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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
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