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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 | |
| def generate_voice_segments(audio_path, target_dir='processed', vad=True): | |
| audio_name = os.path.basename(audio_path).rsplit('.', 1)[0] | |
| if 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!') | |