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Running
on
Zero
| import librosa | |
| import soundfile as sf | |
| from tqdm import tqdm | |
| from Preprocessing.TextFrontend import get_feature_to_index_lookup | |
| from Utility.path_to_transcript_dicts import * | |
| def make_silence_cleaned_versions(train_sets): | |
| torch.hub._validate_not_a_forked_repo = lambda a, b, c: True # torch 1.9 has a bug in the hub loading, this is a workaround | |
| # careful: assumes 16kHz or 8kHz audio | |
| silero_model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', | |
| model='silero_vad', | |
| force_reload=False, | |
| onnx=False, | |
| verbose=False) | |
| (get_speech_timestamps, | |
| save_audio, | |
| read_audio, | |
| VADIterator, | |
| collect_chunks) = utils | |
| torch.set_grad_enabled(True) # finding this issue was very infuriating: silero sets | |
| # this to false globally during model loading rather than using inference mode or no_grad | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| silero_model = silero_model.to(device) | |
| for train_set in train_sets: | |
| for index in tqdm(range(len(train_set))): | |
| filepath = train_set.datapoints[index][8] | |
| phonemes = train_set.datapoints[index][0] | |
| speech_length = train_set.datapoints[index][3] | |
| durations = train_set.datapoints[index][4] | |
| cumsum = 0 | |
| legal_silences = list() | |
| for phoneme_index, phone in enumerate(phonemes): | |
| if phone[get_feature_to_index_lookup()["silence"]] == 1 or phone[get_feature_to_index_lookup()["end of sentence"]] == 1 or phone[get_feature_to_index_lookup()["questionmark"]] == 1 or phone[get_feature_to_index_lookup()["exclamationmark"]] == 1 or phone[get_feature_to_index_lookup()["fullstop"]] == 1: | |
| legal_silences.append([cumsum, cumsum + durations[phoneme_index]]) | |
| cumsum = cumsum + durations[phoneme_index] | |
| wave, sr = sf.read(filepath) | |
| resampled_wave = librosa.resample(wave, orig_sr=sr, target_sr=16000) | |
| with torch.inference_mode(): | |
| speech_timestamps = get_speech_timestamps(torch.Tensor(resampled_wave).to(device), silero_model, sampling_rate=16000) | |
| silences = list() | |
| prev_end = 0 | |
| for speech_segment in speech_timestamps: | |
| if prev_end != 0: | |
| silences.append([prev_end, speech_segment["start"]]) | |
| prev_end = speech_segment["end"] | |
| # at this point we know all the silences and we know the legal silences. | |
| # We have to transform them both into ratios, so we can compare them. | |
| # If a silence overlaps with a legal silence, it can stay. | |
| illegal_silences = list() | |
| for silence in silences: | |
| illegal = True | |
| start = silence[0] / len(resampled_wave) | |
| end = silence[1] / len(resampled_wave) | |
| for legal_silence in legal_silences: | |
| legal_start = legal_silence[0] / speech_length | |
| legal_end = legal_silence[1] / speech_length | |
| if legal_start < start < legal_end or legal_start < end < legal_end: | |
| illegal = False | |
| break | |
| if illegal: | |
| # If it is an illegal silence, it is marked for removal in the original wave according to ration with real samplingrate. | |
| illegal_silences.append([start, end]) | |
| # print(f"{len(illegal_silences)} illegal silences detected. ({len(silences) - len(illegal_silences)} legal silences left)") | |
| wave = list(wave) | |
| orig_wave_length = len(wave) | |
| for illegal_silence in reversed(illegal_silences): | |
| wave = wave[:int(illegal_silence[0] * orig_wave_length)] + wave[int(illegal_silence[1] * orig_wave_length):] | |
| # Audio with illegal silences removed will be saved into a new directory. | |
| new_filepath_list = filepath.split("/") | |
| new_filepath_list[-2] = new_filepath_list[-2] + "_silence_removed" | |
| os.makedirs("/".join(new_filepath_list[:-1]), exist_ok=True) | |
| sf.write("/".join(new_filepath_list), wave, sr) | |