mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
174 lines
7.0 KiB
Python
174 lines
7.0 KiB
Python
import os
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import torch
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import json
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import torch.distributed as dist
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import numpy as np
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import kaldiio
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import librosa
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import torchaudio
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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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try:
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from funasr.download.file import download_from_url
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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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import pdb
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import subprocess
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from subprocess import CalledProcessError, run
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def is_ffmpeg_installed():
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try:
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# 尝试运行ffmpeg命令并获取其版本信息
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output = subprocess.check_output(['ffmpeg', '-version'], stderr=subprocess.STDOUT)
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return 'ffmpeg version' in output.decode('utf-8')
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except (subprocess.CalledProcessError, FileNotFoundError):
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# 若运行ffmpeg命令失败，则认为ffmpeg未安装
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return False
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use_ffmpeg=False
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if is_ffmpeg_installed():
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use_ffmpeg
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else:
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print("Warning: ffmpeg is not installed. torchaudio is used to load audio")
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def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs: int = 16000, data_type="sound", tokenizer=None, **kwargs):
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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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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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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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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)
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data_or_path_or_list_ret[j].append(data_or_path_or_list_j)
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return data_or_path_or_list_ret
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else:
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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]
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if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith('http'): # download url to local file
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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): # local file
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if data_type is None or data_type == "sound":
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if use_ffmpeg:
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data_or_path_or_list = _load_audio_ffmpeg(data_or_path_or_list, sr=fs)
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data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze() # [n_samples,]
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else:
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data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list)
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if kwargs.get("reduce_channels", True):
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data_or_path_or_list = data_or_path_or_list.mean(0)
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elif 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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elif data_type == "image": # undo
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pass
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elif data_type == "video": # undo
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pass
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# if data_in is a file or url, set is_final=True
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if "cache" in kwargs:
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kwargs["cache"]["is_final"] = True
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kwargs["cache"]["is_streaming_input"] = False
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elif isinstance(data_or_path_or_list, str) 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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elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point
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data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze() # [n_samples,]
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elif isinstance(data_or_path_or_list, str) and data_type == "kaldi_ark":
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data_mat = kaldiio.load_mat(data_or_path_or_list)
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if isinstance(data_mat, tuple):
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audio_fs, mat = data_mat
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else:
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mat = data_mat
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if mat.dtype == 'int16' or mat.dtype == 'int32':
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mat = mat.astype(np.float64)
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mat = mat / 32768
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if mat.ndim ==2:
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mat = mat[:,0]
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data_or_path_or_list = mat
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else:
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pass
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# print(f"unsupport data type: {data_or_path_or_list}, return raw data")
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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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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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def load_bytes(input):
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middle_data = np.frombuffer(input, dtype=np.int16)
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middle_data = np.asarray(middle_data)
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if middle_data.dtype.kind not in 'iu':
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raise TypeError("'middle_data' must be an array of integers")
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dtype = np.dtype('float32')
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if dtype.kind != 'f':
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raise TypeError("'dtype' must be a floating point type")
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i = np.iinfo(middle_data.dtype)
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abs_max = 2 ** (i.bits - 1)
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offset = i.min + abs_max
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array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
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return array
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def extract_fbank(data, data_len = None, data_type: str="sound", frontend=None, **kwargs):
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if isinstance(data, np.ndarray):
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data = torch.from_numpy(data)
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if len(data.shape) < 2:
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data = data[None, :] # data: [batch, N]
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data_len = [data.shape[1]] if data_len is None else data_len
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elif isinstance(data, torch.Tensor):
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if len(data.shape) < 2:
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data = data[None, :] # data: [batch, N]
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data_len = [data.shape[1]] if data_len is None else data_len
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elif isinstance(data, (list, tuple)):
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data_list, data_len = [], []
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for data_i in data:
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if isinstance(data_i, np.ndarray):
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data_i = torch.from_numpy(data_i)
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data_list.append(data_i)
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data_len.append(data_i.shape[0])
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data = pad_sequence(data_list, batch_first=True) # data: [batch, N]
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data, data_len = frontend(data, data_len, **kwargs)
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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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def _load_audio_ffmpeg(file: str, sr: int = 16000):
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"""
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Open an audio file and read as mono waveform, resampling as necessary
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Parameters
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----------
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file: str
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The audio file to open
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sr: int
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The sample rate to resample the audio if necessary
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Returns
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-------
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A NumPy array containing the audio waveform, in float32 dtype.
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"""
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# This launches a subprocess to decode audio while down-mixing
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# and resampling as necessary. Requires the ffmpeg CLI in PATH.
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# fmt: off
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cmd = [
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"ffmpeg",
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"-nostdin",
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"-threads", "0",
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"-i", file,
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"-f", "s16le",
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"-ac", "1",
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"-acodec", "pcm_s16le",
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"-ar", str(sr),
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"-"
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]
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# fmt: on
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try:
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out = run(cmd, capture_output=True, check=True).stdout
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except CalledProcessError as e:
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raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
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return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
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