Source code for

import numpy as np

from ..base import BaseAudioPreprocessor
from ...proto import array2blob, blob2array

[docs]class VggishPreprocessor(BaseAudioPreprocessor): def __init__(self, num_frames: int = 96, num_bands: int = 64, sample_rate: int = 16000, log_offset: float = 0.01, example_window_seconds: float = 0.96, example_hop_seconds: float = 0.96, stft_window_length_seconds: float = 0.025, stft_hop_length_seconds: float = 0.01, mel_min_hz: int = 125, mel_max_hz: int = 7500, *args, **kwargs): super().__init__(*args, **kwargs) self.num_frames = num_frames self.num_bands = num_bands self.sample_rate = sample_rate self.log_offset = log_offset self.example_window_seconds = example_window_seconds self.example_hop_seconds = example_hop_seconds self.stft_window_length_seconds = stft_window_length_seconds self.stft_hop_length_seconds = stft_hop_length_seconds self.mel_min_hz = mel_min_hz self.mel_max_hz = mel_max_hz self.num_mel_binds = num_bands
[docs] def apply(self, doc: 'gnes_pb2.Document') -> None: super().apply(doc) if doc.raw_bytes: for chunks in doc.chunks: chunks.blob.CopyFrom(array2blob(np.array(self.waveform_to_examples(blob2array(chunks.blob), sample_rate=self.sample_rate), dtype=np.float32))) else: self.logger.error('bad document: "raw_bytes" is empty!')
[docs] def waveform_to_examples(self, data, sample_rate): """Converts audio waveform into an array of examples for VGGish. Args: data: np.array of either one dimension (mono) or two dimensions (multi-channel, with the outer dimension representing channels). Each sample is generally expected to lie in the range [-1.0, +1.0], although this is not required. sample_rate: Sample rate of data. Returns: 3-D np.array of shape [num_examples, num_frames, num_bands] which represents a sequence of examples, each of which contains a patch of log mel spectrogram, covering num_frames frames of audio and num_bands mel frequency bands, where the frame length is vggish_params.STFT_HOP_LENGTH_SECONDS. """ from .vggish_example_helper import mel_features import resampy # Convert to mono. print(type(data)) if len(data.shape) > 1: data = np.mean(data, axis=1) # Resample to the rate assumed by VGGish. if sample_rate != self.sample_rate: data = resampy.resample(data, sample_rate, self.sample_rate) # Compute log mel spectrogram features. log_mel = mel_features.log_mel_spectrogram( data, audio_sample_rate=self.sample_rate, log_offset=self.log_offset, window_length_secs=self.stft_window_length_seconds, hop_length_secs=self.stft_hop_length_seconds, num_mel_bins=self.num_mel_binds, lower_edge_hertz=self.mel_min_hz, upper_edge_hertz=self.mel_max_hz) # Frame features into examples. features_sample_rate = 1.0 / self.stft_hop_length_seconds example_window_length = int(round( self.example_window_seconds * features_sample_rate)) example_hop_length = int(round( self.example_hop_seconds * features_sample_rate)) log_mel_examples = mel_features.frame( log_mel, window_length=example_window_length, hop_length=example_hop_length) return log_mel_examples