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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_org, audio_segments_new, intervals, sr_orig, sr_new):
"""
Merges audio segments back into a single audio signal, filling gaps with silence.
Assumes audio segments are already at sr_new.
Parameters:
- audio_segments_org (list of np.ndarray): The non-silent audio segments (at sr_orig).
- audio_segments_new (list of np.ndarray): The non-silent audio segments (at sr_new).
- 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.
"""
merged_audio = np.array([], dtype=audio_segments_new[0].dtype)
sr_ratio = sr_new / sr_orig
for i, (start, end) in enumerate(intervals):
start_new = int(start * sr_ratio)
end_new = int(end * sr_ratio)
original_duration = len(audio_segments_org[i]) / sr_orig
new_duration = len(audio_segments_new[i]) / sr_new
duration_diff = new_duration - original_duration
silence_samples = int(abs(duration_diff) * sr_new)
silence_compensation = np.zeros(
silence_samples, dtype=audio_segments_new[0].dtype
)
if i == 0 and start_new > 0:
initial_silence = np.zeros(start_new, dtype=audio_segments_new[0].dtype)
merged_audio = np.concatenate((merged_audio, initial_silence))
if duration_diff > 0:
merged_audio = np.concatenate((merged_audio, silence_compensation))
merged_audio = np.concatenate((merged_audio, audio_segments_new[i]))
if duration_diff < 0:
merged_audio = np.concatenate((merged_audio, silence_compensation))
if i < len(intervals) - 1:
next_start_new = int(intervals[i + 1][0] * sr_ratio)
silence_duration = next_start_new - end_new
if silence_duration > 0:
silence = np.zeros(silence_duration, dtype=audio_segments_new[0].dtype)
merged_audio = np.concatenate((merged_audio, silence))
return merged_audio
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