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load_audio_text_image_video

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游雁 2024-01-05 17:00:11 +08:00
parent 4f98546f36
commit e6a7bbe1ca
1 changed files with 29 additions and 29 deletions
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58
funasr/utils/load_utils.py
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@ -10,52 +10,52 @@ import time
import logging
from torch.nn.utils.rnn import pad_sequence
# def load_audio(audio_or_path_or_list, fs: int=16000, audio_fs: int=16000):
# def load_audio(data_or_path_or_list, fs: int=16000, audio_fs: int=16000):
#
# if isinstance(audio_or_path_or_list, (list, tuple)):
# return [load_audio(audio, fs=fs, audio_fs=audio_fs) for audio in audio_or_path_or_list]
# if isinstance(data_or_path_or_list, (list, tuple)):
# return [load_audio(audio, fs=fs, audio_fs=audio_fs) for audio in data_or_path_or_list]
#
# if isinstance(audio_or_path_or_list, str) and os.path.exists(audio_or_path_or_list):
# audio_or_path_or_list, audio_fs = torchaudio.load(audio_or_path_or_list)
# audio_or_path_or_list = audio_or_path_or_list[0, :]
# elif isinstance(audio_or_path_or_list, np.ndarray): # audio sample point
# audio_or_path_or_list = np.squeeze(audio_or_path_or_list) #[n_samples,]
# if isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list):
# data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list)
# data_or_path_or_list = data_or_path_or_list[0, :]
# elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point
# data_or_path_or_list = np.squeeze(data_or_path_or_list) #[n_samples,]
#
# if audio_fs != fs:
# resampler = torchaudio.transforms.Resample(audio_fs, fs)
# audio_or_path_or_list = resampler(audio_or_path_or_list[None, :])[0, :]
# return audio_or_path_or_list
# data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :]
# return data_or_path_or_list
def load_audio_text_image_video(audio_or_path_or_list, fs: int = 16000, audio_fs: int = 16000, data_type=None, tokenizer=None):
if isinstance(audio_or_path_or_list, (list, tuple)):
def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs: int = 16000, data_type=None, tokenizer=None):
if isinstance(data_or_path_or_list, (list, tuple)):
if data_type is not None and isinstance(data_type, (list, tuple)):
data_types = [data_type] * len(audio_or_path_or_list)
audio_or_path_or_list_ret = [[] for d in data_type]
for i, (data_type_i, audio_or_path_or_list_i) in enumerate(zip(data_types, audio_or_path_or_list)):
data_types = [data_type] * len(data_or_path_or_list)
data_or_path_or_list_ret = [[] for d in data_type]
for i, (data_type_i, data_or_path_or_list_i) in enumerate(zip(data_types, data_or_path_or_list)):
for j, (data_type_j, audio_or_path_or_list_j) in enumerate(zip(data_type_i, audio_or_path_or_list_i)):
for j, (data_type_j, data_or_path_or_list_j) in enumerate(zip(data_type_i, data_or_path_or_list_i)):
audio_or_path_or_list_j = load_audio_text_image_video(audio_or_path_or_list_j, fs=fs, audio_fs=audio_fs, data_type=data_type_j, tokenizer=tokenizer)
audio_or_path_or_list_ret[j].append(audio_or_path_or_list_j)
data_or_path_or_list_j = load_audio_text_image_video(data_or_path_or_list_j, fs=fs, audio_fs=audio_fs, data_type=data_type_j, tokenizer=tokenizer)
data_or_path_or_list_ret[j].append(data_or_path_or_list_j)
return audio_or_path_or_list_ret
return data_or_path_or_list_ret
else:
return [load_audio_text_image_video(audio, fs=fs, audio_fs=audio_fs) for audio in audio_or_path_or_list]
return [load_audio_text_image_video(audio, fs=fs, audio_fs=audio_fs) for audio in data_or_path_or_list]
if isinstance(audio_or_path_or_list, str) and os.path.exists(audio_or_path_or_list):
audio_or_path_or_list, audio_fs = torchaudio.load(audio_or_path_or_list)
audio_or_path_or_list = audio_or_path_or_list[0, :]
elif isinstance(audio_or_path_or_list, np.ndarray): # audio sample point
audio_or_path_or_list = np.squeeze(audio_or_path_or_list) # [n_samples,]
elif isinstance(audio_or_path_or_list, str) and data_type is not None and data_type == "text" and tokenizer is not None:
audio_or_path_or_list = tokenizer.encode(audio_or_path_or_list)
if isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list):
data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list)
data_or_path_or_list = data_or_path_or_list[0, :]
elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point
data_or_path_or_list = np.squeeze(data_or_path_or_list) # [n_samples,]
elif isinstance(data_or_path_or_list, str) and data_type is not None and data_type == "text" and tokenizer is not None:
data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
if audio_fs != fs and data_type != "text":
resampler = torchaudio.transforms.Resample(audio_fs, fs)
audio_or_path_or_list = resampler(audio_or_path_or_list[None, :])[0, :]
return audio_or_path_or_list
data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :]
return data_or_path_or_list
def load_bytes(input):
middle_data = np.frombuffer(input, dtype=np.int16)