| import io |
| import time |
| import torch |
| import numpy as np |
| from pydub import AudioSegment |
|
|
|
|
| def tokenise(audio_np_array: np.ndarray) -> torch.Tensor: |
| """ |
| Function to tokenise an audio file represented as a NumPy array. |
| |
| Args: |
| - audio_np_array (np.ndarray): The audio file as a NumPy array. |
| |
| Returns: |
| - torch.Tensor: A random 1D tensor with dtype int16 and variable length in range (20, 1000). |
| """ |
|
|
| |
| if not isinstance(audio_np_array, np.ndarray): |
| raise ValueError("Input should be a NumPy array") |
|
|
| |
| time.sleep(0.15) |
|
|
| tensor_length = np.random.randint(20, 1001) |
| return torch.randint(low=-32768, high=32767, size=(tensor_length,), dtype=torch.int16) |
|
|
|
|
| def convert_flac_to_wav(flac_data: np.array): |
| """ |
| Convert FLAC data to WAV using pydub |
| Args: |
| - flac_data (np.ndarray): The flac audio file as a NumPy array. |
| """ |
| audio = AudioSegment.from_file(io.BytesIO(flac_data), format='flac') |
| audio = audio.set_channels(1) |
| audio = audio.set_sample_width(2) |
| return audio.export(format='wav').read() |
