import os
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import torch
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import json
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import torch.distributed as dist
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import numpy as np
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import kaldiio
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import librosa
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import torchaudio
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import time
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import logging
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from torch.nn.utils.rnn import pad_sequence
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# def load_audio(audio_or_path_or_list, fs: int=16000, audio_fs: int=16000):
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#
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# if isinstance(audio_or_path_or_list, (list, tuple)):
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# return [load_audio(audio, fs=fs, audio_fs=audio_fs) for audio in audio_or_path_or_list]
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#
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# if isinstance(audio_or_path_or_list, str) and os.path.exists(audio_or_path_or_list):
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# audio_or_path_or_list, audio_fs = torchaudio.load(audio_or_path_or_list)
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# audio_or_path_or_list = audio_or_path_or_list[0, :]
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# elif isinstance(audio_or_path_or_list, np.ndarray): # audio sample point
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# audio_or_path_or_list = np.squeeze(audio_or_path_or_list) #[n_samples,]
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#
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# if audio_fs != fs:
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# resampler = torchaudio.transforms.Resample(audio_fs, fs)
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# audio_or_path_or_list = resampler(audio_or_path_or_list[None, :])[0, :]
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# return audio_or_path_or_list
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def load_audio_and_text_image_video(audio_or_path_or_list, fs: int = 16000, audio_fs: int = 16000, data_type=None, tokenizer=None):
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if isinstance(audio_or_path_or_list, (list, tuple)):
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if data_type is not None and isinstance(data_type, (list, tuple)):
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data_types = [data_type] * len(audio_or_path_or_list)
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audio_or_path_or_list_ret = [[] for d in data_type]
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for i, (data_type_i, audio_or_path_or_list_i) in enumerate(zip(data_types, audio_or_path_or_list)):
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for j, (data_type_j, audio_or_path_or_list_j) in enumerate(zip(data_type_i, audio_or_path_or_list_i)):
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audio_or_path_or_list_j = load_audio_and_text_image_video(audio_or_path_or_list_j, fs=fs, audio_fs=audio_fs, data_type=data_type_j, tokenizer=tokenizer)
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audio_or_path_or_list_ret[j].append(audio_or_path_or_list_j)
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return audio_or_path_or_list_ret
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else:
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return [load_audio_and_text_image_video(audio, fs=fs, audio_fs=audio_fs) for audio in audio_or_path_or_list]
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if isinstance(audio_or_path_or_list, str) and os.path.exists(audio_or_path_or_list):
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audio_or_path_or_list, audio_fs = torchaudio.load(audio_or_path_or_list)
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audio_or_path_or_list = audio_or_path_or_list[0, :]
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elif isinstance(audio_or_path_or_list, np.ndarray): # audio sample point
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audio_or_path_or_list = np.squeeze(audio_or_path_or_list) # [n_samples,]
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elif isinstance(audio_or_path_or_list, str) and data_type is not None and data_type == "text" and tokenizer is not None:
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audio_or_path_or_list = tokenizer.encode(audio_or_path_or_list)
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if audio_fs != fs and data_type != "text":
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resampler = torchaudio.transforms.Resample(audio_fs, fs)
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audio_or_path_or_list = resampler(audio_or_path_or_list[None, :])[0, :]
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return audio_or_path_or_list
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def load_bytes(input):
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middle_data = np.frombuffer(input, dtype=np.int16)
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middle_data = np.asarray(middle_data)
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if middle_data.dtype.kind not in 'iu':
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raise TypeError("'middle_data' must be an array of integers")
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dtype = np.dtype('float32')
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if dtype.kind != 'f':
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raise TypeError("'dtype' must be a floating point type")
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i = np.iinfo(middle_data.dtype)
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abs_max = 2 ** (i.bits - 1)
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offset = i.min + abs_max
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array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
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return array
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def extract_fbank(data, data_len = None, data_type: str="sound", frontend=None):
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# import pdb;
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# pdb.set_trace()
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if isinstance(data, np.ndarray):
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data = torch.from_numpy(data)
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if len(data.shape) < 2:
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data = data[None, :] # data: [batch, N]
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data_len = [data.shape[1]] if data_len is None else data_len
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elif isinstance(data, torch.Tensor):
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if len(data.shape) < 2:
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data = data[None, :] # data: [batch, N]
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data_len = [data.shape[1]] if data_len is None else data_len
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elif isinstance(data, (list, tuple)):
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data_list, data_len = [], []
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for data_i in data:
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if isinstance(data, np.ndarray):
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data_i = torch.from_numpy(data_i)
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data_list.append(data_i)
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data_len.append(data_i.shape[0])
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data = pad_sequence(data_list, batch_first=True) # data: [batch, N]
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# import pdb;
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# pdb.set_trace()
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# if data_type == "sound":
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data, data_len = frontend(data, data_len)
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if isinstance(data_len, (list, tuple)):
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data_len = torch.tensor([data_len])
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return data.to(torch.float32), data_len.to(torch.int32)
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