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# Copyright 2026 Xiaomi Corp. (authors: Han Zhu)
#
# See ../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Audio I/O and processing utilities.
Provides functions for loading, resampling, silence removal,
chunking, cross-fading, and format conversion.
All public functions in this module operate on **numpy float32 arrays**
with shape ``(C, T)`` (channels-first).
"""
import io
import logging
import numpy as np
import soundfile as sf
import torch
import torchaudio
from pydub import AudioSegment
from pydub.silence import detect_leading_silence, detect_nonsilent, split_on_silence
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Loading
# ---------------------------------------------------------------------------
def load_waveform(audio_path: str):
"""Load audio from a file path, returning (data, sample_rate).
Tries two backends in order:
1. soundfile — covers WAV/FLAC/OGG etc., no ffmpeg needed.
2. librosa — covers MP3/M4A etc. via audioread + ffmpeg.
Returns:
(data, sample_rate) where data is a numpy float32 array of
shape (C, T).
"""
try:
data, sr = sf.read(audio_path, dtype="float32", always_2d=True)
return data.T, sr # (T, C) → (C, T)
except Exception:
# soundfile cannot handle MP3/M4A etc., fall back to librosa.
import librosa
data, sr = librosa.load(audio_path, sr=None, mono=False)
if data.ndim == 1:
data = data[np.newaxis, :]
return data, sr
def load_audio(audio_path: str, sampling_rate: int) -> np.ndarray:
"""Load a waveform from file and resample to the target rate.
Parameters:
audio_path: path of the audio.
sampling_rate: target sampling rate.
Returns:
Numpy float32 array of shape (1, T).
"""
data, sr = load_waveform(audio_path)
if data.shape[0] > 1:
data = np.mean(data, axis=0, keepdims=True)
if sr != sampling_rate:
data = torchaudio.functional.resample(
torch.from_numpy(data), orig_freq=sr, new_freq=sampling_rate
).numpy()
return data
def load_audio_bytes(raw: bytes, sampling_rate: int) -> np.ndarray:
"""Load audio from in-memory bytes and resample.
Parameters:
raw: raw audio file bytes (e.g. from WebDataset).
sampling_rate: target sampling rate.
Returns:
Numpy float32 array of shape (1, T).
"""
buf = io.BytesIO(raw)
try:
data, sr = sf.read(buf, dtype="float32", always_2d=True)
data = data.T # (T, C) → (C, T)
except Exception:
import librosa
buf.seek(0)
data, sr = librosa.load(buf, sr=None, mono=False)
if data.ndim == 1:
data = data[np.newaxis, :]
if data.shape[0] > 1:
data = np.mean(data, axis=0, keepdims=True)
if sr != sampling_rate:
data = torchaudio.functional.resample(
torch.from_numpy(data), orig_freq=sr, new_freq=sampling_rate
).numpy()
return data
# ---------------------------------------------------------------------------
# Audio processing (all numpy in / numpy out)
# ---------------------------------------------------------------------------
def numpy_to_audiosegment(audio: np.ndarray, sample_rate: int) -> AudioSegment:
"""Convert a numpy float32 array of shape (C, T) to a pydub AudioSegment."""
audio_int = (audio * 32768.0).clip(-32768, 32767).astype(np.int16)
if audio_int.shape[0] > 1:
audio_int = audio_int.T.flatten() # interleave channels
return AudioSegment(
data=audio_int.tobytes(),
sample_width=2,
frame_rate=sample_rate,
channels=audio.shape[0],
)
def audiosegment_to_numpy(aseg: AudioSegment) -> np.ndarray:
"""Convert a pydub AudioSegment to a numpy float32 array of shape (C, T)."""
data = np.array(aseg.get_array_of_samples()).astype(np.float32) / 32768.0
if aseg.channels == 1:
return data[np.newaxis, :]
return data.reshape(-1, aseg.channels).T
def remove_silence(
audio: np.ndarray,
sampling_rate: int,
mid_sil: int = 300,
lead_sil: int = 100,
trail_sil: int = 300,
) -> np.ndarray:
"""Remove middle silences longer than *mid_sil* ms and trim edge silences.
Parameters:
audio: numpy array with shape (C, T).
sampling_rate: sampling rate of the audio.
mid_sil: middle-silence threshold in ms (0 to skip).
lead_sil: kept leading silence in ms.
trail_sil: kept trailing silence in ms.
Returns:
Numpy array with shape (C, T').
"""
wave = numpy_to_audiosegment(audio, sampling_rate)
if mid_sil > 0:
non_silent_segs = split_on_silence(
wave,
min_silence_len=mid_sil,
silence_thresh=-50,
keep_silence=mid_sil,
seek_step=10,
)
wave = AudioSegment.silent(duration=0)
for seg in non_silent_segs:
wave += seg
wave = remove_silence_edges(wave, lead_sil, trail_sil, -50)
return audiosegment_to_numpy(wave)
def remove_silence_edges(
audio: AudioSegment,
lead_sil: int = 100,
trail_sil: int = 300,
silence_threshold: float = -50,
) -> AudioSegment:
"""Remove edge silences, keeping *lead_sil* / *trail_sil* ms."""
start_idx = detect_leading_silence(audio, silence_threshold=silence_threshold)
start_idx = max(0, start_idx - lead_sil)
audio = audio[start_idx:]
audio = audio.reverse()
start_idx = detect_leading_silence(audio, silence_threshold=silence_threshold)
start_idx = max(0, start_idx - trail_sil)
audio = audio[start_idx:]
audio = audio.reverse()
return audio
def fade_and_pad_audio(
audio: np.ndarray,
pad_duration: float = 0.1,
fade_duration: float = 0.1,
sample_rate: int = 24000,
) -> np.ndarray:
"""Apply fade-in/out and pad with silence to prevent clicks.
Args:
audio: numpy array of shape (C, T).
pad_duration: silence padding duration per side (seconds).
fade_duration: fade curve duration (seconds).
sample_rate: audio sampling rate.
Returns:
Processed numpy array of shape (C, T_new).
"""
if audio.shape[-1] == 0:
return audio
fade_samples = int(fade_duration * sample_rate)
pad_samples = int(pad_duration * sample_rate)
processed = audio.copy()
if fade_samples > 0:
k = min(fade_samples, processed.shape[-1] // 2)
if k > 0:
fade_in = np.linspace(0, 1, k, dtype=np.float32)[np.newaxis, :]
processed[..., :k] *= fade_in
fade_out = np.linspace(1, 0, k, dtype=np.float32)[np.newaxis, :]
processed[..., -k:] *= fade_out
if pad_samples > 0:
silence = np.zeros(
(processed.shape[0], pad_samples),
dtype=processed.dtype,
)
processed = np.concatenate([silence, processed, silence], axis=-1)
return processed
def trim_long_audio(
audio: np.ndarray,
sampling_rate: int,
max_duration: float = 15.0,
min_duration: float = 3.0,
trim_threshold: float = 20.0,
) -> np.ndarray:
"""Trim audio to <= *max_duration* by splitting at the largest silence gap.
Only trims when the audio exceeds *trim_threshold* seconds.
Args:
audio: numpy array of shape (C, T).
sampling_rate: audio sampling rate.
max_duration: maximum duration in seconds.
min_duration: minimum duration in seconds.
trim_threshold: only trim if audio is longer than this (seconds).
Returns:
Trimmed numpy array.
"""
duration = audio.shape[-1] / sampling_rate
if duration <= trim_threshold:
return audio
seg = numpy_to_audiosegment(audio, sampling_rate)
nonsilent = detect_nonsilent(
seg, min_silence_len=100, silence_thresh=-40, seek_step=10
)
if not nonsilent:
return audio
max_ms = int(max_duration * 1000)
min_ms = int(min_duration * 1000)
best_split = 0
for start, end in nonsilent:
if start > best_split and start <= max_ms:
best_split = start
if end > max_ms:
break
if best_split < min_ms:
best_split = min(max_ms, len(seg))
trimmed = seg[:best_split]
return audiosegment_to_numpy(trimmed)
def cross_fade_chunks(
chunks: list[np.ndarray],
sample_rate: int,
silence_duration: float = 0.3,
) -> np.ndarray:
"""Concatenate audio chunks with silence gaps and cross-fade at boundaries.
Args:
chunks: list of numpy arrays, each (C, T).
sample_rate: audio sample rate.
silence_duration: total silence gap duration in seconds.
Returns:
Merged numpy array (C, T_total).
"""
if len(chunks) == 1:
return chunks[0]
total_n = int(silence_duration * sample_rate)
fade_n = total_n // 3
silence_n = fade_n
merged = chunks[0].copy()
for chunk in chunks[1:]:
parts = [merged]
fout_n = min(fade_n, merged.shape[-1])
if fout_n > 0:
w_out = np.linspace(1, 0, fout_n, dtype=np.float32)[np.newaxis, :]
parts[-1][..., -fout_n:] *= w_out
parts.append(np.zeros((chunks[0].shape[0], silence_n), dtype=np.float32))
fade_in = chunk.copy()
fin_n = min(fade_n, fade_in.shape[-1])
if fin_n > 0:
w_in = np.linspace(0, 1, fin_n, dtype=np.float32)[np.newaxis, :]
fade_in[..., :fin_n] *= w_in
parts.append(fade_in)
merged = np.concatenate(parts, axis=-1)
return merged
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