added licensing info

egs/libritts/CODEC/encodec/base_discriminators.py

@@ -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

egs/libritts/CODEC/encodec/binary.py

@@ -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)

egs/libritts/CODEC/encodec/discriminators.py

@@ -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

egs/libritts/CODEC/encodec/encodec.py

@@ -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

egs/libritts/CODEC/encodec/loss.py

@@ -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,

egs/libritts/CODEC/encodec/modules/__init__.py

@@ -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 (

egs/libritts/CODEC/encodec/modules/conv.py

@@ -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

egs/libritts/CODEC/encodec/modules/lstm.py

@@ -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
 

egs/libritts/CODEC/encodec/modules/norm.py

@@ -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

egs/libritts/CODEC/encodec/modules/seanet.py

@@ -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,

egs/libritts/CODEC/encodec/modules/transformer.py

@@ -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

egs/libritts/CODEC/encodec/quantization/__init__.py

@@ -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

egs/libritts/CODEC/encodec/quantization/ac.py

@@ -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

egs/libritts/CODEC/encodec/quantization/core_vq.py

@@ -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)

egs/libritts/CODEC/encodec/quantization/distrib.py

@@ -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
 

egs/libritts/CODEC/encodec/quantization/vq.py

@@ -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

egs/libritts/CODEC/encodec/scheduler.py

@@ -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
 

egs/libritts/CODEC/encodec/train.py

@@ -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