From 2eabd03b48170f6177589065fb290cc2e3b03ecb Mon Sep 17 00:00:00 2001
From: Brandon Thomas <echelon@gmail.com>
Date: Sat, 25 Jul 2020 22:19:20 -0400
Subject: [PATCH] REMOVE DEPRECATED CODE/FILES, SUCH AS VOICE CONVERSION

---
 Cargo.lock                                    | 728 ------------------
 Cargo.toml                                    |   6 -
 data/mels/LJ002-0320.mel                      | Bin 180509 -> 0 bytes
 data/mels/trump_2018_02_15-001.mel            | Bin 311393 -> 0 bytes
 data/text/tacotron_text_sequence.pt           | Bin 826 -> 0 bytes
 deployment.yaml                               | 153 ----
 deployment_multi.yaml                         | 358 ---------
 pod_cleanup.sh                                |  35 -
 tacotron_melgan/Cargo.toml                    |  19 -
 tacotron_melgan/README.md                     |  16 -
 tacotron_melgan/convert_mel_to_torch.py       |   0
 tacotron_melgan/jit_containerize_tensor.py    |  82 --
 tacotron_melgan/layers.py                     |  81 --
 tacotron_melgan/requirements.txt              |   2 -
 tacotron_melgan/src/main.rs                   |  68 --
 tacotron_melgan/src/melgan.rs                 |  99 ---
 tacotron_melgan/src/model.rs                  | 130 ----
 tacotron_melgan/src/server.rs                 | 135 ----
 tacotron_melgan/src/tacotron.rs               | 116 ---
 tacotron_melgan/tacotron_model.py             | 604 ---------------
 tacotron_melgan/wrap_melgan_model.py          | 148 ----
 tacotron_melgan/wrap_tacotron2_model.py       | 140 ----
 tacotron_tensorflow/Cargo.toml                |  15 -
 tacotron_tensorflow/src/main.rs               | 280 -------
 tacotron_tensorflow/src/model.rs              | 149 ----
 voice_converter_sidecar/Cargo.toml            |  23 -
 voice_converter_sidecar/README.md             |  34 -
 voice_converter_sidecar/build.rs              |  11 -
 .../cycle_gan/demo_server.py                  | 278 -------
 voice_converter_sidecar/cycle_gan/model.py    | 183 -----
 voice_converter_sidecar/cycle_gan/module.py   | 214 -----
 .../cycle_gan/preprocess.py                   | 238 ------
 .../cycle_gan/protos/audio_pb2.py             | 260 -------
 .../cycle_gan/requirements.txt                |  36 -
 .../cycle_gan/script/recorder.js              | 357 ---------
 voice_converter_sidecar/cycle_gan/sidecar.py  | 264 -------
 voice_converter_sidecar/cycle_gan/utils.py    |  17 -
 voice_converter_sidecar/protos/audio.proto    |  64 --
 voice_converter_sidecar/src/audio_hardware.rs |  84 --
 voice_converter_sidecar/src/audio_queue.rs    |  78 --
 voice_converter_sidecar/src/main.rs           |  72 --
 voice_converter_sidecar/src/protos/mod.rs     |   1 -
 .../src/protos/voder_audio.rs                 |  70 --
 voice_converter_sidecar/src/sidecar.rs        | 217 ------
 voice_converter_sidecar/src/synthesis.rs      |  38 -
 45 files changed, 5903 deletions(-)
 delete mode 100644 data/mels/LJ002-0320.mel
 delete mode 100644 data/mels/trump_2018_02_15-001.mel
 delete mode 100644 data/text/tacotron_text_sequence.pt
 delete mode 100644 deployment.yaml
 delete mode 100644 deployment_multi.yaml
 delete mode 100755 pod_cleanup.sh
 delete mode 100644 tacotron_melgan/Cargo.toml
 delete mode 100644 tacotron_melgan/README.md
 delete mode 100644 tacotron_melgan/convert_mel_to_torch.py
 delete mode 100755 tacotron_melgan/jit_containerize_tensor.py
 delete mode 100644 tacotron_melgan/layers.py
 delete mode 100644 tacotron_melgan/requirements.txt
 delete mode 100644 tacotron_melgan/src/main.rs
 delete mode 100644 tacotron_melgan/src/melgan.rs
 delete mode 100644 tacotron_melgan/src/model.rs
 delete mode 100644 tacotron_melgan/src/server.rs
 delete mode 100644 tacotron_melgan/src/tacotron.rs
 delete mode 100644 tacotron_melgan/tacotron_model.py
 delete mode 100755 tacotron_melgan/wrap_melgan_model.py
 delete mode 100755 tacotron_melgan/wrap_tacotron2_model.py
 delete mode 100644 tacotron_tensorflow/Cargo.toml
 delete mode 100644 tacotron_tensorflow/src/main.rs
 delete mode 100644 tacotron_tensorflow/src/model.rs
 delete mode 100644 voice_converter_sidecar/Cargo.toml
 delete mode 100644 voice_converter_sidecar/README.md
 delete mode 100644 voice_converter_sidecar/build.rs
 delete mode 100755 voice_converter_sidecar/cycle_gan/demo_server.py
 delete mode 100644 voice_converter_sidecar/cycle_gan/model.py
 delete mode 100644 voice_converter_sidecar/cycle_gan/module.py
 delete mode 100644 voice_converter_sidecar/cycle_gan/preprocess.py
 delete mode 100644 voice_converter_sidecar/cycle_gan/protos/audio_pb2.py
 delete mode 100644 voice_converter_sidecar/cycle_gan/requirements.txt
 delete mode 100644 voice_converter_sidecar/cycle_gan/script/recorder.js
 delete mode 100755 voice_converter_sidecar/cycle_gan/sidecar.py
 delete mode 100644 voice_converter_sidecar/cycle_gan/utils.py
 delete mode 100644 voice_converter_sidecar/protos/audio.proto
 delete mode 100644 voice_converter_sidecar/src/audio_hardware.rs
 delete mode 100644 voice_converter_sidecar/src/audio_queue.rs
 delete mode 100644 voice_converter_sidecar/src/main.rs
 delete mode 100644 voice_converter_sidecar/src/protos/mod.rs
 delete mode 100644 voice_converter_sidecar/src/protos/voder_audio.rs
 delete mode 100644 voice_converter_sidecar/src/sidecar.rs
 delete mode 100644 voice_converter_sidecar/src/synthesis.rs

diff --git a/Cargo.lock b/Cargo.lock
index 38cfed1798..d066b84f44 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -289,33 +289,6 @@ dependencies = [
  "memchr 2.3.3 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "aligned_alloc"
-version = "0.1.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "kernel32-sys 0.2.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "libc 0.2.66 (registry+https://github.com/rust-lang/crates.io-index)",
- "winapi 0.2.8 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
-name = "alsa-sys"
-version = "0.1.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "libc 0.2.66 (registry+https://github.com/rust-lang/crates.io-index)",
- "pkg-config 0.3.17 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
-name = "ansi_term"
-version = "0.11.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "winapi 0.3.8 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "anyhow"
 version = "1.0.30"
@@ -423,28 +396,6 @@ name = "base64"
 version = "0.12.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
-name = "bindgen"
-version = "0.51.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "bitflags 1.2.1 (registry+https://github.com/rust-lang/crates.io-index)",
- "cexpr 0.3.6 (registry+https://github.com/rust-lang/crates.io-index)",
- "cfg-if 0.1.10 (registry+https://github.com/rust-lang/crates.io-index)",
- "clang-sys 0.28.1 (registry+https://github.com/rust-lang/crates.io-index)",
- "clap 2.33.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "env_logger 0.6.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "lazy_static 1.4.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "log 0.4.8 (registry+https://github.com/rust-lang/crates.io-index)",
- "peeking_take_while 0.1.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "proc-macro2 1.0.7 (registry+https://github.com/rust-lang/crates.io-index)",
- "quote 1.0.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "regex 1.3.7 (registry+https://github.com/rust-lang/crates.io-index)",
- "rustc-hash 1.0.1 (registry+https://github.com/rust-lang/crates.io-index)",
- "shlex 0.1.1 (registry+https://github.com/rust-lang/crates.io-index)",
- "which 3.1.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "bitflags"
 version = "1.2.1"
@@ -468,11 +419,6 @@ dependencies = [
  "libc 0.2.66 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "build_const"
-version = "0.2.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
 name = "byteorder"
 version = "1.3.2"
@@ -531,14 +477,6 @@ name = "cc"
 version = "1.0.48"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
-name = "cexpr"
-version = "0.3.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "nom 4.2.3 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "cfg-if"
 version = "0.1.10"
@@ -555,30 +493,6 @@ dependencies = [
  "time 0.1.42 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "clang-sys"
-version = "0.28.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "glob 0.3.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "libc 0.2.66 (registry+https://github.com/rust-lang/crates.io-index)",
- "libloading 0.5.2 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
-name = "clap"
-version = "2.33.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "ansi_term 0.11.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "atty 0.2.14 (registry+https://github.com/rust-lang/crates.io-index)",
- "bitflags 1.2.1 (registry+https://github.com/rust-lang/crates.io-index)",
- "strsim 0.8.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "textwrap 0.11.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "unicode-width 0.1.7 (registry+https://github.com/rust-lang/crates.io-index)",
- "vec_map 0.8.1 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "cloudabi"
 version = "0.0.3"
@@ -595,11 +509,6 @@ dependencies = [
  "cc 1.0.48 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "color_quant"
-version = "1.0.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
 name = "combine"
 version = "3.8.1"
@@ -617,52 +526,6 @@ name = "copyless"
 version = "0.1.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
-name = "core-foundation-sys"
-version = "0.6.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-
-[[package]]
-name = "coreaudio-rs"
-version = "0.9.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "bitflags 1.2.1 (registry+https://github.com/rust-lang/crates.io-index)",
- "coreaudio-sys 0.2.3 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
-name = "coreaudio-sys"
-version = "0.2.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "bindgen 0.51.1 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
-name = "cpal"
-version = "0.10.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "alsa-sys 0.1.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "core-foundation-sys 0.6.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "coreaudio-rs 0.9.1 (registry+https://github.com/rust-lang/crates.io-index)",
- "failure 0.1.6 (registry+https://github.com/rust-lang/crates.io-index)",
- "lazy_static 1.4.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "libc 0.2.66 (registry+https://github.com/rust-lang/crates.io-index)",
- "num-traits 0.2.11 (registry+https://github.com/rust-lang/crates.io-index)",
- "stdweb 0.1.3 (registry+https://github.com/rust-lang/crates.io-index)",
- "winapi 0.3.8 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
-name = "crc"
-version = "1.8.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "build_const 0.2.1 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "crc32fast"
 version = "1.2.0"
@@ -671,37 +534,6 @@ dependencies = [
  "cfg-if 0.1.10 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "crossbeam-deque"
-version = "0.7.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "crossbeam-epoch 0.8.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "crossbeam-utils 0.7.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
-name = "crossbeam-epoch"
-version = "0.8.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "autocfg 0.1.7 (registry+https://github.com/rust-lang/crates.io-index)",
- "cfg-if 0.1.10 (registry+https://github.com/rust-lang/crates.io-index)",
- "crossbeam-utils 0.7.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "lazy_static 1.4.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "memoffset 0.5.3 (registry+https://github.com/rust-lang/crates.io-index)",
- "scopeguard 1.0.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
-name = "crossbeam-queue"
-version = "0.2.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "cfg-if 0.1.10 (registry+https://github.com/rust-lang/crates.io-index)",
- "crossbeam-utils 0.7.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "crossbeam-utils"
 version = "0.7.0"
@@ -740,15 +572,6 @@ dependencies = [
  "winapi 0.3.8 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "deflate"
-version = "0.7.20"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "adler32 1.0.4 (registry+https://github.com/rust-lang/crates.io-index)",
- "byteorder 1.3.2 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "derive_more"
 version = "0.99.2"
@@ -812,18 +635,6 @@ dependencies = [
  "syn 1.0.13 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "env_logger"
-version = "0.6.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "atty 0.2.14 (registry+https://github.com/rust-lang/crates.io-index)",
- "humantime 1.3.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "log 0.4.8 (registry+https://github.com/rust-lang/crates.io-index)",
- "regex 1.3.7 (registry+https://github.com/rust-lang/crates.io-index)",
- "termcolor 1.0.5 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "env_logger"
 version = "0.7.1"
@@ -836,11 +647,6 @@ dependencies = [
  "termcolor 1.0.5 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "error-chain"
-version = "0.10.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
 name = "failure"
 version = "0.1.6"
@@ -861,22 +667,6 @@ dependencies = [
  "synstructure 0.12.3 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "filetime"
-version = "0.2.8"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "cfg-if 0.1.10 (registry+https://github.com/rust-lang/crates.io-index)",
- "libc 0.2.66 (registry+https://github.com/rust-lang/crates.io-index)",
- "redox_syscall 0.1.56 (registry+https://github.com/rust-lang/crates.io-index)",
- "winapi 0.3.8 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
-name = "fixedbitset"
-version = "0.1.9"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
 name = "flate2"
 version = "1.0.13"
@@ -1021,20 +811,6 @@ dependencies = [
  "wasi 0.7.0 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "gif"
-version = "0.10.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "color_quant 1.0.1 (registry+https://github.com/rust-lang/crates.io-index)",
- "lzw 0.10.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
-name = "glob"
-version = "0.3.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
 name = "h2"
 version = "0.2.1"
@@ -1053,11 +829,6 @@ dependencies = [
  "tokio-util 0.2.0 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "half"
-version = "1.4.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
 name = "heck"
 version = "0.3.1"
@@ -1131,22 +902,6 @@ dependencies = [
  "unicode-normalization 0.1.11 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "image"
-version = "0.23.0-preview.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "byteorder 1.3.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "gif 0.10.3 (registry+https://github.com/rust-lang/crates.io-index)",
- "jpeg-decoder 0.1.18 (registry+https://github.com/rust-lang/crates.io-index)",
- "num-iter 0.1.40 (registry+https://github.com/rust-lang/crates.io-index)",
- "num-rational 0.2.4 (registry+https://github.com/rust-lang/crates.io-index)",
- "num-traits 0.2.11 (registry+https://github.com/rust-lang/crates.io-index)",
- "png 0.15.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "scoped_threadpool 0.1.9 (registry+https://github.com/rust-lang/crates.io-index)",
- "tiff 0.4.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "indexmap"
 version = "1.3.0"
@@ -1155,14 +910,6 @@ dependencies = [
  "autocfg 0.1.7 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "inflate"
-version = "0.4.5"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "adler32 1.0.4 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "iovec"
 version = "0.1.4"
@@ -1195,15 +942,6 @@ name = "itoa"
 version = "0.4.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
-name = "jpeg-decoder"
-version = "0.1.18"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "byteorder 1.3.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "rayon 1.3.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "kernel32-sys"
 version = "0.2.2"
@@ -1228,15 +966,6 @@ name = "libc"
 version = "0.2.66"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
-name = "libloading"
-version = "0.5.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "cc 1.0.48 (registry+https://github.com/rust-lang/crates.io-index)",
- "winapi 0.3.8 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "libz-sys"
 version = "1.0.25"
@@ -1288,11 +1017,6 @@ dependencies = [
  "linked-hash-map 0.5.2 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "lzw"
-version = "0.10.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
 name = "match_cfg"
 version = "0.1.0"
@@ -1321,24 +1045,6 @@ name = "memchr"
 version = "2.3.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
-name = "memoffset"
-version = "0.5.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "rustc_version 0.2.3 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
-name = "metadeps"
-version = "1.1.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "error-chain 0.10.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "pkg-config 0.3.17 (registry+https://github.com/rust-lang/crates.io-index)",
- "toml 0.2.1 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "mime"
 version = "0.3.14"
@@ -1400,11 +1106,6 @@ dependencies = [
  "ws2_32-sys 0.2.1 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
 name = "mysqlclient-sys"
 version = "0.2.4"
@@ -1601,24 +1302,11 @@ dependencies = [
  "winapi 0.3.8 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
-name = "peeking_take_while"
-version = "0.1.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
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-[[package]]
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-version = "0.4.13"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "fixedbitset 0.1.9 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "phf"
 version = "0.8.0"
@@ -1692,17 +1380,6 @@ name = "pkg-config"
 version = "0.3.17"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
-name = "png"
-version = "0.15.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "bitflags 1.2.1 (registry+https://github.com/rust-lang/crates.io-index)",
- "crc32fast 1.2.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "deflate 0.7.20 (registry+https://github.com/rust-lang/crates.io-index)",
- "inflate 0.4.5 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
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@@ -1728,14 +1405,6 @@ name = "proc-macro-nested"
 version = "0.1.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
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-version = "0.4.30"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "unicode-xid 0.1.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
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@@ -1744,67 +1413,11 @@ dependencies = [
  "unicode-xid 0.2.0 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
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-version = "0.5.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "byteorder 1.3.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "bytes 0.4.12 (registry+https://github.com/rust-lang/crates.io-index)",
- "prost-derive 0.5.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "bytes 0.4.12 (registry+https://github.com/rust-lang/crates.io-index)",
- "heck 0.3.1 (registry+https://github.com/rust-lang/crates.io-index)",
- "itertools 0.8.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "log 0.4.8 (registry+https://github.com/rust-lang/crates.io-index)",
- "multimap 0.4.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "petgraph 0.4.13 (registry+https://github.com/rust-lang/crates.io-index)",
- "prost 0.5.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "prost-types 0.5.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "tempfile 3.1.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "which 2.0.1 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
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- "itertools 0.8.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "proc-macro2 0.4.30 (registry+https://github.com/rust-lang/crates.io-index)",
- "quote 0.6.13 (registry+https://github.com/rust-lang/crates.io-index)",
- "syn 0.15.44 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
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-dependencies = [
- "bytes 0.4.12 (registry+https://github.com/rust-lang/crates.io-index)",
- "prost 0.5.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
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-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "proc-macro2 0.4.30 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
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@@ -1970,28 +1583,6 @@ name = "rawpointer"
 version = "0.2.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
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-version = "1.3.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "crossbeam-deque 0.7.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "either 1.5.3 (registry+https://github.com/rust-lang/crates.io-index)",
- "rayon-core 1.7.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
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- "crossbeam-queue 0.2.1 (registry+https://github.com/rust-lang/crates.io-index)",
- "crossbeam-utils 0.7.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "lazy_static 1.4.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "num_cpus 1.11.1 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
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@@ -2042,14 +1633,6 @@ name = "regex-syntax"
 version = "0.6.17"
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-[[package]]
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-version = "0.5.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "winapi 0.3.8 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "resolv-conf"
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@@ -2064,14 +1647,6 @@ name = "rustc-demangle"
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 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
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-version = "1.0.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
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-]
-
 [[package]]
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@@ -2085,11 +1660,6 @@ name = "ryu"
 version = "1.0.2"
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-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
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@@ -2107,11 +1677,6 @@ dependencies = [
  "parking_lot 0.10.0 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-
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@@ -2183,11 +1748,6 @@ name = "sha1"
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-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
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@@ -2231,26 +1791,6 @@ dependencies = [
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 ]
 
-[[package]]
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-
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-
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- "quote 0.6.13 (registry+https://github.com/rust-lang/crates.io-index)",
- "unicode-xid 0.1.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
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@@ -2272,39 +1812,6 @@ dependencies = [
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 ]
 
-[[package]]
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- "hound 3.4.0 (registry+https://github.com/rust-lang/crates.io-index)",
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- "serde 1.0.104 (registry+https://github.com/rust-lang/crates.io-index)",
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- "tch 0.1.7 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
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- "hound 3.4.0 (registry+https://github.com/rust-lang/crates.io-index)",
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- "sample 0.10.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "tensorflow 0.14.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
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- "xattr 0.2.2 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
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@@ -2319,57 +1826,6 @@ dependencies = [
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 ]
 
-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
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- "libc 0.2.66 (registry+https://github.com/rust-lang/crates.io-index)",
- "rand 0.7.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "redox_syscall 0.1.56 (registry+https://github.com/rust-lang/crates.io-index)",
- "remove_dir_all 0.5.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "winapi 0.3.8 (registry+https://github.com/rust-lang/crates.io-index)",
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- "libc 0.2.66 (registry+https://github.com/rust-lang/crates.io-index)",
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- "tensorflow-sys 0.17.0 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
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- "quote 0.6.13 (registry+https://github.com/rust-lang/crates.io-index)",
- "syn 0.15.44 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
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-source = "registry+https://github.com/rust-lang/crates.io-index"
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- "flate2 1.0.13 (registry+https://github.com/rust-lang/crates.io-index)",
- "libc 0.2.66 (registry+https://github.com/rust-lang/crates.io-index)",
- "pkg-config 0.3.17 (registry+https://github.com/rust-lang/crates.io-index)",
- "semver 0.9.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "tar 0.4.26 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
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 version = "1.0.5"
@@ -2378,14 +1834,6 @@ dependencies = [
  "wincolor 1.0.2 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
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-]
-
 [[package]]
 name = "thread_local"
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@@ -2402,16 +1850,6 @@ dependencies = [
  "num_cpus 1.11.1 (registry+https://github.com/rust-lang/crates.io-index)",
 ]
 
-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-dependencies = [
- "byteorder 1.3.2 (registry+https://github.com/rust-lang/crates.io-index)",
- "lzw 0.10.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "miniz_oxide 0.3.5 (registry+https://github.com/rust-lang/crates.io-index)",
-]
-
 [[package]]
 name = "time"
 version = "0.1.42"
@@ -2541,11 +1979,6 @@ dependencies = [
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 ]
 
-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
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@@ -2663,16 +2096,6 @@ name = "unicode-segmentation"
 version = "1.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-
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-
 [[package]]
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@@ -2729,11 +2152,6 @@ name = "vcpkg"
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-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
-
 [[package]]
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@@ -2748,22 +2166,6 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 name = "voder"
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-[[package]]
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-]
-
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@@ -2774,31 +2176,6 @@ name = "wasi"
 version = "0.7.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 
-[[package]]
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-]
-
 [[package]]
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@@ -2866,14 +2243,6 @@ dependencies = [
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 ]
 
-[[package]]
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-]
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@@ -2883,14 +2252,6 @@ dependencies = [
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 ]
 
-[[package]]
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-]
-
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@@ -2903,26 +2264,6 @@ dependencies = [
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 ]
 
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@@ -2942,9 +2283,6 @@ dependencies = [
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-"checksum wavy 0.1.2 (registry+https://github.com/rust-lang/crates.io-index)" = "feea5ba5ef38798f371bc995c1ca542d5162923d1bcbdaaf89916042bb61f006"
-"checksum which 2.0.1 (registry+https://github.com/rust-lang/crates.io-index)" = "b57acb10231b9493c8472b20cb57317d0679a49e0bdbee44b3b803a6473af164"
-"checksum which 3.1.0 (registry+https://github.com/rust-lang/crates.io-index)" = "5475d47078209a02e60614f7ba5e645ef3ed60f771920ac1906d7c1cc65024c8"
 "checksum widestring 0.4.0 (registry+https://github.com/rust-lang/crates.io-index)" = "effc0e4ff8085673ea7b9b2e3c73f6bd4d118810c9009ed8f1e16bd96c331db6"
 "checksum winapi 0.2.8 (registry+https://github.com/rust-lang/crates.io-index)" = "167dc9d6949a9b857f3451275e911c3f44255842c1f7a76f33c55103a909087a"
 "checksum winapi 0.3.8 (registry+https://github.com/rust-lang/crates.io-index)" = "8093091eeb260906a183e6ae1abdba2ef5ef2257a21801128899c3fc699229c6"
@@ -3231,7 +2506,4 @@ dependencies = [
 "checksum winreg 0.6.2 (registry+https://github.com/rust-lang/crates.io-index)" = "b2986deb581c4fe11b621998a5e53361efe6b48a151178d0cd9eeffa4dc6acc9"
 "checksum winutil 0.1.1 (registry+https://github.com/rust-lang/crates.io-index)" = "7daf138b6b14196e3830a588acf1e86966c694d3e8fb026fb105b8b5dca07e6e"
 "checksum ws2_32-sys 0.2.1 (registry+https://github.com/rust-lang/crates.io-index)" = "d59cefebd0c892fa2dd6de581e937301d8552cb44489cdff035c6187cb63fa5e"
-"checksum xattr 0.2.2 (registry+https://github.com/rust-lang/crates.io-index)" = "244c3741f4240ef46274860397c7c74e50eb23624996930e484c16679633a54c"
 "checksum zip 0.5.3 (registry+https://github.com/rust-lang/crates.io-index)" = "3c21bb410afa2bd823a047f5bda3adb62f51074ac7e06263b2c97ecdd47e9fc6"
-"checksum zmq 0.9.2 (registry+https://github.com/rust-lang/crates.io-index)" = "aad98a7a617d608cd9e1127147f630d24af07c7cd95ba1533246d96cbdd76c66"
-"checksum zmq-sys 0.11.0 (registry+https://github.com/rust-lang/crates.io-index)" = "d33a2c51dde24d5b451a2ed4b488266df221a5eaee2ee519933dc46b9a9b3648"
diff --git a/Cargo.toml b/Cargo.toml
index 40aea7e427..daaf3f5814 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -6,16 +6,10 @@ authors = [ "Brandon Thomas <bt@brand.io>",
 
 [workspace]
 members = [
-  "tacotron_melgan",
-  "tacotron_tensorflow",
   "tts_service",
-  "voice_converter_sidecar",
 ]
 
 default-members = [
-  "tacotron_melgan",
-  "tacotron_tensorflow",
   "tts_service",
-  "voice_converter_sidecar",
 ]
 
diff --git a/data/mels/LJ002-0320.mel b/data/mels/LJ002-0320.mel
deleted file mode 100644
index 685ed5f5f567146921e6c57405623029dfc66698..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/data/text/tacotron_text_sequence.pt b/data/text/tacotron_text_sequence.pt
deleted file mode 100644
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zmhKZyIfzd_r+SX=$A&K9#Ovrj<tiVhJfu(io4%Bn_8?u-AwKoVk8s+DaLPgbzj)m$
Uo<;9P^E(=+eJMBPGJR;zAM<@^c>n+a

diff --git a/deployment.yaml b/deployment.yaml
deleted file mode 100644
index 7ff7025d6f..0000000000
--- a/deployment.yaml
+++ /dev/null
@@ -1,153 +0,0 @@
----
-kind: Service
-apiVersion: v1
-metadata:
-  name: tts
-  namespace: mumble
-spec:
-  type: LoadBalancer
-  selector:
-    app: tts
-  ports:
-  - name: http
-    protocol: TCP
-    port: 80
-    targetPort: 12345
----
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: tts
-  namespace: mumble
-  labels:
-    app: tts
-spec:
-  replicas: 3 # 4 would never launch with cluster configs as of 2020-06-20
-  selector:
-    matchLabels:
-      app: tts
-  template:
-    metadata:
-      labels:
-        app: tts
-      annotations:
-        bump: '1'
-    spec:
-      terminationGracePeriodSeconds: 60
-      imagePullSecrets:
-      - name: regcred
-      volumes:
-      - name: downloads
-        hostPath:
-          path: /downloads
-          type: DirectoryOrCreate
-      containers:
-      - name: downloader
-        image: docker.pkg.github.com/ml-applications/do-spaces-downloader/do-spaces-downloader:58ec25e3aa3c
-        command: ["./launch.sh"]
-        args: [""]
-        #command: ["sleep"]
-        #args: ["300000"]
-        volumeMounts:
-        - name: downloads
-          mountPath: /data/downloads
-        env:
-        - name: ACCESS_KEY
-          valueFrom:
-            secretKeyRef:
-              name: spaces-secrets
-              key: aws_access_key_id
-        - name: SECRET_KEY
-          valueFrom:
-            secretKeyRef:
-              name: spaces-secrets
-              key: aws_secret_access_key
-        - name: BUCKET_NAME
-          value: "upload-models"
-        - name: REGION
-          value: "nyc3"
-        # NB: These filesystem paths are strage because they need to be on the
-        # same filesystem. Rust can't handle moving files between filesystems yet: 
-        #   "Error: Invalid cross-device link (os error 18)"
-        - name: DOWNLOAD_DIR
-          value: "/data/downloads/downloaded"
-        - name: TEMP_DIR
-          value: "/data/downloads/tmp"
-        - name: MATCH_PATH
-          value: "" # previously "models/"
-        - name: SLEEP_TIMEOUT
-          value: "300"
-        - name: RUST_LOG
-          value: "info,do_spaces_downloader=debug"
-      - name: tts
-        # NB: Container builds are triggered by Github Actions
-
-        # The following image runs a non-Arpabet engine that can run an 
-        # LSpeech model that sounds 100% amazing
-        # image: docker.pkg.github.com/echelon/voder/tts-service:6b5c22567614
-        #
-        image: docker.pkg.github.com/ml-applications/voder/tts-service:60211d6df69a
-        resources:
-          limits:
-            # 4 GiB (4096 MiB)
-            # 3 GiB (3072 MiB)
-            # 2 GiB (2048 MiB)
-            memory: "4Gi" # Just under 4 GB
-            cpu: "700m" # 700 milliCPU
-          requests:
-            #memory: "50Mi" - we're getting evicted!
-            # Warning  FailedScheduling   <unknown>  default-scheduler   0/6 nodes are available: 6 Insufficient memory.
-            # Normal   NotTriggerScaleUp  29s        cluster-autoscaler  pod didn't trigger scale-up (it wouldn't fit if 
-            #                                                            a new node is added):
-            #
-            memory: "4Gi" # Just under 4 GB
-            cpu: "700m" # 700 milliCPU
-        volumeMounts:
-        - name: downloads
-          mountPath: /data/downloads
-        env:
-        - name: DATABASE_URL
-          valueFrom:
-            secretKeyRef:
-              name: db-secrets
-              key: url
-        - name: BIND_ADDRESS
-          value: "0.0.0.0:12345"
-        - name: NUM_WORKERS
-          value: "5"
-        - name: ASSET_DIRECTORY
-          value: "/data/downloads/downloaded/frontend/build" # TODO
-        - name: MODEL_CONFIG_FILE
-          value: "/data/downloads/downloaded/models/deployed_models.toml"
-        - name: MAX_CHAR_LEN
-          value: "220"
-        command: ["./launch.sh"]
-        args: [""]
-        #command: ["sleep"]
-        #args: ["300000"]
-        ports:
-        - containerPort: 12345
-          protocol: TCP
-        # If the readiness probe fails, the container is removed from the service.
-        readinessProbe:
-          httpGet:
-            path: /readiness
-            port: 12345
-          # Check every two seconds.
-          periodSeconds: 2
-          # Warmup period before first check.
-          initialDelaySeconds: 5
-          # How long it takes for the probe to time out.
-          timeoutSeconds: 1
-          # Number of attempts before the pod is marked unready
-          failureThreshold: 1
-        # If the liveness probe fails, the container is subject to its restart policy
-        # Since the container can crash on its own, we'll omit this.
-        # livenessProbe:
-        #   httpGet:
-        #     path: /liveness
-        #     port: 12345
-        #   initialDelaySeconds: 20
-        #   periodSeconds: 10
-        #   timeoutSeconds: 3
-        #   failureThreshold: 3
diff --git a/deployment_multi.yaml b/deployment_multi.yaml
deleted file mode 100644
index 8cefc4c1d1..0000000000
--- a/deployment_multi.yaml
+++ /dev/null
@@ -1,358 +0,0 @@
----
-kind: Service
-apiVersion: v1
-metadata:
-  name: service-trump
-  namespace: vocodes
-spec:
-  #type: LoadBalancer
-  #type: ClusterIP
-  selector:
-    #model: trump
-    app: tts-trump
-  ports:
-  - name: http
-    protocol: TCP
-    port: 12345
-    targetPort: 12345
-
----
-
-kind: Service
-apiVersion: v1
-metadata:
-  name: service-other
-  namespace: vocodes
-spec:
-  #type: LoadBalancer
-  selector:
-    model: other
-  ports:
-  - name: http
-    protocol: TCP
-    port: 80
-    targetPort: 12345
----
-
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: tts-service-proxy
-  namespace: vocodes
-  labels:
-    app: tts-service-proxy
-spec:
-  replicas: 1
-  selector:
-    matchLabels:
-      app: tts-service-proxy
-  template:
-    metadata:
-      labels:
-        app: tts-service-proxy
-      annotations:
-        bump: '2'
-    spec:
-      terminationGracePeriodSeconds: 60
-      imagePullSecrets:
-      - name: regcred
-      containers:
-      - name: tts-service-proxy
-        image: docker.pkg.github.com/ml-applications/voder-proxy/tts-service-proxy:67d094d38ef4
-        #command: ["./launch.sh"]
-        #args: [""]
-        #command: ["sleep"]
-        #args: ["300000"]
-        env:
-        - name: ROUTE_ONE
-          value: "http://10.245.22.66:80"
-        - name: ROUTE_TWO
-          value: "http://10.245.179.52:80"
-        - name: RUST_LOG
-          value: "info"
-        ports:
-        - containerPort: 13900
-          protocol: TCP
-
----
-
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: tts-trump
-  namespace: vocodes
-  labels:
-    app: tts-trump
-    #model: trump
-spec:
-  replicas: 1 # 4 would never launch with cluster configs as of 2020-06-20
-  selector:
-    matchLabels:
-      app: tts-trump
-  template:
-    metadata:
-      labels:
-        app: tts-trump
-      annotations:
-        bump: '1'
-    spec:
-      terminationGracePeriodSeconds: 60
-      imagePullSecrets:
-      - name: regcred
-      volumes:
-      - name: downloads
-        hostPath:
-          path: /downloads
-          type: DirectoryOrCreate
-      containers:
-      - name: downloader
-        image: docker.pkg.github.com/ml-applications/do-spaces-downloader/do-spaces-downloader:58ec25e3aa3c
-        command: ["./launch.sh"]
-        args: [""]
-        #command: ["sleep"]
-        #args: ["300000"]
-        volumeMounts:
-        - name: downloads
-          mountPath: /data/downloads
-        env:
-        - name: ACCESS_KEY
-          valueFrom:
-            secretKeyRef:
-              name: spaces-secrets
-              key: aws_access_key_id
-        - name: SECRET_KEY
-          valueFrom:
-            secretKeyRef:
-              name: spaces-secrets
-              key: aws_secret_access_key
-        - name: BUCKET_NAME
-          value: "upload-models"
-        - name: REGION
-          value: "nyc3"
-        # NB: These filesystem paths are strage because they need to be on the
-        # same filesystem. Rust can't handle moving files between filesystems yet: 
-        #   "Error: Invalid cross-device link (os error 18)"
-        - name: DOWNLOAD_DIR
-          value: "/data/downloads/downloaded"
-        - name: TEMP_DIR
-          value: "/data/downloads/tmp"
-        - name: MATCH_PATH
-          value: "" # previously "models/"
-        - name: SLEEP_TIMEOUT
-          value: "300"
-        - name: RUST_LOG
-          value: "info,do_spaces_downloader=debug"
-      - name: tts
-        # NB: Container builds are triggered by Github Actions
-
-        # The following image runs a non-Arpabet engine that can run an 
-        # LSpeech model that sounds 100% amazing
-        # image: docker.pkg.github.com/echelon/voder/tts-service:6b5c22567614
-        #
-        image: docker.pkg.github.com/ml-applications/voder/tts-service:60211d6df69a
-        resources:
-          limits:
-            # 4 GiB (4096 MiB)
-            # 3 GiB (3072 MiB)
-            # 2 GiB (2048 MiB)
-            memory: "4Gi" # Just under 4 GB
-            cpu: "700m" # 700 milliCPU
-          requests:
-            #memory: "50Mi" - we're getting evicted!
-            # Warning  FailedScheduling   <unknown>  default-scheduler   0/6 nodes are available: 6 Insufficient memory.
-            # Normal   NotTriggerScaleUp  29s        cluster-autoscaler  pod didn't trigger scale-up (it wouldn't fit if 
-            #                                                            a new node is added):
-            #
-            memory: "2Gi" # Just under 4 GB
-            cpu: "300m" # 700 milliCPU
-        volumeMounts:
-        - name: downloads
-          mountPath: /data/downloads
-        env:
-        - name: DATABASE_URL
-          valueFrom:
-            secretKeyRef:
-              name: db-secrets
-              key: url
-        - name: BIND_ADDRESS
-          value: "0.0.0.0:12345"
-        - name: NUM_WORKERS
-          value: "5"
-        - name: ASSET_DIRECTORY
-          value: "/data/downloads/downloaded/frontend/build" # TODO
-        - name: MODEL_CONFIG_FILE
-          value: "/data/downloads/downloaded/models/deployed_models.toml"
-        - name: MAX_CHAR_LEN
-          value: "220"
-        command: ["./launch.sh"]
-        args: [""]
-        #command: ["sleep"]
-        #args: ["300000"]
-        ports:
-        - containerPort: 12345
-          protocol: TCP
-        # If the readiness probe fails, the container is removed from the service.
-        readinessProbe:
-          httpGet:
-            path: /readiness
-            port: 12345
-          # Check every two seconds.
-          periodSeconds: 2
-          # Warmup period before first check.
-          initialDelaySeconds: 5
-          # How long it takes for the probe to time out.
-          timeoutSeconds: 1
-          # Number of attempts before the pod is marked unready
-          failureThreshold: 1
-        # If the liveness probe fails, the container is subject to its restart policy
-        # Since the container can crash on its own, we'll omit this.
-        # livenessProbe:
-        #   httpGet:
-        #     path: /liveness
-        #     port: 12345
-        #   initialDelaySeconds: 20
-        #   periodSeconds: 10
-        #   timeoutSeconds: 3
-        #   failureThreshold: 3
-        #
-
----
-
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: tts-other
-  namespace: vocodes
-  labels:
-    app: tts-other
-    model: other
-spec:
-  replicas: 1 # 4 would never launch with cluster configs as of 2020-06-20
-  selector:
-    matchLabels:
-      app: tts-other
-  template:
-    metadata:
-      labels:
-        app: tts-other
-      annotations:
-        bump: '1'
-    spec:
-      terminationGracePeriodSeconds: 60
-      imagePullSecrets:
-      - name: regcred
-      volumes:
-      - name: downloads
-        hostPath:
-          path: /downloads
-          type: DirectoryOrCreate
-      containers:
-      - name: downloader
-        image: docker.pkg.github.com/ml-applications/do-spaces-downloader/do-spaces-downloader:58ec25e3aa3c
-        command: ["./launch.sh"]
-        args: [""]
-        #command: ["sleep"]
-        #args: ["300000"]
-        volumeMounts:
-        - name: downloads
-          mountPath: /data/downloads
-        env:
-        - name: ACCESS_KEY
-          valueFrom:
-            secretKeyRef:
-              name: spaces-secrets
-              key: aws_access_key_id
-        - name: SECRET_KEY
-          valueFrom:
-            secretKeyRef:
-              name: spaces-secrets
-              key: aws_secret_access_key
-        - name: BUCKET_NAME
-          value: "upload-models"
-        - name: REGION
-          value: "nyc3"
-        # NB: These filesystem paths are strage because they need to be on the
-        # same filesystem. Rust can't handle moving files between filesystems yet: 
-        #   "Error: Invalid cross-device link (os error 18)"
-        - name: DOWNLOAD_DIR
-          value: "/data/downloads/downloaded"
-        - name: TEMP_DIR
-          value: "/data/downloads/tmp"
-        - name: MATCH_PATH
-          value: "" # previously "models/"
-        - name: SLEEP_TIMEOUT
-          value: "300"
-        - name: RUST_LOG
-          value: "info,do_spaces_downloader=debug"
-      - name: tts
-        # NB: Container builds are triggered by Github Actions
-
-        # The following image runs a non-Arpabet engine that can run an 
-        # LSpeech model that sounds 100% amazing
-        # image: docker.pkg.github.com/echelon/voder/tts-service:6b5c22567614
-        #
-        image: docker.pkg.github.com/ml-applications/voder/tts-service:60211d6df69a
-        resources:
-          limits:
-            # 4 GiB (4096 MiB)
-            # 3 GiB (3072 MiB)
-            # 2 GiB (2048 MiB)
-            memory: "4Gi" # Just under 4 GB
-            cpu: "700m" # 700 milliCPU
-          requests:
-            #memory: "50Mi" - we're getting evicted!
-            # Warning  FailedScheduling   <unknown>  default-scheduler   0/6 nodes are available: 6 Insufficient memory.
-            # Normal   NotTriggerScaleUp  29s        cluster-autoscaler  pod didn't trigger scale-up (it wouldn't fit if 
-            #                                                            a new node is added):
-            #
-            memory: "2Gi" # Just under 4 GB
-            cpu: "300m" # 700 milliCPU
-        volumeMounts:
-        - name: downloads
-          mountPath: /data/downloads
-        env:
-        - name: DATABASE_URL
-          valueFrom:
-            secretKeyRef:
-              name: db-secrets
-              key: url
-        - name: BIND_ADDRESS
-          value: "0.0.0.0:12345"
-        - name: NUM_WORKERS
-          value: "5"
-        - name: ASSET_DIRECTORY
-          value: "/data/downloads/downloaded/frontend/build" # TODO
-        - name: MODEL_CONFIG_FILE
-          value: "/data/downloads/downloaded/models/deployed_models.toml"
-        - name: MAX_CHAR_LEN
-          value: "220"
-        command: ["./launch.sh"]
-        args: [""]
-        #command: ["sleep"]
-        #args: ["300000"]
-        ports:
-        - containerPort: 12345
-          protocol: TCP
-        # If the readiness probe fails, the container is removed from the service.
-        readinessProbe:
-          httpGet:
-            path: /readiness
-            port: 12345
-          # Check every two seconds.
-          periodSeconds: 2
-          # Warmup period before first check.
-          initialDelaySeconds: 5
-          # How long it takes for the probe to time out.
-          timeoutSeconds: 1
-          # Number of attempts before the pod is marked unready
-          failureThreshold: 1
-        # If the liveness probe fails, the container is subject to its restart policy
-        # Since the container can crash on its own, we'll omit this.
-        # livenessProbe:
-        #   httpGet:
-        #     path: /liveness
-        #     port: 12345
-        #   initialDelaySeconds: 20
-        #   periodSeconds: 10
-        #   timeoutSeconds: 3
-        #   failureThreshold: 3
diff --git a/pod_cleanup.sh b/pod_cleanup.sh
deleted file mode 100755
index f5f4232790..0000000000
--- a/pod_cleanup.sh
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/bin/bash
-
-# For some reason, pods aren't going away after they're marked for deletion. 
-# They're getting stuck as "Terminating" for days at a time, leading to an 
-# accumulation of limitless stale pods.
-#
-# I'm not sure what the cause is, but there are lots of reports of this on
-# Google. 
-#
-# One thing I uncovered (in kubernetes events) that might be related:
-#
-# 22m         Normal    TaintManagerEviction     pod/{name}    Marking for deletion Pod {name}
-#
-# But I honestly don't know what is causing this. Perhaps the fuse filesytem?
-# Perhaps DigitalOcean weirdness?
-#
-# This stop-gap non-solution is taken from https://stackoverflow.com/q/50336665
-#
-function cleanup_pods() {
-  namespace=$1
-
-  echo "Cleanup pods in namespace ${namespace}"
-
-  pods=$( kubectl -n ${namespace} get pods | grep -v Running | tail -n +2 | awk -F " " '{print $1}' )
-
-  for pod in $pods;
-  do
-    echo "Deleting pod ${pod}"
-    kubectl -n ${namespace} delete pod $pod --grace-period=0 --force 2> /dev/null
-  done
-}
-
-cleanup_pods "mumble"
-cleanup_pods "trumped"
-
diff --git a/tacotron_melgan/Cargo.toml b/tacotron_melgan/Cargo.toml
deleted file mode 100644
index 5f219f846e..0000000000
--- a/tacotron_melgan/Cargo.toml
+++ /dev/null
@@ -1,19 +0,0 @@
-[package]
-name = "tacotron_melgan"
-version = "0.0.1"
-authors = [ "Brandon Thomas <bt@brand.io>",
-            "Brandon Thomas <echelon@gmail.com>" ]
-
-[[bin]]
-name = "tacotron_melgan"
-path = "src/main.rs"
-
-[dependencies]
-hound = "3.4.0" # wav codec
-rand = "0.7.2" # random sampling for testing (TODO: temp)
-#rocket = "0.4.2" # http server
-#rocket_contrib = "0.4.2" # http server
-serde = "1.0.104"
-serde_derive = "1.0.104"
-serde_json = "1.0.44"
-tch = "0.1.3" # libtorch bindings (pytorch)
diff --git a/tacotron_melgan/README.md b/tacotron_melgan/README.md
deleted file mode 100644
index 65b4e71463..0000000000
--- a/tacotron_melgan/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
-Tacotron + Melgan
-=================
-Runs Tacotron2 + Melgan as a fully end-to-end TTS system.
-
-Compiling
----------
-Libtorch has been problematic, so I have a good version here:
-
-```
-export LIBTORCH=/home/bt/dev/3rd/libtorch
-export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH
-```
-
-This was downloaded from 
-https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-1.3.1%2Bcpu.zip
-
diff --git a/tacotron_melgan/convert_mel_to_torch.py b/tacotron_melgan/convert_mel_to_torch.py
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/tacotron_melgan/jit_containerize_tensor.py b/tacotron_melgan/jit_containerize_tensor.py
deleted file mode 100755
index 5d3f61aa70..0000000000
--- a/tacotron_melgan/jit_containerize_tensor.py
+++ /dev/null
@@ -1,82 +0,0 @@
-#!/usr/bin/env python3
-# parts taken from: https://github.com/pytorch/pytorch/issues/20356#issuecomment-545572400
-"""
-SUPER STUPID HACK
-
-libtensor/tch.rs do not seem to be able to load tensors saved from
-mytorch with `foo_tensor.save()`. This gets around the problem by
-wrapping tensors in JIT containers that can be loaded. The source
-tensor can be pulled out by calling `forward` on the module.
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import sys
-from collections import OrderedDict
-
-# If set, change the tensor type.
-output_type = 'double'
-
-if output_type == 'float':
-    torch.set_default_tensor_type('torch.FloatTensor')
-elif output_type == 'double':
-    torch.set_default_tensor_type('torch.DoubleTensor')
-
-def cuda_to_cpu(model):
-    """Recursively make everything non-CUDA"""
-    if isinstance(model, dict) or isinstance(model, OrderedDict):
-        for key, value in model.items():
-            model[key] = cuda_to_cpu(value)
-        return model
-    if isinstance(model, list):
-        for i, value in enumerate(model):
-            model[i] = cuda_to_cpu(value)
-    elif isinstance(model, torch.Tensor):
-        return model.cpu()
-    else:
-        return model
-
-class Container(torch.nn.Module):
-    def __init__(self, payload):
-        super(Container, self).__init__()
-        self.tensor = payload
-    def forward(self, x):
-        return self.tensor # XXX: This is how we return the wrapped tensor. Such a hack.
-
-def main(filename):
-    output_filename = '{}.containerized.pt'.format(filename)
-
-    print('Loading tensor file: {}'.format(filename))
-    tensor = torch.load(filename, map_location=torch.device('cpu'))
-
-    if output_type == 'float':
-        tensor = tensor.float()
-    elif output_type == 'double':
-        tensor = tensor.double()
-
-    if not isinstance(tensor, torch.Tensor):
-        type_ = type(tensor)
-        raise Exception('File should contain object of type Tensor, not {}'.format(type_))
-
-    print('Switching device from CUDA to CPU (if necessary)')
-    tensor = cuda_to_cpu(tensor)
-
-    print('Containerizing. Libtorch can\'t read raw tensors from ' \
-        + 'pytorch (at least I don\'t know how)')
-
-    container = Container(tensor)
-    jit_container = torch.jit.script(container)
-
-    print('Saving: {}'.format(output_filename))
-    jit_container.save(output_filename)
-
-if __name__ == '__main__':
-    if len(sys.argv) != 2:
-        print('Must supply input filename')
-        sys.exit(1)
-
-    filename = sys.argv[1]
-    main(filename)
-
diff --git a/tacotron_melgan/layers.py b/tacotron_melgan/layers.py
deleted file mode 100644
index 66816f98b2..0000000000
--- a/tacotron_melgan/layers.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import torch
-#from librosa.filters import mel as librosa_mel_fn
-#from audio_processing import dynamic_range_compression
-#from audio_processing import dynamic_range_decompression
-#from stft import STFT
-
-
-class LinearNorm(torch.nn.Module):
-    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
-        super(LinearNorm, self).__init__()
-        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
-
-        torch.nn.init.xavier_uniform_(
-            self.linear_layer.weight,
-            gain=torch.nn.init.calculate_gain(w_init_gain))
-
-    def forward(self, x):
-        return self.linear_layer(x)
-
-
-class ConvNorm(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
-                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
-        super(ConvNorm, self).__init__()
-        if padding is None:
-            assert(kernel_size % 2 == 1)
-            padding = int(dilation * (kernel_size - 1) / 2)
-
-        self.conv = torch.nn.Conv1d(in_channels, out_channels,
-                                    kernel_size=kernel_size, stride=stride,
-                                    padding=padding, dilation=dilation,
-                                    bias=bias)
-
-        torch.nn.init.xavier_uniform_(
-            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
-
-    def forward(self, signal):
-        conv_signal = self.conv(signal)
-        return conv_signal
-
-
-class TacotronSTFT(torch.nn.Module):
-    def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
-                 n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
-                 mel_fmax=8000.0):
-        super(TacotronSTFT, self).__init__()
-        self.n_mel_channels = n_mel_channels
-        self.sampling_rate = sampling_rate
-        self.stft_fn = STFT(filter_length, hop_length, win_length)
-        mel_basis = librosa_mel_fn(
-            sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
-        mel_basis = torch.from_numpy(mel_basis).float()
-        self.register_buffer('mel_basis', mel_basis)
-
-    def spectral_normalize(self, magnitudes):
-        output = dynamic_range_compression(magnitudes)
-        return output
-
-    @torch.jit.ignore
-    def spectral_de_normalize(self, magnitudes):
-        output = dynamic_range_decompression(magnitudes)
-        return output
-
-    def mel_spectrogram(self, y):
-        """Computes mel-spectrograms from a batch of waves
-        PARAMS
-        ------
-        y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
-
-        RETURNS
-        -------
-        mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
-        """
-        assert(torch.min(y.data) >= -1)
-        assert(torch.max(y.data) <= 1)
-
-        magnitudes, phases = self.stft_fn.transform(y)
-        magnitudes = magnitudes.data
-        mel_output = torch.matmul(self.mel_basis, magnitudes)
-        mel_output = self.spectral_normalize(mel_output)
-        return mel_output
diff --git a/tacotron_melgan/requirements.txt b/tacotron_melgan/requirements.txt
deleted file mode 100644
index 8636b28531..0000000000
--- a/tacotron_melgan/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-PyYAML==5.2
-torch==1.3.1
diff --git a/tacotron_melgan/src/main.rs b/tacotron_melgan/src/main.rs
deleted file mode 100644
index d4aea75c30..0000000000
--- a/tacotron_melgan/src/main.rs
+++ /dev/null
@@ -1,68 +0,0 @@
-#![feature(proc_macro_hygiene, decl_macro)]
-
-//#[macro_use] extern crate rocket;
-//#[macro_use] extern crate rocket_contrib;
-#[macro_use] extern crate serde_derive;
-extern crate hound;
-extern crate rand;
-extern crate serde;
-extern crate serde_json;
-extern crate tch;
-
-use tch::CModule;
-use tch::Tensor;
-use tch::nn::Module;
-use tch::nn::ModuleT;
-//use server::run_server;
-
-pub mod melgan;
-pub mod model;
-//pub mod server;
-pub mod tacotron;
-
-pub fn main() {
-  println!("Tacotron2 + MelGan");
-
-  //melgan::run_melgan_network();
-  //tacotron::run_tacotron();
-  //run_end_to_end();
-
-  //run_server();
-}
-
-fn run_end_to_end() {
-  println!("Load Tacotron Model");
-  let tacotron_model =
-      model::load_model_file("/home/bt/dev/voder/tacotron_melgan/tacotron_jit_model_voder_c0cac635.pt");
-
-  println!("Load Melgan Model");
-  let melgan_model =
-      model::load_model_file("/home/bt/dev/voder/tacotron_melgan/melgan_jit_model_voder_c0cac635.pt");
-
-  println!("Load Text Sequence Tensor");
-  let text_sequence =
-      model::load_wrapped_tensor_file("/home/bt/dev/voder/data/text/tacotron_text_sequence.pt.containerized.pt");
-
-  //for i in 0..10 {
-    println!(">>> Evaluating Tacotron Model");
-    let mut mel_tensor = tacotron_model.forward(&text_sequence);
-
-    println!("Result mel tensor: {:?}", mel_tensor);
-
-    if mel_tensor.dim() == 2 {
-      println!("mel unsqeeze");
-      mel_tensor = mel_tensor.unsqueeze(0);
-    }
-
-    let audio_tensor = melgan_model.forward(&mel_tensor);
-
-    println!("Result audio tensor: {:?}", audio_tensor);
-
-    let audio = melgan::audio_tensor_to_audio_signal(audio_tensor);
-
-    println!("Writing audio file...");
-    melgan::write_audio_file(audio, "double_model_results_2.wav");
-
-    println!("Done.");
-  //}
-}
diff --git a/tacotron_melgan/src/melgan.rs b/tacotron_melgan/src/melgan.rs
deleted file mode 100644
index 980b94beb1..0000000000
--- a/tacotron_melgan/src/melgan.rs
+++ /dev/null
@@ -1,99 +0,0 @@
-use tch::CModule;
-use tch::Tensor;
-use tch::nn::Module;
-use tch;
-
-use rand::Rng;
-
-use model::{
-  load_model_file,
-  load_wrapped_tensor_file,
-};
-use hound::Sample;
-
-const WRAPPED_MODEL_PATH : &'static str = "/home/bt/dev/voder/tacotron_melgan/melgan_jit_model_voder_c0cac635.pt";
-
-const EXAMPLE_MEL_1: &'static str = "/home/bt/dev/voder/data/mels/LJ002-0320.mel.containerized.pt";
-const EXAMPLE_MEL_2 : &'static str = "/home/bt/dev/voder/data/mels/trump_2018_02_15-001.mel.containerized.pt";
-
-//pub const MAX_WAV_VALUE : f32 = 32768.0f32;
-//pub const HALF_WAV_VALUE : f32 = MAX_WAV_VALUE * 0.5f32;
-
-// TODO: This is an hparam and should be dynamic.
-//pub const HOP_LENGTH : i64 = 256;
-
-pub fn audio_tensor_to_audio_signal(mel: Tensor) -> Vec<i16> {
-  let mut flat_audio_tensor = mel.squeeze();
-
-  println!("Sqeueezed tensor: {:?}, dim: {}",
-    flat_audio_tensor,
-    flat_audio_tensor.dim());
-
-  let length = flat_audio_tensor.size1().unwrap() as usize;
-  println!("Length: {}", length);
-
-  //let trim_back = HOP_LENGTH * 10;
-  //let new_size = length as i64 - trim_back;
-  //println!("Old size: {}", length);
-  //println!("New size: {}", new_size);
-  //flat_audio_tensor = flat_audio_tensor.resize_(&[new_size]);
-
-  //flat_audio_tensor = flat_audio_tensor * MAX_WAV_VALUE as f64;
-
-  let mut data : Vec<f32> = Vec::with_capacity(length);
-
-  for i in 0 .. length {
-    data.push(0.0f32);
-  }
-
-  flat_audio_tensor.copy_data(data.as_mut_slice(), length as usize);
-
-  data.iter().map(|x| x.trunc() as i16).collect()
-}
-
-fn debug_print_sample(audio: &Vec<f32>, num_samples: usize) {
-  let mut rng = rand::thread_rng();
-  for _ in 0..num_samples {
-    let ri = rng.gen_range(0, audio.len());
-    println!("audio_data[{}]: {:?}", ri, audio.get(ri as usize));
-  }
-}
-
-pub fn run_melgan_network() {
-  let mut mel = load_wrapped_tensor_file(EXAMPLE_MEL_1);
-  println!("Got mel: {:?} of dim {}", mel, mel.dim());
-
-  if mel.dim() == 2 {
-    println!("mel unsqeeze");
-    mel = mel.unsqueeze(0);
-  }
-
-  let melgan_model = load_model_file(WRAPPED_MODEL_PATH);
-
-  println!("Evaluating model...");
-  let output = melgan_model.forward(&mel);
-
-  println!("Result tensor: {:?}", output);
-
-  let audio = audio_tensor_to_audio_signal(output);
-
-  //debug_print_sample(&audio, 10);
-
-  write_audio_file(audio, "melgan_output.wav");
-}
-
-pub fn write_audio_file(audio_signal: Vec<i16>, filename: &str) {
-  let spec = hound::WavSpec {
-    channels: 1,
-    sample_rate: 20000,
-    bits_per_sample: 16,
-    sample_format: hound::SampleFormat::Int,
-  };
-
-  let mut writer = hound::WavWriter::create(filename, spec).unwrap();
-
-  for sample in audio_signal {
-    //let sample = sample * 0.0001f32;
-    writer.write_sample(sample).unwrap();
-  }
-}
diff --git a/tacotron_melgan/src/model.rs b/tacotron_melgan/src/model.rs
deleted file mode 100644
index 87617daa21..0000000000
--- a/tacotron_melgan/src/model.rs
+++ /dev/null
@@ -1,130 +0,0 @@
-use hound::SampleFormat;
-use hound::WavSpec;
-use hound::WavWriter;
-
-use tch::{CModule, Device, Kind};
-use tch::Tensor;
-use tch::nn::Module;
-use tch;
-
-use std::io::Cursor;
-use std::io::BufWriter;
-
-use melgan::audio_tensor_to_audio_signal;
-
-/// Load a Torch model
-pub fn load_model_file(filename: &str) -> CModule {
-  println!("Loading model: {}", filename);
-  tch::CModule::load(filename).unwrap()
-}
-
-/// This is a _HACK_ to load a single Tensor.
-///
-/// It seems currently impossible to load tensors into tch.rs (libtorch),
-/// (eg with 'Tensor::load(filename)') that are saved from pytorch. They
-/// use different serialization formats. Luckily, I can embed a tensor in
-/// a JIT module and unpack it from there instead. The most
-/// straightforward way of getting it out is to define a 'forward()'
-/// method that simply returns the wrapped tensor.
-///
-/// I wrote a script to wrap tensors: 'jit_containerize_tensor.py'.
-pub fn load_wrapped_tensor_file(filename: &str) -> Tensor {
-  println!("Loading wrapped tensor file: {}", filename);
-  let module = tch::CModule::load(filename).unwrap();
-  let mut temp = Tensor::zeros(
-    &[10, 10, 10],
-    (tch::Kind::Float, tch::Device::Cpu)
-  );
-  module.forward(&temp)
-}
-
-pub struct TacoMelModel {
-  tacotron_model: CModule,
-  melgan_model: CModule,
-}
-
-impl TacoMelModel {
-
-  pub fn create(tacotron_filename: &str, melgan_filename: &str) -> Self {
-    let tacotron_model = load_model_file(tacotron_filename);
-    let melgan_model = load_model_file(melgan_filename);
-
-    Self {
-      tacotron_model,
-      melgan_model,
-    }
-  }
-
-  pub fn run_tts(&self, text: &str) -> Vec<i16> {
-    println!("Text : {:?}", text);
-    let copied = text.to_string().to_ascii_lowercase();
-    let mut text_buffer : Vec<i64> = Vec::new();
-
-    for ch in copied.chars() {
-      // TODO: HORRIBLE EXPERIMENTAL HACK. Write a formal module to clean and process text
-      let mut v = ch as i64 - 59;
-      if v < 1 {
-        v = 11; // NB: Space
-      }
-      text_buffer.push(v);
-    }
-
-    println!("Text buffer: {:?}", text_buffer);
-
-    let text_tensor = Tensor::of_slice(text_buffer.as_slice());
-
-    println!("Text tensor: {:?}", text_tensor);
-
-    let text_tensor = text_tensor.unsqueeze(0);
-
-    println!("Text tensor unsq: {:?}", text_tensor);
-
-    let mut mel_tensor = self.tacotron_model.forward(&text_tensor);
-
-    //println!("Loading mel...");
-    //let mel_tensor = load_wrapped_tensor_file("/home/bt/dev/tacotron-melgan/saved_mel.pt.containerized.pt");
-
-    println!("\n\n>>> Mel tensor:\n{:?}\n\n", mel_tensor);
-
-    //let repr = mel_tensor.to_string(20000).unwrap();
-    //println!("\n{:?}\n\n", repr);
-
-    println!("Running melgan...");
-
-    let audio_tensor = self.melgan_model.forward(&mel_tensor);
-
-    println!("\n\n>>> Audio tensor:\n{:?}\n\n", audio_tensor);
-
-    audio_tensor_to_audio_signal(audio_tensor)
-  }
-
-  pub fn run_tts_audio(&self, text: &str) -> Vec<u8> {
-    let audio_signal = self.run_tts(text);
-
-    audio_signal_to_wav_bytes(audio_signal)
-  }
-}
-
-pub fn audio_signal_to_wav_bytes(audio_signal: Vec<i16>) -> Vec<u8> {
-  let spec = WavSpec {
-    channels: 1,
-    sample_rate: 20000,
-    bits_per_sample: 16,
-    sample_format: SampleFormat::Int,
-  };
-  let bytes: Vec<u8> = Vec::new();
-  let seek: Cursor<Vec<u8>> = Cursor::new(bytes);
-  let mut buffer = BufWriter::new(seek);
-  {
-    let mut writer = WavWriter::new(&mut buffer, spec).unwrap();
-    for s in audio_signal {
-      //let s = s * 0.00001f32; // TODO: Find a more appropriate multiplier
-      writer.write_sample(s).unwrap();
-    }
-    writer.finalize().unwrap(); // TODO: Error
-  }
-  match buffer.into_inner() {
-    Err(_) => { Vec::new() }, // TODO: Error
-    Ok(r) => { r.get_ref().to_vec() },
-  }
-}
diff --git a/tacotron_melgan/src/server.rs b/tacotron_melgan/src/server.rs
deleted file mode 100644
index f921c42662..0000000000
--- a/tacotron_melgan/src/server.rs
+++ /dev/null
@@ -1,135 +0,0 @@
-use rocket::{Request, State, Response};
-use rocket::response::content::Content;
-use rocket::http::{RawStr, ContentType};
-use rocket_contrib::json::{Json, JsonValue};
-use model::{TacoMelModel, load_wrapped_tensor_file, load_model_file, audio_signal_to_wav_bytes};
-use tch::nn::Module;
-use melgan::audio_tensor_to_audio_signal;
-use tch::Tensor;
-use std::panic;
-
-#[derive(Serialize, Deserialize)]
-struct Message {
-  contents: String,
-}
-
-#[get("/")]
-fn get_index() -> String {
-  format!("<b>Hello</b>")
-}
-
-#[get("/tts?<text>")]
-fn get_tts(model: State<TacoMelModel>, text: &RawStr) -> Content<Vec<u8>> {
-
-  let result = panic::catch_unwind(|| {
-    let audio_bytes = model.inner().run_tts_audio(text.as_str());
-    audio_bytes
-  });
-
-  match result {
-    Err(_) => {
-      Content(ContentType::Plain, "there was an error".as_bytes().to_vec())
-    },
-    Ok(audio_bytes) => {
-      Content(ContentType::new("audio", "wav"), audio_bytes)
-    }
-  }
-}
-
-fn print_mel(tensor: &Tensor, i: i64) {
-  let t = tensor.squeeze();
-  print!("M[{}][0:5] =", i);
-  for j in 0..5 {
-    print!(" {:?}", t.get(i).get(j));
-  }
-  println!();
-}
-
-fn print_audio(tensor: &Tensor, i: i64) {
-  let t = tensor.squeeze();
-  println!("A[{}] = {:?}", i, t.get(i));
-}
-
-fn print_audio_signal(signal: &Vec<i16>, i: usize) {
-  println!("W[{}] = {:?}", i, signal.get(i).unwrap());
-}
-
-
-#[get("/tts_test")]
-fn get_tts_test() -> Content<Vec<u8>> {
-  println!("Loading melgan model...");
-  let melgan_filename  = "/home/bt/dev/tacotron-melgan/shared_melgan_container.pt";
-  let melgan_filename  = "/home/bt/dev/tacotron-melgan/shared_melgan_container2.pt";
-  let melgan_model = load_model_file(melgan_filename);
-
-  println!("Loading mel...");
-  let mel_tensor = load_wrapped_tensor_file("/home/bt/dev/tacotron-melgan/saved_mel.pt.containerized.pt");
-
-  println!("⚙️");
-  print_mel(&mel_tensor, 0);
-  print_mel(&mel_tensor, 1);
-  print_mel(&mel_tensor, 2);
-  print_mel(&mel_tensor, 3);
-  print_mel(&mel_tensor, 4);
-  print_mel(&mel_tensor, 79);
-
-  //println!("\n\n>>> Mel tensor:\n{:?}\n\n", mel_tensor);
-
-  let audio_tensor = melgan_model.forward(&mel_tensor);
-
-  print_audio(&audio_tensor, 0);
-  print_audio(&audio_tensor, 1);
-  print_audio(&audio_tensor, 2);
-  print_audio(&audio_tensor, 100);
-  print_audio(&audio_tensor, 101);
-  print_audio(&audio_tensor, 102);
-
-  //println!("\n\n>>> Audio tensor:\n{:?}\n\n", mel_tensor);
-
-  let audio_signal = audio_tensor_to_audio_signal(audio_tensor);
-
-  print_audio_signal(&audio_signal, 0);
-  print_audio_signal(&audio_signal, 1);
-  print_audio_signal(&audio_signal, 2);
-  print_audio_signal(&audio_signal, 100);
-  print_audio_signal(&audio_signal, 101);
-  print_audio_signal(&audio_signal, 102);
-
-  let audio_bytes = audio_signal_to_wav_bytes(audio_signal);
-  Content(ContentType::new("audio", "wav"), audio_bytes)
-}
-
-#[catch(404)]
-fn not_found(req: &Request) -> String {
-  format!("I couldn't find '{}'. Try something else?", req.uri())
-}
-
-pub fn run_server() {
-  /*let options = Options::Index | Options::DotFiles;
-  rocket::ignite()
-      .mount("/static", StaticFiles::from("/www/public"))
-      .mount("/pub", StaticFiles::new("/www/public", options).rank(-1))*/
-
-  //let tacotron = "/home/bt/dev/voder/tacotron_melgan/tacotron_jit_model_voder_c0cac635.pt";
-  let tacotron = "/home/bt/dev/voder/tacotron_melgan/tacotron_container4.pt";
-  //let melgan = "/home/bt/dev/voder/tacotron_melgan/melgan_jit_model_voder_c0cac635.pt";
-  let melgan = "/home/bt/dev/voder/tacotron_melgan/melgan_container2.pt";
-  let melgan = "/home/bt/dev/tacotron-melgan/shared_melgan_container.pt";
-  let melgan = "/home/bt/dev/tacotron-melgan/shared_melgan_container2.pt";
-  let model = TacoMelModel::create(
-    tacotron,
-    melgan);
-
-  rocket::ignite()
-      .mount("/", routes![
-          get_index,
-          get_tts,
-          get_tts_test,
-      ])
-      .manage(model)
-      .register(catchers![not_found])
-      .launch();
-}
-
-
-
diff --git a/tacotron_melgan/src/tacotron.rs b/tacotron_melgan/src/tacotron.rs
deleted file mode 100644
index df37f5bc1b..0000000000
--- a/tacotron_melgan/src/tacotron.rs
+++ /dev/null
@@ -1,116 +0,0 @@
-use tch::Tensor;
-use tch::CModule;
-use tch;
-use tch::nn::Module;
-
-const WRAPPED_MODEL_PATH : &'static str = "/home/bt/dev/voder/tacotron_melgan/tacotron_jit_model_voder_c0cac635.pt";
-//const EXAMPLE_TEXT_SEQUENCE : &'static str = "/home/bt/dev/voder/data/text/tacotron_text_sequence.pt";
-const EXAMPLE_TEXT_SEQUENCE : &'static str = "/home/bt/dev/voder/data/text/tacotron_text_sequence.pt.containerized.pt";
-
-pub fn load_tacotron_model(filename: &str) -> CModule {
-  // TODO
-  println!("Loading model: {}", filename);
-  tch::CModule::load(filename).unwrap()
-}
-
-pub fn load_wrapped_text(filename: &str) -> Tensor {
-  println!("Loading wrapped text sequence file: {}", filename);
-  let module = tch::CModule::load(filename).unwrap();
-  let mut temp = Tensor::zeros(
-    &[10, 10, 10],
-    (tch::Kind::Float, tch::Device::Cpu)
-  );
-  module.forward(&temp)
-}
-
-
-pub fn run_tacotron() {
-  let mut text_sequence = load_wrapped_text(EXAMPLE_TEXT_SEQUENCE);
-  println!("Got text_sequence: {:?} of dim {}", text_sequence, text_sequence.dim());
-
-  let mut vs = tch::nn::VarStore::new(tch::Device::Cpu);
-  let tacotron_model = load_tacotron_model(WRAPPED_MODEL_PATH);
-
-  for i in 0..10 {
-    println!("Evaluating model... {}", i);
-    let output = tacotron_model.forward(&text_sequence);
-
-    println!("Result tensor: {:?}", output);
-  }
-}
-
-/*println!("Tacotron2 + MelGan");
-
-// Create the model and load the weights from the file.
-let mut vs = tch::nn::VarStore::new(tch::Device::Cpu);
-
-println!("Loading Wrapped model: {}", WRAPPED_MODEL_PATH);
-
-println!("loaded");
-
-/* Just gotta do this...
-  model = Generator(hp.audio.n_mel_channels).cuda()
-  model.load_state_dict(checkpoint['model_g'])
-  model.eval(inference=False)
-
-  with torch.no_grad():
-      for melpath in tqdm.tqdm(glob.glob(os.path.join(args.input_folder, '*.mel'))):
-          mel = torch.load(melpath)
-          if len(mel.shape) == 2:
-              mel = mel.unsqueeze(0)
-
-          filename = melpath.replace('.mel', '_reconstructed_epoch%04d.png' % checkpoint['epoch'])
-          #render_histogram(mel, filename)
-          mel = mel.cuda()
-
-          audio = model.inference(mel)
-          audio = audio.cpu().detach().numpy()
-
-          out_path = melpath.replace('.mel', '_reconstructed_epoch%04d.wav' % checkpoint['epoch'])
-          write(out_path, hp.audio.sampling_rate, audio)
-*/
-
-//vs.load(WRAPPED_MODEL_PATH).unwrap();
-
-println!("Loading trained model...");
-let model = tch::CModule::load(WRAPPED_MODEL_PATH).unwrap();
-
-// NB: This is just random data for now. Kind of mel shaped since it's an image.
-//let path = "/home/bt/Downloads/virtual-studio-design.jpg";
-//let image = imagenet::load_image_and_resize(path, 100, 100).unwrap();
-//let image = imagenet::load_image(EXAMPLE_MEL_PATH).unwrap();
-
-println!("Loading mel file...");
-let mel_file = Tensor::load(EXAMPLE_MEL_PATH).unwrap();
-
-println!("Forwarding mel to model...");
-let output = model.forward_ts(&[mel_file.unsqueeze(0)]).unwrap();
-
-
-/*// TODO: Now how do I evaluate the model?
-//let resnet18 = tch::vision::resnet::resnet18(&vs.root(), imagenet::CLASS_COUNT);
-
-let path = "/home/bt/Downloads/virtual-studio-design.jpg";
-let image = imagenet::load_image_and_resize(path, 100, 100).unwrap();
-
-// NB: This works
-let net = Box::new(resnet::resnet18(&vs.root(), imagenet::CLASS_COUNT));
-
-for _ in 0..10 {
-  let output = net
-      .forward_t(&image.unsqueeze(0), /*train=*/ false)
-      .softmax(-1, tch::Kind::Float); // Convert to probability.
-
-  for (probability, class) in imagenet::top(&output, 5).iter() {
-    println!("{:50} {:5.2}%", class, 100.0 * probability)
-  }
-}*/
-
-/*// Apply the forward pass of the model to get the logits.
-let output = net
-    .forward_t(&image.unsqueeze(0), /*train=*/ false)
-    .softmax(-1, tch::Kind::Float); // Convert to probability.
-
-// Print the top 5 categories for this image.
-Ok(())*/
-*/
diff --git a/tacotron_melgan/tacotron_model.py b/tacotron_melgan/tacotron_model.py
deleted file mode 100644
index 2f470da0a2..0000000000
--- a/tacotron_melgan/tacotron_model.py
+++ /dev/null
@@ -1,604 +0,0 @@
-# Adapted from nvidia's tacotron2 codebase
-
-from math import sqrt
-import torch
-from torch.autograd import Variable
-from torch import nn
-from torch.nn import functional as F
-from layers import ConvNorm, LinearNorm
-#from utils import to_gpu, get_mask_from_lengths
-
-class LocationLayer(nn.Module):
-    def __init__(self, attention_n_filters, attention_kernel_size,
-                 attention_dim):
-        super(LocationLayer, self).__init__()
-        padding = int((attention_kernel_size - 1) / 2)
-        self.location_conv = ConvNorm(2, attention_n_filters,
-                                      kernel_size=attention_kernel_size,
-                                      padding=padding, bias=False, stride=1,
-                                      dilation=1)
-        self.location_dense = LinearNorm(attention_n_filters, attention_dim,
-                                         bias=False, w_init_gain='tanh')
-
-    def forward(self, attention_weights_cat):
-        processed_attention = self.location_conv(attention_weights_cat)
-        processed_attention = processed_attention.transpose(1, 2)
-        processed_attention = self.location_dense(processed_attention)
-        return processed_attention
-
-
-class Attention(nn.Module):
-    def __init__(self, attention_rnn_dim, embedding_dim, attention_dim,
-                 attention_location_n_filters, attention_location_kernel_size):
-        super(Attention, self).__init__()
-        self.query_layer = LinearNorm(attention_rnn_dim, attention_dim,
-                                      bias=False, w_init_gain='tanh')
-        self.memory_layer = LinearNorm(embedding_dim, attention_dim, bias=False,
-                                       w_init_gain='tanh')
-        self.v = LinearNorm(attention_dim, 1, bias=False)
-        self.location_layer = LocationLayer(attention_location_n_filters,
-                                            attention_location_kernel_size,
-                                            attention_dim)
-        self.score_mask_value = -float("inf")
-
-    def get_alignment_energies(self, query, processed_memory,
-                               attention_weights_cat):
-        """
-        PARAMS
-        ------
-        query: decoder output (batch, n_mel_channels * n_frames_per_step)
-        processed_memory: processed encoder outputs (B, T_in, attention_dim)
-        attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)
-
-        RETURNS
-        -------
-        alignment (batch, max_time)
-        """
-
-        processed_query = self.query_layer(query.unsqueeze(1))
-        processed_attention_weights = self.location_layer(attention_weights_cat)
-        energies = self.v(torch.tanh(
-            processed_query + processed_attention_weights + processed_memory))
-
-        energies = energies.squeeze(-1)
-        return energies
-
-    def forward(self, attention_hidden_state, memory, processed_memory,
-                #attention_weights_cat, mask):
-                attention_weights_cat):
-        """
-        PARAMS
-        ------
-        attention_hidden_state: attention rnn last output
-        memory: encoder outputs
-        processed_memory: processed encoder outputs
-        attention_weights_cat: previous and cummulative attention weights
-        mask: binary mask for padded data
-        """
-        alignment = self.get_alignment_energies(
-            attention_hidden_state, processed_memory, attention_weights_cat)
-
-        #if mask is not None:
-        #    alignment.data.masked_fill_(mask, self.score_mask_value)
-
-        attention_weights = F.softmax(alignment, dim=1)
-        attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
-        attention_context = attention_context.squeeze(1)
-
-        return attention_context, attention_weights
-
-
-class Prenet(nn.Module):
-    def __init__(self, in_dim, sizes):
-        super(Prenet, self).__init__()
-        in_sizes = [in_dim] + sizes[:-1]
-        self.layers = nn.ModuleList(
-            [LinearNorm(in_size, out_size, bias=False)
-             for (in_size, out_size) in zip(in_sizes, sizes)])
-
-    def forward(self, x):
-        for linear in self.layers:
-            x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
-        return x
-
-
-class Postnet(nn.Module):
-    """Postnet
-        - Five 1-d convolution with 512 channels and kernel size 5
-    """
-
-    def __init__(self, hparams):
-        super(Postnet, self).__init__()
-        self.convolutions = nn.ModuleList()
-
-        # TODO(TEMP)
-        """
-        self.convolutions.append(
-            nn.Sequential(
-                ConvNorm(hparams.n_mel_channels, hparams.postnet_embedding_dim,
-                         kernel_size=hparams.postnet_kernel_size, stride=1,
-                         padding=int((hparams.postnet_kernel_size - 1) / 2),
-                         dilation=1, w_init_gain='tanh'),
-                nn.BatchNorm1d(hparams.postnet_embedding_dim))
-        )
-
-        for i in range(1, hparams.postnet_n_convolutions - 1):
-            self.convolutions.append(
-                nn.Sequential(
-                    ConvNorm(hparams.postnet_embedding_dim,
-                             hparams.postnet_embedding_dim,
-                             kernel_size=hparams.postnet_kernel_size, stride=1,
-                             padding=int((hparams.postnet_kernel_size - 1) / 2),
-                             dilation=1, w_init_gain='tanh'),
-                    nn.BatchNorm1d(hparams.postnet_embedding_dim))
-            )
-
-        self.convolutions.append(
-            nn.Sequential(
-                ConvNorm(hparams.postnet_embedding_dim, hparams.n_mel_channels,
-                         kernel_size=hparams.postnet_kernel_size, stride=1,
-                         padding=int((hparams.postnet_kernel_size - 1) / 2),
-                         dilation=1, w_init_gain='linear'),
-                nn.BatchNorm1d(hparams.n_mel_channels))
-            )
-        """
-
-    def forward(self, x):
-        for i in range(len(self.convolutions) - 1):
-            x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training)
-        x = F.dropout(self.convolutions[-1](x), 0.5, self.training)
-
-        return x
-
-
-class Encoder(nn.Module):
-    """Encoder module:
-        - Three 1-d convolution banks
-        - Bidirectional LSTM
-    """
-    def __init__(self, hparams):
-        super(Encoder, self).__init__()
-
-        convolutions = []
-        for _ in range(hparams.encoder_n_convolutions):
-            conv_layer = nn.Sequential(
-                ConvNorm(hparams.encoder_embedding_dim,
-                         hparams.encoder_embedding_dim,
-                         kernel_size=hparams.encoder_kernel_size, stride=1,
-                         padding=int((hparams.encoder_kernel_size - 1) / 2),
-                         dilation=1, w_init_gain='relu'),
-                nn.BatchNorm1d(hparams.encoder_embedding_dim))
-            convolutions.append(conv_layer)
-        self.convolutions = nn.ModuleList(convolutions)
-
-        self.lstm = nn.LSTM(hparams.encoder_embedding_dim,
-                            int(hparams.encoder_embedding_dim / 2), 1,
-                            batch_first=True, bidirectional=True)
-
-    # def forward(self, x, input_lengths):
-    #     for conv in self.convolutions:
-    #         x = F.dropout(F.relu(conv(x)), 0.5, self.training)
-
-    #     x = x.transpose(1, 2)
-
-    #     # pytorch tensor are not reversible, hence the conversion
-    #     #input_lengths = input_lengths.cpu().numpy()
-    #     x = nn.utils.rnn.pack_padded_sequence(
-    #         x, input_lengths, batch_first=True)
-
-    #     self.lstm.flatten_parameters()
-    #     outputs, _ = self.lstm(x)
-
-    #     outputs, _ = nn.utils.rnn.pad_packed_sequence(
-    #         outputs, batch_first=True)
-
-    #     return outputs
-
-    # NB: This used to be 'inference'
-    def forward(self, x):
-        for conv in self.convolutions:
-            x = F.dropout(F.relu(conv(x)), 0.5, self.training)
-
-        x = x.transpose(1, 2)
-
-        # NB(bt): Don't need to flatten, per the method comments:
-        # Right now, this works only if the module is on the GPU and cuDNN is enabled.
-        #self.lstm.flatten_parameters()
-        outputs, _ = self.lstm(x)
-
-        return outputs
-
-class Decoder(nn.Module):
-    def __init__(self, hparams):
-        super(Decoder, self).__init__()
-
-        # NB(bt): Initializing these upfront because JIT script fails otherwise:
-        #  "Tried to set nonexistent attribute: attention_hidden.
-        #   Did you forget to initialize it in __init__()?:"
-        self.attention_hidden = torch.zeros(1,1)
-        self.attention_cell = torch.zeros(1, 1)
-        self.decoder_hidden = torch.zeros(1, 1)
-        self.decoder_cell = torch.zeros(1, 1)
-        self.attention_weights = torch.zeros(1, 1)
-        self.attention_weights_cum = torch.zeros(1, 1)
-        self.attention_context = torch.zeros(1, 1)
-        self.memory = torch.zeros(1, 1)
-        self.processed_memory = torch.zeros(1, 1)
-
-        self.n_mel_channels = hparams.n_mel_channels
-        self.n_frames_per_step = hparams.n_frames_per_step
-        self.encoder_embedding_dim = hparams.encoder_embedding_dim
-        self.attention_rnn_dim = hparams.attention_rnn_dim
-        self.decoder_rnn_dim = hparams.decoder_rnn_dim
-        self.prenet_dim = hparams.prenet_dim
-        self.max_decoder_steps = hparams.max_decoder_steps
-        self.gate_threshold = hparams.gate_threshold
-        self.p_attention_dropout = hparams.p_attention_dropout
-        self.p_decoder_dropout = hparams.p_decoder_dropout
-
-        self.prenet = Prenet(
-            hparams.n_mel_channels * hparams.n_frames_per_step,
-            [hparams.prenet_dim, hparams.prenet_dim])
-
-        self.attention_rnn = nn.LSTMCell(
-            hparams.prenet_dim + hparams.encoder_embedding_dim,
-            hparams.attention_rnn_dim)
-
-        self.attention_layer = Attention(
-            hparams.attention_rnn_dim, hparams.encoder_embedding_dim,
-            hparams.attention_dim, hparams.attention_location_n_filters,
-            hparams.attention_location_kernel_size)
-
-        self.decoder_rnn = nn.LSTMCell(
-            hparams.attention_rnn_dim + hparams.encoder_embedding_dim,
-            hparams.decoder_rnn_dim, 1)
-
-        self.linear_projection = LinearNorm(
-            hparams.decoder_rnn_dim + hparams.encoder_embedding_dim,
-            hparams.n_mel_channels * hparams.n_frames_per_step)
-
-        self.gate_layer = LinearNorm(
-            hparams.decoder_rnn_dim + hparams.encoder_embedding_dim, 1,
-            bias=True, w_init_gain='sigmoid')
-
-    def get_go_frame(self, memory):
-        """ Gets all zeros frames to use as first decoder input
-        PARAMS
-        ------
-        memory: decoder outputs
-
-        RETURNS
-        -------
-        decoder_input: all zeros frames
-        """
-
-        B = memory.size(0)
-        # NB(bt): Rewriting this as it doesn't work with scripting (but
-        # does with tracing).
-        # mem = memory.data.new(B, self.n_mel_channels * self.n_frames_per_step)
-        # decoder_input = Variable(mem.zero_())
-        mem = torch.zeros(B, self.n_mel_channels * self.n_frames_per_step)
-        # NB(bt): I don't think I need a variable as we don't need to do backprop.
-        # decoder_input = Variable(mem)
-        # return decoder_input
-        return mem
-
-    #def initialize_decoder_states(self, memory, mask):
-    def initialize_decoder_states(self, memory):
-        """ Initializes attention rnn states, decoder rnn states, attention
-        weights, attention cumulative weights, attention context, stores memory
-        and stores processed memory
-        PARAMS
-        ------
-        memory: Encoder outputs
-        mask: Mask for padded data if training, expects None for inference
-        """
-        B = memory.size(0)
-        MAX_TIME = memory.size(1)
-
-        # NB(bt): Rewriting for scripting
-        #self.attention_hidden = Variable(memory.data.new(
-        #    B, self.attention_rnn_dim).zero_())
-        self.attention_hidden = torch.zeros(B, self.attention_rnn_dim)
-        #self.attention_cell = Variable(memory.data.new(
-        #    B, self.attention_rnn_dim).zero_())
-        self.attention_cell = torch.zeros(B, self.attention_rnn_dim)
-
-        #self.decoder_hidden = Variable(memory.data.new(
-        #    B, self.decoder_rnn_dim).zero_())
-        self.decoder_hidden = torch.zeros(B, self.decoder_rnn_dim)
-        #self.decoder_cell = Variable(memory.data.new(
-        #    B, self.decoder_rnn_dim).zero_())
-        self.decoder_cell = torch.zeros(B, self.decoder_rnn_dim)
-
-        #self.attention_weights = Variable(memory.data.new(
-        #    B, MAX_TIME).zero_())
-        self.attention_weights = torch.zeros(B, MAX_TIME)
-        #self.attention_weights_cum = Variable(memory.data.new(
-        #    B, MAX_TIME).zero_())
-        self.attention_weights_cum = torch.zeros(B, MAX_TIME)
-        #self.attention_context = Variable(memory.data.new(
-        #    B, self.encoder_embedding_dim).zero_())
-        self.attention_context = torch.zeros(B, self.encoder_embedding_dim)
-
-        self.memory = memory
-        self.processed_memory = self.attention_layer.memory_layer(memory)
-        # NB(bt): Can't set to None in the CTOR as it gets optimized out.
-        # self.mask = mask
-
-    def parse_decoder_inputs(self, decoder_inputs):
-        """ Prepares decoder inputs, i.e. mel outputs
-        PARAMS
-        ------
-        decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs
-
-        RETURNS
-        -------
-        inputs: processed decoder inputs
-
-        """
-        # (B, n_mel_channels, T_out) -> (B, T_out, n_mel_channels)
-        decoder_inputs = decoder_inputs.transpose(1, 2)
-        decoder_inputs = decoder_inputs.view(
-            decoder_inputs.size(0),
-            int(decoder_inputs.size(1)/self.n_frames_per_step), -1)
-        # (B, T_out, n_mel_channels) -> (T_out, B, n_mel_channels)
-        decoder_inputs = decoder_inputs.transpose(0, 1)
-        return decoder_inputs
-
-    def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments):
-        """ Prepares decoder outputs for output
-        PARAMS
-        ------
-        mel_outputs:
-        gate_outputs: gate output energies
-        alignments:
-
-        RETURNS
-        -------
-        mel_outputs:
-        gate_outpust: gate output energies
-        alignments:
-        """
-        # type: (List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
-
-        # (T_out, B) -> (B, T_out)
-        alignments = torch.stack(alignments).transpose(0, 1)
-        # (T_out, B) -> (B, T_out)
-        gate_outputs = torch.stack(gate_outputs).transpose(0, 1)
-        gate_outputs = gate_outputs.contiguous()
-        # (T_out, B, n_mel_channels) -> (B, T_out, n_mel_channels)
-        mel_outputs = torch.stack(mel_outputs).transpose(0, 1).contiguous()
-        # decouple frames per step
-        mel_outputs = mel_outputs.view(
-            mel_outputs.size(0), -1, self.n_mel_channels)
-        # (B, T_out, n_mel_channels) -> (B, n_mel_channels, T_out)
-        mel_outputs = mel_outputs.transpose(1, 2)
-
-        return mel_outputs, gate_outputs, alignments
-
-    def decode(self, decoder_input):
-        """ Decoder step using stored states, attention and memory
-        PARAMS
-        ------
-        decoder_input: previous mel output
-
-        RETURNS
-        -------
-        mel_output:
-        gate_output: gate output energies
-        attention_weights:
-        """
-        cell_input = torch.cat((decoder_input, self.attention_context), -1)
-
-        #self.attention_hidden, self.attention_cell = self.attention_rnn(
-        results = self.attention_rnn(cell_input, (self.attention_hidden, self.attention_cell))
-        self.attention_hidden = results[0]
-        self.attention_cell = results[1]
-        self.attention_hidden = F.dropout(
-            self.attention_hidden, self.p_attention_dropout, self.training)
-
-        attention_weights_cat = torch.cat(
-            (self.attention_weights.unsqueeze(1),
-             self.attention_weights_cum.unsqueeze(1)), dim=1)
-        #self.attention_context, self.attention_weights = self.attention_layer(
-        results = self.attention_layer(
-            self.attention_hidden, self.memory, self.processed_memory,
-            #attention_weights_cat, self.mask)
-            attention_weights_cat)
-        self.attention_context = results[0]
-        self.attention_weights = results[1]
-
-        self.attention_weights_cum += self.attention_weights
-        decoder_input = torch.cat(
-            (self.attention_hidden, self.attention_context), -1)
-        #self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
-        results = self.decoder_rnn(
-            decoder_input, (self.decoder_hidden, self.decoder_cell))
-        self.decoder_hidden = results[0]
-        self.decoder_cell = results[1]
-        self.decoder_hidden = F.dropout(
-            self.decoder_hidden, self.p_decoder_dropout, self.training)
-
-        decoder_hidden_attention_context = torch.cat(
-            (self.decoder_hidden, self.attention_context), dim=1)
-        decoder_output = self.linear_projection(
-            decoder_hidden_attention_context)
-
-        gate_prediction = self.gate_layer(decoder_hidden_attention_context)
-        return decoder_output, gate_prediction, self.attention_weights
-
-#    def forward(self, memory, decoder_inputs, memory_lengths):
-#        """ Decoder forward pass for training
-#        PARAMS
-#        ------
-#        memory: Encoder outputs
-#        decoder_inputs: Decoder inputs for teacher forcing. i.e. mel-specs
-#        memory_lengths: Encoder output lengths for attention masking.
-#
-#        RETURNS
-#        -------
-#        mel_outputs: mel outputs from the decoder
-#        gate_outputs: gate outputs from the decoder
-#        alignments: sequence of attention weights from the decoder
-#        """
-#
-#        decoder_input = self.get_go_frame(memory).unsqueeze(0)
-#        decoder_inputs = self.parse_decoder_inputs(decoder_inputs)
-#        decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
-#        decoder_inputs = self.prenet(decoder_inputs)
-#
-#        self.initialize_decoder_states(
-#            memory, mask=~get_mask_from_lengths(memory_lengths))
-#
-#        mel_outputs, gate_outputs, alignments = [], [], []
-#        while len(mel_outputs) < decoder_inputs.size(0) - 1:
-#            decoder_input = decoder_inputs[len(mel_outputs)]
-#            mel_output, gate_output, attention_weights = self.decode(
-#                decoder_input)
-#            mel_outputs += [mel_output.squeeze(1)]
-#            gate_outputs += [gate_output.squeeze()]
-#            alignments += [attention_weights]
-#
-#        mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
-#            mel_outputs, gate_outputs, alignments)
-#
-#        return mel_outputs, gate_outputs, alignments
-
-    def forward(self, memory):
-        """ Decoder inference
-        PARAMS
-        ------
-        memory: Encoder outputs
-
-        RETURNS
-        -------
-        mel_outputs: mel outputs from the decoder
-        gate_outputs: gate outputs from the decoder
-        alignments: sequence of attention weights from the decoder
-        """
-        decoder_input = self.get_go_frame(memory)
-
-        #self.initialize_decoder_states(memory, mask=None)
-        self.initialize_decoder_states(memory)
-
-        mel_outputs, gate_outputs, alignments = [], [], []
-        while True:
-            decoder_input = self.prenet(decoder_input)
-            mel_output, gate_output, alignment = self.decode(decoder_input)
-
-            mel_outputs += [mel_output.squeeze(1)]
-            gate_outputs += [gate_output]
-            alignments += [alignment]
-
-            if torch.sigmoid(gate_output.data) > self.gate_threshold:
-                break
-            elif len(mel_outputs) == self.max_decoder_steps:
-                print("Warning! Reached max decoder steps")
-                break
-
-            decoder_input = mel_output
-
-        mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
-            mel_outputs, gate_outputs, alignments)
-
-        return mel_outputs, gate_outputs, alignments
-
-class Tacotron2(nn.Module):
-    def __init__(self, hparams):
-        super(Tacotron2, self).__init__()
-        self.mask_padding = hparams.mask_padding
-        self.fp16_run = hparams.fp16_run
-        self.n_mel_channels = hparams.n_mel_channels
-        self.n_frames_per_step = hparams.n_frames_per_step
-
-        # https://pytorch.org/docs/stable/tensors.html
-        #torch.set_default_tensor_type('torch.HalfTensor')
-        #torch.set_default_tensor_type('torch.DoubleTensor')
-
-        self.embedding = nn.Embedding(
-            hparams.n_symbols, hparams.symbols_embedding_dim)
-
-        std = sqrt(2.0 / (hparams.n_symbols + hparams.symbols_embedding_dim))
-        val = sqrt(3.0) * std  # uniform bounds for std
-        self.embedding.weight.data.uniform_(-val, val)
-        self.encoder = Encoder(hparams)
-        self.decoder = Decoder(hparams)
-        # NB(bt): Postnet does not seem to have different output per inference experiments
-        #self.postnet = Postnet(hparams)
-
-#    def parse_batch(self, batch):
-#        text_padded, input_lengths, mel_padded, gate_padded, \
-#            output_lengths = batch
-#        text_padded = to_gpu(text_padded).long()
-#        input_lengths = to_gpu(input_lengths).long()
-#        max_len = torch.max(input_lengths.data).item()
-#        mel_padded = to_gpu(mel_padded).float()
-#        gate_padded = to_gpu(gate_padded).float()
-#        output_lengths = to_gpu(output_lengths).long()
-#
-#        return (
-#            (text_padded, input_lengths, mel_padded, max_len, output_lengths),
-#            (mel_padded, gate_padded))
-#
-    #def parse_output(self, outputs, output_lengths=None):
-    def parse_output(self, outputs):
-        # type: (List[torch.Tensor]) -> List[torch.Tensor]
-        # NB(bt): Not calling this currently
-        """
-        if self.mask_padding and output_lengths is not None:
-            mask = ~get_mask_from_lengths(output_lengths)
-            mask = mask.expand(self.n_mel_channels, mask.size(0), mask.size(1))
-            mask = mask.permute(1, 0, 2)
-
-            outputs[0].data.masked_fill_(mask, 0.0)
-            # NB(bt): No postnet. outputs[1].data.masked_fill_(mask, 0.0)
-            #outputs[2].data.masked_fill_(mask[:, 0, :], 1e3)  # gate energies
-            outputs[1].data.masked_fill_(mask[:, 0, :], 1e3)  # gate energies
-        """
-
-        return outputs
-
-    """
-    #def forward(self, inputs):
-    def forward(self, text_inputs, text_lengths, mels, max_len, output_lengths):
-        #text_inputs, text_lengths, mels, max_len, output_lengths = inputs
-        text_lengths, output_lengths = text_lengths.data, output_lengths.data
-
-        embedded_inputs = self.embedding(text_inputs).transpose(1, 2)
-
-        encoder_outputs = self.encoder(embedded_inputs, text_lengths)
-
-        mel_outputs, gate_outputs, alignments = self.decoder(
-            encoder_outputs, mels, memory_lengths=text_lengths)
-
-        mel_outputs_postnet = self.postnet(mel_outputs)
-        mel_outputs_postnet = mel_outputs + mel_outputs_postnet
-
-        return self.parse_output(
-            [mel_outputs, mel_outputs_postnet, gate_outputs, alignments],
-            output_lengths)
-    """
-
-    # NB: This was the old 'inference' method. It's the single entrypoint into mel inference.
-    def forward(self, inputs):
-        #torch.set_default_tensor_type('torch.DoubleTensor')
-        temp = self.embedding(inputs)
-        embedded_inputs = temp.transpose(1, 2)
-        encoder_outputs = self.encoder.forward(embedded_inputs)
-        mel_outputs, gate_outputs, alignments = self.decoder.forward(encoder_outputs)
-
-        # TODO(bt): The postnet seemed to produce the *same* output, but perhaps not necessary
-        #mel_outputs_postnet = self.postnet(mel_outputs)
-        #mel_outputs_postnet = mel_outputs + mel_outputs_postnet
-
-        outputs = self.parse_output(
-            [
-              mel_outputs,
-              #mel_outputs_postnet,
-              gate_outputs,
-              alignments
-            ])
-
-        #return outputs
-        return outputs[0].float()
diff --git a/tacotron_melgan/wrap_melgan_model.py b/tacotron_melgan/wrap_melgan_model.py
deleted file mode 100755
index b96d9b0613..0000000000
--- a/tacotron_melgan/wrap_melgan_model.py
+++ /dev/null
@@ -1,148 +0,0 @@
-#!/usr/bin/env python3
-# From: https://github.com/pytorch/pytorch/issues/20356#issuecomment-545572400
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from collections import OrderedDict
-import re
-import yaml
-
-#torch.set_default_tensor_type('torch.DoubleTensor')
-
-class ResStack(nn.Module):
-    def __init__(self, channel):
-        super(ResStack, self).__init__()
-
-        block_list = [
-            nn.Sequential(
-                nn.LeakyReLU(0.2),
-                nn.ReflectionPad1d(3**i),
-                nn.utils.weight_norm(nn.Conv1d(channel, channel, kernel_size=3, dilation=3**i)),
-                nn.LeakyReLU(0.2),
-                nn.utils.weight_norm(nn.Conv1d(channel, channel, kernel_size=1)),
-            )
-            for i in range(3)
-        ]
-
-        self.block0 = block_list[0]
-        self.block1 = block_list[1]
-        self.block2 = block_list[2]
-
-        shortcut_list = [
-            nn.utils.weight_norm(nn.Conv1d(channel, channel, kernel_size=1))
-            for i in range(3)
-        ]
-
-        self.shortcut0 = shortcut_list[0]
-        self.shortcut1 = shortcut_list[1]
-        self.shortcut2 = shortcut_list[2]
-
-
-    def forward(self, x):
-        x = self.shortcut0(x) + self.block0(x)
-        x = self.shortcut1(x) + self.block1(x)
-        x = self.shortcut2(x) + self.block2(x)
-        return x
-
-class Container(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-        mel_channel = 80 # TODO: Load from yaml
-
-        self.generator = nn.Sequential(
-            nn.ReflectionPad1d(3),
-            nn.utils.weight_norm(nn.Conv1d(mel_channel, 512, kernel_size=7, stride=1)),
-
-            nn.LeakyReLU(0.2),
-            nn.utils.weight_norm(nn.ConvTranspose1d(512, 256, kernel_size=16, stride=8, padding=4)),
-
-            ResStack(256),
-
-            nn.LeakyReLU(0.2),
-            nn.utils.weight_norm(nn.ConvTranspose1d(256, 128, kernel_size=16, stride=8, padding=4)),
-
-            ResStack(128),
-
-            nn.LeakyReLU(0.2),
-            nn.utils.weight_norm(nn.ConvTranspose1d(128, 64, kernel_size=4, stride=2, padding=1)),
-
-            ResStack(64),
-
-            nn.LeakyReLU(0.2),
-            nn.utils.weight_norm(nn.ConvTranspose1d(64, 32, kernel_size=4, stride=2, padding=1)),
-
-            ResStack(32),
-
-            nn.LeakyReLU(0.2),
-            nn.ReflectionPad1d(3),
-            nn.utils.weight_norm(nn.Conv1d(32, 1, kernel_size=7, stride=1)),
-            nn.Tanh(),
-        )
-
-    # TODO: Looks unnecessary. Remove once this works.
-    def eval(self):
-        super(Container, self).eval()
-
-
-    def forward(self, mel):
-        mel = (mel + 5.0) / 5.0 # roughly normalize spectrogram
-        audio = self.generator(mel)
-
-        # From inference() method:
-        audio = audio.squeeze()
-        #MAX_WAV_VALUE = 32768.0
-        #audio = MAX_WAV_VALUE * audio
-
-        #return audio.float()
-        return audio
-
-
-print('Loading melgan model...')
-melgan_model_file = '/home/bt/models/melgan-swpark/firstgo_a7c2351_3650.pt'
-melgan_model_file = '/home/bt/models/melgan-swpark/ljs-high-pitch_8741385_6975.pt'
-melgan_model = torch.load(melgan_model_file, map_location=torch.device('cpu'))
-
-module = Container()
-
-# NB: This is done so we can rename the model components above since `zip` won't work :(
-BLOCK_REGEX = re.compile('blocks\.(\d+)\.')
-SHORTCUT_REGEX = re.compile('shortcuts\.(\d+)\.')
-
-new_model_g = [] # Rebuild the ordered dict
-for key, value in melgan_model['model_g'].items():
-    if 'blocks.' in key:
-        new_key = BLOCK_REGEX.sub(r'block\1.', key)
-        key = new_key
-    elif 'shortcuts.' in key:
-        new_key = SHORTCUT_REGEX.sub(r'shortcut\1.', key)
-        key = new_key
-    new_model_g.append((key, value))
-
-
-melgan_model['model_g'] = OrderedDict(new_model_g)
-
-print('Load state dict...')
-module.load_state_dict(melgan_model['model_g'])
-
-#output_filename = 'melgan_container2.pt'
-output_filename = 'melgan_ljs-high-pitch_container.pt'
-
-# NB: Tracing evaluates the model on input and unrolls and hardcodes branching
-# and loops. Scripting allows these to remain by converting the entire program
-# to TorchScript. Scripting seems MUCH faster, is also harder get working.
-trace_model = True
-if trace_model:
-    print('Tracing model and saving as: {}'.format(output_filename))
-    mel_file = '/home/bt/dev/voder/data/mels/LJ002-0320.mel'
-    mel = torch.load(mel_file, map_location=torch.device('cpu'))
-    # NB: Getting `Tracing failed sanity checks! Graphs differed across invocations!`
-    traced_script_module = torch.jit.trace(module, mel, check_trace=True)
-    traced_script_module.save(output_filename)
-else:
-    print('Saving script model as: {}'.format(output_filename))
-    container = torch.jit.script(module)
-    container.save(output_filename)
-
-
diff --git a/tacotron_melgan/wrap_tacotron2_model.py b/tacotron_melgan/wrap_tacotron2_model.py
deleted file mode 100755
index 17c5035426..0000000000
--- a/tacotron_melgan/wrap_tacotron2_model.py
+++ /dev/null
@@ -1,140 +0,0 @@
-#!/usr/bin/env python3
-# Adapted From: https://github.com/pytorch/pytorch/issues/20356#issuecomment-545572400
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from collections import OrderedDict
-import re
-import yaml
-from math import sqrt
-
-from tacotron_model import Tacotron2
-
-# Going back in time, this is an old trump txlearned model:
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_ljs_arpabet_ckpt30000_2020_02_06'
-# Starting again with new samples:
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-14_chkpt7000'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-14_chkpt11500'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-14_chkpt17000'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-14_chkpt27000'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-14_chkpt27000-18000'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-14_chkpt53500'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-14_chkpt112500'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-23_chkpt8000'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-23_chkpt13000'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-24_chkpt2000'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-24_chkpt16000'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-25_chkpt7000'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-25_chkpt17000'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-25_chkpt26500'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-25_chkpt40000'
-tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_trump_txlearn_arpabet_2020-05-25_chkpt50000'
-
-
-#output_filename = 'tacotron_arpabet_1.pt'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_ckpt30000_2020_02_06.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-14_ckpt7000.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-14_ckpt11500.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-14_ckpt17000.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-14_ckpt27000.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-14_ckpt27000-18000.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-14_ckpt53500.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-14_chkpt112500.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-23_chkpt8000.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-23_chkpt13000.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-24_chkpt2000.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-24_chkpt16000.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-25_chkpt7000.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-25_chkpt17000.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-25_chkpt26500.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-25_chkpt40000.jit'
-output_filename = 'tacotron2_trump_txlearn_ljs_arpabet_2020-05-25_chkpt50000.jit'
-
-
-# NB: Tacotron's original shape is [148, 512]. Arpabet encoding is [256, 512].
-NUM_SYMBOLS_ENCODED = 256 # 148 by default, 256 with my custom Arpabet encoding
-
-#torch.set_default_tensor_type('torch.DoubleTensor')
-#torch.set_default_tensor_type('torch.FloatTensor')
-
-print('Loading tacotron model...')
-#tacotron_model_file = '/home/bt/models/tacotron2-nvidia/tacotron2_statedict.pt'
-tacotron_model = torch.load(tacotron_model_file, map_location=torch.device('cpu'))
-
-def cuda_to_cpu(model):
-    """Recursively make everything non-CUDA"""
-    if isinstance(model, dict) or isinstance(model, OrderedDict):
-        for key, value in model.items():
-            model[key] = cuda_to_cpu(value)
-        return model
-    if isinstance(model, list):
-        for i, value in enumerate(model):
-            model[i] = cuda_to_cpu(value)
-    elif isinstance(model, torch.Tensor):
-        return model.cpu()
-    else:
-        return model
-
-# TODO: Maybe not necessary anymore with `map_location=torch.device('cpu')`
-#print('Converitng to CPU model...')
-#tacotron_model = cuda_to_cpu(tacotron_model)
-
-print('Remove unused keys...')
-new_state_dict = [] # Rebuild the ordered dict
-for key, value in tacotron_model['state_dict'].items():
-    if key.startswith('postnet'):
-        continue
-    new_state_dict.append((key, value))
-
-tacotron_model['state_dict'] = OrderedDict(new_state_dict)
-
-print('Containerizing model...')
-# Save arbitrary values supported by TorchScript
-# https://pytorch.org/docs/master/jit.html#supported-type
-
-# Yay duck typing
-class HParams:
-    mask_padding = True
-    fp16_run = False
-    n_mel_channels = 80
-    n_frames_per_step = 1
-    n_symbols = NUM_SYMBOLS_ENCODED
-    symbols_embedding_dim = 512
-    encoder_kernel_size = 5
-    encoder_n_convolutions = 3
-    encoder_embedding_dim = 512
-    attention_rnn_dim = 1024
-    decoder_rnn_dim = 1024
-    prenet_dim = 256
-    max_decoder_steps = 1000
-    gate_threshold = 0.5
-    p_attention_dropout = 0.1
-    p_decoder_dropout = 0.1
-    attention_dim = 128
-    attention_location_n_filters = 32
-    attention_location_kernel_size = 31
-
-module = Tacotron2(HParams())
-
-print('Load state dict...')
-module.load_state_dict(tacotron_model['state_dict'])
-#module.eval() # NB: Complains unless called: Did you forget call .eval() on your model?
-
-# NB: Tracing evaluates the model on input and unrolls and hardcodes branching
-# and loops. Scripting allows these to remain by converting the entire program
-# to TorchScript. Scripting seems MUCH faster, is also harder get working.
-trace_model = False
-if trace_model:
-    print('Tracing model and saving as:'.format(output_filename))
-    text_sequence_file = '/home/bt/dev/voder/data/text/tacotron_text_sequence.pt'
-    text_sequence = torch.load(text_sequence_file, map_location=torch.device('cpu'))
-    # NB: Getting `Tracing failed sanity checks! Graphs differed across invocations!`
-    traced_script_module = torch.jit.trace(module, text_sequence, check_trace=False)
-    traced_script_module.save(output_filename)
-else:
-    print('Saving script model as: {}'.format(output_filename))
-    container = torch.jit.script(module)
-    container.save(output_filename)
-
-print('Done.')
diff --git a/tacotron_tensorflow/Cargo.toml b/tacotron_tensorflow/Cargo.toml
deleted file mode 100644
index 3ccde92641..0000000000
--- a/tacotron_tensorflow/Cargo.toml
+++ /dev/null
@@ -1,15 +0,0 @@
-[package]
-name = "tacotron_tensorflow"
-version = "0.0.1"
-authors = [ "Brandon Thomas <bt@brand.io>",
-            "Brandon Thomas <echelon@gmail.com>" ]
-
-[[bin]]
-name = "tacotron_tensorflow"
-path = "src/main.rs"
-
-[dependencies]
-hound = "3.4.0" # wav encoder/decoder
-image = "0.23.0-preview.0"
-sample = "0.10.0" # DSP toolkit
-tensorflow = "0.14.0"
diff --git a/tacotron_tensorflow/src/main.rs b/tacotron_tensorflow/src/main.rs
deleted file mode 100644
index e835c1b967..0000000000
--- a/tacotron_tensorflow/src/main.rs
+++ /dev/null
@@ -1,280 +0,0 @@
-/**
- * This takes SavedModels from Keith Ito's Tacotron impl and runs them in Rust.
- * Critically, it can short-circuit the entire model evaluation and only run the pipeline
- * pieces that we care about.
- */
-extern crate hound;
-extern crate image;
-extern crate sample;
-extern crate tensorflow;
-
-pub mod model;
-
-use tensorflow::{
-  SessionOptions,
-  Session,
-  Graph,
-  Tensor,
-  SessionRunArgs
-};
-
-use model::print_tensorfow_version;
-use sample::ring_buffer::Slice;
-use image::RgbImage;
-use image::ImageBuffer;
-use image::Rgb;
-use image::imageops::{rotate90, flip_horizontal, flip_vertical, resize, FilterType, crop};
-
-const INPUT_NAME : &'static str = "inputs";
-const INPUT_LENGTHS_NAME : &'static str = "input_lengths";
-
-// NB: This goes through the griffin-lim pipeline, but is hella slow off GPU
-// The output of this is *actual* audio, though there are a few more steps in the
-// network to run before the audio is a 1:1 match with Tacotron Python.
-// Tensor("model/griffinlim/Squeeze:0", shape=(?,), dtype=float32)
-//const OUTPUT_NAME : &'static str = "model/griffinlim/Squeeze";
-
-// This appears to be the mel spectrogram before griffin-lim reconstruction.
-// Image output shows visible formants. If my thinking holds, we can short circuit
-// Griffin-Lim reconstruction and send this to a trained MelGan network for a HUGE
-// performance boost end-to-end. It should even be realtime on a CPU.
-// Executing the model to this point is *extremely* fast.
-const OUTPUT_NAME : &'static str = "model/Pow_1";
-
-/**
- * To save:
-     print('Saving model...')
-
-    # https://github.com/tensorflow/models/issues/3530#issuecomment-395968881
-    output_dir = './tx_trained2/'
-    builder = tf.saved_model.builder.SavedModelBuilder(output_dir)
-
-    builder.add_meta_graph_and_variables(
-      self.session,
-      [tf.saved_model.tag_constants.SERVING],
-      main_op=tf.tables_initializer(),
-    )
-
-    builder.save()
-
-    # Save the model text labels to dereference
-    definition = self.session.graph_def
-    directory = 'saved_model_labels'
-    tf.train.write_graph(definition, directory, 'model_labels.pb', as_text=True)
- */
-pub fn main() {
-  println!("Tacotron");
-  print_tensorfow_version();
-
-  let dir_name = "/home/bt/dev/2nd/tacotron/tx_trained2";
-
-  let mut graph = Graph::new();
-  let session = Session::from_saved_model(
-    &SessionOptions::new(),
-    &["serve"],
-    &mut graph,
-    dir_name,
-  ).expect("Should load");
-
-  println!("Model Loaded!");
-
-  // cleaned string "my name is donald trump"
-  let sentence = vec![
-    40, 52, 64,
-    41, 28, 40, 32, 64,
-    36, 46, 64,
-    31, 42, 41, 28, 39, 31, 64,
-    47, 45, 48, 40, 43, 1
-  ];
-
-  /*let sentence = vec![
-    40, 52, 64,
-    40, 52, 64,
-    40, 52, 64,
-    40, 52, 64,
-    40, 52, 64,
-    40, 52, 64,
-    40, 52, 64,
-    41, 28, 40, 32, 64,
-    41, 28, 40, 32, 64,
-    41, 28, 40, 32, 64,
-    41, 28, 40, 32, 64,
-    41, 28, 40, 32, 64,
-    41, 28, 40, 32, 64,
-    41, 28, 40, 32, 64,
-    36, 46, 64,
-    36, 46, 64,
-    36, 46, 64,
-    36, 46, 64,
-    36, 46, 64,
-    31, 42, 41, 28, 39, 31, 64,
-    47, 45, 48, 40, 43, 1
-  ];*/
-
-  //for _i in 0..32 {
-    convert_sentence(&mut graph, &session, &sentence)
-  //}
-}
-
-fn convert_sentence(graph: &mut Graph, session: &Session, sentence: &Vec<i32>) -> () {
-  let mut input = Tensor::new(&[1, sentence.len() as u64])
-      .with_values(
-        // cleaned string "my name is donald trump"
-        sentence.slice()
-      )
-      .unwrap();
-  let mut input_length = Tensor::new(&[1])
-      .with_values(&[sentence.len() as i32])
-      .unwrap();
-  println!(">>> Input tensor dims: {:?}", input.dims());
-  {
-    let mut args = SessionRunArgs::new();
-
-    println!(">>> Inputs ...");
-    args.add_feed(&graph.operation_by_name_required(INPUT_NAME)
-        .expect(INPUT_NAME), 0, &input);
-
-    println!(">>> Input Lengths ...");
-    args.add_feed(&graph.operation_by_name_required(INPUT_LENGTHS_NAME)
-        .expect(INPUT_LENGTHS_NAME), 0, &input_length);
-
-    let z = args.request_fetch(
-      &graph.operation_by_name_required(OUTPUT_NAME)
-          .expect(OUTPUT_NAME), 0);
-
-    println!(">>> Running...");
-
-    session.run(&mut args).expect("Run success");
-
-    // Check our results.
-    let z_res = args.fetch::<f32>(z).expect("ret");
-
-    println!("z_rez.dims(): {:?}", z_res.dims());
-    println!("z_rez: {:?}", z_res);
-
-    println!("Data[0]: {:?}", &z_res.get(0));
-    println!("Data[1]: {:?}", &z_res.get(1));
-    println!("Data[2]: {:?}", &z_res.get(2));
-
-    visualize(z_res);
-
-    //let processed = process_audio(z_res.to_vec());
-    //write_wav(processed);
-  }
-}
-
-fn visualize(tensor : Tensor<f32>) {
-  let dims = tensor.dims();
-  let width = *dims.get(0).unwrap() as u32;
-  let height = *dims.get(1).unwrap() as u32;
-
-  let mut image: RgbImage = ImageBuffer::new(width, height);
-
-  let mut i: u32 = 0;
-  let mut j: u32 = 0;
-  let mut k: u32 = 0;
-
-  for x in tensor.iter() {
-    if j >= height {
-      j = 0;
-      i += 1
-    }
-    if i >= width {
-      println!("Break @ {}", k);
-      break;
-    }
-    /*if i % 5 == 0  {
-      image.put_pixel(i, j, Rgb([0, 0, 0]));
-    } else {
-      image.put_pixel(i, j, Rgb([0, 255, 0]));
-    }*/
-    // [-1.0, 1.0]
-    let val = (*x) * 255f32;
-    let val = val.abs() as u8;
-    image.put_pixel(i, j, Rgb([val, val, val]));
-    j += 1;
-    k += 1;
-  }
-
-  //image = rotate90(&image);
-  //image = flip_horizontal(&image);
-  image = flip_vertical(&image);
-  let cropped = crop(&mut image,0, 0, 200, height);
-  let mut image = cropped.to_image();
-  image = resize(&image, 200, 100, FilterType::Triangle);
-  image.save("output.png").unwrap();
-
-  println!("Dimensions: {:?}", dims);
-  println!("Width: {:?}", width);
-  println!("Height: {:?}", height);
-  println!("Num pixels calc: {:?}", (width * height));
-  println!("Num pixels covered: {:?}", k);
-}
-
-fn process_audio(signal: Vec<f32>) -> Vec<f64> {
-  //  wav *= 32767 / max(0.01, np.max(np.abs(wav)))
-  //  scipy.io.wavfile.write(path, hparams.sample_rate, wav.astype(np.int16))
-
-  // abs
-  //[0.0000000e+00 4.1254760e-12 7.1285297e-11 ... 5.9092813e-14 4.0335224e-14
-  // 2.1358613e-15]
-  //max abs
-  //0.08918631
-
-  let signal = signal.iter()
-      .map(|f| *f as f64)
-      .collect::<Vec<f64>>();
-
-  let abs = signal.iter()
-      .map(|f|  f.abs())
-      .collect::<Vec<f64>>();
-
-  println!("Abs[0]: {:?}", &abs.get(0));
-  println!("Abs[1]: {:?}", &abs.get(1));
-  println!("Abs[2]: {:?}", &abs.get(2));
-
-  let max : f64 = abs.iter()
-      .fold(0.01f64, |x, y| x.max(*y));
-
-
-  let signal_mult = signal.iter()
-      .map(|f| *f * 32767.0f64)
-      .collect::<Vec<f64>>();
-
-  let signal_div = signal_mult.iter()
-      .map(|f| *f / max)
-      .collect::<Vec<f64>>();
-
-  println!("Max: {:?}", max);
-
-  let mult = 32767.0f64 / max;
-  let rust_mult = 367225.3669259117;
-  let python_mult = 367399.4329151011;
-
-  println!("Mult: {:?}", mult);
-
-  /*let result = signal.iter()
-      .map(|f| f * python_mult)
-      .collect::<Vec<f64>>();
-
-  println!("Res[0]: {:?}", &abs.get(0));
-  println!("Res[1]: {:?}", &abs.get(1));
-  println!("Res[2]: {:?}", &abs.get(2));*/
-
-  signal_div
-}
-
-fn write_wav(signal: Vec<f64>) {
-  let spec = hound::WavSpec {
-    channels: 1,
-    sample_rate: 16000,
-    bits_per_sample: 32,
-    sample_format: hound::SampleFormat::Float,
-  };
-  let mut writer = hound::WavWriter::create("output.wav", spec).unwrap();
-
-  for sample in signal {
-    writer.write_sample(sample as f32).unwrap();
-  }
-  writer.finalize().unwrap();
-}
\ No newline at end of file
diff --git a/tacotron_tensorflow/src/model.rs b/tacotron_tensorflow/src/model.rs
deleted file mode 100644
index e3e7650c41..0000000000
--- a/tacotron_tensorflow/src/model.rs
+++ /dev/null
@@ -1,149 +0,0 @@
-use tensorflow::Code;
-use tensorflow::Graph;
-use tensorflow::ImportGraphDefOptions;
-use tensorflow::Session;
-use tensorflow::SessionOptions;
-use tensorflow::SessionRunArgs;
-use tensorflow::Status;
-use tensorflow::Tensor;
-use tensorflow::version;
-
-const INPUT_NAME : &'static str = "input_A_test";
-const OUTPUT_NAME : &'static str = "generator_A2B_3/output_transpose";
-
-pub struct VoiceModel {
-  graph: Graph,
-  session: Session,
-}
-
-impl VoiceModel {
-  pub fn load(dir_name: &str) -> Self {
-    let mut graph = Graph::new();
-    let session = Session::from_saved_model(
-      &SessionOptions::new(),
-      &["serve"],
-      &mut graph,
-      dir_name,
-    ).expect("Should load");
-
-    VoiceModel {
-      graph,
-      session,
-    }
-  }
-
-  pub fn evaluate(&self, input: &Tensor<f32>) {
-    println!(">>> Input tensor dims: {:?}", input.dims());
-
-    // Run the graph.
-    let mut args = SessionRunArgs::new();
-
-    // input_A_test:
-    // Tensor("input_A_test:0", shape=(?, 24, ?), dtype=float32)
-    args.add_feed(&self.graph.operation_by_name_required(INPUT_NAME)
-        .expect(INPUT_NAME), 0, &input);
-
-    // generation_B_test:
-    // Tensor("generator_A2B_3/output_transpose:0", shape=(?, 24, ?), dtype=float32)
-    let z = args.request_fetch(
-      &self.graph.operation_by_name_required(OUTPUT_NAME)
-          .expect(OUTPUT_NAME), 0);
-
-    self.session.run(&mut args).expect("Run success");
-
-    // Check our results.
-    let z_res = args.fetch::<f32>(z).expect("ret");
-
-    println!("z_rez.dims(): {:?}", z_res.dims());
-    println!("z_rez: {:?}", z_res);
-  }
-}
-
-pub fn load_model() {
-  let model_dir = "/home/bt/dev/voder/saved_model";
-  let model = VoiceModel::load(model_dir);
-
-  // ] generation_B_test: Tensor("generator_A2B_3/output_transpose:0", shape=(?, 24, ?), dtype=float32)
-  // ] input_A_test: Tensor("input_A_test:0", shape=(?, 24, ?), dtype=float32)
-  // ] inputs.shape: (1, 24, 380) -- the last dimension is the signal
-
-  let mut input  = Tensor::new(&[1, 24, 10])
-      .with_values(&[
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-        0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-      ])
-      .unwrap();
-
-  model.evaluate(&input);
-  model.evaluate(&input);
-  model.evaluate(&input);
-}
-
-/*fn load_model_2() {
-  // from regression_checkpoint.rs example
-
-  let filename = "/home/bt/dev/voder/saved_model/saved_model.pb";
-
-  let mut graph = Graph::new();
-  let mut proto = Vec::new();
-
-  File::open(filename)
-      .expect("opened")
-      .read_to_end(&mut proto)
-      .expect("cannot read");
-
-  graph.import_graph_def(&proto, &ImportGraphDefOptions::new()).expect("cannot import");
-
-  let session = Session::new(&SessionOptions::new(), &graph).expect("cannot session");
-
-  /*let op_x = graph.operation_by_name_required("x").expect("x");
-  let op_y = graph.operation_by_name_required("y").expect("y");
-  let op_init = graph.operation_by_name_required("init").expect("init");
-  let op_train = graph.operation_by_name_required("train").expect("train");
-  let op_w = graph.operation_by_name_required("w").expect("w");
-  let op_b = graph.operation_by_name_required("b").expect("b");
-  let op_file_path = graph.operation_by_name_required("save/Const").expect("const");
-  let op_save = graph.operation_by_name_required("save/control_dependency").expect("save");*/
-
-  //let file_path_tensor: Tensor<String> =
-  //    Tensor::from(String::from("/home/bt/dev/voder/extra_model/sf1_tm1.ckpt.data-00000-of-00001"));
-  //println!("Tensor: {:?}", file_path_tensor);
-}*/
-
-pub fn print_tensorfow_version() {
-  // Python TensorFlow version: 1.14.0
-  // Rust TensorFlow version:   1.13.1 (default, must be hand-upgraded)
-  let version = version().expect("version");
-  println!("Tensorflow version: {}", version);
-
-  let graph = Graph::new();
-  let options = SessionOptions::new();
-  let session = Session::new(&options, &graph).unwrap();
-  let devices = session.device_list().unwrap();
-  for device in devices {
-    println!("Device: {:?}", device);
-  }
-}
-
diff --git a/voice_converter_sidecar/Cargo.toml b/voice_converter_sidecar/Cargo.toml
deleted file mode 100644
index 44a4c412b7..0000000000
--- a/voice_converter_sidecar/Cargo.toml
+++ /dev/null
@@ -1,23 +0,0 @@
-[package]
-name = "voice_conversion_sidecar"
-version = "0.1.0"
-authors = [ "Brandon Thomas <bt@brand.io>",
-            "Brandon Thomas <echelon@gmail.com>" ]
-[[bin]]
-name = "voice_converter_sidecar"
-path = "src/main.rs"
-
-[dependencies]
-byteorder = "1.3.2" # byte-level operations
-bytes = "0.4.12" # required by 'prost'
-cpal = "0.10.0" # hardware audio out
-failure = "0.1.5" # required by cpal
-prost = "0.5.*" # protobuf
-prost-types = "0.5.0" # protobuf
-wavy = "0.1.2" # real time audio capture and playback
-# TODO: Move these into the voder project workspace.
-world-sys = { path = "/home/bt/dev/world-sys" } # AWESOME WORLD VOCODER BINDINGS HAH HA HAH
-zmq = "0.9.2" # zeromq
-
-[build-dependencies]
-prost-build = "0.5.0"
diff --git a/voice_converter_sidecar/README.md b/voice_converter_sidecar/README.md
deleted file mode 100644
index 53648631d8..0000000000
--- a/voice_converter_sidecar/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
-Voice Converter Sidecar
-=======================
-This project spins up a Rust binary to handle audio input and output, then 
-ships microphone audio over proto/zeromq to a Python sidecar running
-CycleGAN-VC to convert it into target speech.
-
-### CycleGAN
-I've included a pared down copy of CycleGAN for the purposes of model
-evaluation. It handles all of the audio buffering / sidecar integration
-pieces. [The original CycleGAN I used is here](https://github.com/leimao/Voice_Converter_CycleGAN).
-
-#### Note on Training
-It's possible to train and evaluate at the same time using dual GPUs
-(at least my dual 1080Ti setup). To run the sidecar on a particular
-GPU (0-indexded), use:
-
-```
-CUDA_VISIBLE_DEVICES=1 ./sidecar.py
-```
-
-### Proto Compilation (for Python)
-Codegen for Rust is built in. Codegen for Python uses,
-
-```
-protoc --python_out=cycle_gan protos/audio.proto
-```
-
-### Current Results
-Currently there is 4.39 seconds of delay between speaking and generated output
-with the sidecar setup on my desktop computer. This is really great and seems
-promising.
-
-This gets up to 6.0 seconds later. Drift continues to accrue, but it's a slow
-build.
diff --git a/voice_converter_sidecar/build.rs b/voice_converter_sidecar/build.rs
deleted file mode 100644
index 5c0635ef0c..0000000000
--- a/voice_converter_sidecar/build.rs
+++ /dev/null
@@ -1,11 +0,0 @@
-extern crate prost_build;
-
-fn main() {
-  //prost_build::compile_protos(&["protos/audio.proto"],
-  //                        &["protos/"]).unwrap();
-  prost_build::Config::new()
-      .out_dir("src/protos/")
-      .compile_protos(&["protos/audio.proto"],
-                      &["protos/"])
-      .unwrap();
-}
\ No newline at end of file
diff --git a/voice_converter_sidecar/cycle_gan/demo_server.py b/voice_converter_sidecar/cycle_gan/demo_server.py
deleted file mode 100755
index 3b767ba631..0000000000
--- a/voice_converter_sidecar/cycle_gan/demo_server.py
+++ /dev/null
@@ -1,278 +0,0 @@
-#!/usr/bin/env python3
-
-"""
-Starts a demo HTTP server to capture and transform audio
-as a live demonstration of the trained model.
-
-Brandon Thomas 2019-07-29 <bt@brand.io> <echelon@gmail.com>
-"""
-
-import argparse
-import falcon
-import io
-import librosa
-import numpy as np
-import os
-import scipy
-import soundfile
-import tensorflow as tf
-import subprocess
-import tempfile
-
-from falcon_multipart.middleware import MultipartMiddleware
-from model import CycleGAN
-from preprocess import *
-from wsgiref import simple_server
-
-print("TensorFlow version: {}".format(tf.version.VERSION))
-
-INDEX_HTML = '''
-<!doctype html>
-<html>
-  <body>
-    <h1>Voice Demo</h1>
-    <script src="./script/recorder.js" type="application/javascript"></script>
-    <script type="application/javascript">
-      var audio_context;
-      var recorder;
-
-      // NB: Capture code originates from 'recorder.js' demo.
-      function startUserMedia(stream) {
-        var input = audio_context.createMediaStreamSource(stream);
-        window.recorder = new Recorder(input);
-      }
-
-      function createDownloadLink() {
-        window.recorder && window.recorder.exportWAV(function(blob) {
-          var xhr = new XMLHttpRequest();
-          xhr.responseType = 'blob';
-          xhr.onload=function(e) {
-            console.log('response received');
-            var objectUrl = window.URL.createObjectURL(this.response);
-            console.log('audio URL', objectUrl);
-            var audio = document.getElementById('audio');
-            audio.src = objectUrl;
-            audio.play();
-          };
-
-          var fd = new FormData();
-          fd.append("audio_data", blob, "audio_file.wav");
-          console.log(blob);
-          console.log('sending...');
-          xhr.open("POST","/upload",true);
-          xhr.send(fd);
-          console.log('sent');
-        });
-      }
-
-      function startRecording(button) {
-        window.recorder && window.recorder.record();
-        document.getElementById('start').disabled = true;
-        document.getElementById('stop').disabled = false;
-      }
-
-      function stopRecording(button) {
-        window.recorder && window.recorder.stop();
-        document.getElementById('start').disabled = false;
-        document.getElementById('stop').disabled = true;
-
-        createDownloadLink();
-        window.recorder.clear();
-      }
-
-      window.onload = function init() {
-        try {
-          audio_context = new AudioContext();
-        } catch (e) {
-          alert('No web audio support in this browser!');
-        }
-
-        var media = navigator.mediaDevices.getUserMedia({ audio: true, video: false });
-        window.media = media;
-        console.log('media', media);
-        media.then(startUserMedia);
-      };
-    </script>
-
-    <button id="start" onclick="startRecording(this);">record</button>
-    <br>
-    <br>
-    <button id="stop" onclick="stopRecording(this);" disabled>stop</button>
-    <br>
-    <br>
-    <audio controls id="audio">
-      <source id="source" src="" type="audio/wav">
-      Your browser does not support the audio element.
-    </audio>
-  </body>
-</html>
-'''
-
-
-class Converter():
-    def __init__(self, model_dir, model_name):
-        self.num_features = 24
-        self.sampling_rate = 16000
-        self.frame_period = 5.0
-
-        self.model = CycleGAN(num_features = self.num_features, mode = 'test')
-
-        self.model.load(filepath = os.path.join(model_dir, model_name))
-
-        """
-        # NB: Save the graph
-        definition = self.model.sess.graph_def
-        directory = 'saved_model_2'
-        tf.train.write_graph(definition, directory, 'saved_model_2.pb', as_text=True)
-
-        # https://github.com/tensorflow/models/issues/3530#issuecomment-395968881
-        output_dir = './saved_model/'
-        builder = tf.saved_model.builder.SavedModelBuilder(output_dir)
-
-        builder.add_meta_graph_and_variables(
-            self.model.sess,
-            [tf.saved_model.tag_constants.SERVING],
-            main_op=tf.tables_initializer(),
-        )
-
-        builder.save()
-        """
-
-        """
-        builder.add_meta_graph_and_variables(
-            self.model.sess,
-            [tf.saved_model.tag_constants.SERVING],
-            signature_def_map={
-                'predict_images':
-                    prediction_signature,
-                signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
-                    classification_signature,
-            },
-            main_op=tf.tables_initializer())
-        """
-
-        self.mcep_normalization_params = np.load(os.path.join(model_dir, 'mcep_normalization.npz'))
-        self.mcep_mean_A = self.mcep_normalization_params['mean_A']
-        self.mcep_std_A = self.mcep_normalization_params['std_A']
-        self.mcep_mean_B = self.mcep_normalization_params['mean_B']
-        self.mcep_std_B = self.mcep_normalization_params['std_B']
-
-        self.logf0s_normalization_params = np.load(os.path.join(model_dir, 'logf0s_normalization.npz'))
-        self.logf0s_mean_A = self.logf0s_normalization_params['mean_A']
-        self.logf0s_std_A = self.logf0s_normalization_params['std_A']
-        self.logf0s_mean_B = self.logf0s_normalization_params['mean_B']
-        self.logf0s_std_B = self.logf0s_normalization_params['std_B']
-
-    def convert(self, wav, conversion_direction='A2B'):
-        wav = wav_padding(wav = wav, sr = self.sampling_rate, frame_period = self.frame_period, multiple = 4)
-        f0, timeaxis, sp, ap = world_decompose(wav = wav, fs = self.sampling_rate, frame_period = self.frame_period)
-        coded_sp = world_encode_spectral_envelop(sp = sp, fs = self.sampling_rate, dim = self.num_features)
-        coded_sp_transposed = coded_sp.T
-
-        if conversion_direction == 'A2B':
-            f0_converted = pitch_conversion(f0 = f0, mean_log_src = self.logf0s_mean_A, std_log_src = self.logf0s_std_A, mean_log_target = self.logf0s_mean_B, std_log_target = self.logf0s_std_B)
-            coded_sp_norm = (coded_sp_transposed - self.mcep_mean_A) / self.mcep_std_A
-            coded_sp_converted_norm = self.model.test(inputs = np.array([coded_sp_norm]), direction = conversion_direction)[0]
-            coded_sp_converted = coded_sp_converted_norm * self.mcep_std_B + self.mcep_mean_B
-        else:
-            f0_converted = pitch_conversion(f0 = f0, mean_log_src = self.logf0s_mean_B, std_log_src = self.logf0s_std_B, mean_log_target = self.logf0s_mean_A, std_log_target = self.logf0s_std_A)
-            coded_sp_norm = (coded_sp_transposed - self.mcep_mean_B) / self.mcep_std_B
-            coded_sp_converted_norm = self.model.test(inputs = np.array([coded_sp_norm]), direction = conversion_direction)[0]
-            coded_sp_converted = coded_sp_converted_norm * self.mcep_std_A + self.mcep_mean_A
-
-        coded_sp_converted = coded_sp_converted.T
-        coded_sp_converted = np.ascontiguousarray(coded_sp_converted)
-        decoded_sp_converted = world_decode_spectral_envelop(coded_sp = coded_sp_converted, fs = self.sampling_rate)
-        wav_transformed = world_speech_synthesis(f0 = f0_converted, decoded_sp = decoded_sp_converted, ap = ap, fs = self.sampling_rate, frame_period = self.frame_period)
-
-        # For debugging model output, uncomment the following line:
-        # librosa.output.write_wav('model_output.wav', wav_transformed, self.sampling_rate)
-
-        # TODO: Perhaps ditch this. It's probably unnecessary work.
-        upsampled = librosa.resample(wav_transformed, self.sampling_rate, 48000)
-        pcm_data = upsampled.astype(np.float64)
-        stereo_pcm_data = np.tile(pcm_data, (2,1)).T
-
-        buf = io.BytesIO()
-        scipy.io.wavfile.write(buf, 48000, stereo_pcm_data.astype(np.float32))
-        return buf
-
-# Set up model
-# This should live long in memory, so we do it up front.
-model_dir_default = './model/sf1_tm1'
-model_name_default = 'sf1_tm1.ckpt'
-converter = Converter(model_dir_default, model_name_default)
-
-class IndexHandler():
-    def on_get(self, request, response):
-        response.content_type = 'text/html'
-        response.body = INDEX_HTML
-
-class ApiHandler():
-    def on_post(self, request, response):
-        # NB: uses middleware to pull out data.
-        form_data = request.params['audio_data'].file
-        data, samplerate = soundfile.read(form_data)
-
-        print('samplerate', samplerate)
-        print('data', data)
-        print('data.shape', data.shape)
-        print('data.dtype', data.dtype)
-
-        # For debugging browser input, uncomment the following line:
-        # scipy.io.wavfile.write('browser_input_audio.wav', samplerate, data)
-
-        # NB: Convert the input stereo signal into mono.
-        # In the future the frontend should be responsible for sampling details.
-        mono = data[:, 0]
-        print('mono', mono)
-        print('mono.shape', mono.shape)
-        print('mono.dtype', mono.dtype)
-
-        # NB: We must downsample to the rate that the network is trained on.
-        downsampled = librosa.resample(mono, samplerate, 16000)
-        print('downsampled', downsampled)
-        print('downsampled.shape', downsampled.shape)
-        print('downsampled.dtype', downsampled.dtype)
-
-        # Evaluate the model
-        print(">>> Converting...")
-        results = converter.convert(downsampled, conversion_direction = 'A2B')
-
-        print('type(results)', type(results))
-
-        temp_dir = tempfile.TemporaryDirectory(prefix='tmp_ml_audio')
-        temp_file = tempfile.NamedTemporaryFile(suffix='.wav')
-
-        temp_file.write(results.read())
-
-        out_file = temp_dir.name + '/output.ogg'
-
-        # NB: Browsers have a great deal of trouble decoding WAV files unless they are in the
-        # narrow slice of the WAV spec expected. None of the {librosa, scipy, soundfile} python
-        # tools do a good job of this, so here we shell out to ffmpeg and generate OGG.
-        # It's lazy and messy, but it works for now.
-        # See https://github.com/librosa/librosa/issues/361 for a survey of the library landscape
-        # See https://bugzilla.mozilla.org/show_bug.cgi?id=523837 for one of dozens of browser codec bugs
-        _stdout = subprocess.check_output(['ffmpeg', '-i', temp_file.name, '-acodec', 'libvorbis', out_file])
-
-        response.content_type = 'audio/ogg'
-        with open(out_file, mode='rb') as f:
-            response.data = f.read()
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--port', type=int, default=8000)
-    args = parser.parse_args()
-
-    api = falcon.API(middleware=[MultipartMiddleware()])
-    api.add_route('/', IndexHandler())
-    api.add_route('/upload', ApiHandler())
-    api.add_static_route('/script', os.path.abspath('./script'))
-    api.add_static_route('/sound', os.path.abspath('./sound'))
-    print('Serving on 0.0.0.0:%d' % args.port)
-    simple_server.make_server('0.0.0.0', args.port, api).serve_forever()
-
-if __name__ == '__main__':
-    main()
-
diff --git a/voice_converter_sidecar/cycle_gan/model.py b/voice_converter_sidecar/cycle_gan/model.py
deleted file mode 100644
index cf7a32c1e7..0000000000
--- a/voice_converter_sidecar/cycle_gan/model.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import os
-import tensorflow as tf
-from module import discriminator, generator_gatedcnn
-from utils import l1_loss, l2_loss, cross_entropy_loss
-from datetime import datetime
-
-class CycleGAN(object):
-
-    def __init__(self, num_features, discriminator = discriminator, generator = generator_gatedcnn, mode = 'train', log_dir = './log'):
-
-        self.num_features = num_features
-        self.input_shape = [None, num_features, None] # [batch_size, num_features, num_frames]
-
-        self.discriminator = discriminator
-        self.generator = generator
-        self.mode = mode
-
-        self.build_model()
-        self.optimizer_initializer()
-
-        self.saver = tf.train.Saver()
-
-        #config = tf.ConfigProto(device_count = {'GPU': 1})
-        #self.sess = tf.Session(config=config)
-        self.sess = tf.Session()
-        self.sess.run(tf.global_variables_initializer())
-
-        if self.mode == 'train':
-            self.train_step = 0
-            now = datetime.now()
-            self.log_dir = os.path.join(log_dir, now.strftime('%Y%m%d-%H%M%S'))
-            self.writer = tf.summary.FileWriter(self.log_dir, tf.get_default_graph())
-            self.generator_summaries, self.discriminator_summaries = self.summary()
-
-    def build_model(self):
-
-        # Placeholders for real training samples
-        self.input_A_real = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_A_real')
-        self.input_B_real = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_B_real')
-        # Placeholders for fake generated samples
-        self.input_A_fake = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_A_fake')
-        self.input_B_fake = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_B_fake')
-        # Placeholder for test samples
-        self.input_A_test = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_A_test')
-        self.input_B_test = tf.placeholder(tf.float32, shape = self.input_shape, name = 'input_B_test')
-
-        self.generation_B = self.generator(inputs = self.input_A_real, reuse = False, scope_name = 'generator_A2B')
-        self.cycle_A = self.generator(inputs = self.generation_B, reuse = False, scope_name = 'generator_B2A')
-
-        self.generation_A = self.generator(inputs = self.input_B_real, reuse = True, scope_name = 'generator_B2A')
-        self.cycle_B = self.generator(inputs = self.generation_A, reuse = True, scope_name = 'generator_A2B')
-
-        self.generation_A_identity = self.generator(inputs = self.input_A_real, reuse = True, scope_name = 'generator_B2A')
-        self.generation_B_identity = self.generator(inputs = self.input_B_real, reuse = True, scope_name = 'generator_A2B')
-
-        self.discrimination_A_fake = self.discriminator(inputs = self.generation_A, reuse = False, scope_name = 'discriminator_A')
-        self.discrimination_B_fake = self.discriminator(inputs = self.generation_B, reuse = False, scope_name = 'discriminator_B')
-
-        # Cycle loss
-        self.cycle_loss = l1_loss(y = self.input_A_real, y_hat = self.cycle_A) + l1_loss(y = self.input_B_real, y_hat = self.cycle_B)
-
-        # Identity loss
-        self.identity_loss = l1_loss(y = self.input_A_real, y_hat = self.generation_A_identity) + l1_loss(y = self.input_B_real, y_hat = self.generation_B_identity)
-
-        # Place holder for lambda_cycle and lambda_identity
-        self.lambda_cycle = tf.placeholder(tf.float32, None, name = 'lambda_cycle')
-        self.lambda_identity = tf.placeholder(tf.float32, None, name = 'lambda_identity')
-
-        # Generator loss
-        # Generator wants to fool discriminator
-        self.generator_loss_A2B = l2_loss(y = tf.ones_like(self.discrimination_B_fake), y_hat = self.discrimination_B_fake)
-        self.generator_loss_B2A = l2_loss(y = tf.ones_like(self.discrimination_A_fake), y_hat = self.discrimination_A_fake)
-
-        # Merge the two generators and the cycle loss
-        self.generator_loss = self.generator_loss_A2B + self.generator_loss_B2A + self.lambda_cycle * self.cycle_loss + self.lambda_identity * self.identity_loss
-
-        # Discriminator loss
-        self.discrimination_input_A_real = self.discriminator(inputs = self.input_A_real, reuse = True, scope_name = 'discriminator_A')
-        self.discrimination_input_B_real = self.discriminator(inputs = self.input_B_real, reuse = True, scope_name = 'discriminator_B')
-        self.discrimination_input_A_fake = self.discriminator(inputs = self.input_A_fake, reuse = True, scope_name = 'discriminator_A')
-        self.discrimination_input_B_fake = self.discriminator(inputs = self.input_B_fake, reuse = True, scope_name = 'discriminator_B')
-
-        # Discriminator wants to classify real and fake correctly
-        self.discriminator_loss_input_A_real = l2_loss(y = tf.ones_like(self.discrimination_input_A_real), y_hat = self.discrimination_input_A_real)
-        self.discriminator_loss_input_A_fake = l2_loss(y = tf.zeros_like(self.discrimination_input_A_fake), y_hat = self.discrimination_input_A_fake)
-        self.discriminator_loss_A = (self.discriminator_loss_input_A_real + self.discriminator_loss_input_A_fake) / 2
-
-        self.discriminator_loss_input_B_real = l2_loss(y = tf.ones_like(self.discrimination_input_B_real), y_hat = self.discrimination_input_B_real)
-        self.discriminator_loss_input_B_fake = l2_loss(y = tf.zeros_like(self.discrimination_input_B_fake), y_hat = self.discrimination_input_B_fake)
-        self.discriminator_loss_B = (self.discriminator_loss_input_B_real + self.discriminator_loss_input_B_fake) / 2
-
-        # Merge the two discriminators into one
-        self.discriminator_loss = self.discriminator_loss_A + self.discriminator_loss_B
-
-        # Categorize variables because we have to optimize the two sets of the variables separately
-        trainable_variables = tf.trainable_variables()
-        self.discriminator_vars = [var for var in trainable_variables if 'discriminator' in var.name]
-        self.generator_vars = [var for var in trainable_variables if 'generator' in var.name]
-        #for var in t_vars: print(var.name)
-
-        # Reserved for test
-        self.generation_B_test = self.generator(inputs = self.input_A_test, reuse = True, scope_name = 'generator_A2B')
-        self.generation_A_test = self.generator(inputs = self.input_B_test, reuse = True, scope_name = 'generator_B2A')
-
-
-    def optimizer_initializer(self):
-
-        self.generator_learning_rate = tf.placeholder(tf.float32, None, name = 'generator_learning_rate')
-        self.discriminator_learning_rate = tf.placeholder(tf.float32, None, name = 'discriminator_learning_rate')
-        self.discriminator_optimizer = tf.train.AdamOptimizer(learning_rate = self.discriminator_learning_rate, beta1 = 0.5).minimize(self.discriminator_loss, var_list = self.discriminator_vars)
-        self.generator_optimizer = tf.train.AdamOptimizer(learning_rate = self.generator_learning_rate, beta1 = 0.5).minimize(self.generator_loss, var_list = self.generator_vars)
-
-    def train(self, input_A, input_B, lambda_cycle, lambda_identity, generator_learning_rate, discriminator_learning_rate):
-
-        generation_A, generation_B, generator_loss, _, generator_summaries = self.sess.run(
-            [self.generation_A, self.generation_B, self.generator_loss, self.generator_optimizer, self.generator_summaries], \
-            feed_dict = {self.lambda_cycle: lambda_cycle, self.lambda_identity: lambda_identity, self.input_A_real: input_A, self.input_B_real: input_B, self.generator_learning_rate: generator_learning_rate})
-
-        self.writer.add_summary(generator_summaries, self.train_step)
-
-        discriminator_loss, _, discriminator_summaries = self.sess.run([self.discriminator_loss, self.discriminator_optimizer, self.discriminator_summaries], \
-            feed_dict = {self.input_A_real: input_A, self.input_B_real: input_B, self.discriminator_learning_rate: discriminator_learning_rate, self.input_A_fake: generation_A, self.input_B_fake: generation_B})
-
-        self.writer.add_summary(discriminator_summaries, self.train_step)
-
-        self.train_step += 1
-
-        return generator_loss, discriminator_loss
-
-
-    def test(self, inputs, direction):
-        #print(">>> model.test()!")
-
-        if direction == 'A2B':
-            #print("generation_B_test: {}".format(self.generation_B_test))
-            #print("input_A_test: {}".format(self.input_A_test))
-            generation = self.sess.run(self.generation_B_test, feed_dict = {self.input_A_test: inputs})
-        elif direction == 'B2A':
-            #print("generation_A_test: {}".format(self.generation_A_test))
-            #print("input_B_test: {}".format(self.input_B_test))
-            generation = self.sess.run(self.generation_A_test, feed_dict = {self.input_B_test: inputs})
-        else:
-            raise Exception('Conversion direction must be specified.')
-
-        return generation
-
-
-    def save(self, directory, filename):
-
-        if not os.path.exists(directory):
-            os.makedirs(directory)
-        self.saver.save(self.sess, os.path.join(directory, filename))
-
-        return os.path.join(directory, filename)
-
-    def load(self, filepath):
-
-        self.saver.restore(self.sess, filepath)
-
-
-    def summary(self):
-
-        with tf.name_scope('generator_summaries'):
-            cycle_loss_summary = tf.summary.scalar('cycle_loss', self.cycle_loss)
-            identity_loss_summary = tf.summary.scalar('identity_loss', self.identity_loss)
-            generator_loss_A2B_summary = tf.summary.scalar('generator_loss_A2B', self.generator_loss_A2B)
-            generator_loss_B2A_summary = tf.summary.scalar('generator_loss_B2A', self.generator_loss_B2A)
-            generator_loss_summary = tf.summary.scalar('generator_loss', self.generator_loss)
-            generator_summaries = tf.summary.merge([cycle_loss_summary, identity_loss_summary, generator_loss_A2B_summary, generator_loss_B2A_summary, generator_loss_summary])
-
-        with tf.name_scope('discriminator_summaries'):
-            discriminator_loss_A_summary = tf.summary.scalar('discriminator_loss_A', self.discriminator_loss_A)
-            discriminator_loss_B_summary = tf.summary.scalar('discriminator_loss_B', self.discriminator_loss_B)
-            discriminator_loss_summary = tf.summary.scalar('discriminator_loss', self.discriminator_loss)
-            discriminator_summaries = tf.summary.merge([discriminator_loss_A_summary, discriminator_loss_B_summary, discriminator_loss_summary])
-
-        return generator_summaries, discriminator_summaries
-
-
-if __name__ == '__main__':
-
-    model = CycleGAN(num_features = 24)
-    #print('Graph Compile Successeded.')
diff --git a/voice_converter_sidecar/cycle_gan/module.py b/voice_converter_sidecar/cycle_gan/module.py
deleted file mode 100644
index 4319d9a1e2..0000000000
--- a/voice_converter_sidecar/cycle_gan/module.py
+++ /dev/null
@@ -1,214 +0,0 @@
-import tensorflow as tf 
-
-def gated_linear_layer(inputs, gates, name = None):
-
-    activation = tf.multiply(x = inputs, y = tf.sigmoid(gates), name = name)
-
-    return activation
-
-def instance_norm_layer(
-    inputs, 
-    epsilon = 1e-06, 
-    activation_fn = None, 
-    name = None):
-
-    instance_norm_layer = tf.contrib.layers.instance_norm(
-        inputs = inputs,
-        epsilon = epsilon,
-        activation_fn = activation_fn)
-
-    return instance_norm_layer
-
-def conv1d_layer(
-    inputs, 
-    filters, 
-    kernel_size, 
-    strides = 1, 
-    padding = 'same', 
-    activation = None,
-    kernel_initializer = None,
-    name = None):
-
-    conv_layer = tf.layers.conv1d(
-        inputs = inputs,
-        filters = filters,
-        kernel_size = kernel_size,
-        strides = strides,
-        padding = padding,
-        activation = activation,
-        kernel_initializer = kernel_initializer,
-        name = name)
-
-    return conv_layer
-
-def conv2d_layer(
-    inputs, 
-    filters, 
-    kernel_size, 
-    strides, 
-    padding = 'same', 
-    activation = None,
-    kernel_initializer = None,
-    name = None):
-
-    conv_layer = tf.layers.conv2d(
-        inputs = inputs,
-        filters = filters,
-        kernel_size = kernel_size,
-        strides = strides,
-        padding = padding,
-        activation = activation,
-        kernel_initializer = kernel_initializer,
-        name = name)
-
-    return conv_layer
-
-def residual1d_block(
-    inputs, 
-    filters = 1024, 
-    kernel_size = 3, 
-    strides = 1,
-    name_prefix = 'residule_block_'):
-
-    h1 = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_conv')
-    h1_norm = instance_norm_layer(inputs = h1, activation_fn = None, name = name_prefix + 'h1_norm')
-    h1_gates = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_gates')
-    h1_norm_gates = instance_norm_layer(inputs = h1_gates, activation_fn = None, name = name_prefix + 'h1_norm_gates')
-    h1_glu = gated_linear_layer(inputs = h1_norm, gates = h1_norm_gates, name = name_prefix + 'h1_glu')
-    h2 = conv1d_layer(inputs = h1_glu, filters = filters // 2, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h2_conv')
-    h2_norm = instance_norm_layer(inputs = h2, activation_fn = None, name = name_prefix + 'h2_norm')
-    
-    h3 = inputs + h2_norm
-
-    return h3
-
-def downsample1d_block(
-    inputs, 
-    filters, 
-    kernel_size, 
-    strides,
-    name_prefix = 'downsample1d_block_'):
-
-    h1 = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_conv')
-    h1_norm = instance_norm_layer(inputs = h1, activation_fn = None, name = name_prefix + 'h1_norm')
-    h1_gates = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_gates')
-    h1_norm_gates = instance_norm_layer(inputs = h1_gates, activation_fn = None, name = name_prefix + 'h1_norm_gates')
-    h1_glu = gated_linear_layer(inputs = h1_norm, gates = h1_norm_gates, name = name_prefix + 'h1_glu')
-
-    return h1_glu
-
-def downsample2d_block(
-    inputs, 
-    filters, 
-    kernel_size, 
-    strides,
-    name_prefix = 'downsample2d_block_'):
-
-    h1 = conv2d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_conv')
-    h1_norm = instance_norm_layer(inputs = h1, activation_fn = None, name = name_prefix + 'h1_norm')
-    h1_gates = conv2d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_gates')
-    h1_norm_gates = instance_norm_layer(inputs = h1_gates, activation_fn = None, name = name_prefix + 'h1_norm_gates')
-    h1_glu = gated_linear_layer(inputs = h1_norm, gates = h1_norm_gates, name = name_prefix + 'h1_glu')
-
-    return h1_glu
-
-def upsample1d_block(
-    inputs, 
-    filters, 
-    kernel_size, 
-    strides,
-    shuffle_size = 2,
-    name_prefix = 'upsample1d_block_'):
-    
-    h1 = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_conv')
-    h1_shuffle = pixel_shuffler(inputs = h1, shuffle_size = shuffle_size, name = name_prefix + 'h1_shuffle')
-    h1_norm = instance_norm_layer(inputs = h1_shuffle, activation_fn = None, name = name_prefix + 'h1_norm')
-
-    h1_gates = conv1d_layer(inputs = inputs, filters = filters, kernel_size = kernel_size, strides = strides, activation = None, name = name_prefix + 'h1_gates')
-    h1_shuffle_gates = pixel_shuffler(inputs = h1_gates, shuffle_size = shuffle_size, name = name_prefix + 'h1_shuffle_gates')
-    h1_norm_gates = instance_norm_layer(inputs = h1_shuffle_gates, activation_fn = None, name = name_prefix + 'h1_norm_gates')
-
-    h1_glu = gated_linear_layer(inputs = h1_norm, gates = h1_norm_gates, name = name_prefix + 'h1_glu')
-
-    return h1_glu
-
-def pixel_shuffler(inputs, shuffle_size = 2, name = None):
-
-    n = tf.shape(inputs)[0]
-    w = tf.shape(inputs)[1]
-    c = inputs.get_shape().as_list()[2]
-
-    oc = c // shuffle_size
-    ow = w * shuffle_size
-
-    outputs = tf.reshape(tensor = inputs, shape = [n, ow, oc], name = name)
-
-    return outputs
-
-def generator_gatedcnn(inputs, reuse = False, scope_name = 'generator_gatedcnn'):
-
-    # inputs has shape [batch_size, num_features, time]
-    # we need to convert it to [batch_size, time, num_features] for 1D convolution
-    inputs = tf.transpose(inputs, perm = [0, 2, 1], name = 'input_transpose')
-
-    with tf.variable_scope(scope_name) as scope:
-        # Discriminator would be reused in CycleGAN
-        if reuse:
-            scope.reuse_variables()
-        else:
-            assert scope.reuse is False
-
-        h1 = conv1d_layer(inputs = inputs, filters = 128, kernel_size = 15, strides = 1, activation = None, name = 'h1_conv')
-        h1_gates = conv1d_layer(inputs = inputs, filters = 128, kernel_size = 15, strides = 1, activation = None, name = 'h1_conv_gates')
-        h1_glu = gated_linear_layer(inputs = h1, gates = h1_gates, name = 'h1_glu')
-
-        # Downsample
-        d1 = downsample1d_block(inputs = h1_glu, filters = 256, kernel_size = 5, strides = 2, name_prefix = 'downsample1d_block1_')
-        d2 = downsample1d_block(inputs = d1, filters = 512, kernel_size = 5, strides = 2, name_prefix = 'downsample1d_block2_')
-
-        # Residual blocks
-        r1 = residual1d_block(inputs = d2, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block1_')
-        r2 = residual1d_block(inputs = r1, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block2_')
-        r3 = residual1d_block(inputs = r2, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block3_')
-        r4 = residual1d_block(inputs = r3, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block4_')
-        r5 = residual1d_block(inputs = r4, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block5_')
-        r6 = residual1d_block(inputs = r5, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block6_')
-
-        # Upsample
-        u1 = upsample1d_block(inputs = r6, filters = 1024, kernel_size = 5, strides = 1, shuffle_size = 2, name_prefix = 'upsample1d_block1_')
-        u2 = upsample1d_block(inputs = u1, filters = 512, kernel_size = 5, strides = 1, shuffle_size = 2, name_prefix = 'upsample1d_block2_')
-
-        # Output
-        o1 = conv1d_layer(inputs = u2, filters = 24, kernel_size = 15, strides = 1, activation = None, name = 'o1_conv')
-        o2 = tf.transpose(o1, perm = [0, 2, 1], name = 'output_transpose')
-
-    return o2
-    
-
-def discriminator(inputs, reuse = False, scope_name = 'discriminator'):
-
-    # inputs has shape [batch_size, num_features, time]
-    # we need to add channel for 2D convolution [batch_size, num_features, time, 1]
-    inputs = tf.expand_dims(inputs, -1)
-
-    with tf.variable_scope(scope_name) as scope:
-        # Discriminator would be reused in CycleGAN
-        if reuse:
-            scope.reuse_variables()
-        else:
-            assert scope.reuse is False
-
-        h1 = conv2d_layer(inputs = inputs, filters = 128, kernel_size = [3, 3], strides = [1, 2], activation = None, name = 'h1_conv')
-        h1_gates = conv2d_layer(inputs = inputs, filters = 128, kernel_size = [3, 3], strides = [1, 2], activation = None, name = 'h1_conv_gates')
-        h1_glu = gated_linear_layer(inputs = h1, gates = h1_gates, name = 'h1_glu')
-
-        # Downsample
-        d1 = downsample2d_block(inputs = h1_glu, filters = 256, kernel_size = [3, 3], strides = [2, 2], name_prefix = 'downsample2d_block1_')
-        d2 = downsample2d_block(inputs = d1, filters = 512, kernel_size = [3, 3], strides = [2, 2], name_prefix = 'downsample2d_block2_')
-        d3 = downsample2d_block(inputs = d2, filters = 1024, kernel_size = [6, 3], strides = [1, 2], name_prefix = 'downsample2d_block3_')
-
-        # Output
-        o1 = tf.layers.dense(inputs = d3, units = 1, activation = tf.nn.sigmoid)
-
-        return o1
-
diff --git a/voice_converter_sidecar/cycle_gan/preprocess.py b/voice_converter_sidecar/cycle_gan/preprocess.py
deleted file mode 100644
index f073b0bbb8..0000000000
--- a/voice_converter_sidecar/cycle_gan/preprocess.py
+++ /dev/null
@@ -1,238 +0,0 @@
-import librosa
-import numpy as np
-import os
-import pyworld
-
-def load_wavs(wav_dir, sr):
-
-    wavs = list()
-    for file in os.listdir(wav_dir):
-        file_path = os.path.join(wav_dir, file)
-        wav, _ = librosa.load(file_path, sr = sr, mono = True)
-        #wav = wav.astype(np.float64)
-        wavs.append(wav)
-
-    return wavs
-
-def world_decompose(wav, fs, frame_period = 5.0):
-
-    # Decompose speech signal into f0, spectral envelope and aperiodicity using WORLD
-    wav = wav.astype(np.float64)
-    f0, timeaxis = pyworld.harvest(wav, fs, frame_period = frame_period, f0_floor = 71.0, f0_ceil = 800.0)
-    sp = pyworld.cheaptrick(wav, f0, timeaxis, fs)
-    ap = pyworld.d4c(wav, f0, timeaxis, fs)
-
-    return f0, timeaxis, sp, ap
-
-def world_encode_spectral_envelop(sp, fs, dim = 24):
-
-    # Get Mel-cepstral coefficients (MCEPs)
-
-    #sp = sp.astype(np.float64)
-    coded_sp = pyworld.code_spectral_envelope(sp, fs, dim)
-
-    return coded_sp
-
-def world_decode_spectral_envelop(coded_sp, fs):
-
-    fftlen = pyworld.get_cheaptrick_fft_size(fs)
-    #coded_sp = coded_sp.astype(np.float32)
-    #coded_sp = np.ascontiguousarray(coded_sp)
-    decoded_sp = pyworld.decode_spectral_envelope(coded_sp, fs, fftlen)
-
-    return decoded_sp
-
-
-def world_encode_data(wavs, fs, frame_period = 5.0, coded_dim = 24):
-
-    f0s = list()
-    timeaxes = list()
-    sps = list()
-    aps = list()
-    coded_sps = list()
-
-    for wav in wavs:
-        f0, timeaxis, sp, ap = world_decompose(wav = wav, fs = fs, frame_period = frame_period)
-        coded_sp = world_encode_spectral_envelop(sp = sp, fs = fs, dim = coded_dim)
-        f0s.append(f0)
-        timeaxes.append(timeaxis)
-        sps.append(sp)
-        aps.append(ap)
-        coded_sps.append(coded_sp)
-
-    return f0s, timeaxes, sps, aps, coded_sps
-
-
-def transpose_in_list(lst):
-
-    transposed_lst = list()
-    for array in lst:
-        transposed_lst.append(array.T)
-    return transposed_lst
-
-
-def world_decode_data(coded_sps, fs):
-
-    decoded_sps =  list()
-
-    for coded_sp in coded_sps:
-        decoded_sp = world_decode_spectral_envelop(coded_sp, fs)
-        decoded_sps.append(decoded_sp)
-
-    return decoded_sps
-
-
-def world_speech_synthesis(f0, decoded_sp, ap, fs, frame_period):
-
-    #decoded_sp = decoded_sp.astype(np.float64)
-    wav = pyworld.synthesize(f0, decoded_sp, ap, fs, frame_period)
-    # Librosa could not save wav if not doing so
-    wav = wav.astype(np.float32)
-
-    return wav
-
-
-def world_synthesis_data(f0s, decoded_sps, aps, fs, frame_period):
-
-    wavs = list()
-
-    for f0, decoded_sp, ap in zip(f0s, decoded_sps, aps):
-        wav = world_speech_synthesis(f0, decoded_sp, ap, fs, frame_period)
-        wavs.append(wav)
-
-    return wavs
-
-
-def coded_sps_normalization_fit_transoform(coded_sps):
-
-    coded_sps_concatenated = np.concatenate(coded_sps, axis = 1)
-    coded_sps_mean = np.mean(coded_sps_concatenated, axis = 1, keepdims = True)
-    coded_sps_std = np.std(coded_sps_concatenated, axis = 1, keepdims = True)
-
-    coded_sps_normalized = list()
-    for coded_sp in coded_sps:
-        coded_sps_normalized.append((coded_sp - coded_sps_mean) / coded_sps_std)
-    
-    return coded_sps_normalized, coded_sps_mean, coded_sps_std
-
-def coded_sps_normalization_transoform(coded_sps, coded_sps_mean, coded_sps_std):
-
-    coded_sps_normalized = list()
-    for coded_sp in coded_sps:
-        coded_sps_normalized.append((coded_sp - coded_sps_mean) / coded_sps_std)
-    
-    return coded_sps_normalized
-
-def coded_sps_normalization_inverse_transoform(normalized_coded_sps, coded_sps_mean, coded_sps_std):
-
-    coded_sps = list()
-    for normalized_coded_sp in normalized_coded_sps:
-        coded_sps.append(normalized_coded_sp * coded_sps_std + coded_sps_mean)
-
-    return coded_sps
-
-def coded_sp_padding(coded_sp, multiple = 4):
-
-    num_features = coded_sp.shape[0]
-    num_frames = coded_sp.shape[1]
-    num_frames_padded = int(np.ceil(num_frames / multiple)) * multiple
-    num_frames_diff = num_frames_padded - num_frames
-    num_pad_left = num_frames_diff // 2
-    num_pad_right = num_frames_diff - num_pad_left
-    coded_sp_padded = np.pad(coded_sp, ((0, 0), (num_pad_left, num_pad_right)), 'constant', constant_values = 0)
-
-    return coded_sp_padded
-
-def wav_padding(wav, sr, frame_period, multiple = 4):
-
-    assert wav.ndim == 1 
-    num_frames = len(wav)
-    num_frames_padded = int((np.ceil((np.floor(num_frames / (sr * frame_period / 1000)) + 1) / multiple + 1) * multiple - 1) * (sr * frame_period / 1000))
-    num_frames_diff = num_frames_padded - num_frames
-    num_pad_left = num_frames_diff // 2
-    num_pad_right = num_frames_diff - num_pad_left
-    wav_padded = np.pad(wav, (num_pad_left, num_pad_right), 'constant', constant_values = 0)
-
-    return wav_padded
-
-
-def logf0_statistics(f0s):
-
-    log_f0s_concatenated = np.ma.log(np.concatenate(f0s))
-    log_f0s_mean = log_f0s_concatenated.mean()
-    log_f0s_std = log_f0s_concatenated.std()
-
-    return log_f0s_mean, log_f0s_std
-
-def pitch_conversion(f0, mean_log_src, std_log_src, mean_log_target, std_log_target):
-
-    # Logarithm Gaussian normalization for Pitch Conversions
-    f0_converted = np.exp((np.log(f0) - mean_log_src) / std_log_src * std_log_target + mean_log_target)
-
-    return f0_converted
-
-def wavs_to_specs(wavs, n_fft = 1024, hop_length = None):
-
-    stfts = list()
-    for wav in wavs:
-        stft = librosa.stft(wav, n_fft = n_fft, hop_length = hop_length)
-        stfts.append(stft)
-
-    return stfts
-
-
-def wavs_to_mfccs(wavs, sr, n_fft = 1024, hop_length = None, n_mels = 128, n_mfcc = 24):
-
-    mfccs = list()
-    for wav in wavs:
-        mfcc = librosa.feature.mfcc(y = wav, sr = sr, n_fft = n_fft, hop_length = hop_length, n_mels = n_mels, n_mfcc = n_mfcc)
-        mfccs.append(mfcc)
-
-    return mfccs
-
-
-def mfccs_normalization(mfccs):
-
-    mfccs_concatenated = np.concatenate(mfccs, axis = 1)
-    mfccs_mean = np.mean(mfccs_concatenated, axis = 1, keepdims = True)
-    mfccs_std = np.std(mfccs_concatenated, axis = 1, keepdims = True)
-
-    mfccs_normalized = list()
-    for mfcc in mfccs:
-        mfccs_normalized.append((mfcc - mfccs_mean) / mfccs_std)
-    
-    return mfccs_normalized, mfccs_mean, mfccs_std
-
-
-def sample_train_data(dataset_A, dataset_B, n_frames = 128):
-
-    num_samples = min(len(dataset_A), len(dataset_B))
-    train_data_A_idx = np.arange(len(dataset_A))
-    train_data_B_idx = np.arange(len(dataset_B))
-    np.random.shuffle(train_data_A_idx)
-    np.random.shuffle(train_data_B_idx)
-    train_data_A_idx_subset = train_data_A_idx[:num_samples]
-    train_data_B_idx_subset = train_data_B_idx[:num_samples]
-
-    train_data_A = list()
-    train_data_B = list()
-
-    for idx_A, idx_B in zip(train_data_A_idx_subset, train_data_B_idx_subset):
-        data_A = dataset_A[idx_A]
-        frames_A_total = data_A.shape[1]
-        assert frames_A_total >= n_frames
-        start_A = np.random.randint(frames_A_total - n_frames + 1)
-        end_A = start_A + n_frames
-        train_data_A.append(data_A[:,start_A:end_A])
-
-        data_B = dataset_B[idx_B]
-        frames_B_total = data_B.shape[1]
-        assert frames_B_total >= n_frames
-        start_B = np.random.randint(frames_B_total - n_frames + 1)
-        end_B = start_B + n_frames
-        train_data_B.append(data_B[:,start_B:end_B])
-
-    train_data_A = np.array(train_data_A)
-    train_data_B = np.array(train_data_B)
-
-    return train_data_A, train_data_B
\ No newline at end of file
diff --git a/voice_converter_sidecar/cycle_gan/protos/audio_pb2.py b/voice_converter_sidecar/cycle_gan/protos/audio_pb2.py
deleted file mode 100644
index 735b5e398c..0000000000
--- a/voice_converter_sidecar/cycle_gan/protos/audio_pb2.py
+++ /dev/null
@@ -1,260 +0,0 @@
-# Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: protos/audio.proto
-
-import sys
-_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
-from google.protobuf import descriptor as _descriptor
-from google.protobuf import message as _message
-from google.protobuf import reflection as _reflection
-from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
-# @@protoc_insertion_point(imports)
-
-_sym_db = _symbol_database.Default()
-
-
-
-
-DESCRIPTOR = _descriptor.FileDescriptor(
-  name='protos/audio.proto',
-  package='voder_audio',
-  syntax='proto3',
-  serialized_pb=_b('\n\x12protos/audio.proto\x12\x0bvoder_audio\"\xe6\x04\n\x12VocodeAudioRequest\x12\x13\n\x0b\x66loat_audio\x18\x01 \x03(\x02\x12\x1c\n\x14request_batch_number\x18\x02 \x01(\x03\x12\x43\n\rvocode_params\x18\n \x01(\x0b\x32,.voder_audio.VocodeAudioRequest.VocodeParams\x12\x13\n\x0bskip_vocode\x18\x04 \x01(\x08\x12\x1b\n\x13\x62uffer_size_minimum\x18\x06 \x01(\x05\x12\x1d\n\x15\x64iscard_vocoded_audio\x18\x08 \x01(\x08\x1a\x86\x03\n\x0cVocodeParams\x12\x1b\n\x13initial_sample_rate\x18\x01 \x01(\x05\x12\x1c\n\x14original_source_rate\x18\t \x01(\x05\x12!\n\x19original_source_save_file\x18\n \x01(\x08\x12\x1c\n\x14pre_convert_resample\x18\x02 \x01(\x08\x12!\n\x19pre_convert_resample_rate\x18\x03 \x01(\x05\x12&\n\x1epre_convert_resample_save_file\x18\x04 \x01(\x08\x12*\n\"model_hyperparameter_sampling_rate\x18\x05 \x01(\x05\x12\x17\n\x0fmodel_save_file\x18\x0b \x01(\x08\x12\x1d\n\x15post_convert_resample\x18\x06 \x01(\x08\x12\"\n\x1apost_convert_resample_rate\x18\x07 \x01(\x05\x12\'\n\x1fpost_convert_resample_save_file\x18\x08 \x01(\x08\"I\n\x13VocodeAudioResponse\x12\x13\n\x0b\x66loat_audio\x18\x01 \x03(\x02\x12\x1d\n\x15response_batch_number\x18\x02 \x01(\x03\x62\x06proto3')
-)
-_sym_db.RegisterFileDescriptor(DESCRIPTOR)
-
-
-
-
-_VOCODEAUDIOREQUEST_VOCODEPARAMS = _descriptor.Descriptor(
-  name='VocodeParams',
-  full_name='voder_audio.VocodeAudioRequest.VocodeParams',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='initial_sample_rate', full_name='voder_audio.VocodeAudioRequest.VocodeParams.initial_sample_rate', index=0,
-      number=1, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='original_source_rate', full_name='voder_audio.VocodeAudioRequest.VocodeParams.original_source_rate', index=1,
-      number=9, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='original_source_save_file', full_name='voder_audio.VocodeAudioRequest.VocodeParams.original_source_save_file', index=2,
-      number=10, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='pre_convert_resample', full_name='voder_audio.VocodeAudioRequest.VocodeParams.pre_convert_resample', index=3,
-      number=2, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='pre_convert_resample_rate', full_name='voder_audio.VocodeAudioRequest.VocodeParams.pre_convert_resample_rate', index=4,
-      number=3, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='pre_convert_resample_save_file', full_name='voder_audio.VocodeAudioRequest.VocodeParams.pre_convert_resample_save_file', index=5,
-      number=4, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='model_hyperparameter_sampling_rate', full_name='voder_audio.VocodeAudioRequest.VocodeParams.model_hyperparameter_sampling_rate', index=6,
-      number=5, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='model_save_file', full_name='voder_audio.VocodeAudioRequest.VocodeParams.model_save_file', index=7,
-      number=11, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='post_convert_resample', full_name='voder_audio.VocodeAudioRequest.VocodeParams.post_convert_resample', index=8,
-      number=6, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='post_convert_resample_rate', full_name='voder_audio.VocodeAudioRequest.VocodeParams.post_convert_resample_rate', index=9,
-      number=7, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='post_convert_resample_save_file', full_name='voder_audio.VocodeAudioRequest.VocodeParams.post_convert_resample_save_file', index=10,
-      number=8, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=260,
-  serialized_end=650,
-)
-
-_VOCODEAUDIOREQUEST = _descriptor.Descriptor(
-  name='VocodeAudioRequest',
-  full_name='voder_audio.VocodeAudioRequest',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='float_audio', full_name='voder_audio.VocodeAudioRequest.float_audio', index=0,
-      number=1, type=2, cpp_type=6, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='request_batch_number', full_name='voder_audio.VocodeAudioRequest.request_batch_number', index=1,
-      number=2, type=3, cpp_type=2, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='vocode_params', full_name='voder_audio.VocodeAudioRequest.vocode_params', index=2,
-      number=10, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='skip_vocode', full_name='voder_audio.VocodeAudioRequest.skip_vocode', index=3,
-      number=4, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='buffer_size_minimum', full_name='voder_audio.VocodeAudioRequest.buffer_size_minimum', index=4,
-      number=6, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='discard_vocoded_audio', full_name='voder_audio.VocodeAudioRequest.discard_vocoded_audio', index=5,
-      number=8, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-  ],
-  extensions=[
-  ],
-  nested_types=[_VOCODEAUDIOREQUEST_VOCODEPARAMS, ],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=36,
-  serialized_end=650,
-)
-
-
-_VOCODEAUDIORESPONSE = _descriptor.Descriptor(
-  name='VocodeAudioResponse',
-  full_name='voder_audio.VocodeAudioResponse',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='float_audio', full_name='voder_audio.VocodeAudioResponse.float_audio', index=0,
-      number=1, type=2, cpp_type=6, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-    _descriptor.FieldDescriptor(
-      name='response_batch_number', full_name='voder_audio.VocodeAudioResponse.response_batch_number', index=1,
-      number=2, type=3, cpp_type=2, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=652,
-  serialized_end=725,
-)
-
-_VOCODEAUDIOREQUEST_VOCODEPARAMS.containing_type = _VOCODEAUDIOREQUEST
-_VOCODEAUDIOREQUEST.fields_by_name['vocode_params'].message_type = _VOCODEAUDIOREQUEST_VOCODEPARAMS
-DESCRIPTOR.message_types_by_name['VocodeAudioRequest'] = _VOCODEAUDIOREQUEST
-DESCRIPTOR.message_types_by_name['VocodeAudioResponse'] = _VOCODEAUDIORESPONSE
-
-VocodeAudioRequest = _reflection.GeneratedProtocolMessageType('VocodeAudioRequest', (_message.Message,), dict(
-
-  VocodeParams = _reflection.GeneratedProtocolMessageType('VocodeParams', (_message.Message,), dict(
-    DESCRIPTOR = _VOCODEAUDIOREQUEST_VOCODEPARAMS,
-    __module__ = 'protos.audio_pb2'
-    # @@protoc_insertion_point(class_scope:voder_audio.VocodeAudioRequest.VocodeParams)
-    ))
-  ,
-  DESCRIPTOR = _VOCODEAUDIOREQUEST,
-  __module__ = 'protos.audio_pb2'
-  # @@protoc_insertion_point(class_scope:voder_audio.VocodeAudioRequest)
-  ))
-_sym_db.RegisterMessage(VocodeAudioRequest)
-_sym_db.RegisterMessage(VocodeAudioRequest.VocodeParams)
-
-VocodeAudioResponse = _reflection.GeneratedProtocolMessageType('VocodeAudioResponse', (_message.Message,), dict(
-  DESCRIPTOR = _VOCODEAUDIORESPONSE,
-  __module__ = 'protos.audio_pb2'
-  # @@protoc_insertion_point(class_scope:voder_audio.VocodeAudioResponse)
-  ))
-_sym_db.RegisterMessage(VocodeAudioResponse)
-
-
-# @@protoc_insertion_point(module_scope)
diff --git a/voice_converter_sidecar/cycle_gan/requirements.txt b/voice_converter_sidecar/cycle_gan/requirements.txt
deleted file mode 100644
index 143c687f82..0000000000
--- a/voice_converter_sidecar/cycle_gan/requirements.txt
+++ /dev/null
@@ -1,36 +0,0 @@
-absl-py==0.7.1
-astor==0.8.0
-audioread==2.1.8
-cffi==1.12.3
-Cython==0.29.12
-decorator==4.4.0
-falcon==2.0.0
-falcon-multipart==0.2.0
-gast==0.2.2
-google-pasta==0.1.7
-grpcio==1.22.0
-h5py==2.9.0
-joblib==0.13.2
-Keras-Applications==1.0.8
-Keras-Preprocessing==1.1.0
-librosa==0.7.0
-llvmlite==0.29.0
-Markdown==3.1.1
-numba==0.45.0
-numpy==1.16.4
-protobuf==3.9.0
-pycparser==2.19
-pyworld==0.2.8
-pyzmq==18.1.0
-resampy==0.2.1
-scikit-learn==0.21.2
-scipy==1.3.0
-six==1.12.0
-SoundFile==0.10.2
-tensorboard==1.14.0
-tensorflow-estimator==1.14.0
-tensorflow-gpu==1.14.0
-termcolor==1.1.0
-Werkzeug==0.15.5
-wrapt==1.11.2
-zmq==0.0.0
diff --git a/voice_converter_sidecar/cycle_gan/script/recorder.js b/voice_converter_sidecar/cycle_gan/script/recorder.js
deleted file mode 100644
index 0fcb6b3cab..0000000000
--- a/voice_converter_sidecar/cycle_gan/script/recorder.js
+++ /dev/null
@@ -1,357 +0,0 @@
-(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}g.Recorder = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s})({1:[function(require,module,exports){
-"use strict";
-
-module.exports = require("./recorder").Recorder;
-
-},{"./recorder":2}],2:[function(require,module,exports){
-'use strict';
-
-var _createClass = (function () {
-    function defineProperties(target, props) {
-        for (var i = 0; i < props.length; i++) {
-            var descriptor = props[i];descriptor.enumerable = descriptor.enumerable || false;descriptor.configurable = true;if ("value" in descriptor) descriptor.writable = true;Object.defineProperty(target, descriptor.key, descriptor);
-        }
-    }return function (Constructor, protoProps, staticProps) {
-        if (protoProps) defineProperties(Constructor.prototype, protoProps);if (staticProps) defineProperties(Constructor, staticProps);return Constructor;
-    };
-})();
-
-Object.defineProperty(exports, "__esModule", {
-    value: true
-});
-exports.Recorder = undefined;
-
-var _inlineWorker = require('inline-worker');
-
-var _inlineWorker2 = _interopRequireDefault(_inlineWorker);
-
-function _interopRequireDefault(obj) {
-    return obj && obj.__esModule ? obj : { default: obj };
-}
-
-function _classCallCheck(instance, Constructor) {
-    if (!(instance instanceof Constructor)) {
-        throw new TypeError("Cannot call a class as a function");
-    }
-}
-
-var Recorder = exports.Recorder = (function () {
-    function Recorder(source, cfg) {
-        var _this = this;
-
-        _classCallCheck(this, Recorder);
-
-        this.config = {
-            bufferLen: 4096,
-            numChannels: 2,
-            mimeType: 'audio/wav'
-        };
-        this.recording = false;
-        this.callbacks = {
-            getBuffer: [],
-            exportWAV: []
-        };
-
-        Object.assign(this.config, cfg);
-        this.context = source.context;
-        this.node = (this.context.createScriptProcessor || this.context.createJavaScriptNode).call(this.context, this.config.bufferLen, this.config.numChannels, this.config.numChannels);
-
-        this.node.onaudioprocess = function (e) {
-            if (!_this.recording) return;
-
-            var buffer = [];
-            for (var channel = 0; channel < _this.config.numChannels; channel++) {
-                buffer.push(e.inputBuffer.getChannelData(channel));
-            }
-            _this.worker.postMessage({
-                command: 'record',
-                buffer: buffer
-            });
-        };
-
-        source.connect(this.node);
-        this.node.connect(this.context.destination); //this should not be necessary
-
-        var self = {};
-        this.worker = new _inlineWorker2.default(function () {
-            var recLength = 0,
-                recBuffers = [],
-                sampleRate = undefined,
-                numChannels = undefined;
-
-            self.onmessage = function (e) {
-                switch (e.data.command) {
-                    case 'init':
-                        init(e.data.config);
-                        break;
-                    case 'record':
-                        record(e.data.buffer);
-                        break;
-                    case 'exportWAV':
-                        exportWAV(e.data.type);
-                        break;
-                    case 'getBuffer':
-                        getBuffer();
-                        break;
-                    case 'clear':
-                        clear();
-                        break;
-                }
-            };
-
-            function init(config) {
-                sampleRate = config.sampleRate;
-                numChannels = config.numChannels;
-                initBuffers();
-            }
-
-            function record(inputBuffer) {
-                for (var channel = 0; channel < numChannels; channel++) {
-                    recBuffers[channel].push(inputBuffer[channel]);
-                }
-                recLength += inputBuffer[0].length;
-            }
-
-            function exportWAV(type) {
-                var buffers = [];
-                for (var channel = 0; channel < numChannels; channel++) {
-                    buffers.push(mergeBuffers(recBuffers[channel], recLength));
-                }
-                var interleaved = undefined;
-                if (numChannels === 2) {
-                    interleaved = interleave(buffers[0], buffers[1]);
-                } else {
-                    interleaved = buffers[0];
-                }
-                var dataview = encodeWAV(interleaved);
-                var audioBlob = new Blob([dataview], { type: type });
-
-                self.postMessage({ command: 'exportWAV', data: audioBlob });
-            }
-
-            function getBuffer() {
-                var buffers = [];
-                for (var channel = 0; channel < numChannels; channel++) {
-                    buffers.push(mergeBuffers(recBuffers[channel], recLength));
-                }
-                self.postMessage({ command: 'getBuffer', data: buffers });
-            }
-
-            function clear() {
-                recLength = 0;
-                recBuffers = [];
-                initBuffers();
-            }
-
-            function initBuffers() {
-                for (var channel = 0; channel < numChannels; channel++) {
-                    recBuffers[channel] = [];
-                }
-            }
-
-            function mergeBuffers(recBuffers, recLength) {
-                var result = new Float32Array(recLength);
-                var offset = 0;
-                for (var i = 0; i < recBuffers.length; i++) {
-                    result.set(recBuffers[i], offset);
-                    offset += recBuffers[i].length;
-                }
-                return result;
-            }
-
-            function interleave(inputL, inputR) {
-                var length = inputL.length + inputR.length;
-                var result = new Float32Array(length);
-
-                var index = 0,
-                    inputIndex = 0;
-
-                while (index < length) {
-                    result[index++] = inputL[inputIndex];
-                    result[index++] = inputR[inputIndex];
-                    inputIndex++;
-                }
-                return result;
-            }
-
-            function floatTo16BitPCM(output, offset, input) {
-                for (var i = 0; i < input.length; i++, offset += 2) {
-                    var s = Math.max(-1, Math.min(1, input[i]));
-                    output.setInt16(offset, s < 0 ? s * 0x8000 : s * 0x7FFF, true);
-                }
-            }
-
-            function writeString(view, offset, string) {
-                for (var i = 0; i < string.length; i++) {
-                    view.setUint8(offset + i, string.charCodeAt(i));
-                }
-            }
-
-            function encodeWAV(samples) {
-                var buffer = new ArrayBuffer(44 + samples.length * 2);
-                var view = new DataView(buffer);
-
-                /* RIFF identifier */
-                writeString(view, 0, 'RIFF');
-                /* RIFF chunk length */
-                view.setUint32(4, 36 + samples.length * 2, true);
-                /* RIFF type */
-                writeString(view, 8, 'WAVE');
-                /* format chunk identifier */
-                writeString(view, 12, 'fmt ');
-                /* format chunk length */
-                view.setUint32(16, 16, true);
-                /* sample format (raw) */
-                view.setUint16(20, 1, true);
-                /* channel count */
-                view.setUint16(22, numChannels, true);
-                /* sample rate */
-                view.setUint32(24, sampleRate, true);
-                /* byte rate (sample rate * block align) */
-                view.setUint32(28, sampleRate * 4, true);
-                /* block align (channel count * bytes per sample) */
-                view.setUint16(32, numChannels * 2, true);
-                /* bits per sample */
-                view.setUint16(34, 16, true);
-                /* data chunk identifier */
-                writeString(view, 36, 'data');
-                /* data chunk length */
-                view.setUint32(40, samples.length * 2, true);
-
-                floatTo16BitPCM(view, 44, samples);
-
-                return view;
-            }
-        }, self);
-
-        this.worker.postMessage({
-            command: 'init',
-            config: {
-                sampleRate: this.context.sampleRate,
-                numChannels: this.config.numChannels
-            }
-        });
-
-        this.worker.onmessage = function (e) {
-            var cb = _this.callbacks[e.data.command].pop();
-            if (typeof cb == 'function') {
-                cb(e.data.data);
-            }
-        };
-    }
-
-    _createClass(Recorder, [{
-        key: 'record',
-        value: function record() {
-            this.recording = true;
-        }
-    }, {
-        key: 'stop',
-        value: function stop() {
-            this.recording = false;
-        }
-    }, {
-        key: 'clear',
-        value: function clear() {
-            this.worker.postMessage({ command: 'clear' });
-        }
-    }, {
-        key: 'getBuffer',
-        value: function getBuffer(cb) {
-            cb = cb || this.config.callback;
-            if (!cb) throw new Error('Callback not set');
-
-            this.callbacks.getBuffer.push(cb);
-
-            this.worker.postMessage({ command: 'getBuffer' });
-        }
-    }, {
-        key: 'exportWAV',
-        value: function exportWAV(cb, mimeType) {
-            mimeType = mimeType || this.config.mimeType;
-            cb = cb || this.config.callback;
-            if (!cb) throw new Error('Callback not set');
-
-            this.callbacks.exportWAV.push(cb);
-
-            this.worker.postMessage({
-                command: 'exportWAV',
-                type: mimeType
-            });
-        }
-    }], [{
-        key: 'forceDownload',
-        value: function forceDownload(blob, filename) {
-            var url = (window.URL || window.webkitURL).createObjectURL(blob);
-            var link = window.document.createElement('a');
-            link.href = url;
-            link.download = filename || 'output.wav';
-            var click = document.createEvent("Event");
-            click.initEvent("click", true, true);
-            link.dispatchEvent(click);
-        }
-    }]);
-
-    return Recorder;
-})();
-
-exports.default = Recorder;
-
-},{"inline-worker":3}],3:[function(require,module,exports){
-"use strict";
-
-module.exports = require("./inline-worker");
-},{"./inline-worker":4}],4:[function(require,module,exports){
-(function (global){
-"use strict";
-
-var _createClass = (function () { function defineProperties(target, props) { for (var key in props) { var prop = props[key]; prop.configurable = true; if (prop.value) prop.writable = true; } Object.defineProperties(target, props); } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; })();
-
-var _classCallCheck = function (instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } };
-
-var WORKER_ENABLED = !!(global === global.window && global.URL && global.Blob && global.Worker);
-
-var InlineWorker = (function () {
-  function InlineWorker(func, self) {
-    var _this = this;
-
-    _classCallCheck(this, InlineWorker);
-
-    if (WORKER_ENABLED) {
-      var functionBody = func.toString().trim().match(/^function\s*\w*\s*\([\w\s,]*\)\s*{([\w\W]*?)}$/)[1];
-      var url = global.URL.createObjectURL(new global.Blob([functionBody], { type: "text/javascript" }));
-
-      return new global.Worker(url);
-    }
-
-    this.self = self;
-    this.self.postMessage = function (data) {
-      setTimeout(function () {
-        _this.onmessage({ data: data });
-      }, 0);
-    };
-
-    setTimeout(function () {
-      func.call(self);
-    }, 0);
-  }
-
-  _createClass(InlineWorker, {
-    postMessage: {
-      value: function postMessage(data) {
-        var _this = this;
-
-        setTimeout(function () {
-          _this.self.onmessage({ data: data });
-        }, 0);
-      }
-    }
-  });
-
-  return InlineWorker;
-})();
-
-module.exports = InlineWorker;
-}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
-},{}]},{},[1])(1)
-});
\ No newline at end of file
diff --git a/voice_converter_sidecar/cycle_gan/sidecar.py b/voice_converter_sidecar/cycle_gan/sidecar.py
deleted file mode 100755
index 9af7fb9fe9..0000000000
--- a/voice_converter_sidecar/cycle_gan/sidecar.py
+++ /dev/null
@@ -1,264 +0,0 @@
-#!/usr/bin/env python3
-
-"""
-Starts a demo HTTP server to capture and transform audio
-as a live demonstration of the trained model.
-
-Brandon Thomas 2019-07-29 <bt@brand.io> <echelon@gmail.com>
-"""
-
-# noinspection PyInterpreter
-import argparse
-import io
-import librosa
-import numpy as np
-import os
-import pathlib
-import scipy
-import soundfile
-import struct
-import subprocess
-import tempfile
-import tensorflow as tf
-import zmq
-
-from model import CycleGAN
-from preprocess import *
-from protos.audio_pb2 import VocodeAudioRequest
-from protos.audio_pb2 import VocodeAudioResponse
-
-print("TensorFlow version: {}".format(tf.version.VERSION))
-
-
-class Converter():
-    def __init__(self, model_dir, model_name):
-        self.num_features = 24
-        self.frame_period = 5.0
-
-        self.model = CycleGAN(num_features = self.num_features, mode = 'test')
-
-        self.model.load(filepath = os.path.join(model_dir, model_name))
-
-        self.mcep_normalization_params = np.load(os.path.join(model_dir, 'mcep_normalization.npz'))
-        self.mcep_mean_A = self.mcep_normalization_params['mean_A']
-        self.mcep_std_A = self.mcep_normalization_params['std_A']
-        self.mcep_mean_B = self.mcep_normalization_params['mean_B']
-        self.mcep_std_B = self.mcep_normalization_params['std_B']
-
-        self.logf0s_normalization_params = np.load(os.path.join(model_dir,
-          'logf0s_normalization.npz'))
-        self.logf0s_mean_A = self.logf0s_normalization_params['mean_A']
-        self.logf0s_std_A = self.logf0s_normalization_params['std_A']
-        self.logf0s_mean_B = self.logf0s_normalization_params['mean_B']
-        self.logf0s_std_B = self.logf0s_normalization_params['std_B']
-
-    def convert_partial(self, wav, conversion_direction='A2B', model_sampling_rate=16000):
-        wav = wav_padding(wav = wav,
-            sr = model_sampling_rate,
-            frame_period = self.frame_period,
-            multiple = 4)
-
-        #librosa.output.write_wav('wav_padding.wav', wav, model_sampling_rate)
-
-        f0, timeaxis, sp, ap = world_decompose(wav = wav,
-            fs = model_sampling_rate,
-            frame_period = self.frame_period)
-        coded_sp = world_encode_spectral_envelop(sp = sp,
-            fs = model_sampling_rate,
-            dim = self.num_features)
-        coded_sp_transposed = coded_sp.T
-
-        f0_converted = pitch_conversion(f0 = f0,
-            mean_log_src = self.logf0s_mean_A,
-            std_log_src = self.logf0s_std_A,
-            mean_log_target = self.logf0s_mean_B,
-            std_log_target = self.logf0s_std_B)
-        coded_sp_norm = (coded_sp_transposed - self.mcep_mean_A) / self.mcep_std_A
-
-        coded_sp_converted_norm = self.model.test(inputs = np.array([coded_sp_norm]),
-            direction = conversion_direction)[0]
-        coded_sp_converted = coded_sp_converted_norm * self.mcep_std_B + self.mcep_mean_B
-
-        coded_sp_converted = coded_sp_converted.T
-        coded_sp_converted = np.ascontiguousarray(coded_sp_converted)
-        decoded_sp_converted = world_decode_spectral_envelop(coded_sp = coded_sp_converted,
-            fs = model_sampling_rate)
-        wav_transformed = world_speech_synthesis(f0 = f0_converted,
-            decoded_sp = decoded_sp_converted,
-            ap = ap,
-            fs = model_sampling_rate,
-            frame_period = self.frame_period)
-
-        # For debugging model output, uncomment the following line:
-        #librosa.output.write_wav('model_output.wav', wav_transformed, model_sampling_rate)
-
-        # TODO: Perhaps ditch this. It's probably unnecessary work.
-        #upsampled = librosa.resample(wav_transformed, model_sampling_rate, 48000)
-        #pcm_data = upsampled.astype(np.float64)
-        #stereo_pcm_data = np.tile(pcm_data, (2,1)).T
-        #return stereo_pcm_data.astype(np.float32)
-        #librosa.output.write_wav('model_output.wav', pcm_data, 48000)
-
-        #return wav
-        return wav_transformed
-
-    #def convert(self, wav, conversion_direction='A2B'):
-    #    pcm_data = self.convert_partial(wav, conversion_direction=conversion_direction)
-    #    buf = io.BytesIO()
-    #    # pcm_data: A 1-D or 2-D numpy array of either integer or float data-type.
-    #    # To write multiple-channels, use a 2-D array of shape (Nsamples, Nchannels).
-    #    scipy.io.wavfile.write(buf, 48000, pcm_data)
-    #    return buf
-
-# Set up model
-# This should live long in memory, so we do it up front.
-model_dir_default = './model/sf1_tm1'
-model_name_default = 'sf1_tm1.ckpt'
-
-# TODO: UNCOMMENT
-converter = Converter(model_dir_default, model_name_default)
-
-TEMP_DIR = tempfile.TemporaryDirectory(prefix='queue_audio')
-
-def temp_file_name(suffix='.wav'):
-    # NB: Not actually using the tempfile. Just the random name.
-    temp_file = tempfile.NamedTemporaryFile(suffix=suffix)
-    name = os.path.basename(temp_file.name)
-    return os.path.join(TEMP_DIR.name, name)
-
-def convert(audio,
-            vocode_params=None,
-            request_batch_number=0,
-            skip_vocode=False,
-            save_files=False,
-            discard_vocoded_audio=False):
-    #audio = np.array(audio, dtype=np.int16)
-    #data, samplerate = soundfile.read(audio)
-    #print('samplerate', samplerate)
-    """
-    samplerate 44100
-    data.shape (77824, 2)
-    data.dtype float64
-    mono [0.0050354  0.00518799 0.0050354  ... 0.11651611 0.11935425 0.1164856 ]
-    mono.shape (77824,)
-    mono.dtype float64
-    downsampled [0.00329925 0.00577342 0.00474898 ... 0.0851728  0.1166483  0.        ]
-    downsampled.shape (28236,)
-    downsampled.dtype float64
-
-    1) Data should be float64
-    2) Output is BYTES!! Not floats.
-    3) Result is still somehow mono!?
-    --------------------------------------------------
-
-    demo_server.py
-
-       capture sample rate = 44100
-       downsample for algorithm = 16000
-       upsample after done = 48000
-
-    """
-
-    audio = np.array(audio, dtype=np.float64)
-    print('audio.shape', audio.shape)
-    print('audio.type', audio.dtype)
-
-    #source_rate = 88000 # Experimentally determined for Rust library 'CPAL'
-    #source_rate = 44100
-
-    if vocode_params.original_source_save_file:
-        #filename = temp_file_name('.wav')
-        filename = 'debug/{}_original_source.wav'.format(request_batch_number)
-        print('----- Original wav file out: {}'.format(filename))
-        scipy.io.wavfile.write(filename, vocode_params.original_source_rate, audio)
-
-    # NB: Convert the input stereo signal into mono.
-    # In the future the frontend should be responsible for sampling details.
-    #audio = audio[:, 0]
-
-    if vocode_params.pre_convert_resample:
-        print("Resampling audio from {} Hz to {} Hz".format(vocode_params.original_source_rate,
-                                                            vocode_params.pre_convert_resample_rate))
-        audio = librosa.resample(audio, vocode_params.original_source_rate, vocode_params.pre_convert_resample_rate)
-        print('resampled_audio.shape', audio.shape)
-        print('resampled_audio.type', audio.dtype)
-        if vocode_params.pre_convert_resample_save_file:
-            #filename = temp_file_name('.wav')
-            filename = 'debug/{}_pre_convert_resample.wav'.format(request_batch_number)
-            print('----- Pre-downsampled file out: {}'.format(filename))
-            scipy.io.wavfile.write(filename, vocode_params.pre_convert_resample_rate, audio)
-
-    if skip_vocode:
-        return audio
-
-    results = converter.convert_partial(audio,
-                                        conversion_direction='A2B',
-                                        model_sampling_rate=vocode_params.model_hyperparameter_sampling_rate)
-
-    if vocode_params.model_save_file:
-        #filename = temp_file_name('.wav')
-        filename = 'debug/{}_model_output.wav'.format(request_batch_number)
-        print('----- Model file out: {}'.format(filename))
-        scipy.io.wavfile.write(filename, vocode_params.model_hyperparameter_sampling_rate, results)
-
-    print('results.type', type(results))
-    print('results.len', len(results))
-
-    if vocode_params.post_convert_resample:
-        print("Resampling output audio from {} Hz to {} Hz".format(vocode_params.model_hyperparameter_sampling_rate,
-                                                                   vocode_params.post_convert_resample_rate))
-        #consume_rate = 68000 # Experimentally determined for Rust lib 'CPAL'
-        results = librosa.resample(results,
-                                   vocode_params.model_hyperparameter_sampling_rate,
-                                   vocode_params.post_convert_resample_rate)
-        if vocode_params.post_convert_resample_save_file:
-            #filename = temp_file_name('.wav')
-            filename = 'debug/{}_post_convert_resample.wav'.format(request_batch_number)
-            print('----- Upsampled (transformed) file out: {}'.format(filename))
-            scipy.io.wavfile.write(filename, vocode_params.post_convert_resample_rate, results)
-
-    if discard_vocoded_audio:
-        return audio
-    else:
-        return results
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--port', type=int, default=5555)
-    args = parser.parse_args()
-
-    context = zmq.Context()
-    socket = context.socket(zmq.REP)
-    socket.bind("tcp://*:{}".format(args.port))
-    print('Running server...')
-
-    queue = []
-    while True:
-        #  Wait for next request from client
-        message = socket.recv()
-
-        vocode_request = VocodeAudioRequest.FromString(message)
-        vocode_params = vocode_request.vocode_params
-
-        queue.extend(vocode_request.float_audio)
-
-        if len(queue) >= vocode_request.buffer_size_minimum:
-            #results = queue[:]
-            results = convert(queue,
-                              vocode_params=vocode_params,
-                              request_batch_number=vocode_request.request_batch_number,
-                              skip_vocode=vocode_request.skip_vocode,
-                              discard_vocoded_audio=vocode_request.discard_vocoded_audio)
-            queue = []
-
-            vocode_response = VocodeAudioResponse()
-            vocode_response.float_audio[:] = results
-
-            socket.send(vocode_response.SerializeToString())
-        else:
-            # Must send reply back to client
-            socket.send(b"OK")
-
-if __name__ == '__main__':
-    main()
-
diff --git a/voice_converter_sidecar/cycle_gan/utils.py b/voice_converter_sidecar/cycle_gan/utils.py
deleted file mode 100644
index 858af12c74..0000000000
--- a/voice_converter_sidecar/cycle_gan/utils.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import tensorflow as tf
-import os
-import random
-import numpy as np
-
-def l1_loss(y, y_hat):
-
-    return tf.reduce_mean(tf.abs(y - y_hat))
-
-def l2_loss(y, y_hat):
-
-    return tf.reduce_mean(tf.square(y - y_hat))
-
-def cross_entropy_loss(logits, labels):
-    return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = logits, labels = labels))
-
-
diff --git a/voice_converter_sidecar/protos/audio.proto b/voice_converter_sidecar/protos/audio.proto
deleted file mode 100644
index 46e26b60a1..0000000000
--- a/voice_converter_sidecar/protos/audio.proto
+++ /dev/null
@@ -1,64 +0,0 @@
-syntax = "proto3";
-package voder_audio;
-
-message VocodeAudioRequest {
-  repeated float float_audio = 1;
-
-  // An autoincrement sent with each request.
-  int64 request_batch_number = 2;
-
-  message VocodeParams {
-    // The initial sample rate coming from the microphone.
-    int32 initial_sample_rate = 1;
-
-    // Original sample
-    int32 original_source_rate = 9;
-    bool original_source_save_file = 10;
-
-    // Resample before passing to the algorithm?
-    bool pre_convert_resample = 2;
-    int32 pre_convert_resample_rate = 3;
-    bool pre_convert_resample_save_file = 4;
-
-    // The hard-coded hyperparameter of the model.
-    // Probably don't want to change from 16000.
-    int32 model_hyperparameter_sampling_rate = 5;
-    bool model_save_file = 11;
-
-    // Resample after passing to the algorithm?
-    bool post_convert_resample = 6;
-    int32 post_convert_resample_rate = 7;
-    bool post_convert_resample_save_file = 8;
-  }
-
-  VocodeParams vocode_params = 10;
-
-  // We'll resend these params for every batch, but will only use
-  // the last batch's settings. Kind of a dumb API, but it'll work.
-  //int32 sample_rate = 2;
-
-  //bool skip_resample = 3;
-  bool skip_vocode = 4;
-
-  // Debugging
-  //bool save_files = 5;
-
-  // How big we let the buffer grow before running 'convert'.
-  int32 buffer_size_minimum = 6;
-
-  // Requested output rate
-  //int32 output_rate = 7;
-
-  // Discard the vocoded audio and return the original
-  bool discard_vocoded_audio = 8;
-
-  // Rate param for the model
-  //int32 model_sampling_rate = 9;
-}
-
-message VocodeAudioResponse {
-  repeated float float_audio = 1;
-
-  // A sidecar-side autoincrement sent with each request.
-  int64 response_batch_number = 2;
-}
diff --git a/voice_converter_sidecar/src/audio_hardware.rs b/voice_converter_sidecar/src/audio_hardware.rs
deleted file mode 100644
index 32f31fc866..0000000000
--- a/voice_converter_sidecar/src/audio_hardware.rs
+++ /dev/null
@@ -1,84 +0,0 @@
-use cpal::traits::{DeviceTrait, EventLoopTrait, HostTrait};
-use cpal;
-use audio_queue::AudioQueue;
-use std::sync::Arc;
-
-/// CPAL Audio Device
-/// Access audio input and output streams.
-pub struct AudioHardware {
-  host: cpal::Host,
-  event_loop: cpal::EventLoop,
-  microphone_queue: Arc<AudioQueue>,
-  speaker_queue: Arc<AudioQueue>,
-}
-
-impl AudioHardware {
-  pub fn new(microphone_queue: Arc<AudioQueue>, speaker_queue: Arc<AudioQueue>) -> Result<Self, failure::Error> {
-    let host = cpal::default_host();
-    let event_loop = host.event_loop();
-
-    let input_device = host.default_input_device().expect("failed to get default input device");
-    let output_device = host.default_output_device().expect("failed to get default output device");
-
-    println!("Using default input device: \"{}\"", input_device.name()?);
-    println!("Using default output device: \"{}\"", output_device.name()?);
-
-    // We'll try and use the same format between streams to keep it simple
-    let mut format = input_device.default_input_format()?;
-    format.data_type = cpal::SampleFormat::F32;
-
-    // Build streams.
-    println!("Attempting to build both streams with `{:?}`.", format);
-    let input_stream_id = event_loop.build_input_stream(&input_device, &format)?;
-    let output_stream_id = event_loop.build_output_stream(&output_device, &format)?;
-    println!("Successfully built streams.");
-
-    Ok(AudioHardware {
-      host,
-      event_loop,
-      microphone_queue,
-      speaker_queue,
-    })
-  }
-
-  pub fn run(&mut self) {
-    self.event_loop.run(|id, result| {
-      let data = match result {
-        Ok(data) => data,
-        Err(err) => {
-          eprintln!("An error occurred on stream {:?}: {}", id, err);
-          return;
-        }
-      };
-
-      match data {
-        cpal::StreamData::Input { buffer: cpal::UnknownTypeInputBuffer::F32(buffer) } => {
-          //assert_eq!(id, input_stream_id);
-          let mut output_fell_behind = false;
-          for &sample in buffer.iter() {
-            self.microphone_queue.push_back(sample);
-          }
-        },
-        cpal::StreamData::Output { buffer: cpal::UnknownTypeOutputBuffer::F32(mut buffer) } => {
-          //println!("Audio out buffer len: {}", self.speaker_queue.len());
-          let request_size = buffer.len();
-          let mut drained = self.speaker_queue.drain_size((request_size));
-          match drained {
-            None => {
-              for sample in buffer.iter_mut() {
-                *sample = 0.0;
-              }
-            },
-            Some(mut drained) => {
-              for (i, sample) in buffer.iter_mut().enumerate() {
-                *sample = drained.get(i).copied().unwrap();
-                //*sample = 0.0;
-              }
-            },
-          }
-        },
-        _ => panic!("We're expecting f32 data."),
-      }
-    })
-  }
-}
diff --git a/voice_converter_sidecar/src/audio_queue.rs b/voice_converter_sidecar/src/audio_queue.rs
deleted file mode 100644
index f8314aa7c7..0000000000
--- a/voice_converter_sidecar/src/audio_queue.rs
+++ /dev/null
@@ -1,78 +0,0 @@
-use zmq;
-use zmq::{Error, Socket};
-use std::collections::VecDeque;
-use std::sync::{RwLock, PoisonError};
-
-pub struct AudioQueue {
-  queue: RwLock<VecDeque<f32>>,
-}
-
-impl AudioQueue {
-  pub fn new() -> Self {
-    Self {
-      queue: RwLock::new(VecDeque::new()),
-    }
-  }
-
-  pub fn push_back(&self, val: f32) {
-    match self.queue.write() {
-      Ok(mut queue) => {
-        queue.push_back(val);
-      },
-      Err(_) => {
-        unreachable!("This shouldn't happen (push_back)");
-      },
-    }
-  }
-
-  pub fn extend(&self, buf: Vec<f32>) {
-    match self.queue.write() {
-      Ok(mut queue) => {
-        queue.extend(buf);
-      },
-      Err(_) => {
-        unreachable!("This shouldn't happen (extend)");
-      },
-    }
-  }
-
-  pub fn drain(&self) -> Vec<f32> {
-    match self.queue.write() {
-      Ok(mut queue) => {
-        return queue.drain(..)
-            .collect::<Vec<_>>();
-      },
-      Err(_) => {
-        unreachable!("This shouldn't happen (drain)");
-      },
-    }
-  }
-
-  pub fn drain_size(&self, size: usize) -> Option<Vec<f32>> {
-    match self.queue.write() {
-      Ok(mut queue) => {
-        if queue.len() < size {
-          return None;
-        }
-        let vec = queue.drain(0..size)
-            .collect::<Vec<_>>();
-
-        return Some(vec);
-      },
-      Err(_) => {
-        unreachable!("This shouldn't happen (drain_size)");
-      },
-    }
-  }
-
-  pub fn len(&self) -> usize {
-    match self.queue.read() {
-      Ok(mut queue) => {
-        return queue.len();
-      },
-      Err(_) => {
-        unreachable!("This shouldn't happen (len)");
-      },
-    }
-  }
-}
diff --git a/voice_converter_sidecar/src/main.rs b/voice_converter_sidecar/src/main.rs
deleted file mode 100644
index 80f840b0c5..0000000000
--- a/voice_converter_sidecar/src/main.rs
+++ /dev/null
@@ -1,72 +0,0 @@
-//!
-//! This is a process that captures microphone input, ships proto-over-zeromq to a python
-//! CycleGAN-VC (tensorflow) sidecar server, collects the results, then plays them over
-//! the speaker.
-//!
-
-extern crate bytes;
-extern crate byteorder;
-extern crate cpal;
-extern crate failure;
-extern crate prost;
-extern crate prost_types;
-extern crate wavy;
-extern crate world_sys;
-extern crate zmq;
-
-//include!(concat!(env!("OUT_DIR"), "/voder.audio.rs"));
-
-pub mod audio_hardware;
-pub mod audio_queue;
-//pub mod model;
-pub mod protos;
-pub mod sidecar;
-pub mod synthesis;
-
-use audio_hardware::AudioHardware;
-use protos::voder_audio::VocodeAudioRequest;
-use protos::voder_audio::VocodeAudioResponse;
-use sidecar::Sidecar;
-
-use wavy::*;
-
-use byteorder::{ByteOrder, BigEndian, LittleEndian, ReadBytesExt};
-use failure::_core::time::Duration;
-use audio_queue::AudioQueue;
-use prost::Message;
-//use model::load_model;
-//use model::print_version;
-use std::collections::VecDeque;
-use std::fs::File;
-use std::io::{Read, Cursor};
-use std::path::Path;
-use std::process::exit;
-use std::sync::Arc;
-use std::thread;
-use zmq::{Error, Socket};
-use protos::voder_audio::vocode_audio_request::VocodeParams;
-
-fn main() {
-  //print_version();
-  //load_model(); // TODO: This works. Temporarily commented out
-  run_audio().expect("Should work");
-}
-
-const LATENCY_MS: f32 = 50.0;
-
-fn run_audio() -> Result<(), failure::Error> {
-  let mut microphone_queue = Arc::new(AudioQueue::new());
-  let mut microphone_queue_2 = microphone_queue.clone();
-
-  let mut post_process_queue = Arc::new(AudioQueue::new());
-  let mut post_process_queue_2 = post_process_queue.clone();
-  let mut sidecar = Sidecar::new("tcp://127.0.0.1:5555", microphone_queue_2, post_process_queue);
-
-  thread::spawn(move || {
-    sidecar.run();
-  });
-
-  let mut audio_hardware = AudioHardware::new(microphone_queue, post_process_queue_2).expect("");
-  audio_hardware.run();
-  Ok(())
-}
diff --git a/voice_converter_sidecar/src/protos/mod.rs b/voice_converter_sidecar/src/protos/mod.rs
deleted file mode 100644
index 5e3380f41f..0000000000
--- a/voice_converter_sidecar/src/protos/mod.rs
+++ /dev/null
@@ -1 +0,0 @@
-pub mod voder_audio;
\ No newline at end of file
diff --git a/voice_converter_sidecar/src/protos/voder_audio.rs b/voice_converter_sidecar/src/protos/voder_audio.rs
deleted file mode 100644
index 4dde4f4b21..0000000000
--- a/voice_converter_sidecar/src/protos/voder_audio.rs
+++ /dev/null
@@ -1,70 +0,0 @@
-#[derive(Clone, PartialEq, ::prost::Message)]
-pub struct VocodeAudioRequest {
-    #[prost(float, repeated, tag="1")]
-    pub float_audio: ::std::vec::Vec<f32>,
-    /// An autoincrement sent with each request.
-    #[prost(int64, tag="2")]
-    pub request_batch_number: i64,
-    #[prost(message, optional, tag="10")]
-    pub vocode_params: ::std::option::Option<vocode_audio_request::VocodeParams>,
-    // We'll resend these params for every batch, but will only use
-    // the last batch's settings. Kind of a dumb API, but it'll work.
-    //int32 sample_rate = 2;
-
-    ///bool skip_resample = 3;
-    #[prost(bool, tag="4")]
-    pub skip_vocode: bool,
-    // Debugging
-    //bool save_files = 5;
-
-    /// How big we let the buffer grow before running 'convert'.
-    #[prost(int32, tag="6")]
-    pub buffer_size_minimum: i32,
-    // Requested output rate
-    //int32 output_rate = 7;
-
-    /// Discard the vocoded audio and return the original
-    #[prost(bool, tag="8")]
-    pub discard_vocoded_audio: bool,
-}
-pub mod vocode_audio_request {
-    #[derive(Clone, PartialEq, ::prost::Message)]
-    pub struct VocodeParams {
-        /// The initial sample rate coming from the microphone.
-        #[prost(int32, tag="1")]
-        pub initial_sample_rate: i32,
-        /// Original sample
-        #[prost(int32, tag="9")]
-        pub original_source_rate: i32,
-        #[prost(bool, tag="10")]
-        pub original_source_save_file: bool,
-        /// Resample before passing to the algorithm?
-        #[prost(bool, tag="2")]
-        pub pre_convert_resample: bool,
-        #[prost(int32, tag="3")]
-        pub pre_convert_resample_rate: i32,
-        #[prost(bool, tag="4")]
-        pub pre_convert_resample_save_file: bool,
-        /// The hard-coded hyperparameter of the model.
-        /// Probably don't want to change from 16000.
-        #[prost(int32, tag="5")]
-        pub model_hyperparameter_sampling_rate: i32,
-        #[prost(bool, tag="11")]
-        pub model_save_file: bool,
-        /// Resample after passing to the algorithm?
-        #[prost(bool, tag="6")]
-        pub post_convert_resample: bool,
-        #[prost(int32, tag="7")]
-        pub post_convert_resample_rate: i32,
-        #[prost(bool, tag="8")]
-        pub post_convert_resample_save_file: bool,
-    }
-}
-#[derive(Clone, PartialEq, ::prost::Message)]
-pub struct VocodeAudioResponse {
-    #[prost(float, repeated, tag="1")]
-    pub float_audio: ::std::vec::Vec<f32>,
-    /// A sidecar-side autoincrement sent with each request.
-    #[prost(int64, tag="2")]
-    pub response_batch_number: i64,
-}
diff --git a/voice_converter_sidecar/src/sidecar.rs b/voice_converter_sidecar/src/sidecar.rs
deleted file mode 100644
index 600ee00ba2..0000000000
--- a/voice_converter_sidecar/src/sidecar.rs
+++ /dev/null
@@ -1,217 +0,0 @@
-use audio_queue::AudioQueue;
-use prost::Message;
-use protos::voder_audio::VocodeAudioRequest;
-use protos::voder_audio::VocodeAudioResponse;
-use protos::voder_audio::vocode_audio_request::VocodeParams;
-use std::collections::VecDeque;
-use std::sync::Arc;
-use zmq::{Error, Socket};
-use zmq;
-
-/// Talks a custom protobuf over ZeroMQ protocol to a Python TensorFlow sidecar process
-/// that evaluates the model and ships back audio byte results.
-pub struct Sidecar {
-  socket_endpoint: String,
-  microphone_queue: Arc<AudioQueue>,
-  processed_queue: Arc<AudioQueue>,
-}
-
-impl Sidecar {
-  pub fn new(socket_endpoint: &str,
-             microphone_queue: Arc<AudioQueue>,
-             processed_queue: Arc<AudioQueue>) -> Self {
-    Self {
-      socket_endpoint: socket_endpoint.into(),
-      microphone_queue,
-      processed_queue,
-    }
-  }
-
-  /// Heuristic to determine if a speaker is speaking.
-  /// Basically just checks audio loudness.
-  fn is_speaking(audio: &Vec<f32>) -> bool {
-    let mut avg_pos = 0.0f32;
-    let mut num_pos = 0;
-
-    for x in audio {
-      if x > &0.0f32 {
-        avg_pos += x;
-        num_pos += 1;
-      }
-    }
-
-    avg_pos = avg_pos / num_pos as f32;
-    avg_pos > 0.006f32
-  }
-
-  pub fn run(&mut self) {
-    let mut context = zmq::Context::new();
-    let mut socket = context.socket(zmq::REQ).unwrap();
-
-    socket.connect(&self.socket_endpoint).unwrap();
-
-    let mut reconnect = false;
-    let mut fail_count = 0;
-
-    const SEND_SIZE : usize = 5000;
-
-    let mut request_batch_number = 0i64;
-
-    let mut activated = false;
-    let mut frames_activated = 0;
-    let mut frames_deactivated = 0;
-
-    let mut ring_buffer = VecDeque::with_capacity(5);
-
-    loop {
-      let mut drained = match self.microphone_queue.drain_size(SEND_SIZE) {
-        None => { continue; },
-        Some(d) => d,
-      };
-
-      /*
-      Heuristic for audio. Don't record when silent (using threshold).
-
-        mic: act act act dec act act act act dec dec dec dec dec act dec
-        sent:         | ok ok ok ok ok ok ok ok ok ok ok|
-      */
-
-
-      // NB: We don't want to lose audio right at the activating edge.
-      // Save some bounded history to replay once activated.
-      ring_buffer.push_back(drained.clone());
-      if ring_buffer.len() > 5 {
-        ring_buffer.pop_front();
-      }
-
-      if activated {
-        if !Self::is_speaking(&drained) {
-          frames_deactivated += 1;
-        } else {
-          frames_deactivated = 0;
-        }
-
-        if frames_deactivated > 20 {
-          println!("Deactivating edge");
-          activated = false;
-          frames_activated = 0;
-          frames_deactivated = 0;
-        }
-
-      } else {
-        if Self::is_speaking(&drained) {
-          frames_activated += 1;
-        } else {
-          frames_activated = 0;
-        }
-
-        if frames_activated > 3 {
-          println!("Activating edge");
-          activated = true;
-          frames_activated = 0;
-          frames_deactivated = 0;
-        }
-      }
-
-      if !activated {
-        continue;
-      }
-
-      let drained = ring_buffer.drain(..)
-          .flat_map(|sample| sample)
-          .collect::<Vec<_>>();
-
-      request_batch_number += 1;
-
-      let mut vocode_request = VocodeAudioRequest::default();
-      //vocode_request.sample_rate = 16000;
-      //vocode_request.output_rate = 16000;
-      //vocode_request.model_sampling_rate = 88000; // This is close!
-      //vocode_request.model_sampling_rate = 88000;
-      //vocode_request.skip_resample = false;
-      vocode_request.skip_vocode = false;
-      vocode_request.discard_vocoded_audio = false;
-      //vocode_request.buffer_size_minimum = 5000; // AWFUL. SO CHOPPY.
-      //vocode_request.buffer_size_minimum = 50000; // Practically real time, but lots more phase distortion.
-      //vocode_request.buffer_size_minimum = 30000; // Hm, hmm... not bad
-      vocode_request.buffer_size_minimum = 70000; // Sounds pretty good.
-      //vocode_request.buffer_size_minimum = 100000; // This sounds good! A bit slow ~3seconds.
-      //vocode_request.buffer_size_minimum = 200000;
-      vocode_request.float_audio = drained.clone();
-      vocode_request.request_batch_number = request_batch_number;
-
-      let mut vocode_params = VocodeParams::default();
-      vocode_params.original_source_rate = 88000; // This is correct for saving
-
-      vocode_params.pre_convert_resample = true;
-      vocode_params.pre_convert_resample_rate = 16000;
-
-      vocode_params.model_hyperparameter_sampling_rate = 16000;
-
-      vocode_params.post_convert_resample = true;
-      vocode_params.post_convert_resample_rate = 88000;
-
-      vocode_params.original_source_save_file = false;
-      vocode_params.pre_convert_resample_save_file = false;
-      vocode_params.model_save_file = false;
-      vocode_params.post_convert_resample_save_file = false;
-
-      vocode_request.vocode_params = Some(vocode_params);
-
-      let mut encoded_bytes = Vec::with_capacity(vocode_request.encoded_len());
-      vocode_request.encode(&mut encoded_bytes).unwrap();
-
-      if reconnect {
-        reconnect = false;
-        println!("RECONNECT");
-
-        //thread::sleep(Duration::from_millis(200));
-
-        socket = match context.socket(zmq::REQ) {
-          Ok(s) => s,
-          Err(e) => {
-            println!("Error creating socket: {:?}", e);
-            continue
-          },
-        };
-
-        match socket.connect("tcp://127.0.0.1:5555") {
-          Ok(_) => {},
-          Err(err) => {
-            println!("Err B: {:?}", err);
-          },
-        }
-      }
-
-      match socket.send(&encoded_bytes, 0) {
-        Ok(_) => {
-          //println!("Sent len: {}", bytes.len());
-        },
-        Err(e) => {
-          println!("send err {}: {:?}", e.to_raw(), e);
-          fail_count += 1;
-        },
-      }
-
-      match socket.recv_bytes(0) {
-        Ok(buf) => {
-          //println!("---> Buf len: {}", buf.len());
-          if buf.len() > 2 {
-            // Receive data condition.
-            let vocode_response = VocodeAudioResponse::decode(buf).unwrap();
-            self.processed_queue.extend(vocode_response.float_audio);
-          }
-        },
-        Err(e) => {
-          fail_count += 1;
-          println!("recv err: {:?}", e);
-        },
-      }
-
-      if fail_count > 5 {
-        fail_count = 0;
-        reconnect = true;
-      }
-    }
-  }
-}
\ No newline at end of file
diff --git a/voice_converter_sidecar/src/synthesis.rs b/voice_converter_sidecar/src/synthesis.rs
deleted file mode 100644
index b7c926be21..0000000000
--- a/voice_converter_sidecar/src/synthesis.rs
+++ /dev/null
@@ -1,38 +0,0 @@
-use world_sys::core::synthesize::SynthesizeError;
-use world_sys::core::synthesize::SynthesizeResult;
-use world_sys::core::synthesize::synthesize;
-
-use world_sys::core::spectral::code_spectral_envelope;
-
-/*
-TODO: ALGORITHM TO GET VOICE TRANSFORMATION:
-
-- [mono]
-- [librosa.resample (downsample)]
-- core convert()
-    - wav_padding()
-        - [maths]
-        - [np.pad]
-    - world_decompose()
-        - pyworld.harvest
-        - pyworld.cheaptrick
-        - pyworld.d4c
-    - world_encode_spectral_envelope()
-        - pyworld.code_spectral_envelope
-    - [matrix transpose]
-    - pitch_conversion()
-        - [maths]
-    - [maths : coded_sp_norm]
-    - << evaluate ML model >>
-    - [maths : coded_sp_converted]
-    - [matrix transpose]
-    - [np.ascontiguousarray]
-    - world_decode_spectral_envelop
-        - pyworld.get_cheaptrick_fft_size
-        - pyworld.decode_spectral_envelope
-    - world_speech_synthesis
-        - pyworld.synthesize
-    - [librosa.resample (upsample)]
-    - [wav things]
-*/
-