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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 |
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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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waveform = waveform.unsqueeze(0) |
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else: |
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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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