Spaces:
Runtime error
Runtime error
File size: 4,529 Bytes
4318c41 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
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
|