| | |
|---|
| | | import time |
|---|
| | | import logging |
|---|
| | | from torch.nn.utils.rnn import pad_sequence |
|---|
| | | |
|---|
| | | try: |
|---|
| | | from urllib.parse import urlparse |
|---|
| | | from funasr.download.file import HTTPStorage |
|---|
| | | import tempfile |
|---|
| | | from funasr.download.file import download_from_url |
|---|
| | | except: |
|---|
| | | print("urllib is not installed, if you infer from url, please install it first.") |
|---|
| | | # def load_audio(data_or_path_or_list, fs: int=16000, audio_fs: int=16000): |
|---|
| | | # |
|---|
| | | # 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(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) |
|---|
| | | # data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :] |
|---|
| | | # return data_or_path_or_list |
|---|
| | | print("urllib is not installed, if you infer from url, please install it first.") |
|---|
| | | import pdb |
|---|
| | | import subprocess |
|---|
| | | from subprocess import CalledProcessError, run |
|---|
| | | |
|---|
| | | |
|---|
| | | 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)): |
|---|
| | | def is_ffmpeg_installed(): |
|---|
| | | try: |
|---|
| | | output = subprocess.check_output(["ffmpeg", "-version"], stderr=subprocess.STDOUT) |
|---|
| | | return "ffmpeg version" in output.decode("utf-8") |
|---|
| | | except (subprocess.CalledProcessError, FileNotFoundError): |
|---|
| | | return False |
|---|
| | | |
|---|
| | | 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, data_or_path_or_list_j) in enumerate(zip(data_type_i, data_or_path_or_list_i)): |
|---|
| | | |
|---|
| | | 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 data_or_path_or_list_ret |
|---|
| | | else: |
|---|
| | | return [load_audio_text_image_video(audio, fs=fs, audio_fs=audio_fs) for audio in data_or_path_or_list] |
|---|
| | | if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith('http'): |
|---|
| | | 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): |
|---|
| | | 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) |
|---|
| | | data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :] |
|---|
| | | return data_or_path_or_list |
|---|
| | | use_ffmpeg = False |
|---|
| | | if is_ffmpeg_installed(): |
|---|
| | | use_ffmpeg = True |
|---|
| | | else: |
|---|
| | | print( |
|---|
| | | "Notice: ffmpeg is not installed. torchaudio is used to load audio\n" |
|---|
| | | "If you want to use ffmpeg backend to load audio, please install it by:" |
|---|
| | | "\n\tsudo apt install ffmpeg # ubuntu" |
|---|
| | | "\n\t# brew install ffmpeg # mac" |
|---|
| | | ) |
|---|
| | | |
|---|
| | | |
|---|
| | | def load_audio_text_image_video( |
|---|
| | | data_or_path_or_list, |
|---|
| | | fs: int = 16000, |
|---|
| | | audio_fs: int = 16000, |
|---|
| | | data_type="sound", |
|---|
| | | tokenizer=None, |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | 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(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, data_or_path_or_list_j) in enumerate( |
|---|
| | | zip(data_type_i, data_or_path_or_list_i) |
|---|
| | | ): |
|---|
| | | 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, |
|---|
| | | ) |
|---|
| | | 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 |
|---|
| | | ] |
|---|
| | | if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith( |
|---|
| | | ("http://", "https://") |
|---|
| | | ): # download url to local file |
|---|
| | | 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 data_type is None or data_type == "sound": |
|---|
| | | # if use_ffmpeg: |
|---|
| | | # 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,] |
|---|
| | | # else: |
|---|
| | | # 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) |
|---|
| | | 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 data_type == "text" and tokenizer is not None: |
|---|
| | | data_or_path_or_list = tokenizer.encode(data_or_path_or_list) |
|---|
| | | elif data_type == "image": # undo |
|---|
| | | pass |
|---|
| | | elif data_type == "video": # undo |
|---|
| | | pass |
|---|
| | | |
|---|
| | | # if data_in is a file or url, set is_final=True |
|---|
| | | if "cache" in kwargs: |
|---|
| | | kwargs["cache"]["is_final"] = True |
|---|
| | | kwargs["cache"]["is_streaming_input"] = False |
|---|
| | | elif isinstance(data_or_path_or_list, str) and data_type == "text" and tokenizer is not None: |
|---|
| | | 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): |
|---|
| | | audio_fs, mat = 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 = mat |
|---|
| | | else: |
|---|
| | | pass |
|---|
| | | # print(f"unsupport data type: {data_or_path_or_list}, return raw data") |
|---|
| | | |
|---|
| | | if audio_fs != fs and data_type != "text": |
|---|
| | | resampler = torchaudio.transforms.Resample(audio_fs, fs) |
|---|
| | | 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) |
|---|
| | | middle_data = np.asarray(middle_data) |
|---|
| | | if middle_data.dtype.kind not in 'iu': |
|---|
| | | raise TypeError("'middle_data' must be an array of integers") |
|---|
| | | dtype = np.dtype('float32') |
|---|
| | | if dtype.kind != 'f': |
|---|
| | | raise TypeError("'dtype' must be a floating point type") |
|---|
| | | |
|---|
| | | i = np.iinfo(middle_data.dtype) |
|---|
| | | abs_max = 2 ** (i.bits - 1) |
|---|
| | | offset = i.min + abs_max |
|---|
| | | array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32) |
|---|
| | | return array |
|---|
| | | middle_data = np.frombuffer(input, dtype=np.int16) |
|---|
| | | middle_data = np.asarray(middle_data) |
|---|
| | | if middle_data.dtype.kind not in "iu": |
|---|
| | | raise TypeError("'middle_data' must be an array of integers") |
|---|
| | | dtype = np.dtype("float32") |
|---|
| | | if dtype.kind != "f": |
|---|
| | | raise TypeError("'dtype' must be a floating point type") |
|---|
| | | |
|---|
| | | def extract_fbank(data, data_len = None, data_type: str="sound", frontend=None): |
|---|
| | | # import pdb; |
|---|
| | | # pdb.set_trace() |
|---|
| | | if isinstance(data, np.ndarray): |
|---|
| | | data = torch.from_numpy(data) |
|---|
| | | if len(data.shape) < 2: |
|---|
| | | data = data[None, :] # data: [batch, N] |
|---|
| | | data_len = [data.shape[1]] if data_len is None else data_len |
|---|
| | | elif isinstance(data, torch.Tensor): |
|---|
| | | if len(data.shape) < 2: |
|---|
| | | data = data[None, :] # data: [batch, N] |
|---|
| | | data_len = [data.shape[1]] if data_len is None else data_len |
|---|
| | | elif isinstance(data, (list, tuple)): |
|---|
| | | data_list, data_len = [], [] |
|---|
| | | for data_i in data: |
|---|
| | | if isinstance(data, np.ndarray): |
|---|
| | | data_i = torch.from_numpy(data_i) |
|---|
| | | data_list.append(data_i) |
|---|
| | | data_len.append(data_i.shape[0]) |
|---|
| | | data = pad_sequence(data_list, batch_first=True) # data: [batch, N] |
|---|
| | | # import pdb; |
|---|
| | | # pdb.set_trace() |
|---|
| | | # if data_type == "sound": |
|---|
| | | data, data_len = frontend(data, data_len) |
|---|
| | | |
|---|
| | | if isinstance(data_len, (list, tuple)): |
|---|
| | | data_len = torch.tensor([data_len]) |
|---|
| | | return data.to(torch.float32), data_len.to(torch.int32) |
|---|
| | | i = np.iinfo(middle_data.dtype) |
|---|
| | | abs_max = 2 ** (i.bits - 1) |
|---|
| | | offset = i.min + abs_max |
|---|
| | | array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32) |
|---|
| | | return array |
|---|
| | | |
|---|
| | | def download_from_url(url): |
|---|
| | | |
|---|
| | | result = urlparse(url) |
|---|
| | | file_path = None |
|---|
| | | if result.scheme is not None and len(result.scheme) > 0: |
|---|
| | | storage = HTTPStorage() |
|---|
| | | # bytes |
|---|
| | | data = storage.read(url) |
|---|
| | | work_dir = tempfile.TemporaryDirectory().name |
|---|
| | | if not os.path.exists(work_dir): |
|---|
| | | os.makedirs(work_dir) |
|---|
| | | file_path = os.path.join(work_dir, os.path.basename(url)) |
|---|
| | | with open(file_path, 'wb') as fb: |
|---|
| | | fb.write(data) |
|---|
| | | assert file_path is not None, f"failed to download: {url}" |
|---|
| | | return file_path |
|---|
| | | |
|---|
| | | def extract_fbank(data, data_len=None, data_type: str = "sound", frontend=None, **kwargs): |
|---|
| | | if isinstance(data, np.ndarray): |
|---|
| | | data = torch.from_numpy(data) |
|---|
| | | if len(data.shape) < 2: |
|---|
| | | data = data[None, :] # data: [batch, N] |
|---|
| | | data_len = [data.shape[1]] if data_len is None else data_len |
|---|
| | | elif isinstance(data, torch.Tensor): |
|---|
| | | if len(data.shape) < 2: |
|---|
| | | data = data[None, :] # data: [batch, N] |
|---|
| | | data_len = [data.shape[1]] if data_len is None else data_len |
|---|
| | | elif isinstance(data, (list, tuple)): |
|---|
| | | data_list, data_len = [], [] |
|---|
| | | for data_i in data: |
|---|
| | | if isinstance(data_i, np.ndarray): |
|---|
| | | data_i = torch.from_numpy(data_i) |
|---|
| | | data_list.append(data_i) |
|---|
| | | data_len.append(data_i.shape[0]) |
|---|
| | | data = pad_sequence(data_list, batch_first=True) # data: [batch, N] |
|---|
| | | |
|---|
| | | data, data_len = frontend(data, data_len, **kwargs) |
|---|
| | | |
|---|
| | | if isinstance(data_len, (list, tuple)): |
|---|
| | | data_len = torch.tensor([data_len]) |
|---|
| | | return data.to(torch.float32), data_len.to(torch.int32) |
|---|
| | | |
|---|
| | | |
|---|
| | | def _load_audio_ffmpeg(file: str, sr: int = 16000): |
|---|
| | | """ |
|---|
| | | Open an audio file and read as mono waveform, resampling as necessary |
|---|
| | | |
|---|
| | | Parameters |
|---|
| | | ---------- |
|---|
| | | file: str |
|---|
| | | The audio file to open |
|---|
| | | |
|---|
| | | sr: int |
|---|
| | | The sample rate to resample the audio if necessary |
|---|
| | | |
|---|
| | | Returns |
|---|
| | | ------- |
|---|
| | | A NumPy array containing the audio waveform, in float32 dtype. |
|---|
| | | """ |
|---|
| | | |
|---|
| | | # This launches a subprocess to decode audio while down-mixing |
|---|
| | | # and resampling as necessary. Requires the ffmpeg CLI in PATH. |
|---|
| | | # fmt: off |
|---|
| | | cmd = [ |
|---|
| | | "ffmpeg", |
|---|
| | | "-nostdin", |
|---|
| | | "-threads", "0", |
|---|
| | | "-i", file, |
|---|
| | | "-f", "s16le", |
|---|
| | | "-ac", "1", |
|---|
| | | "-acodec", "pcm_s16le", |
|---|
| | | "-ar", str(sr), |
|---|
| | | "-" |
|---|
| | | ] |
|---|
| | | # fmt: on |
|---|
| | | try: |
|---|
| | | out = run(cmd, capture_output=True, check=True).stdout |
|---|
| | | except CalledProcessError as e: |
|---|
| | | raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e |
|---|
| | | |
|---|
| | | return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 |
|---|
