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语帆 2024-02-23 17:38:54 +08:00
parent 5130d2406d
commit 70a236b652
2 changed files with 15 additions and 9 deletions
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2
examples/industrial_data_pretraining/lcbnet/demo2.sh
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@ -8,7 +8,7 @@ python -m funasr.bin.inference \
++tokenizer_conf.token_list=${file_dir}/tokens.txt \
++frontend_conf.cmvn_file=${file_dir}/am.mvn \
++input=[${file_dir}/wav.scp,${file_dir}/ocr_text] \
+data_type='["sound", "text"]' \
+data_type='["kaldi_ark", "text"]' \
++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
++output_dir="./outputs/debug" \
++device="" \

22
funasr/utils/load_utils.py
View File

@ -17,35 +17,28 @@ import pdb
def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs: int = 16000, data_type="sound", tokenizer=None, **kwargs):
pdb.set_trace()
if isinstance(data_or_path_or_list, (list, tuple)):
if data_type is not None and isinstance(data_type, (list, tuple)):
pdb.set_trace()
data_types = [data_type] * len(data_or_path_or_list)
data_or_path_or_list_ret = [[] for d in data_type]
pdb.set_trace()
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, data_or_path_or_list_j) in enumerate(zip(data_type_i, data_or_path_or_list_i)):
pdb.set_trace()
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, **kwargs)
pdb.set_trace()
data_or_path_or_list_ret[j].append(data_or_path_or_list_j)
return data_or_path_or_list_ret
else:
return [load_audio_text_image_video(audio, fs=fs, audio_fs=audio_fs, data_type=data_type, **kwargs) for audio in data_or_path_or_list]
pdb.set_trace()
if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith('http'): # download url to local file
data_or_path_or_list = download_from_url(data_or_path_or_list)
pdb.set_trace()
if isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list): # local file
pdb.set_trace()
if data_type is None or data_type == "sound":
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)
elif data_type == "text" and tokenizer is not None:
pdb.set_trace()
data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
elif data_type == "image": # undo
pass
@ -60,6 +53,19 @@ def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs:
data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
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,]
elif isinstance(data_or_path_or_list, str) and data_type == "kaldi_ark":
data_mat = kaldiio.load_mat(data_or_path_or_list)
if isinstance(data_mat, tuple):
sampling_rate, mat = data_mat
assert sampling_rate == audio_fs
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 = mat
else:
pass
# print(f"unsupport data type: {data_or_path_or_list}, return raw data")