wehub-resource-sync
365 lines
13 KiB
Python
365 lines
13 KiB
Python
import os
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import torch
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import json
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from io import BytesIO
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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 subprocess
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from subprocess import CalledProcessError, run
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try:
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from pydub import AudioSegment
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except:
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pass
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def is_ffmpeg_installed():
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"""Is ffmpeg installed."""
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try:
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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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return False
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use_ffmpeg = False
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if is_ffmpeg_installed():
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use_ffmpeg = True
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else:
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print(
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"Notice: ffmpeg is not installed. torchaudio is used to load audio\n"
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"If you want to use ffmpeg backend to load audio, please install it by:"
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"\n\tsudo apt install ffmpeg # ubuntu"
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"\n\t# brew install ffmpeg # mac"
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)
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def load_audio_text_image_video(
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data_or_path_or_list,
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fs: int = 16000,
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audio_fs: int = 16000,
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data_type="sound",
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tokenizer=None,
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**kwargs,
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):
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"""Load audio/text/image/video data from various input formats.
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Args:
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data_or_path_or_list: File path, URL, numpy array, torch Tensor, bytes, or list.
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fs (int): Target sample rate (default 16000).
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audio_fs (int): Source audio sample rate.
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data_type (str): Input type ("sound", "text", "fbank").
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Returns:
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torch.Tensor or list: Loaded and resampled audio tensor(s).
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"""
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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(
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zip(data_types, data_or_path_or_list)
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):
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for j, (data_type_j, data_or_path_or_list_j) in enumerate(
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zip(data_type_i, data_or_path_or_list_i)
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):
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data_or_path_or_list_j = load_audio_text_image_video(
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data_or_path_or_list_j,
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fs=fs,
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audio_fs=audio_fs,
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data_type=data_type_j,
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tokenizer=tokenizer,
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**kwargs,
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)
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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 [
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load_audio_text_image_video(
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audio, fs=fs, audio_fs=audio_fs, data_type=data_type, **kwargs
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)
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for audio in data_or_path_or_list
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]
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if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith(
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("http://", "https://")
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): # 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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# Fail fast with a clear error if an audio file path does not exist, instead of
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# silently passing the string downstream (which later crashes with a cryptic
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# "expected Tensor ... but got str" deep inside the model).
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if (
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isinstance(data_or_path_or_list, str)
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and data_type in (None, "sound")
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and not data_or_path_or_list.startswith(("http://", "https://"))
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and not os.path.exists(data_or_path_or_list)
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):
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raise FileNotFoundError(
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f"Audio file not found: {data_or_path_or_list!r}. Pass a valid local file "
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f"path, URL, numpy array, torch.Tensor, or bytes."
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)
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if (isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list)) or hasattr(data_or_path_or_list, 'read'): # local file or bytes io
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if data_type is None or data_type == "sound":
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if hasattr(data_or_path_or_list, "read") and hasattr(data_or_path_or_list, "seek"):
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data_or_path_or_list.seek(0)
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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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try:
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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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except:
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try:
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import soundfile as sf
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data_np, audio_fs = sf.read(data_or_path_or_list, dtype="float32")
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data_or_path_or_list = torch.from_numpy(data_np).squeeze()
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if data_or_path_or_list.ndim > 1 and kwargs.get("reduce_channels", True):
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data_or_path_or_list = data_or_path_or_list.mean(-1)
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except:
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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(
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data_or_path_or_list
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).squeeze() # [n_samples,]
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elif data_type == "text" and tokenizer is not None:
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with open(data_or_path_or_list, "r") as f:
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data_or_path_or_list = tokenizer.encode(f.read().strip())
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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 _is_audio_container(data: bytes) -> bool:
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"""Return True if *data* starts with a recognised container-format magic header.
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Raw PCM byte streams have no header, so they will return False and the
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expensive pydub/ffmpeg validation round-trip can be skipped entirely.
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"""
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if len(data) < 4:
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return False
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# WAV – RIFF....WAVE
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if data[:4] == b"RIFF":
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return True
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# MP3 – ID3 tag or sync word (0xFF 0xEx)
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if data[:3] == b"ID3" or (data[0] == 0xFF and (data[1] & 0xE0) == 0xE0):
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return True
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# OGG
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if data[:4] == b"OggS":
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return True
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# FLAC
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if data[:4] == b"fLaC":
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return True
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# MP4 / M4A / AAC – 'ftyp' box at offset 4
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if len(data) >= 8 and data[4:8] == b"ftyp":
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return True
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# WebM / MKV
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if data[:4] == b"\x1a\x45\xdf\xa3":
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return True
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return False
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def load_bytes(input):
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"""Convert audio bytes to numpy array.
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Args:
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input (bytes): Raw audio bytes.
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Returns:
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numpy.ndarray: Decoded audio samples.
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"""
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# Only run the (expensive) frame-rate validation when the payload is an
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# actual audio container (WAV, MP3, OGG, …). Raw PCM buffers have no
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# recognisable header and would cause pydub to spend ~200 ms before
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# raising an exception that is then silently swallowed anyway.
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if _is_audio_container(input):
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try:
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input = validate_frame_rate(input)
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except Exception:
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pass
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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 validate_frame_rate(
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input,
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fs: int = 16000,
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):
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# 将文件读取为字节流
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"""Validate frame rate.
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Args:
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input: Input audio/text data.
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fs: TODO.
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"""
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byte_data = BytesIO(input)
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# 使用 pydub 加载音频
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try:
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audio = AudioSegment.from_file(byte_data)
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except:
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raise RuntimeError(
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"You are decoding the pcm data, please install pydub first. via `pip install pydub`."
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)
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# 确保采样率为 16000 Hz
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if audio.frame_rate != fs:
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audio = audio.set_frame_rate(fs)
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# 将重新采样后的音频导出为字节流
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output = BytesIO()
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audio.export(output, format="wav")
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output.seek(0)
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# 获取重新采样后的字节流数据
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input = output.read()
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return input
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def extract_fbank(data, data_len=None, data_type: str = "sound", frontend=None, **kwargs):
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"""Extract filter-bank features from audio data.
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Args:
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data: Audio samples (list of numpy arrays or tensors).
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data_len: Lengths of each sample.
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data_type (str): Input type ("sound", "fbank").
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frontend: Frontend instance for feature extraction.
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Returns:
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tuple: (features_tensor, feature_lengths, feature_times)
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"""
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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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elif data.shape[0] > 1:
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data = data.mean(dim=0, keepdim=True) # convert stereo/multi-channel to mono
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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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elif data.shape[0] > 1:
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data = data.mean(dim=0, keepdim=True) # convert stereo/multi-channel to mono
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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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pcm_params = []
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if file.lower().endswith('.pcm'):
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pcm_params = [
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"-f", "s16le",
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"-ar", str(sr),
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"-ac", "1"
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]
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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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*pcm_params, # PCM files need input format specified before -i since PCM is raw data without headers
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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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