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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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| | | |
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| | | # def load_audio(audio_or_path_or_list, fs: int=16000, audio_fs: int=16000): |
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| | | try: |
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| | | from urllib.parse import urlparse |
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| | | from funasr.download.file import HTTPStorage |
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| | | import tempfile |
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| | | except: |
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| | | print("urllib is not installed, if you infer from url, please install it first.") |
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| | | # def load_audio(data_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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| | | # if isinstance(data_or_path_or_list, (list, tuple)): |
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| | | # return [load_audio(audio, fs=fs, audio_fs=audio_fs) for audio in data_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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| | | # if isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list): |
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| | | # data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list) |
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| | | # data_or_path_or_list = data_or_path_or_list[0, :] |
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| | | # elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point |
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| | | # data_or_path_or_list = np.squeeze(data_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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| | | # data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :] |
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| | | # return data_or_path_or_list |
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| | | |
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| | | |
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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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| | | def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs: int = 16000, data_type=None, tokenizer=None): |
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| | | if isinstance(data_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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| | | |
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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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| | | data_types = [data_type] * len(data_or_path_or_list) |
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| | | data_or_path_or_list_ret = [[] for d in data_type] |
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| | | for i, (data_type_i, data_or_path_or_list_i) in enumerate(zip(data_types, data_or_path_or_list)): |
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| | | |
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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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| | | for j, (data_type_j, data_or_path_or_list_j) in enumerate(zip(data_type_i, data_or_path_or_list_i)): |
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| | | |
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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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| | | 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) |
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| | | data_or_path_or_list_ret[j].append(data_or_path_or_list_j) |
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| | | |
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| | | return audio_or_path_or_list_ret |
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| | | return data_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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| | | |
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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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| | | return [load_audio_text_image_video(audio, fs=fs, audio_fs=audio_fs) for audio in data_or_path_or_list] |
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| | | if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith('http'): |
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| | | data_or_path_or_list = download_from_url(data_or_path_or_list) |
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| | | if isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list): |
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| | | data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list) |
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| | | data_or_path_or_list = data_or_path_or_list[0, :] |
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| | | elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point |
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| | | data_or_path_or_list = np.squeeze(data_or_path_or_list) # [n_samples,] |
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| | | elif isinstance(data_or_path_or_list, str) and data_type is not None and data_type == "text" and tokenizer is not None: |
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| | | data_or_path_or_list = tokenizer.encode(data_or_path_or_list) |
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| | | |
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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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| | | data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :] |
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| | | return data_or_path_or_list |
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| | | |
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| | | def load_bytes(input): |
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| | | middle_data = np.frombuffer(input, dtype=np.int16) |
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| | |
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| | | |
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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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| | | return data.to(torch.float32), data_len.to(torch.int32) |
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| | | |
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| | | def download_from_url(url): |
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| | | |
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| | | result = urlparse(url) |
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| | | file_path = None |
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| | | if result.scheme is not None and len(result.scheme) > 0: |
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| | | storage = HTTPStorage() |
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| | | # bytes |
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| | | data = storage.read(url) |
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| | | work_dir = tempfile.TemporaryDirectory().name |
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| | | if not os.path.exists(work_dir): |
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| | | os.makedirs(work_dir) |
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| | | file_path = os.path.join(work_dir, os.path.basename(url)) |
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| | | with open(file_path, 'wb') as fb: |
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| | | fb.write(data) |
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| | | assert file_path is not None, f"failed to download: {url}" |
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| | | return file_path |
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