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| import torch | |
| import torchaudio | |
| def read_audio(path): | |
| wav, sr = torchaudio.load(path) | |
| if wav.size(0) > 1: | |
| wav = wav.mean(dim=0, keepdim=True) | |
| return wav.squeeze(0), sr | |
| def resample_wav(wav, sr, new_sr): | |
| wav = wav.unsqueeze(0) | |
| transform = torchaudio.transforms.Resample(orig_freq=sr, new_freq=new_sr) | |
| wav = transform(wav) | |
| return wav.squeeze(0) | |
| def map_timestamps_to_new_sr(vad_sr, new_sr, timestamps, just_begging_end=False): | |
| factor = new_sr / vad_sr | |
| new_timestamps = [] | |
| if just_begging_end and timestamps: | |
| # get just the start and end timestamps | |
| new_dict = {"start": int(timestamps[0]["start"] * factor), "end": int(timestamps[-1]["end"] * factor)} | |
| new_timestamps.append(new_dict) | |
| else: | |
| for ts in timestamps: | |
| # map to the new SR | |
| new_dict = {"start": int(ts["start"] * factor), "end": int(ts["end"] * factor)} | |
| new_timestamps.append(new_dict) | |
| return new_timestamps | |
| def get_vad_model_and_utils(use_cuda=False, use_onnx=False): | |
| model, utils = torch.hub.load( | |
| repo_or_dir="snakers4/silero-vad", model="silero_vad", force_reload=True, onnx=use_onnx, force_onnx_cpu=True | |
| ) | |
| if use_cuda: | |
| model = model.cuda() | |
| get_speech_timestamps, save_audio, _, _, collect_chunks = utils | |
| return model, get_speech_timestamps, save_audio, collect_chunks | |
| def remove_silence( | |
| model_and_utils, audio_path, out_path, vad_sample_rate=8000, trim_just_beginning_and_end=True, use_cuda=False | |
| ): | |
| # get the VAD model and utils functions | |
| model, get_speech_timestamps, _, collect_chunks = model_and_utils | |
| # read ground truth wav and resample the audio for the VAD | |
| try: | |
| wav, gt_sample_rate = read_audio(audio_path) | |
| except: | |
| print(f"> ❗ Failed to read {audio_path}") | |
| return None, False | |
| # if needed, resample the audio for the VAD model | |
| if gt_sample_rate != vad_sample_rate: | |
| wav_vad = resample_wav(wav, gt_sample_rate, vad_sample_rate) | |
| else: | |
| wav_vad = wav | |
| if use_cuda: | |
| wav_vad = wav_vad.cuda() | |
| # get speech timestamps from full audio file | |
| speech_timestamps = get_speech_timestamps(wav_vad, model, sampling_rate=vad_sample_rate, window_size_samples=768) | |
| # map the current speech_timestamps to the sample rate of the ground truth audio | |
| new_speech_timestamps = map_timestamps_to_new_sr( | |
| vad_sample_rate, gt_sample_rate, speech_timestamps, trim_just_beginning_and_end | |
| ) | |
| # if have speech timestamps else save the wav | |
| if new_speech_timestamps: | |
| wav = collect_chunks(new_speech_timestamps, wav) | |
| is_speech = True | |
| else: | |
| print(f"> The file {audio_path} probably does not have speech please check it !!") | |
| is_speech = False | |
| # save | |
| torchaudio.save(out_path, wav[None, :], gt_sample_rate) | |
| return out_path, is_speech | |