124 lines
4.0 KiB
Python
124 lines
4.0 KiB
Python
from functools import lru_cache
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from typing import Union
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import ffmpeg
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import numpy as np
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import torch
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import torch.nn.functional as F
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from librosa.filters import mel as librosa_mel_fn
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from .utils import exact_div
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# hard-coded audio hyperparameters
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SAMPLE_RATE = 16000
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N_FFT = 400
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N_MELS = 80
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HOP_LENGTH = 160
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CHUNK_LENGTH = 30
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N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
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N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
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def load_audio(file: str, sr: int = SAMPLE_RATE):
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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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try:
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# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
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# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
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out, _ = (
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ffmpeg.input(file, threads=0)
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.output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
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.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
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)
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except ffmpeg.Error 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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def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
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"""
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Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
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"""
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if torch.is_tensor(array):
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if array.shape[axis] > length:
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array = array.index_select(dim=axis, index=torch.arange(length, device=array.device))
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if array.shape[axis] < length:
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pad_widths = [(0, 0)] * array.ndim
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pad_widths[axis] = (0, length - array.shape[axis])
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array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
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else:
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if array.shape[axis] > length:
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array = array.take(indices=range(length), axis=axis)
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if array.shape[axis] < length:
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pad_widths = [(0, 0)] * array.ndim
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pad_widths[axis] = (0, length - array.shape[axis])
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array = np.pad(array, pad_widths)
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return array
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@lru_cache(maxsize=None)
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def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
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"""
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load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
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Allows decoupling librosa dependency; saved using:
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np.savez_compressed(
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"mel_filters.npz",
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mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
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)
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"""
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assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
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return torch.from_numpy(librosa_mel_fn(sr=SAMPLE_RATE,n_fft=N_FFT,n_mels=n_mels)).to(device)
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def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
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"""
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Compute the log-Mel spectrogram of
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Parameters
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----------
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audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
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The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
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n_mels: int
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The number of Mel-frequency filters, only 80 is supported
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Returns
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-------
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torch.Tensor, shape = (80, n_frames)
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A Tensor that contains the Mel spectrogram
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"""
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if not torch.is_tensor(audio):
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if isinstance(audio, str):
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audio = load_audio(audio)
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audio = torch.from_numpy(audio)
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window = torch.hann_window(N_FFT).to(audio.device)
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stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
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magnitudes = stft[..., :-1].abs() ** 2
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filters = mel_filters(audio.device, n_mels)
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mel_spec = filters @ magnitudes
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log_spec = torch.clamp(mel_spec, min=1e-10).log10()
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log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
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log_spec = (log_spec + 4.0) / 4.0
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return log_spec
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