import os import torch import json import torch.distributed as dist import numpy as np import kaldiio import librosa import torchaudio import time import logging from torch.nn.utils.rnn import pad_sequence try: from funasr.download.file import download_from_url except: print("urllib is not installed, if you infer from url, please install it first.") import pdb import subprocess from subprocess import CalledProcessError, run def is_ffmpeg_installed(): try: # 尝试运行ffmpeg命令并获取其版本信息 output = subprocess.check_output(['ffmpeg', '-version'], stderr=subprocess.STDOUT) return 'ffmpeg version' in output.decode('utf-8') except (subprocess.CalledProcessError, FileNotFoundError): # 若运行ffmpeg命令失败，则认为ffmpeg未安装 return False 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 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'): # 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) 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 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