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toolkit.py
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toolkit.py
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import os
import numpy as np
from moviepy import editor
from moviepy.editor import VideoFileClip
from moviepy.video.tools.subtitles import SubtitlesClip
class VideoProcessor:
@staticmethod
def _get_subclip_volume(subclip, second, interval):
cut = subclip.subclip(second, second + interval).audio.to_soundarray(fps=44100)
return np.sqrt(((1.0 * cut) ** 2).mean())
@staticmethod
def _float_to_srt_time(seconds):
hours = int(seconds // 3600)
minutes = int((seconds % 3600) // 60)
seconds = int(seconds % 60)
milliseconds = int((seconds % 1) * 1000)
return f"{hours:02d}:{minutes:02d}:{seconds:02d},{milliseconds:02d}"
@staticmethod
def get_audio(input_video_file_clip, filename):
audio_file_name = f"{filename}_audio.wav"
if os.path.exists(audio_file_name):
os.remove(audio_file_name)
input_video_file_clip.audio.write_audiofile(audio_file_name, codec="pcm_s16le")
return audio_file_name
@staticmethod
def save_video(**kwargs):
filename = kwargs["filename"]
input_video_file_clip = kwargs["input_video_file_clip"]
clip_name = f"{filename}_EDITED.mp4"
input_video_file_clip.write_videofile(clip_name, audio_codec="aac")
kwargs["clips_name"] = clip_name
return kwargs
@staticmethod
def save_joined_video(**kwargs):
if "clips" not in kwargs:
return VideoProcessor.save_video(**kwargs)
filename = kwargs["filename"]
clips = kwargs["clips"]
clip_name = f"{filename}_EDITED.mp4"
if isinstance(clips, list):
concat_clip = editor.concatenate_videoclips(clips)
concat_clip.write_videofile(clip_name, audio_codec="aac")
kwargs["clips_name"] = clip_name
return kwargs
clips.write_videofile(clip_name, audio_codec="aac")
kwargs["clips_name"] = clip_name
return kwargs
@staticmethod
def save_separated_video(**kwargs):
if "clips" not in kwargs:
return VideoProcessor.save_video(**kwargs)
filename = kwargs["filename"]
clips = kwargs["clips"]
clips_format = "{filename}_EDITED_{i}.mp4"
for i, clip in enumerate(clips):
clip.write_videofile(
clips_format.format(filename=filename, i=i), audio_codec="aac"
)
kwargs["clips_name"] = clips_format.format(filename=filename, i="{i}")
return kwargs
@staticmethod
def generate_transcript(**kwargs):
import whisper
input_video_file_clip, filename = (
kwargs["input_video_file_clip"],
kwargs["filename"],
)
audio_file_name = VideoProcessor.get_audio(input_video_file_clip, filename)
model = whisper.load_model("large")
results = model.transcribe(audio_file_name)
transcript = ""
for result in results["segments"]:
start_date = VideoProcessor._float_to_srt_time(result["start"])
end_date = VideoProcessor._float_to_srt_time(result["end"])
text_data = result["text"].strip()
transcript += (
f"{result['id'] + 1}\n{start_date} --> {end_date}\n{text_data}\n\n"
)
transcript_file_name = f"{filename}_transcript.srt"
with open(transcript_file_name, "w", encoding="utf-8") as file:
file.write(transcript)
kwargs["transcript_file_name"] = transcript_file_name
return kwargs
@staticmethod
def denoise_video(**kwargs):
import torch
import torchaudio
from denoiser import pretrained
from denoiser.dsp import convert_audio
input_video_file_clip, filename = (
kwargs["input_video_file_clip"],
kwargs["filename"],
)
audio_file_name = VideoProcessor.get_audio(input_video_file_clip, filename)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = pretrained.dns64().to(device)
wav, source = torchaudio.load(audio_file_name)
wav = convert_audio(wav.to(device), source, model.sample_rate, model.chin)
with torch.no_grad():
denoised = model(wav[None])[0]
denoised_file_name = f"{filename}_denoised.wav"
torchaudio.save(denoised_file_name, denoised.cpu(), model.sample_rate)
# replace audio in video
input_video_file_clip.audio = editor.AudioFileClip(denoised_file_name)
kwargs["input_video_file_clip"] = input_video_file_clip
return kwargs
@staticmethod
def add_subtitles(**kwargs):
filename = kwargs["filename"]
input_video_file_clip = kwargs["input_video_file_clip"]
subtitles_filename = kwargs.get(
"transcript_file_name", f"{filename}_transcript_splitted.srt"
)
config_data = kwargs.get("config_data", {})
if not os.path.exists(subtitles_filename):
subtitles_filename = f"{filename}_transcript.srt"
def generator(txt):
return editor.TextClip(txt, **config_data["text_clip_config"])
subtitles = SubtitlesClip(subtitles_filename, generator)
video_list = [
input_video_file_clip,
subtitles.set_pos(
(
"center",
input_video_file_clip.h + config_data["text_position_y_offset"],
)
),
]
video_with_subs = editor.CompositeVideoClip(video_list)
kwargs["input_video_file_clip"] = video_with_subs
return kwargs
@staticmethod
def set_vertical(**kwargs):
input_video_file_clip = kwargs["input_video_file_clip"]
shape = input_video_file_clip.size
if shape[0] > shape[1]:
shape = [shape[1], shape[0]]
input_video_file_clip = input_video_file_clip.resize(shape)
kwargs["shape"] = input_video_file_clip.size
kwargs["input_video_file_clip"] = input_video_file_clip
return kwargs
@staticmethod
def set_horizontal(**kwargs):
input_video_file_clip = kwargs["input_video_file_clip"]
shape = input_video_file_clip.size
if shape[0] < shape[1]:
shape = [shape[1], shape[0]]
input_video_file_clip = input_video_file_clip.resize(shape)
kwargs["shape"] = input_video_file_clip.size
kwargs["input_video_file_clip"] = input_video_file_clip
return kwargs
@staticmethod
def get_video_data(**kwargs):
video_path = kwargs["video_path"]
filename = video_path.split("/")[-1].split(".")[0]
input_video_file_clip = VideoFileClip(video_path)
kwargs["shape"] = input_video_file_clip.size
kwargs["filename"] = filename
kwargs["input_video_file_clip"] = input_video_file_clip
return kwargs
@staticmethod
def trim_by_silence(**kwargs):
input_video_file_clip = kwargs["input_video_file_clip"]
clip_interval = kwargs["clip_interval"]
sound_threshold = kwargs["sound_threshold"]
discard_silence = kwargs["discard_silence"]
print("Chunking video...")
volumes = []
for i in np.arange(0, input_video_file_clip.duration, clip_interval):
if input_video_file_clip.duration <= i + clip_interval:
continue
volumes.append(
VideoProcessor._get_subclip_volume(
input_video_file_clip, i, clip_interval
)
)
print("Processing silences...")
volumes = np.array(volumes)
volumes_binary = volumes > sound_threshold
change_times = [0]
for i in range(1, len(volumes_binary)):
if volumes_binary[i] == volumes_binary[i - 1]:
continue
change_times.append(i * clip_interval)
change_times.append(input_video_file_clip.duration)
print("Subclipping...")
first_piece_silence = 1 if volumes_binary[0] else 0
clips = []
for i in range(1, len(change_times)):
if discard_silence and i % 2 != first_piece_silence:
continue
new = input_video_file_clip.subclip(change_times[i - 1], change_times[i])
clips.append(new)
kwargs["clips"] = clips
return kwargs