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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Audio IO methods are defined in this module (info, read, write),
We rely on av library for faster read when possible, otherwise on torchaudio.
"""
from dataclasses import dataclass
from pathlib import Path
import logging
import typing as tp
import numpy as np
import soundfile
import torch
from torch.nn import functional as F
import torchaudio as ta
import av
from .audio_utils import f32_pcm, i16_pcm, normalize_audio
_av_initialized = False
def _init_av():
global _av_initialized
if _av_initialized:
return
logger = logging.getLogger('libav.mp3')
logger.setLevel(logging.ERROR)
_av_initialized = True
@dataclass(frozen=True)
class AudioFileInfo:
sample_rate: int
duration: float
channels: int
def _av_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
_init_av()
with av.open(str(filepath)) as af:
stream = af.streams.audio[0]
sample_rate = stream.codec_context.sample_rate
duration = float(stream.duration * stream.time_base)
channels = stream.channels
return AudioFileInfo(sample_rate, duration, channels)
def _soundfile_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
info = soundfile.info(filepath)
return AudioFileInfo(info.samplerate, info.duration, info.channels)
def audio_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
# torchaudio no longer returns useful duration informations for some formats like mp3s.
filepath = Path(filepath)
if filepath.suffix in ['.flac', '.ogg']: # TODO: Validate .ogg can be safely read with av_info
# ffmpeg has some weird issue with flac.
return _soundfile_info(filepath)
else:
return _av_info(filepath)
def _av_read(filepath: tp.Union[str, Path], seek_time: float = 0, duration: float = -1.) -> tp.Tuple[torch.Tensor, int]:
"""FFMPEG-based audio file reading using PyAV bindings.
Soundfile cannot read mp3 and av_read is more efficient than torchaudio.
Args:
filepath (str or Path): Path to audio file to read.
seek_time (float): Time at which to start reading in the file.
duration (float): Duration to read from the file. If set to -1, the whole file is read.
Returns:
Tuple[torch.Tensor, int]: Tuple containing audio data and sample rate
"""
_init_av()
with av.open(str(filepath)) as af:
stream = af.streams.audio[0]
sr = stream.codec_context.sample_rate
num_frames = int(sr * duration) if duration >= 0 else -1
frame_offset = int(sr * seek_time)
# we need a small negative offset otherwise we get some edge artifact
# from the mp3 decoder.
af.seek(int(max(0, (seek_time - 0.1)) / stream.time_base), stream=stream)
frames = []
length = 0
for frame in af.decode(streams=stream.index):
current_offset = int(frame.rate * frame.pts * frame.time_base)
strip = max(0, frame_offset - current_offset)
buf = torch.from_numpy(frame.to_ndarray())
if buf.shape[0] != stream.channels:
buf = buf.view(-1, stream.channels).t()
buf = buf[:, strip:]
frames.append(buf)
length += buf.shape[1]
if num_frames > 0 and length >= num_frames:
break
assert frames
# If the above assert fails, it is likely because we seeked past the end of file point,
# in which case ffmpeg returns a single frame with only zeros, and a weird timestamp.
# This will need proper debugging, in due time.
wav = torch.cat(frames, dim=1)
assert wav.shape[0] == stream.channels
if num_frames > 0:
wav = wav[:, :num_frames]
return f32_pcm(wav), sr
def audio_read(filepath: tp.Union[str, Path], seek_time: float = 0.,
duration: float = -1., pad: bool = False) -> tp.Tuple[torch.Tensor, int]:
"""Read audio by picking the most appropriate backend tool based on the audio format.
Args:
filepath (str or Path): Path to audio file to read.
seek_time (float): Time at which to start reading in the file.
duration (float): Duration to read from the file. If set to -1, the whole file is read.
pad (bool): Pad output audio if not reaching expected duration.
Returns:
Tuple[torch.Tensor, int]: Tuple containing audio data and sample rate.
"""
fp = Path(filepath)
if fp.suffix in ['.flac', '.ogg']: # TODO: check if we can safely use av_read for .ogg
# There is some bug with ffmpeg and reading flac
info = _soundfile_info(filepath)
frames = -1 if duration <= 0 else int(duration * info.sample_rate)
frame_offset = int(seek_time * info.sample_rate)
wav, sr = soundfile.read(filepath, start=frame_offset, frames=frames, dtype=np.float32)
assert info.sample_rate == sr, f"Mismatch of sample rates {info.sample_rate} {sr}"
wav = torch.from_numpy(wav).t().contiguous()
if len(wav.shape) == 1:
wav = torch.unsqueeze(wav, 0)
elif (
fp.suffix in ['.wav', '.mp3'] and fp.suffix[1:] in ta.utils.sox_utils.list_read_formats()
and duration <= 0 and seek_time == 0
):
# Torchaudio is faster if we load an entire file at once.
wav, sr = ta.load(fp)
else:
wav, sr = _av_read(filepath, seek_time, duration)
if pad and duration > 0:
expected_frames = int(duration * sr)
wav = F.pad(wav, (0, expected_frames - wav.shape[-1]))
return wav, sr
def audio_write(stem_name: tp.Union[str, Path],
wav: torch.Tensor, sample_rate: int,
format: str = 'wav', mp3_rate: int = 320, normalize: bool = True,
strategy: str = 'peak', peak_clip_headroom_db: float = 1,
rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
log_clipping: bool = True, make_parent_dir: bool = True,
add_suffix: bool = True) -> Path:
"""Convenience function for saving audio to disk. Returns the filename the audio was written to.
Args:
stem_name (str or Path): Filename without extension which will be added automatically.
format (str): Either "wav" or "mp3".
mp3_rate (int): kbps when using mp3s.
normalize (bool): if `True` (default), normalizes according to the prescribed
strategy (see after). If `False`, the strategy is only used in case clipping
would happen.
strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak',
i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square
with extra headroom to avoid clipping. 'clip' just clips.
peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy.
rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger
than the `peak_clip` one to avoid further clipping.
loudness_headroom_db (float): Target loudness for loudness normalization.
log_clipping (bool): If True, basic logging on stderr when clipping still
occurs despite strategy (only for 'rms').
make_parent_dir (bool): Make parent directory if it doesn't exist.
Returns:
Path: Path of the saved audio.
"""
assert wav.dtype.is_floating_point, "wav is not floating point"
if wav.dim() == 1:
wav = wav[None]
elif wav.dim() > 2:
raise ValueError("Input wav should be at most 2 dimension.")
assert wav.isfinite().all()
wav = normalize_audio(wav, normalize, strategy, peak_clip_headroom_db,
rms_headroom_db, loudness_headroom_db, log_clipping=log_clipping,
sample_rate=sample_rate, stem_name=str(stem_name))
kwargs: dict = {}
if format == 'mp3':
suffix = '.mp3'
kwargs.update({"compression": mp3_rate})
elif format == 'wav':
wav = i16_pcm(wav)
suffix = '.wav'
kwargs.update({"encoding": "PCM_S", "bits_per_sample": 16})
else:
raise RuntimeError(f"Invalid format {format}. Only wav or mp3 are supported.")
if not add_suffix:
suffix = ''
path = Path(str(stem_name) + suffix)
if make_parent_dir:
path.parent.mkdir(exist_ok=True, parents=True)
try:
ta.save(path, wav, sample_rate, **kwargs)
except Exception:
if path.exists():
# we do not want to leave half written files around.
path.unlink()
raise
return path