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added licensing info

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zr_jin 2024-10-21 10:55:27 +08:00
parent 283157268a
commit d752230287
18 changed files with 88 additions and 29 deletions
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23
egs/libritts/CODEC/encodec/base_discriminators.py
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@ -1,3 +1,21 @@
#!/usr/bin/env python3
# Copyright 2024 The Chinese University of HK (Author: Zengrui Jin)
#
# 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.
from typing import List, Tuple
import torch
@ -222,17 +240,12 @@ class DiscriminatorSTFT(nn.Module):
def forward(self, x: torch.Tensor):
fmap = []
# print('x ', x.shape)
z = self.spec_transform(x) # [B, 2, Freq, Frames, 2]
# print('z ', z.shape)
z = torch.cat([z.real, z.imag], dim=1)
# print('cat_z ', z.shape)
z = rearrange(z, "b c w t -> b c t w")
for i, layer in enumerate(self.convs):
z = layer(z)
z = self.activation(z)
# print('z i', i, z.shape)
fmap.append(z)
z = self.conv_post(z)
# print('logit ', z.shape)
return z, fmap

4
egs/libritts/CODEC/encodec/binary.py
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@ -2,7 +2,7 @@
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# LICENSE file at https://github.com/facebookresearch/encodec/blob/main/LICENSE
"""Raw binary format for Encodec compressed audio. Actual compression API is in `encodec.compress`."""
import io
@ -132,7 +132,7 @@ def test():
for rep in range(4):
length: int = torch.randint(10, 2_000, (1,)).item()
bits: int = torch.randint(1, 16, (1,)).item()
tokens: List[int] = torch.randint(2**bits, (length,)).tolist()
tokens: List[int] = torch.randint(2 ** bits, (length,)).tolist()
rebuilt: List[int] = []
buf = io.BytesIO()
packer = BitPacker(bits, buf)

6
egs/libritts/CODEC/encodec/discriminators.py
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@ -1,3 +1,9 @@
# 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.
from typing import List
import torch

17
egs/libritts/CODEC/encodec/encodec.py
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@ -1,3 +1,20 @@
#!/usr/bin/env python3
# Copyright 2024 The Chinese University of HK (Author: Zengrui Jin)
#
# 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.
import math
import random
from typing import List, Optional

2
egs/libritts/CODEC/encodec/loss.py
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@ -235,7 +235,7 @@ class MelSpectrogramReconstructionLoss(torch.nn.Module):
super().__init__()
self.wav_to_specs = []
for i in range(5, 12):
s = 2**i
s = 2 ** i
self.wav_to_specs.append(
MelSpectrogram(
sample_rate=sampling_rate,

2
egs/libritts/CODEC/encodec/modules/__init__.py
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@ -2,7 +2,7 @@
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# LICENSE file at https://github.com/facebookresearch/encodec/blob/main/LICENSE
"""Torch modules."""
# flake8: noqa
from .conv import (

2
egs/libritts/CODEC/encodec/modules/conv.py
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@ -2,7 +2,7 @@
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# LICENSE file at https://github.com/facebookresearch/encodec/blob/main/LICENSE
"""Convolutional layers wrappers and utilities."""
import logging
import math

2
egs/libritts/CODEC/encodec/modules/lstm.py
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@ -2,7 +2,7 @@
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# LICENSE file at https://github.com/facebookresearch/encodec/blob/main/LICENSE
"""LSTM layers module."""
from torch import nn

2
egs/libritts/CODEC/encodec/modules/norm.py
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@ -2,7 +2,7 @@
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# LICENSE file at https://github.com/facebookresearch/encodec/blob/main/LICENSE
"""Normalization modules."""
from typing import List, Union

6
egs/libritts/CODEC/encodec/modules/seanet.py
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@ -2,7 +2,7 @@
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# LICENSE file at https://github.com/facebookresearch/encodec/blob/main/LICENSE
"""Encodec SEANet-based encoder and decoder implementation."""
from typing import Any, Dict, List, Optional
@ -161,7 +161,7 @@ class SEANetEncoder(nn.Module):
SEANetResnetBlock(
mult * n_filters,
kernel_sizes=[residual_kernel_size, 1],
dilations=[dilation_base**j, 1],
dilations=[dilation_base ** j, 1],
norm=norm,
norm_params=norm_params,
activation=activation,
@ -311,7 +311,7 @@ class SEANetDecoder(nn.Module):
SEANetResnetBlock(
mult * n_filters // 2,
kernel_sizes=[residual_kernel_size, 1],
dilations=[dilation_base**j, 1],
dilations=[dilation_base ** j, 1],
activation=activation,
activation_params=activation_params,
norm=norm,

2
egs/libritts/CODEC/encodec/modules/transformer.py
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@ -2,7 +2,7 @@
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# LICENSE file at https://github.com/facebookresearch/encodec/blob/main/LICENSE
"""A streamable transformer."""
import typing as tp
from typing import Any, List, Optional, Union

2
egs/libritts/CODEC/encodec/quantization/__init__.py
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@ -2,6 +2,6 @@
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# LICENSE file at https://github.com/facebookresearch/encodec/blob/main/LICENSE
# flake8: noqa
from .vq import QuantizedResult, ResidualVectorQuantizer

18
egs/libritts/CODEC/encodec/quantization/ac.py
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@ -2,7 +2,7 @@
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# LICENSE file at https://github.com/facebookresearch/encodec/blob/main/LICENSE
"""Arithmetic coder."""
import io
import math
@ -41,7 +41,7 @@ def build_stable_quantized_cdf(
if roundoff:
pdf = (pdf / roundoff).floor() * roundoff
# interpolate with uniform distribution to achieve desired minimum probability.
total_range = 2**total_range_bits
total_range = 2 ** total_range_bits
cardinality = len(pdf)
alpha = min_range * cardinality / total_range
assert alpha <= 1, "you must reduce min_range"
@ -51,7 +51,7 @@ def build_stable_quantized_cdf(
if min_range < 2:
raise ValueError("min_range must be at least 2.")
if check:
assert quantized_cdf[-1] <= 2**total_range_bits, quantized_cdf[-1]
assert quantized_cdf[-1] <= 2 ** total_range_bits, quantized_cdf[-1]
if (
(quantized_cdf[1:] - quantized_cdf[:-1]) < min_range
).any() or quantized_cdf[0] < min_range:
@ -142,7 +142,7 @@ class ArithmeticCoder:
quantized_cdf (Tensor): use `build_stable_quantized_cdf`
to build this from your pdf estimate.
"""
while self.delta < 2**self.total_range_bits:
while self.delta < 2 ** self.total_range_bits:
self.low *= 2
self.high = self.high * 2 + 1
self.max_bit += 1
@ -150,10 +150,10 @@ class ArithmeticCoder:
range_low = 0 if symbol == 0 else quantized_cdf[symbol - 1].item()
range_high = quantized_cdf[symbol].item() - 1
effective_low = int(
math.ceil(range_low * (self.delta / (2**self.total_range_bits)))
math.ceil(range_low * (self.delta / (2 ** self.total_range_bits)))
)
effective_high = int(
math.floor(range_high * (self.delta / (2**self.total_range_bits)))
math.floor(range_high * (self.delta / (2 ** self.total_range_bits)))
)
assert self.low <= self.high
self.high = self.low + effective_high
@ -238,7 +238,7 @@ class ArithmeticDecoder:
to build this from your pdf estimate. This must be **exatly**
the same cdf as the one used at encoding time.
"""
while self.delta < 2**self.total_range_bits:
while self.delta < 2 ** self.total_range_bits:
bit = self.unpacker.pull()
if bit is None:
return None
@ -255,10 +255,10 @@ class ArithmeticDecoder:
range_low = quantized_cdf[mid - 1].item() if mid > 0 else 0
range_high = quantized_cdf[mid].item() - 1
effective_low = int(
math.ceil(range_low * (self.delta / (2**self.total_range_bits)))
math.ceil(range_low * (self.delta / (2 ** self.total_range_bits)))
)
effective_high = int(
math.floor(range_high * (self.delta / (2**self.total_range_bits)))
math.floor(range_high * (self.delta / (2 ** self.total_range_bits)))
)
low = effective_low + self.low
high = effective_high + self.low

2
egs/libritts/CODEC/encodec/quantization/core_vq.py
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@ -76,7 +76,7 @@ def kmeans(samples, num_clusters: int, num_iters: int = 10):
for _ in range(num_iters):
diffs = rearrange(samples, "n d -> n () d") - rearrange(means, "c d -> () c d")
dists = -(diffs**2).sum(dim=-1)
dists = -(diffs ** 2).sum(dim=-1)
buckets = dists.max(dim=-1).indices
bins = torch.bincount(buckets, minlength=num_clusters)

2
egs/libritts/CODEC/encodec/quantization/distrib.py
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@ -2,7 +2,7 @@
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# LICENSE file at https://github.com/facebookresearch/encodec/blob/main/LICENSE
"""Torch distributed utilities."""
from typing import Dict, Iterable, List

2
egs/libritts/CODEC/encodec/quantization/vq.py
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@ -2,7 +2,7 @@
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# LICENSE file at https://github.com/facebookresearch/encodec/blob/main/LICENSE
"""Residual vector quantizer implementation."""
import math
from dataclasses import dataclass, field

5
egs/libritts/CODEC/encodec/scheduler.py
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@ -1,3 +1,8 @@
# original implementation is from https://github.com/ZhikangNiu/encodec-pytorch/blob/main/scheduler.py
# Copyright 2024 Zhi-Kang Niu
# MIT License
import math
from bisect import bisect_right

18
egs/libritts/CODEC/encodec/train.py
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@ -1,3 +1,21 @@
#!/usr/bin/env python3
# Copyright 2023 Xiaomi Corp. (Author: Zengwei Yao)
# 2024 The Chinese University of HK (Author: Zengrui Jin)
#
# 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.
import argparse
import itertools
import logging