speech_recognition/FunASR
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add support mixture of kaldi_ark or sound

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志浩 2024-09-24 19:59:07 +08:00
parent 0a65aaf266
commit ef817d0a7d
1 changed files with 3 additions and 29 deletions
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30
funasr/utils/load_utils.py
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@ -81,7 +81,7 @@ def load_audio_text_image_video(
data_or_path_or_list = download_from_url(data_or_path_or_list) data_or_path_or_list = download_from_url(data_or_path_or_list)
if isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list): # local file if isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list): # local file
if data_type is None or data_type == "sound": if data_type is None or data_type in ["sound", "kaldi_ark_or_sound"]:
# if use_ffmpeg: # if use_ffmpeg:
# data_or_path_or_list = _load_audio_ffmpeg(data_or_path_or_list, sr=fs) # data_or_path_or_list = _load_audio_ffmpeg(data_or_path_or_list, sr=fs)
# data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze() # [n_samples,] # data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze() # [n_samples,]
@ -113,7 +113,7 @@ def load_audio_text_image_video(
data_or_path_or_list = tokenizer.encode(data_or_path_or_list) data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point
data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze() # [n_samples,] data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze() # [n_samples,]
elif isinstance(data_or_path_or_list, str) and data_type == "kaldi_ark": elif isinstance(data_or_path_or_list, str) and data_type in ["kaldi_ark", "kaldi_ark_or_sound"]:
data_mat = kaldiio.load_mat(data_or_path_or_list) data_mat = kaldiio.load_mat(data_or_path_or_list)
if isinstance(data_mat, tuple): if isinstance(data_mat, tuple):
audio_fs, mat = data_mat audio_fs, mat = data_mat
@ -124,33 +124,7 @@ def load_audio_text_image_video(
mat = mat / 32768 mat = mat / 32768
if mat.ndim == 2: if mat.ndim == 2:
mat = mat[:, 0] mat = mat[:, 0]
data_or_path_or_list = mat
elif isinstance(data_or_path_or_list, str) and data_type == "kaldi_ark_or_sound":
if ".ark:" in data_or_path_or_list:
data_mat = kaldiio.load_mat(data_or_path_or_list)
if isinstance(data_mat, tuple):
if isinstance(data_mat[0], int):
audio_fs, mat = data_mat
else:
mat, audio_fs = data_mat
else:
mat = data_mat
if mat.dtype == "int16" or mat.dtype == "int32":
mat = mat.astype(np.float64)
mat = mat / 32768
if mat.ndim == 2:
mat = mat[:, 0]
data_or_path_or_list = torch.from_numpy(mat) data_or_path_or_list = torch.from_numpy(mat)
else:
try:
data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list)
if kwargs.get("reduce_channels", True):
data_or_path_or_list = data_or_path_or_list.mean(0)
except:
data_or_path_or_list = _load_audio_ffmpeg(data_or_path_or_list, sr=fs)
data_or_path_or_list = torch.from_numpy(
data_or_path_or_list
).squeeze() # [n_samples,]
elif isinstance(data_or_path_or_list, bytes): # audio bytes elif isinstance(data_or_path_or_list, bytes): # audio bytes
data_or_path_or_list = load_bytes(data_or_path_or_list) data_or_path_or_list = load_bytes(data_or_path_or_list)
else: else: