mirror of
https://github.com/myshell-ai/OpenVoice.git
synced 2025-12-16 16:37:56 +01:00
89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
import os
|
|
import glob
|
|
import torch
|
|
from glob import glob
|
|
from pydub import AudioSegment
|
|
from faster_whisper import WhisperModel
|
|
|
|
model_size = "medium"
|
|
# Run on GPU with FP16
|
|
model = None
|
|
def split_audio(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 get_se(audio_path, vc_model, target_dir='processed'):
|
|
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
|
|
else:
|
|
wavs_folder = split_audio(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
|