funcineforge/utils/load_utils.py

import os
import torch
import numpy as np
import kaldiio
import librosa
import torchaudio
import torchaudio.compliance.kaldi as Kaldi
from torch.nn.utils.rnn import pad_sequence
import onnxruntime as ort

try:
    from funcineforge.download.file import download_from_url
except:
    print("urllib is not installed, if you infer from url, please install it first.")
import subprocess
from subprocess import CalledProcessError, run


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


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"
    ):  # 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 in ["sound", "kaldi_ark_or_sound"]:
            if kwargs.get("use_ffmpeg", False):
                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:
                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 in ["kaldi_ark", "kaldi_ark_or_sound", "sound"]:
        if len(data_or_path_or_list.split()) == 2:
            data_or_path_or_list, audio_fs = data_or_path_or_list.split()
            audio_fs = int(audio_fs)
        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":
            mat = mat.astype(np.float32)
            mat = mat / (2 ** 16)
        elif mat.dtype == "int32":
            mat = mat.astype(np.float32)
            mat = mat / (2 ** 32)
        if mat.ndim == 2:
            mat = mat[:, 0]
        data_or_path_or_list = torch.from_numpy(mat)
    elif isinstance(data_or_path_or_list, bytes):  # audio bytes
        data_or_path_or_list = load_bytes(data_or_path_or_list)
    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, dtype=data_or_path_or_list.dtype)
        data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :]
    return data_or_path_or_list


class FBank(object):
    def __init__(self,
                 n_mels,
                 sample_rate,
                 mean_nor: bool = False,
                 ):
        self.n_mels = n_mels
        self.sample_rate = sample_rate
        self.mean_nor = mean_nor

    def __call__(self, wav, dither=0):
        sr = 16000
        assert sr == self.sample_rate
        if len(wav.shape) == 1:
            wav = wav.unsqueeze(0)
        if wav.shape[0] > 1:
            wav = torch.mean(wav, dim=0, keepdim=True)
        assert len(wav.shape) == 2 and wav.shape[0] == 1, wav.shape
        feat = Kaldi.fbank(wav, num_mel_bins=self.n_mels,
                           sample_frequency=sr, dither=dither)
        # feat: [T, N]
        if self.mean_nor:
            feat = feat - feat.mean(0, keepdim=True)
        return feat

class OnnxModel(object):
    def __init__(self, pretrained_model):
        session_options = ort.SessionOptions()
        self.model = ort.InferenceSession(pretrained_model, session_options)
        self.input_name = self.model.get_inputs()[0].name
        self.output_name = self.model.get_outputs()[0].name
        self.feature_extractor = FBank(n_mels=80, sample_rate=16000, mean_nor=True)

    def __call__(self, wav):
        feat = self.feature_extractor(torch.as_tensor(wav))
        feat = feat.float().unsqueeze(0).numpy()
        emb = self.model.run([self.output_name], {self.input_name: feat})[0]
        return emb

def extract_campp_xvec(
    wav_path: str = "",
    target_sr: int = 16000,
    **kwargs,
):
    wav, sr = librosa.load(wav_path, dtype=np.float32, sr=target_sr, mono=True)
    if sr != target_sr:
        wav = librosa.resample(wav, orig_sr=sr, target_sr=target_sr)
    onnx_path = kwargs.get("xvec_model", None)
    model = OnnxModel(onnx_path)
    xvec = model(wav)
    return xvec
    
    

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