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import os
import glob
import torch
from glob import glob
import numpy as np
from pydub import AudioSegment
from faster_whisper import WhisperModel
from whisper_timestamped.transcribe import get_audio_tensor, get_vad_segments
model_size = "medium"
# Run on GPU with FP16
model = None
def split_audio_whisper(audio_path, target_dir='processed'):
global model
if model is None:
model = WhisperModel(model_size, device="cuda", compute_type="float16")
audio = AudioSegment.from_file(audio_path)
max_len = len(audio)
audio_name = os.path.basename(audio_path).rsplit('.', 1)[0]
target_folder = os.path.join(target_dir, audio_name)
segments, info = model.transcribe(audio_path, beam_size=5, word_timestamps=True)
segments = list(segments)
# create directory
os.makedirs(target_folder, exist_ok=True)
wavs_folder = os.path.join(target_folder, 'wavs')
os.makedirs(wavs_folder, exist_ok=True)
# segments
s_ind = 0
start_time = None
for k, w in enumerate(segments):
# process with the time
if k == 0:
start_time = max(0, w.start)
end_time = w.end
# calculate confidence
if len(w.words) > 0:
confidence = sum([s.probability for s in w.words]) / len(w.words)
else:
confidence = 0.
# clean text
text = w.text.replace('...', '')
# left 0.08s for each audios
audio_seg = audio[int( start_time * 1000) : min(max_len, int(end_time * 1000) + 80)]
# segment file name
fname = f"{audio_name}_seg{s_ind}.wav"
# filter out the segment shorter than 1.5s and longer than 20s
save = audio_seg.duration_seconds > 1.5 and \
audio_seg.duration_seconds < 20. and \
len(text) >= 2 and len(text) < 200
if save:
output_file = os.path.join(wavs_folder, fname)
audio_seg.export(output_file, format='wav')
if k < len(segments) - 1:
start_time = max(0, segments[k+1].start - 0.08)
s_ind = s_ind + 1
return wavs_folder
def split_audio_vad(audio_path, target_dir, split_seconds=10.0):
SAMPLE_RATE = 16000
audio_vad = get_audio_tensor(audio_path)
segments = get_vad_segments(
audio_vad,
output_sample=True,
min_speech_duration=0.1,
min_silence_duration=1,
method="silero",
)
segments = [(seg["start"], seg["end"]) for seg in segments]
segments = [(float(s) / SAMPLE_RATE, float(e) / SAMPLE_RATE) for s,e in segments]
print(segments)
audio_active = AudioSegment.silent(duration=0)
audio = AudioSegment.from_file(audio_path)
for start_time, end_time in segments:
audio_active += audio[int( start_time * 1000) : int(end_time * 1000)]
audio_dur = audio_active.duration_seconds
print(f'after vad: dur = {audio_dur}')
audio_name = os.path.basename(audio_path).rsplit('.', 1)[0]
target_folder = os.path.join(target_dir, audio_name)
wavs_folder = os.path.join(target_folder, 'wavs')
os.makedirs(wavs_folder, exist_ok=True)
start_time = 0.
count = 0
num_splits = int(np.round(audio_dur / split_seconds))
assert num_splits > 0, 'input audio is too short'
interval = audio_dur / num_splits
for i in range(num_splits):
end_time = min(start_time + interval, audio_dur)
if i == num_splits - 1:
end_time = audio_dur
output_file = f"{wavs_folder}/{audio_name}_seg{count}.wav"
audio_seg = audio_active[int(start_time * 1000): int(end_time * 1000)]
audio_seg.export(output_file, format='wav')
start_time = end_time
count += 1
return wavs_folder
def get_se(audio_path, vc_model, target_dir='processed', vad=True):
device = vc_model.device
audio_name = os.path.basename(audio_path).rsplit('.', 1)[0]
se_path = os.path.join(target_dir, audio_name, 'se.pth')
if os.path.isfile(se_path):
se = torch.load(se_path).to(device)
return se, audio_name
if os.path.isdir(audio_path):
wavs_folder = audio_path
elif vad:
wavs_folder = split_audio_vad(audio_path, target_dir)
else:
wavs_folder = split_audio_whisper(audio_path, target_dir)
audio_segs = glob(f'{wavs_folder}/*.wav')
if len(audio_segs) == 0:
raise NotImplementedError('No audio segments found!')
return vc_model.extract_se(audio_segs, se_save_path=se_path), audio_name
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