wehub-resource-sync
94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
import os.path as op
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from typing import BinaryIO, Optional, Tuple, Union
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import numpy as np
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def get_waveform(
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path_or_fp: Union[str, BinaryIO], normalization=True
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) -> Tuple[np.ndarray, int]:
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"""Get the waveform and sample rate of a 16-bit mono-channel WAV or FLAC.
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Args:
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path_or_fp (str or BinaryIO): the path or file-like object
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normalization (bool): Normalize values to [-1, 1] (Default: True)
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"""
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if isinstance(path_or_fp, str):
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ext = op.splitext(op.basename(path_or_fp))[1]
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if ext not in {".flac", ".wav"}:
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raise ValueError(f"Unsupported audio format: {ext}")
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try:
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import soundfile as sf
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except ImportError:
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raise ImportError("Please install soundfile to load WAV/FLAC file")
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waveform, sample_rate = sf.read(path_or_fp, dtype="float32")
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if not normalization:
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waveform *= 2 ** 15 # denormalized to 16-bit signed integers
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return waveform, sample_rate
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def _get_kaldi_fbank(waveform, sample_rate, n_bins=80) -> Optional[np.ndarray]:
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"""Get mel-filter bank features via PyKaldi."""
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try:
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from kaldi.feat.mel import MelBanksOptions
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from kaldi.feat.fbank import FbankOptions, Fbank
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from kaldi.feat.window import FrameExtractionOptions
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from kaldi.matrix import Vector
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mel_opts = MelBanksOptions()
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mel_opts.num_bins = n_bins
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frame_opts = FrameExtractionOptions()
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frame_opts.samp_freq = sample_rate
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opts = FbankOptions()
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opts.mel_opts = mel_opts
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opts.frame_opts = frame_opts
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fbank = Fbank(opts=opts)
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features = fbank.compute(Vector(waveform), 1.0).numpy()
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return features
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except ImportError:
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return None
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def _get_torchaudio_fbank(waveform, sample_rate, n_bins=80) -> Optional[np.ndarray]:
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"""Get mel-filter bank features via TorchAudio."""
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try:
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import torch
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import torchaudio.compliance.kaldi as ta_kaldi
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import torchaudio.sox_effects as ta_sox
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waveform = torch.from_numpy(waveform)
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if len(waveform.shape) == 1:
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# Mono channel: D -> 1 x D
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waveform = waveform.unsqueeze(0)
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else:
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# Merge multiple channels to one: C x D -> 1 x D
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waveform, _ = ta_sox.apply_effects_tensor(waveform, sample_rate, ['channels', '1'])
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features = ta_kaldi.fbank(
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waveform, num_mel_bins=n_bins, sample_frequency=sample_rate
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)
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return features.numpy()
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except ImportError:
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return None
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def get_fbank(path_or_fp: Union[str, BinaryIO], n_bins=80) -> np.ndarray:
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"""Get mel-filter bank features via PyKaldi or TorchAudio. Prefer PyKaldi
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(faster CPP implementation) to TorchAudio (Python implementation). Note that
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Kaldi/TorchAudio requires 16-bit signed integers as inputs and hence the
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waveform should not be normalized."""
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sound, sample_rate = get_waveform(path_or_fp, normalization=False)
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features = _get_kaldi_fbank(sound, sample_rate, n_bins)
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if features is None:
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features = _get_torchaudio_fbank(sound, sample_rate, n_bins)
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if features is None:
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raise ImportError(
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"Please install pyKaldi or torchaudio to enable "
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"online filterbank feature extraction"
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)
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return features
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