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import os
import librosa
import soundfile as sf
import numpy as np
from pathlib import Path
import io
# Split audio stream at silence points to prevent playback stuttering issues
# caused by AAC encoder frame padding when streaming audio through Gradio audio components.
class AudioStreamProcessor:
def __init__(self, sr=22050, min_silence_duration=0.1, threshold_db=-40):
self.sr = sr
self.min_silence_duration = min_silence_duration
self.threshold_db = threshold_db
self.buffer = np.array([])
def process(self, audio_data, last=False):
"""
Add audio data and process it
params:
audio_data: audio data in numpy array
last: whether this is the last chunk of data
returns:
Processed audio data, returns None if no split point is found
"""
# Add new data to buffer
self.buffer = np.concatenate([self.buffer, audio_data]) if len(self.buffer) > 0 else audio_data
if last:
result = self.buffer
self.buffer = np.array([])
return self._to_wav_bytes(result)
# Find silence boundary
split_point = self._find_silence_boundary(self.buffer)
if split_point is not None:
# Modified: Extend split point to the end of silence
silence_end = self._find_silence_end(split_point)
result = self.buffer[:silence_end]
self.buffer = self.buffer[silence_end:]
return self._to_wav_bytes(result)
return None
def _find_silence_boundary(self, audio):
"""
Find the starting point of silence boundary in audio
"""
# Convert audio to decibels
db = librosa.amplitude_to_db(np.abs(audio), ref=np.max)
# Find points below threshold
silence_points = np.where(db < self.threshold_db)[0]
if len(silence_points) == 0:
return None
# Calculate minimum silence samples
min_silence_samples = int(self.min_silence_duration * self.sr)
# Search backwards for continuous silence segment starting point
for i in range(len(silence_points) - min_silence_samples, -1, -1):
if i < 0:
break
if np.all(np.diff(silence_points[i:i+min_silence_samples]) == 1):
return silence_points[i]
return None
def _find_silence_end(self, start_point):
"""
Find the end point of silence segment
"""
db = librosa.amplitude_to_db(np.abs(self.buffer[start_point:]), ref=np.max)
silence_points = np.where(db >= self.threshold_db)[0]
if len(silence_points) == 0:
return len(self.buffer)
return start_point + silence_points[0]
def _to_wav_bytes(self, audio_data):
"""
trans_to_wav_bytes
"""
wav_buffer = io.BytesIO()
sf.write(wav_buffer, audio_data, self.sr, format='WAV')
return wav_buffer.getvalue()
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