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"""
Audio Processor Module
======================
Handles audio loading, preprocessing, and segmentation.
"""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import torchaudio
from torchaudio.transforms import Resample
try:
import librosa
LIBROSA_AVAILABLE = True
except ImportError:
LIBROSA_AVAILABLE = False
@dataclass
class AudioConfig:
"""Configuration for audio processing"""
sample_rate: int = 16000
mono: bool = True
normalize: bool = True
trim_silence: bool = False
silence_threshold_db: float = -40.0
max_duration_seconds: Optional[float] = None
@dataclass
class AudioInfo:
"""Information about loaded audio"""
path: str
duration_seconds: float
sample_rate: int
num_channels: int
num_samples: int
class AudioProcessor:
"""
Handles all audio preprocessing operations.
Converts input audio to standardized format for downstream processing.
Attributes:
config: AudioConfig object with processing settings
Example:
>>> processor = AudioProcessor()
>>> waveform, sr = processor.load_audio("meeting.wav")
>>> print(f"Duration: {processor.get_duration(waveform, sr):.2f}s")
"""
SUPPORTED_FORMATS = {".wav", ".mp3", ".flac", ".ogg", ".m4a", ".wma", ".aac"}
def __init__(self, config: Optional[AudioConfig] = None):
"""
Initialize AudioProcessor.
Args:
config: AudioConfig object (uses defaults if None)
"""
self.config = config or AudioConfig()
self._resampler_cache: dict = {}
def load_audio(
self,
audio_path: Union[str, Path],
start_time: Optional[float] = None,
end_time: Optional[float] = None,
) -> Tuple[torch.Tensor, int]:
"""
Load and preprocess audio file.
Args:
audio_path: Path to audio file
start_time: Start time in seconds (optional)
end_time: End time in seconds (optional)
Returns:
Tuple of (waveform tensor [1, T], sample_rate)
Raises:
FileNotFoundError: If audio file doesn't exist
ValueError: If audio format is not supported
"""
audio_path = Path(audio_path)
# Validate file exists
if not audio_path.exists():
raise FileNotFoundError(f"Audio file not found: {audio_path}")
# Validate format
if audio_path.suffix.lower() not in self.SUPPORTED_FORMATS:
raise ValueError(
f"Unsupported audio format: {audio_path.suffix}. "
f"Supported formats: {self.SUPPORTED_FORMATS}"
)
# Load audio
try:
waveform, orig_sr = torchaudio.load(str(audio_path))
except Exception as e:
# Fallback to librosa if torchaudio fails
if LIBROSA_AVAILABLE:
try:
audio_np, orig_sr = librosa.load(str(audio_path), sr=None, mono=False)
if audio_np.ndim == 1:
audio_np = audio_np[np.newaxis, :]
waveform = torch.from_numpy(audio_np).float()
except Exception:
# Try pydub (requires ffmpeg) as a robust fallback
try:
from pydub import AudioSegment
seg = AudioSegment.from_file(str(audio_path))
orig_sr = seg.frame_rate
samples = np.array(seg.get_array_of_samples())
if seg.channels > 1:
samples = samples.reshape((-1, seg.channels)).T
else:
samples = samples[np.newaxis, :]
# Normalize based on sample width
max_val = float(1 << (8 * seg.sample_width - 1))
audio_np = samples.astype(np.float32) / max_val
waveform = torch.from_numpy(audio_np).float()
except Exception:
# Try ffmpeg CLI (system binary) to decode to WAV in-memory (no extra Python packages required)
try:
import io
import subprocess
import soundfile as sf
proc = subprocess.run(
[
"ffmpeg",
"-i",
str(audio_path),
"-f",
"wav",
"-ar",
"16000",
"-ac",
"1",
"pipe:1",
],
stdout=subprocess.PIPE,
stderr=subprocess.DEVNULL,
check=True,
)
out = proc.stdout
audio_np, orig_sr = sf.read(io.BytesIO(out), dtype="float32")
if audio_np.ndim == 1:
audio_np = audio_np[np.newaxis, :]
else:
audio_np = audio_np.T
waveform = torch.from_numpy(audio_np).float()
except Exception:
# Last resort: use ffmpeg-python to decode into WAV bytes and read via soundfile
try:
import io
import ffmpeg
import soundfile as sf
out, _ = (
ffmpeg.input(str(audio_path))
.output("pipe:", format="wav", acodec="pcm_s16le")
.run(capture_stdout=True, capture_stderr=True)
)
audio_np, orig_sr = sf.read(io.BytesIO(out), dtype="float32")
if audio_np.ndim == 1:
audio_np = audio_np[np.newaxis, :]
else:
audio_np = audio_np.T
waveform = torch.from_numpy(audio_np).float()
except Exception:
raise RuntimeError(
"Format file tidak didukung atau backend decoding (ffmpeg) tidak tersedia. "
"Silakan install ffmpeg (pastikan tersedia di PATH) atau gunakan format WAV/MP3 yang didukung."
)
else:
raise RuntimeError(f"Failed to load audio: {e}")
# Trim to time range if specified
if start_time is not None or end_time is not None:
waveform = self._trim_to_range(waveform, orig_sr, start_time, end_time)
# Convert to mono if needed
if self.config.mono and waveform.shape[0] > 1:
waveform = torch.mean(waveform, dim=0, keepdim=True)
# Resample if needed
if orig_sr != self.config.sample_rate:
waveform = self._resample(waveform, orig_sr, self.config.sample_rate)
# Normalize amplitude
if self.config.normalize:
waveform = self._normalize(waveform)
# Trim silence if requested
if self.config.trim_silence:
waveform = self._trim_silence(waveform)
# Enforce max duration
if self.config.max_duration_seconds:
max_samples = int(self.config.max_duration_seconds * self.config.sample_rate)
if waveform.shape[-1] > max_samples:
waveform = waveform[:, :max_samples]
return waveform, self.config.sample_rate
def get_audio_info(self, audio_path: Union[str, Path]) -> AudioInfo:
"""
Get information about audio file without loading full waveform.
Args:
audio_path: Path to audio file
Returns:
AudioInfo object with file details
"""
audio_path = Path(audio_path)
if not audio_path.exists():
raise FileNotFoundError(f"Audio file not found: {audio_path}")
info = torchaudio.info(str(audio_path))
return AudioInfo(
path=str(audio_path),
duration_seconds=info.num_frames / info.sample_rate,
sample_rate=info.sample_rate,
num_channels=info.num_channels,
num_samples=info.num_frames,
)
def _trim_to_range(
self,
waveform: torch.Tensor,
sample_rate: int,
start_time: Optional[float],
end_time: Optional[float],
) -> torch.Tensor:
"""Trim waveform to specified time range"""
start_sample = int((start_time or 0) * sample_rate)
end_sample = int((end_time or waveform.shape[-1] / sample_rate) * sample_rate)
start_sample = max(0, start_sample)
end_sample = min(waveform.shape[-1], end_sample)
return waveform[:, start_sample:end_sample]
def _resample(self, waveform: torch.Tensor, orig_sr: int, target_sr: int) -> torch.Tensor:
"""Resample audio to target sample rate with caching"""
cache_key = (orig_sr, target_sr)
if cache_key not in self._resampler_cache:
self._resampler_cache[cache_key] = Resample(orig_freq=orig_sr, new_freq=target_sr)
return self._resampler_cache[cache_key](waveform)
def _normalize(self, waveform: torch.Tensor) -> torch.Tensor:
"""Normalize waveform to [-1, 1] range"""
max_val = torch.max(torch.abs(waveform))
if max_val > 0:
waveform = waveform / max_val
return waveform
def _trim_silence(self, waveform: torch.Tensor) -> torch.Tensor:
"""Remove leading and trailing silence"""
# Convert threshold from dB to amplitude
threshold = 10 ** (self.config.silence_threshold_db / 20)
# Find non-silent regions
amplitude = torch.abs(waveform).squeeze()
non_silent = amplitude > threshold
if not non_silent.any():
return waveform
# Find first and last non-silent sample
non_silent_indices = torch.where(non_silent)[0]
start_idx = non_silent_indices[0].item()
end_idx = non_silent_indices[-1].item() + 1
return waveform[:, start_idx:end_idx]
def get_duration(self, waveform: torch.Tensor, sample_rate: int) -> float:
"""Get duration of waveform in seconds"""
return waveform.shape[-1] / sample_rate
def cut_segment(
self, waveform: torch.Tensor, start_sec: float, end_sec: float, sample_rate: int
) -> torch.Tensor:
"""
Extract a segment from waveform.
Args:
waveform: Input waveform [C, T]
start_sec: Start time in seconds
end_sec: End time in seconds
sample_rate: Sample rate of waveform
Returns:
Segment waveform [C, t]
"""
start_sample = int(max(0, start_sec) * sample_rate)
end_sample = int(min(end_sec * sample_rate, waveform.shape[-1]))
return waveform[:, start_sample:end_sample]
def split_into_chunks(
self,
waveform: torch.Tensor,
chunk_duration: float,
overlap: float = 0.0,
sample_rate: Optional[int] = None,
) -> List[Tuple[torch.Tensor, float, float]]:
"""
Split waveform into overlapping chunks.
Args:
waveform: Input waveform
chunk_duration: Duration of each chunk in seconds
overlap: Overlap between chunks in seconds
sample_rate: Sample rate (uses config if None)
Returns:
List of (chunk_waveform, start_sec, end_sec)
"""
sample_rate = sample_rate or self.config.sample_rate
total_duration = self.get_duration(waveform, sample_rate)
chunks = []
start = 0.0
while start < total_duration:
end = min(start + chunk_duration, total_duration)
chunk = self.cut_segment(waveform, start, end, sample_rate)
chunks.append((chunk, start, end))
start += chunk_duration - overlap
return chunks
def add_noise(
self, waveform: torch.Tensor, noise_level: float = 0.01, noise_type: str = "gaussian"
) -> torch.Tensor:
"""
Add noise to waveform (for data augmentation).
Args:
waveform: Input waveform
noise_level: Noise amplitude (0-1)
noise_type: Type of noise ("gaussian", "uniform")
Returns:
Waveform with added noise
"""
if noise_type == "gaussian":
noise = torch.randn_like(waveform) * noise_level
elif noise_type == "uniform":
noise = (torch.rand_like(waveform) * 2 - 1) * noise_level
else:
raise ValueError(f"Unknown noise type: {noise_type}")
return waveform + noise
def save_audio(
self,
waveform: torch.Tensor,
output_path: Union[str, Path],
sample_rate: Optional[int] = None,
):
"""
Save waveform to audio file.
Args:
waveform: Waveform to save
output_path: Output file path
sample_rate: Sample rate (uses config if None)
"""
sample_rate = sample_rate or self.config.sample_rate
torchaudio.save(str(output_path), waveform, sample_rate)
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