From e6a7bbe1ca6690faa23d29e22cb74a8d67c09ed3 Mon Sep 17 00:00:00 2001 From: 游雁 <zhifu.gzf@alibaba-inc.com> Date: 星期五, 05 一月 2024 17:00:11 +0800 Subject: [PATCH] load_audio_text_image_video --- funasr/utils/load_utils.py | 58 +++++++++++++++++++++++++++++----------------------------- 1 files changed, 29 insertions(+), 29 deletions(-) diff --git a/funasr/utils/load_utils.py b/funasr/utils/load_utils.py index 637e1d2..4fb27c0 100644 --- a/funasr/utils/load_utils.py +++ b/funasr/utils/load_utils.py @@ -10,52 +10,52 @@ 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) -- Gitblit v1.9.1