length = int(min_silence_len / 1000 * sr)
hop_length = frame_length // 2
intervals = librosa.effects.split(
audio, top_db=-silence_thresh, frame_length=frame_length, hop_length=hop_length
)
audio_segments = [audio[start:end] for start, end in intervals]
return audio_segments, intervals
def merge_audio(audio_segments, intervals, sr_orig, sr_new):
"""
Merges audio segments back into a single audio signal, filling gaps with silence.
Parameters:
- audio_segments (list of np.ndarray): The non-silent audio segments.
- intervals (np.ndarray): The intervals used for splitting the original audio.
- sr_orig (int): The sample rate of the original audio
- sr_new (int): The sample rate of the model
Returns:
- np.ndarray: The merged audio signal with silent gaps restored.
"""
sr_ratio = sr_new / sr_orig if sr_new > sr_orig else 1.0
merged_audio = np.zeros(
int(intervals[0][0] * sr_ratio if intervals[0][0] > 0 else 0),
dtype=audio_segments[0].dtype,
)
merged_audio = np.concatenate((merged_audio, audio_segments[0]))
for i in range(1, len(intervals)):
silence_duration = int((intervals[i][0] - intervals[i - 1][1]) * sr_ratio)
silence = np.zeros(silence_duration, dtype=audio_segments[0].dtype)
merged_audio = np.concatenate((merged_audio, silence, audio_segments[i]))
return merged_audio
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