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Modifications to data preparation functions, better duration estimates for non-WAV audio files

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dscripka 2022-09-27 22:54:34 -04:00
parent ea82bc4ae3
commit 37e37722c4
1 changed files with 49 additions and 7 deletions
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56
openwakeword/data.py
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@ -20,12 +20,13 @@ import numpy as np
import torch
from speechbrain.dataio.dataio import read_audio
import torchaudio
import mutagen
# Load audio clips and structure into clips of the same length
def load_audio_clips(files, clip_size=32000):
"""
Loads the specified audio files and shapes them into an array of N by `clip_size`,
Takes the specified audio files and shapes them into an array of N by `clip_size`,
where N is determined by the length of the audio files and `clip_size` at run time.
Clips longer than `clip size` are truncated and extended into the N+1 row.
@ -71,7 +72,7 @@ def load_audio_clips(files, clip_size=32000):
# Dato I/O utils
def filter_wav_paths(target_dirs, min_length, max_length):
def filter_audio_paths(target_dirs, min_length, max_length, duration_method="size"):
"""
Gets the paths of wav files in a flat target directory, automatically filtering
out files below/above the specified length (in seconds). Assumes that all
@ -84,6 +85,10 @@ def filter_wav_paths(target_dirs, min_length, max_length):
target_dir (List[str]): The target directories containing the wav files
min_length_secs (float): The minimum length in seconds (otherwise the clip is skipped)
max_length_secs (float): The maximum length in seconds (otherwise the clip is skipped)
duration_method (str): Whether to use the file size ('size'), or header information ('header')
to estimate the duration of the audio file. 'size' is generally
much faster, but assumes that all files in the target directory
are the same type, sample rate, and bitrate.
Returns:
tuple: A list of strings corresponding to the paths of the wav files that met the length criteria,
@ -92,13 +97,50 @@ def filter_wav_paths(target_dirs, min_length, max_length):
clips = []
for target_dir in target_dirs:
clips_ = [(i.path, get_wav_duration_from_filesize(i.stat().st_size)) for i in os.scandir(target_dir)]
clips_ = [i for i in clips_ if i[1] >= min_length and i[1] <= max_length]
clips.extend(clips_)
return [i[0] for i in clips], [i[1] for i in clips]
file_paths = []
sizes = []
for i in os.scandir(target_dir):
file_paths.append(i.path)
sizes.append(i.stat().st_size)
if duration_method == "size":
durations = estimate_clip_duration(file_paths, sizes)
elif duration_method == "header":
durations = [get_clip_duration(i) for i in file_paths]
return file_paths, durations
def estimate_clip_duration(audio_files: list, sizes: list):
"""Estimates the duration of each audio file in a list.
Assumes that all of the audio files have the same audio format,
bit depth, and sample rate.
Args:
audio_file (str): A list of audio file paths
sizes (int): The size of each audio file in bytes
Returns:
list: A list of durations (in seconds) for the audio files
"""
# Determine file type by checking the first file
details = torchaudio.info(audio_files[0])
# Caculate any correction factors needed from the first file
details = mutagen.File(audio_files[0])
correction = 8*os.path.getsize(audio_files[0]) - details.info.bitrate*details.info.length
# Estimate duration for all remaining clips from file size only
durations = []
for size in sizes:
durations.append((size*8-correction)/details.info.bitrate)
return durations
def get_clip_duration(clip):
"""Gets the duraction of an audio clip in seconds from file header information"""
"""Gets the duration of an audio clip in seconds from file header information"""
metadata = torchaudio.info(clip)
return metadata.num_frames/metadata.sample_rate