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import os
import numpy as np
import librosa
import soundfile as sf
from pydub import AudioSegment
from pydub.silence import split_on_silence
from pydub.playback import play
from tqdm import tqdm
def clean_audio(audio_path, output_path, selected_chunks, min_silence_len=1000, silence_thresh=-40, keep_silence=100):
# Load the audio file
audio_segment = AudioSegment.from_file(audio_path)
# Convert to mono
audio_segment = audio_segment.set_channels(1)
# Normalize the audio
audio_segment = normalize_audio(audio_segment)
# Split on silence
chunks = split_on_silence(
audio_segment,
min_silence_len=min_silence_len,
silence_thresh=silence_thresh,
keep_silence=keep_silence,
)
# Find the main speaker based on total duration
main_speaker_chunk = max(chunks, key=lambda chunk: len(chunk))
# Apply EQ and compression
main_speaker_chunk = apply_eq_and_compression(main_speaker_chunk)
# Export the main speaker's audio
main_speaker_chunk.export(output_path, format="wav")
def normalize_audio(audio_segment):
"""
Normalizes the audio to a target volume.
"""
target_dBFS = -20
change_in_dBFS = target_dBFS - audio_segment.dBFS
return audio_segment.apply_gain(change_in_dBFS)
def apply_eq_and_compression(audio_segment):
"""
Applies equalization and compression to the audio.
"""
# Apply EQ
audio_segment = audio_segment.high_pass_filter(80)
audio_segment = audio_segment.low_pass_filter(12000)
# Apply compression
threshold = -20
ratio = 2
attack = 10
release = 100
audio_segment = audio_segment.compress_dynamic_range(
threshold=threshold,
ratio=ratio,
attack=attack,
release=release,
)
return audio_segment
def process_file(wav_file, srt_file, cleaned_folder):
print(f"Processing file: {wav_file}")
# Create the cleaned folder if it doesn't exist
os.makedirs(cleaned_folder, exist_ok=True)
input_wav_path = wav_file
output_wav_path = os.path.join(cleaned_folder, os.path.basename(wav_file))
# Review and select desired SRT chunks
selected_chunks = review_srt_chunks(input_wav_path, srt_file)
# Clean the audio based on selected chunks
clean_audio(input_wav_path, output_wav_path, selected_chunks)
print(f"Cleaned audio saved to: {output_wav_path}")
def review_srt_chunks(audio_path, srt_path):
audio_segment = AudioSegment.from_wav(audio_path)
selected_chunks = []
with open(srt_path, "r") as srt_file:
srt_content = srt_file.read()
srt_entries = srt_content.strip().split("\n\n")
for entry in tqdm(srt_entries, desc="Reviewing SRT chunks", unit="chunk"):
lines = entry.strip().split("\n")
if len(lines) >= 3:
start_time, end_time = lines[1].split(" --> ")
start_time = convert_to_milliseconds(start_time)
end_time = convert_to_milliseconds(end_time)
chunk = audio_segment[start_time:end_time]
print("Playing chunk...")
play(chunk)
choice = input("Keep this chunk? (y/n): ")
if choice.lower() == "y":
selected_chunks.append((start_time, end_time))
print("Chunk selected.")
else:
print("Chunk skipped.")
return selected_chunks
def convert_to_milliseconds(time_str):
time_str = time_str.replace(",", ".")
hours, minutes, seconds = time_str.strip().split(":")
milliseconds = (int(hours) * 3600 + int(minutes) * 60 + float(seconds)) * 1000
return int(milliseconds)
# Set the WAV file, SRT file, and cleaned folder paths
wav_file = "/path/to/your/audio.wav"
srt_file = "/path/to/your/subtitles.srt"
cleaned_folder = "/path/to/cleaned/folder"
# Process the WAV file
process_file(wav_file, srt_file, cleaned_folder)
print("Processing completed.")
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