NaturalSpeech2 / utils /audio_slicer.py
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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import json
import numpy as np
from tqdm import tqdm
import torch
import torchaudio
from utils.io import save_audio
from utils.audio import load_audio_torch
# This function is obtained from librosa.
def get_rms(
y,
*,
frame_length=2048,
hop_length=512,
pad_mode="constant",
):
padding = (int(frame_length // 2), int(frame_length // 2))
y = np.pad(y, padding, mode=pad_mode)
axis = -1
# put our new within-frame axis at the end for now
out_strides = y.strides + tuple([y.strides[axis]])
# Reduce the shape on the framing axis
x_shape_trimmed = list(y.shape)
x_shape_trimmed[axis] -= frame_length - 1
out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
if axis < 0:
target_axis = axis - 1
else:
target_axis = axis + 1
xw = np.moveaxis(xw, -1, target_axis)
# Downsample along the target axis
slices = [slice(None)] * xw.ndim
slices[axis] = slice(0, None, hop_length)
x = xw[tuple(slices)]
# Calculate power
power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
return np.sqrt(power)
class Slicer:
"""
Copy from: https://github.com/openvpi/audio-slicer/blob/main/slicer2.py
"""
def __init__(
self,
sr: int,
threshold: float = -40.0,
min_length: int = 5000,
min_interval: int = 300,
hop_size: int = 10,
max_sil_kept: int = 5000,
):
if not min_length >= min_interval >= hop_size:
raise ValueError(
"The following condition must be satisfied: min_length >= min_interval >= hop_size"
)
if not max_sil_kept >= hop_size:
raise ValueError(
"The following condition must be satisfied: max_sil_kept >= hop_size"
)
min_interval = sr * min_interval / 1000
self.threshold = 10 ** (threshold / 20.0)
self.hop_size = round(sr * hop_size / 1000)
self.win_size = min(round(min_interval), 4 * self.hop_size)
self.min_length = round(sr * min_length / 1000 / self.hop_size)
self.min_interval = round(min_interval / self.hop_size)
self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
def _apply_slice(self, waveform, begin, end):
begin = begin * self.hop_size
if len(waveform.shape) > 1:
end = min(waveform.shape[1], end * self.hop_size)
return waveform[:, begin:end], begin, end
else:
end = min(waveform.shape[0], end * self.hop_size)
return waveform[begin:end], begin, end
# @timeit
def slice(self, waveform, return_chunks_positions=False):
if len(waveform.shape) > 1:
# (#channle, wave_len) -> (wave_len)
samples = waveform.mean(axis=0)
else:
samples = waveform
if samples.shape[0] <= self.min_length:
return [waveform]
rms_list = get_rms(
y=samples, frame_length=self.win_size, hop_length=self.hop_size
).squeeze(0)
sil_tags = []
silence_start = None
clip_start = 0
for i, rms in enumerate(rms_list):
# Keep looping while frame is silent.
if rms < self.threshold:
# Record start of silent frames.
if silence_start is None:
silence_start = i
continue
# Keep looping while frame is not silent and silence start has not been recorded.
if silence_start is None:
continue
# Clear recorded silence start if interval is not enough or clip is too short
is_leading_silence = silence_start == 0 and i > self.max_sil_kept
need_slice_middle = (
i - silence_start >= self.min_interval
and i - clip_start >= self.min_length
)
if not is_leading_silence and not need_slice_middle:
silence_start = None
continue
# Need slicing. Record the range of silent frames to be removed.
if i - silence_start <= self.max_sil_kept:
pos = rms_list[silence_start : i + 1].argmin() + silence_start
if silence_start == 0:
sil_tags.append((0, pos))
else:
sil_tags.append((pos, pos))
clip_start = pos
elif i - silence_start <= self.max_sil_kept * 2:
pos = rms_list[
i - self.max_sil_kept : silence_start + self.max_sil_kept + 1
].argmin()
pos += i - self.max_sil_kept
pos_l = (
rms_list[
silence_start : silence_start + self.max_sil_kept + 1
].argmin()
+ silence_start
)
pos_r = (
rms_list[i - self.max_sil_kept : i + 1].argmin()
+ i
- self.max_sil_kept
)
if silence_start == 0:
sil_tags.append((0, pos_r))
clip_start = pos_r
else:
sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
clip_start = max(pos_r, pos)
else:
pos_l = (
rms_list[
silence_start : silence_start + self.max_sil_kept + 1
].argmin()
+ silence_start
)
pos_r = (
rms_list[i - self.max_sil_kept : i + 1].argmin()
+ i
- self.max_sil_kept
)
if silence_start == 0:
sil_tags.append((0, pos_r))
else:
sil_tags.append((pos_l, pos_r))
clip_start = pos_r
silence_start = None
# Deal with trailing silence.
total_frames = rms_list.shape[0]
if (
silence_start is not None
and total_frames - silence_start >= self.min_interval
):
silence_end = min(total_frames, silence_start + self.max_sil_kept)
pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
sil_tags.append((pos, total_frames + 1))
# Apply and return slices.
if len(sil_tags) == 0:
return [waveform]
else:
chunks = []
chunks_pos_of_waveform = []
if sil_tags[0][0] > 0:
chunk, begin, end = self._apply_slice(waveform, 0, sil_tags[0][0])
chunks.append(chunk)
chunks_pos_of_waveform.append((begin, end))
for i in range(len(sil_tags) - 1):
chunk, begin, end = self._apply_slice(
waveform, sil_tags[i][1], sil_tags[i + 1][0]
)
chunks.append(chunk)
chunks_pos_of_waveform.append((begin, end))
if sil_tags[-1][1] < total_frames:
chunk, begin, end = self._apply_slice(
waveform, sil_tags[-1][1], total_frames
)
chunks.append(chunk)
chunks_pos_of_waveform.append((begin, end))
return (
chunks
if not return_chunks_positions
else (
chunks,
chunks_pos_of_waveform,
)
)
def split_utterances_from_audio(
wav_file,
output_dir,
max_duration_of_utterance=10.0,
min_interval=300,
db_threshold=-40,
):
"""
Split a long audio into utterances accoring to the silence (VAD).
max_duration_of_utterance (second):
The maximum duration of every utterance (seconds)
min_interval (millisecond):
The smaller min_interval is, the more sliced audio clips this script is likely to generate.
"""
print("File:", wav_file.split("/")[-1])
waveform, fs = torchaudio.load(wav_file)
slicer = Slicer(sr=fs, min_interval=min_interval, threshold=db_threshold)
chunks, positions = slicer.slice(waveform, return_chunks_positions=True)
durations = [(end - begin) / fs for begin, end in positions]
print(
"Slicer's min silence part is {}ms, min and max duration of sliced utterances is {}s and {}s".format(
min_interval, min(durations), max(durations)
)
)
res_chunks, res_positions = [], []
for i, chunk in enumerate(chunks):
if len(chunk.shape) == 1:
chunk = chunk[None, :]
begin, end = positions[i]
assert end - begin == chunk.shape[-1]
max_wav_len = max_duration_of_utterance * fs
if chunk.shape[-1] <= max_wav_len:
res_chunks.append(chunk)
res_positions.append(positions[i])
else:
# TODO: to reserve overlapping and conduct fade-in, fade-out
# Get segments number
number = 2
while chunk.shape[-1] // number >= max_wav_len:
number += 1
seg_len = chunk.shape[-1] // number
# Split
for num in range(number):
s = seg_len * num
t = min(s + seg_len, chunk.shape[-1])
seg_begin = begin + s
seg_end = begin + t
res_chunks.append(chunk[:, s:t])
res_positions.append((seg_begin, seg_end))
# Save utterances
os.makedirs(output_dir, exist_ok=True)
res = {"fs": int(fs)}
for i, chunk in enumerate(res_chunks):
filename = "{:04d}.wav".format(i)
res[filename] = [int(p) for p in res_positions[i]]
save_audio(os.path.join(output_dir, filename), chunk, fs)
# Save positions
with open(os.path.join(output_dir, "positions.json"), "w") as f:
json.dump(res, f, indent=4, ensure_ascii=False)
return res
def is_silence(
wavform,
fs,
threshold=-40.0,
min_interval=300,
hop_size=10,
min_length=5000,
):
"""
Detect whether the given wavform is a silence
wavform: (T, )
"""
threshold = 10 ** (threshold / 20.0)
hop_size = round(fs * hop_size / 1000)
win_size = min(round(min_interval), 4 * hop_size)
min_length = round(fs * min_length / 1000 / hop_size)
if wavform.shape[0] <= min_length:
return True
# (#Frame,)
rms_array = get_rms(y=wavform, frame_length=win_size, hop_length=hop_size).squeeze(
0
)
return (rms_array < threshold).all()
def split_audio(
wav_file, target_sr, output_dir, max_duration_of_segment=10.0, overlap_duration=1.0
):
"""
Split a long audio into segments.
target_sr:
The target sampling rate to save the segments.
max_duration_of_utterance (second):
The maximum duration of every utterance (second)
overlap_duraion:
Each segment has "overlap duration" (second) overlap with its previous and next segment
"""
# (#channel, T) -> (T,)
waveform, fs = torchaudio.load(wav_file)
waveform = torchaudio.functional.resample(
waveform, orig_freq=fs, new_freq=target_sr
)
waveform = torch.mean(waveform, dim=0)
# waveform, _ = load_audio_torch(wav_file, target_sr)
assert len(waveform.shape) == 1
assert overlap_duration < max_duration_of_segment
length = int(max_duration_of_segment * target_sr)
stride = int((max_duration_of_segment - overlap_duration) * target_sr)
chunks = []
for i in range(0, len(waveform), stride):
# (length,)
chunks.append(waveform[i : i + length])
if i + length >= len(waveform):
break
# Save segments
os.makedirs(output_dir, exist_ok=True)
results = []
for i, chunk in enumerate(chunks):
uid = "{:04d}".format(i)
filename = os.path.join(output_dir, "{}.wav".format(uid))
results.append(
{"Uid": uid, "Path": filename, "Duration": len(chunk) / target_sr}
)
save_audio(
filename,
chunk,
target_sr,
turn_up=not is_silence(chunk, target_sr),
add_silence=False,
)
return results
def merge_segments_torchaudio(wav_files, fs, output_path, overlap_duration=1.0):
"""Merge the given wav_files (may have overlaps) into a long audio
fs:
The sampling rate of the wav files.
output_path:
The output path to save the merged audio.
overlap_duration (float, optional):
Each segment has "overlap duration" (second) overlap with its previous and next segment. Defaults to 1.0.
"""
waveforms = []
for file in wav_files:
# (T,)
waveform, _ = load_audio_torch(file, fs)
waveforms.append(waveform)
if len(waveforms) == 1:
save_audio(output_path, waveforms[0], fs, add_silence=False, turn_up=False)
return
overlap_len = int(overlap_duration * fs)
fade_out = torchaudio.transforms.Fade(fade_out_len=overlap_len)
fade_in = torchaudio.transforms.Fade(fade_in_len=overlap_len)
fade_in_and_out = torchaudio.transforms.Fade(fade_out_len=overlap_len)
segments_lens = [len(wav) for wav in waveforms]
merged_waveform_len = sum(segments_lens) - overlap_len * (len(waveforms) - 1)
merged_waveform = torch.zeros(merged_waveform_len)
start = 0
for index, wav in enumerate(
tqdm(waveforms, desc="Merge for {}".format(output_path))
):
wav_len = len(wav)
if index == 0:
wav = fade_out(wav)
elif index == len(waveforms) - 1:
wav = fade_in(wav)
else:
wav = fade_in_and_out(wav)
merged_waveform[start : start + wav_len] = wav
start += wav_len - overlap_len
save_audio(output_path, merged_waveform, fs, add_silence=False, turn_up=True)
def merge_segments_encodec(wav_files, fs, output_path, overlap_duration=1.0):
"""Merge the given wav_files (may have overlaps) into a long audio
fs:
The sampling rate of the wav files.
output_path:
The output path to save the merged audio.
overlap_duration (float, optional):
Each segment has "overlap duration" (second) overlap with its previous and next segment. Defaults to 1.0.
"""
waveforms = []
for file in wav_files:
# (T,)
waveform, _ = load_audio_torch(file, fs)
waveforms.append(waveform)
if len(waveforms) == 1:
save_audio(output_path, waveforms[0], fs, add_silence=False, turn_up=False)
return
device = waveforms[0].device
dtype = waveforms[0].dtype
shape = waveforms[0].shape[:-1]
overlap_len = int(overlap_duration * fs)
segments_lens = [len(wav) for wav in waveforms]
merged_waveform_len = sum(segments_lens) - overlap_len * (len(waveforms) - 1)
sum_weight = torch.zeros(merged_waveform_len, device=device, dtype=dtype)
out = torch.zeros(*shape, merged_waveform_len, device=device, dtype=dtype)
offset = 0
for frame in waveforms:
frame_length = frame.size(-1)
t = torch.linspace(0, 1, frame_length + 2, device=device, dtype=torch.float32)[
1:-1
]
weight = 0.5 - (t - 0.5).abs()
weighted_frame = frame * weight
cur = out[..., offset : offset + frame_length]
cur += weighted_frame[..., : cur.size(-1)]
out[..., offset : offset + frame_length] = cur
cur = sum_weight[offset : offset + frame_length]
cur += weight[..., : cur.size(-1)]
sum_weight[offset : offset + frame_length] = cur
offset += frame_length - overlap_len
assert sum_weight.min() > 0
merged_waveform = out / sum_weight
save_audio(output_path, merged_waveform, fs, add_silence=False, turn_up=True)