| | |
| | | speed = random.choice(self.speed_perturb) |
| | | if speed != 1.0: |
| | | mat, _ = torchaudio.sox_effects.apply_effects_tensor( |
| | | mat, sampling_rate, [['speed', str(speed)], ['rate', str(sampling_rate)]]) |
| | | torch.tensor(mat).view(1, -1), sampling_rate, [['speed', str(speed)], ['rate', str(sampling_rate)]]) |
| | | mat = mat.view(-1).numpy() |
| | | sample_dict[data_name] = mat |
| | | sample_dict["sampling_rate"] = sampling_rate |
| | | if data_name == "speech": |
| | |
| | | import librosa |
| | | from typeguard import check_argument_types |
| | | |
| | | import torch |
| | | import torchaudio |
| | | |
| | | from funasr.fileio.read_text import read_2column_text |
| | |
| | | speed = random.choice(self.speed_perturb) |
| | | if speed != 1.0: |
| | | array, _ = torchaudio.sox_effects.apply_effects_tensor( |
| | | array, rate, |
| | | torch.tensor(array).view(1, -1), rate, |
| | | [['speed', str(speed)], ['rate', str(rate)]]) |
| | | array = array.view(-1).numpy() |
| | | |
| | | return rate, array |
| | | |