Update README.md

AllTalk TTS Generator
Delete templates/TTS_Input_Proof_of_concept.html
2024-01-06 20:49:58 +00:00 · 2024-01-06 20:26:05 +00:00 · 2024-01-06 20:24:20 +00:00 · 2024-01-05 13:27:49 +00:00 · 2024-01-05 13:26:15 +00:00 · 2024-01-05 13:24:20 +00:00
41 changed files with 4872 additions and 1261 deletions
@@ -1,6 +1,7 @@
 voices/*.*
 models/*.*
 outputs/*.*
+finetune/*.*
 config.json
 confignew.json
 models.json
@@ -0,0 +1,9 @@
+{
+    "files.associations": {
+        "*.html": "jinja-html"
+    },
+    "emmet.includeLanguages": {
+        "jinja-html": "html",
+        "jinja2": "html"
+    },
+}
@@ -1,17 +1,18 @@
 # AllTalk TTS
 AllTalk is an updated version of the Coqui_tts extension for Text Generation web UI. Features include:

+- **Can be run as a** [standalone](https://github.com/erew123/alltalk_tts?#-running-alltalk-as-a-standalone-app) **or part of** [Text-generation-webui](https://github.com/erew123/alltalk_tts?#-installation-on-text-generation-web-ui)
+- **Model Finetuning:** Train the model specifically on a voice of your choosing for better reproduction.
+- **Local/Custom models:** Use any of the XTTSv2 models (API Local and XTTSv2 Local).
+- **Bulk TTS Generator/Editor:** Generate hours of TTS into one big file or have something read back to you.
+- **DeepSpeed:** A 3-4x performance boost generating TTS. [DeepSpeed Windows/Linux Instructions](https://github.com/erew123/alltalk_tts?tab=readme-ov-file#deepspeed-installation-options) [Screenshot](https://github.com/erew123/alltalk_tts/assets/35898566/548619c8-5f1b-47d0-a73d-54d2fee3f3db)
+- **Low VRAM mode:** Great for people with small GPU memory or if your VRAM is filled by your LLM. [Screenshot](https://github.com/erew123/alltalk_tts#screenshots)
 - **Custom Start-up Settings:** Adjust your default start-up settings. [Screenshot](https://github.com/erew123/alltalk_tts#screenshots)
 - **Narrarator:** Use different voices for main character and narration. [Example Narration](https://vocaroo.com/18nrv7FR6wuA)
- **Low VRAM mode:** Great for people with small GPU memory or if your VRAM is filled by your LLM. [Screenshot](https://github.com/erew123/alltalk_tts#screenshots)
- **DeepSpeed:** A 3-4x performance boost generating TTS. [DeepSpeed Windows/Linux Instructions](https://github.com/erew123/alltalk_tts?tab=readme-ov-file#deepspeed-installation-options) [Screenshot](https://github.com/erew123/alltalk_tts/assets/35898566/548619c8-5f1b-47d0-a73d-54d2fee3f3db)
- **Local/Custom models:** Use any of the XTTSv2 models (API Local and XTTSv2 Local).
 - **Optional wav file maintenance:** Configurable deletion of old output wav files. [Screenshot](https://github.com/erew123/alltalk_tts#screenshots)
- **Finetuning** Train the model specifically on a voice of your choosing for better reproduction.
 - **Documentation:** Fully documented with a built in webpage. [Screenshot](https://github.com/erew123/alltalk_tts#screenshots)
- **Console output** Clear command line output for any warnings or issues.
- **API Suite and 3rd Party support via JSON calls** Can be used with 3rd party applications via JSON calls.
- **Can be run as a standalone app** Not just inside of text-generation-webui.
+- **Console output:** Clear command line output for any warnings or issues.
+- **API Suite and 3rd Party support via JSON calls:** Can be used with 3rd party applications via JSON calls.

 ## Index

@@ -21,26 +22,25 @@ AllTalk is an updated version of the Coqui_tts extension for Text Generation web
 - 🟨 [Help with problems](https://github.com/erew123/alltalk_tts?#-help-with-problems)
 - ⚫ [Finetuning a model](https://github.com/erew123/alltalk_tts?#-finetuning-a-model)
 - 🔵🟢🟡 [DeepSpeed Installation (Windows & Linux)](https://github.com/erew123/alltalk_tts?#-deepspeed-installation-options)
+- 🟦 [Running AllTalk as a standalone app](https://github.com/erew123/alltalk_tts?#-running-alltalk-as-a-standalone-app)
+- ⬜ [AllTalk TTS Generator](https://github.com/erew123/alltalk_tts?#-alltalk-tts-generator)
 - 🟠 [API Suite and JSON-CURL](https://github.com/erew123/alltalk_tts?#-api-suite-and-json-curl)
 - 🔴 [Future to-do list & Upcoming updates](https://github.com/erew123/alltalk_tts?#-future-to-do-list)

-#### Updates
-Dec 25th - Applied a small update to avoid a possible race condition on file naming with small sentences when generating narrator/character speech. Please update if you are experiencing missing small bits of missing audio on narrator/character.
-
-#### &nbsp;&nbsp;&nbsp;&nbsp;🔄 **Minor updates/bug fixes list** can be found [here](https://github.com/erew123/alltalk_tts/issues/25)
-
-The latest build (13 Dec 2023) has had the entire text filtering engine and narration engine rebuilt from scratch. It's highly complicated how its actually working, but the end result it a much clearer TTS output and much better control over the narrator option and how to handle text that isnt within quotes or asterisks. It does however mean you need to ensure your character card is set up correctly if using the narrator function. Details are below in the installation notes.
-
-DeepSpeed **v11.2** can be installed within the **default text-generation-webui Python 3.11 environment**. Installs in custom Python environments are possible, but can be more complicated. Instructions [here](https://github.com/erew123/alltalk_tts##deepspeed-installation-options) (or scroll down).
+## 🔄 Updates
+Please check the below link to find a list of all recent updates and changes.
+#### &nbsp;&nbsp;&nbsp;&nbsp;🔄 **Updates list & bug fixes list** can be found [here](https://github.com/erew123/alltalk_tts/issues/25)

 ## 🟩 Installation on Text generation web UI
 This has been tested on the current Dec 2023 release of Text generation webUI. If you have not updated it for a while, you may wish to update Text generation webUI, [instructions here](https://github.com/oobabooga/text-generation-webui?tab=readme-ov-file#how-to-install)

+If you want to watch a video of how to do the below [link here](https://youtu.be/9BPKuwaav5w) 
+
 1) In a command prompt/terminal window you need to move into your Text generation webUI folder:<br><br>
 `cd text-generation-webui`

-2) Start the Text generation webUI Python environment for your OS:<br><br>
-`cmd_windows.bat`, `./cmd_linux.sh`, `cmd_macos.sh` or `cmd_wsl.bat`
+2) Start the Text generation webUI Python environment for your OS with whichever **one** of the below is correct for your OS:<br><br>
+`cmd_windows.bat`, `./cmd_linux.sh`, `cmd_macos.sh` or `cmd_wsl.bat`<br><br> Loading Text-generation-webui's Python Environment is $\textcolor{red}{\textsf{VERY IMPORTANT}}$. If you are uncertain what a loaded Python environment looks like, image [here](https://github.com/erew123/alltalk_tts/issues/25#issuecomment-1869344442) and video [here](https://www.youtube.com/watch?v=9BPKuwaav5w)

 3) Move into your extensions folder:<br><br>
 `cd extensions`
@@ -51,17 +51,17 @@ This has been tested on the current Dec 2023 release of Text generation webUI. I
 5) Move into the **alltalk_tts** folder:<br><br>
 `cd alltalk_tts`

-6) Install the requirements that are correct for your machine:<br><br>
+6) Install one of the two requirements files. Whichever one of the two is correct for your machine type:<br><br>
 **Nvidia graphics card machines** - `pip install -r requirements_nvidia.txt`<br><br>
 **Other machines (mac, amd etc)** - `pip install -r requirements_other.txt`

 7) **(Optional DeepSpeed)** If you have an Nvidia Graphics card on a system running Linux or Windows and wish to use **DeepSpeed** please follow these instructions [here](https://github.com/erew123/alltalk_tts?#-deepspeed-installation-options). **However**, I would highly reccommend before you install DeepSpeed, you start text-generation-webui up, confirm AllTalk starts correctly and everything is working, as DeepSpeed can add another layer of complications troubleshooting any potential start-up issues. If necessary you can `pip uninstall deepspeed`.

-8) You can now start move back to the main Text generation webUI folder `cd ..` (a few times), start Text generation webUI (`start_windows.bat`,`./start_linux.sh`, `start_macos.sh` or `start_wsl.bat`)  and load the AllTalk extension in the Text generation webUI **session** tab.
+8) You can now start move back to the main Text generation webUI folder `cd ..` (a few times), start Text generation webUI with whichever **one** of the startup scripts is correct for your OS (`start_windows.bat`,`./start_linux.sh`, `start_macos.sh` or `start_wsl.bat`)  and load the AllTalk extension in the Text generation webUI **session** tab.<br><br> Starting Text-generation-webui with its correct start-up script is $\textcolor{red}{\textsf{VERY IMPORTANT}}$.
   
-9) Please read the note below about start-up times and also the note about ensuring your character cards are set up [correctly](https://github.com/erew123/alltalk_tts#the-one-thing-i-cant-easily-work-around)
+10) Please read the note below about start-up times and also the note about ensuring your character cards are set up [correctly](https://github.com/erew123/alltalk_tts#the-one-thing-i-cant-easily-work-around)

-10) Some extra voices downloadable [here](https://drive.google.com/file/d/1bYdZdr3L69kmzUN3vSiqZmLRD7-A3M47/view?usp=drive_link)
+11) Some extra voices downloadable [here](https://drive.google.com/file/d/1bYdZdr3L69kmzUN3vSiqZmLRD7-A3M47/view?usp=drive_link)

 #### 🟩 Other installation notes
 On first startup, AllTalk will download the Coqui XTTSv2 2.0.2 model to its **models** folder (1.8GB space required). Check the command prompt/terminal window if you want to know what its doing. After it says "Model Loaded" the Text generation webUI is usually available on its IP address a few seconds later, for you to connect to in your browser.
@@ -71,17 +71,20 @@ Once the extension is loaded, please find all documentation and settings on the
 **Where to find voices** https://aiartes.com/voiceai or https://commons.wikimedia.org/ or interviews on youtube etc. Instructions on how to cut down and prepare a voice sample are within the built in documentation.

 #### 🟩 The one thing I cant easily work around
-Narrator function specific - With a RP chat with your AI, **on your character card** `parameters menu` > `character tab` > `greeting` make sure that anything in there that is the **narrator is in asterisks** and anything **spoken is in double quotes**, then hit the `save` (💾) button. Greeting paragraphs/sentences are handled differently from how the AI sends text and so its difficut to account for them both.
+Messages intended for the Narrator should be enclosed in asterisks `*` and those for the character inside quotation marks `"`. However, AI systems often deviate from these rules, resulting in text that is neither in quotes nor asterisks. Sometimes, text may appear with only a single asterisk, and AI models may vary their formatting mid-conversation. For example, they might use asterisks initially and then switch to unmarked text. A properly formatted line should look like this:

-I could force a delimeter in at this stage, but I know it would/may affect things further down the line in the chat and I need a good think about that before just making a change. This issue **only** affects the greeting card/start of conversation and the "example" card that comes with text-generation-webui suffers this issue (if you want to try it for yourself). So you would put double quotes around like this (from the example card):
+`"`Hey! I'm so excited to finally meet you. I've heard so many great things about you and I'm eager to pick your brain about computers.`"` `*`She walked across the room and picked up her cup of coffee`*`

-`"`*Hey! I'm so excited to finally meet you. I've heard so many great things about you and I'm eager to pick your brain about computers. I'm sure you have a wealth of knowledge that I can learn from.*`"`
+Most narrator/character systems switch voices upon encountering an asterisk or quotation marks, which is somewhat effective. AllTalk has undergone several revisions in its sentence splitting and identification methods. While some irregularities and AI deviations in message formatting are inevitable, any line beginning or ending with an asterisk should now be recognized as Narrator dialogue. Lines enclosed in double quotes are identified as Character dialogue. For any other text, you can choose how AllTalk handles it: whether it should be interpreted as Character or Narrator dialogue (most AI systems tend to lean more towards one format when generating text not enclosed in quotes or asterisks).
+
+With improvements to the splitter/processor, I'm confident it's functioning well. You can monitor what AllTalk identifies as Narrator lines on the command line and adjust its behavior if needed (Text Not Inside - Function).

 ## 🟪 Updating
 This is pretty much a repeat of the installation process. 

 1) In a command prompt/terminal window you need to move into your Text generation webUI folder:<br><br>
-`cd text-generation-webui`
+`cd text-generation-webui` and start the Python environment for your OS with whichever **one** of the below is correct for your OS:<br><br>
+`cmd_windows.bat`, `./cmd_linux.sh`, `cmd_macos.sh` or `cmd_wsl.bat`<br>

 2) Move into your extensions and alltalk_tts folder:<br><br>
 `cd extensions` then `cd alltalk_tts`
@@ -103,6 +106,7 @@ I did leave a mistake in the `/extensions/alltalk_tts/.gitignore` file at one po
 voices/*.*
 models/*.*
 outputs/*.*
+finetune/*.*
 config.json
 confignew.json
 models.json
@@ -143,10 +147,11 @@ Assuming its all working fine and you are happy, you can delete the old alltalk_
 </details>

 ## 🟫 Screenshots
-|![image](https://github.com/erew123/alltalk_tts/assets/35898566/4ca9b4c7-60ce-4ac6-82e5-fd1989b84644) | ![image](https://github.com/erew123/alltalk_tts/assets/35898566/548619c8-5f1b-47d0-a73d-54d2fee3f3db) |
+|![image](https://github.com/erew123/alltalk_tts/assets/35898566/15c6ba0b-e6b0-4570-8253-b920dd76174d) | ![image](https://github.com/erew123/alltalk_tts/assets/35898566/548619c8-5f1b-47d0-a73d-54d2fee3f3db) |
 |:---:|:---:|
 |![image](https://github.com/erew123/alltalk_tts/assets/35898566/b0e13dba-c6b1-4ab7-845d-244ac1158330) |  ![image](https://github.com/erew123/alltalk_tts/assets/35898566/e35e987c-543a-486b-b4fb-ee6ebe6f59c6) |
 |![image](https://github.com/erew123/alltalk_tts/assets/35898566/68edc968-bdd2-4f98-9d56-b9ca40decf26) | ![image](https://github.com/erew123/alltalk_tts/assets/35898566/eb5e617b-2345-418d-8267-a9d30f705a10) |
+|![image](https://github.com/erew123/alltalk_tts/assets/35898566/4bd0f02e-298d-4e46-95ea-afc003c1cb70) |

 ## 🟨 Help with problems
 #### &nbsp;&nbsp;&nbsp;&nbsp; 🔄 **Minor updates/bug fixes list** can be found [here](https://github.com/erew123/alltalk_tts/issues/25)
@@ -255,10 +260,29 @@ I would suggest following [Problems Updating](https://github.com/erew123/alltalk
 As far as I am aware, these are to do with the chrome browser the gradio text-generation-webui in some way. I raised an issue about this on the text-generation-webui [here](https://github.com/oobabooga/text-generation-webui/issues/4788) where you can see that AllTalk is not loaded and the messages persist. Either way, this is more a warning than an actual issue, so shouldnt affect any functionality of either AllTalk or text-generation-webui, they are more just an annoyance.
 </details>

+#### 🟨 I have multiple GPU's and I have problems running Finetuning
+
+<details>
+	<summary>Click to expand</summary><br>
+	
+Finetuning pulls in various other scripts and some of those scripts can have issues with multiple Nvidia GPU's being present. Until the people that created those other scripts fix up their code, there is a workaround to temporarily tell your system to only use the 1x of your Nvidia GPU's. To do this:
+
+- **Windows** - You will start the script with `set CUDA_VISIBLE_DEVICES=0 && python finetune.py`<br>
+After you have completed training, you can reset back with `set CUDA_VISIBLE_DEVICES=`<br>
+   
+- **Linux** - You will start the script with `CUDA_VISIBLE_DEVICES=0 python finetune.py`<br>
+After you have completed training, you can reset back with `unset CUDA_VISIBLE_DEVICES`<br>
+
+Rebooting your system will also unset this. The setting is only applied temporarily.
+
+Depending on which of your Nvidia GPU's is the more powerful one, you can change the `0` to `1` or whichever of your GPU's is the most powerful.
+
+</details>
+
 ## ⚫ Finetuning a model
 If you have a voice that the model doesnt quite reproduce correctly, or indeed you just want to improve the reproduced voice, then finetuning is a way to train your "XTTSv2 local" model **(stored in `/alltalk_tts/models/xxxxx/`)** on a specific voice. For this you will need:

- An Nvidia graphics card
+- An Nvidia graphics card. (Please see this [note](https://github.com/erew123/alltalk_tts#-i-have-multiple-gpus-and-i-have-problems-running-finetuning) if you have multiple Nvidia GPU's).
 - To install a few portions of the Nvidia CUDA 11.8 Toolkit (this will not impact text-generation-webui's cuda setup.
 - 18GB of disk space free (most of this is used temporarily)
 - At least 2 minutes of good quality speech from your chosen speaker in mp3, wav or flacc format, in one or more files (have tested as far as 20 minutes worth of audio).
@@ -272,17 +296,23 @@ I would suggest that if its in an interview format, you cut out the interviewer
 #### ⚫ Important requirements CUDA 11.8
 As mentioned you must have a small portion of the Nvidia CUDA Toolkit **11.8** installed. Not higher or lower versions. Specifically **11.8**. You do not have to uninstall any other versions, change any graphics drivers, reinstall torch or anything like that. To keep the download+install as small as possible, you will need to:
 - Download the **xxx (network)** install of the Nvidia Cuda Toolkit 11.8 from [here](https://developer.nvidia.com/cuda-11-8-0-download-archive)
- When you run the installation, select **Custom Advanced** as your installation type. Uncheck `CUDA`, `Other Components` and `Driver Components` at the top then expand `CUDA` > `Development` > `Compiler` > and select `nvcc` then expand `Libraries` and select `CUBLAS`
- Back at the top of `CUDA` expand `Runtime` > `Libraries` and select `CUBLAS`. You can now next through the install.
+- Run the installer. At minimum, you need to [minimally] install the `nvcc` compiler and the `CUBLAS` development and runtime libraries:
+  - Select **Custom Advanced** as your installation type.
+  - Uncheck all the checkboxes in the list.
+  - Now check the following elements:
+    - `CUDA` > `Development` > `Compiler` > `nvcc`
+    - `CUDA` > `Development` > `Compiler` > `Libraries` > `CUBLAS`
+    - `CUDA` > `Runtime` > `Libraries` > `CUBLAS`
+  - You can now proceed through the install.
 - When that has installed, open a terminal/command prompt and type `nvcc --version`. If it reports back `Cuda compilation tools, release 11.8.` you are good to go. **Specifically, 11.8**. If not continue to the next step.
- For both Windows an Linux, you will need to ensure that `nvcc` and the 11.8 cuda library files are in your environments search path. You can undo the changes below after finetuning if you prefer.<br><br>
-  **Windows** - Edit the Windows PATH environment variable and add `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin`<br><br>
-  **Linux** - The path may be different depending on what flavour of Linux you are running, so you may need to seek out specific instructions on the internet. Generic paths **may** be:<br>
-  `export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}` and<br>
-  `export LD_LIBRARY_PATH=/usr/local/cuda-11.8/bin`<br>
-  Add these to your '~/.bashrc' if you want this to be permanent and not something you have to set each time you open a new terminal.<br><br>
- When you have made the changes, open a new terminal/command prompt and `nvcc --version`. It should report back `Cuda compilation tools, release 11.8.` at which point, you are good to go.
- If it doesnt report that, check you have correctly set the search environment paths, dont have overlapping other versions of cuda paths etc.
+- For both Windows and Linux, you need to ensure that `nvcc` and the 11.8 cuda library files are in your environments search path. You can undo the changes below after finetuning if you prefer:
+  - **Windows**: Edit the Windows `Path` environment variable and add `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin`
+  - **Linux**: The path may be different depending on what flavour of Linux you are running, so you may need to seek out specific instructions on the internet. Generic paths **may** be:
+    - `export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}` and
+    - `export LD_LIBRARY_PATH=/usr/local/cuda-11.8/bin`
+    - Add these to your '~/.bashrc' if you want this to be permanent and not something you have to set each time you open a new terminal.
+- When you have made the changes, open a **new** terminal/command prompt (in order to load the new search paths) and `nvcc --version`. It should report back `Cuda compilation tools, release 11.8.` at which point, you are good to go.
+- If it doesn't report that, check you have correctly set the search environment paths, dont have overlapping other versions of cuda paths etc.

 #### ⚫ Starting Finetuning
 **NOTE:** Please make sure you have started AllTalk at least once after updating, so that it downloads the additional files needed for finetuning. 
@@ -310,6 +340,18 @@ As mentioned you must have a small portion of the Nvidia CUDA Toolkit **11.8** i
 11) Follow the on-screen instructions when the web interface starts up.
 12) When you have finished finetuning, the final tab will tell you what to do with your files and how to move your newly trained model to the correct location on disk.

+#### ⚫ Using a Finetuned model in Text-generation-webui
+
+At the end of the finetune process, you will have an option to `Compact and move model to /trainedmodel/` this will compact the raw training file and move it to `/model/trainedmodel/`. When AllTalk starts up within Text-generation-webui, if it finds a model in this location a new loader will appear in the interface for `XTTSv2 FT` and you can use this to load your finetuned model. <br><br>**Be careful** not to train a new model from the base model, then overwrite your current `/model/trainedmodel/` **if** you want a seperately trained model. This is why there is an `OPTION B` to move your just trained model to `/models/lastfinetuned/`.
+
+#### ⚫ Training one model with multiple voices
+
+At the end of the finetune process, you will have an option to `Compact and move model to /trainedmodel/` this will compact the raw training file and move it to `/model/trainedmodel/`. This model will become available when you start up finetuning. You will have a choice to train the Base Model or the `Existing finetuned model` (which is the one in `/model/trainedmodel/`). So you can use this to keep further training this model with additional voices, then copying it back to `/model/trainedmodel/` at the end of training.
+
+#### ⚫ Do I need to keep the raw training data/model?
+
+If you've compacted and moved your model, its highly unlikely you would want to keep that data, however the choice is there to keep it if you wish. It will be between 5-10GB in size, so most people will want to delete it.
+
 ## 🔵🟢🟡 DeepSpeed Installation Options
 **NOTE**: You **DO NOT** need to set Text-generation-webUI's **--deepspeed** setting for AllTalk to be able to use DeepSpeed. These are two completely separate things and incorrectly setting that on Text-generation-webUI may cause other complications.

@@ -463,13 +505,144 @@ def get_shm_size():
 **Removal** - If it became necessary to uninstall DeepSpeed, you can do so with `cmd_windows.bat` and then `pip uninstall deepspeed`<br><br>
 </details>

+## 🟦 Running AllTalk as a standalone app
+AllTalk will run as a standalone app, as long as you install its requirements files into whatever Python environment you are using. You can follow the steps to install the AllTalk's requirements into whatever Python environment you wish. Because I dont know what Python environment you are wanting to use, I can only give you a loose set of installation instructions. 
+
+Please note, at time of writing, the TTS engine requires Python **3.9.x** to **3.11.x** [TTS Engine details here](https://pypi.org/project/TTS/). AllTalk and its requirements are tested on Python **3.11.x**.
+
+#### 🟦 (Option 1) I already have AllTalk installed as an extension of Text-generation-webui
+If you already have AllTalk as a extension of Text-generation-webui, and wish to run it as standalone, load Text-generation-webui's Python environment `cmd_windows.bat`, `./cmd_linux.sh`, `cmd_macos.sh` or `cmd_wsl.bat`, move into the AllTalk folder `cd extensions` > `cd alltalk_tts` and start AllTalk with `python script.py`. There is nothing beyond this you would need to do.
+
+#### 🟦 (Option 2) I wish to do a custom install of AllTalk
+#### 🔹 A very quick understanding of Python Environments.
+Different Python applications have different requirements, some of those requirement’s conflict with other Python applications requirements. To work around this problem, you can create different Python environments that remain separated from one another. A simple way of looking at Python environments, is just like how your house has different rooms for specific purposes (Kitchen, Bathroom, Bedroom etc). You can create a Python environment that is built/customised specifically for your current applications needs/purposes and will **not** interfere with any other Python applications environments/installations. <br><br>If you are adept at managing Python environments, have an existing Python environment and know that you won’t cause any conflicts by installing AllTalk's requirements within that Python environment, then load up your Python environment and install the requirements. For everyone else, here is a basic guide on installing AllTalk in its own custom Python environment (there will be small variations between OS's, but the principle is the same).
+
+**Note:** A standard VENV can cause module path issues, hence Conda is the correct method to create a Python environment for AllTalk. 
+
+#### 🔹 Building a custom Python Environment with Miniconda
+1) Open a terminal/command prompt, and confirm that both `python --version` and `pip` both work. Neither of them should give an error message. If they do you may need to install Python (and maybe Pip).<br><br>[Python Website](https://www.python.org/downloads/)<br>[Pip Website](https://pip.pypa.io/en/stable/installation/)<br><br>Once you have those working you can now continue on.<br><br>
+
+2) Assuming you don't already have Miniconda installed, we will need to download and install Minoconda as this is what we will use to build our custom Python environment.<br><br>[Miniconda Website](https://docs.conda.io/projects/miniconda/en/latest/)<br><br>Download the Miniconda version that is correct for your OS and then install it. Miniconda will create some items in your Start Menu or Application Launcher called something like `Anaconda Prompt`<br><br> You will start the `Anaconda Prompt` and your prompt in your terminal/command prompt will say something like: <br><br>`(base) C:\users\myaccount\>`<br><br> The important bit is that it has the `(base)`, the location after that doesn't matter. `(base)` signifies we are in the base conda Python environment and can now create custom Python environments with Conda.<br><br>
+  
+3) In your Anaconda Prompt command prompt/terminal, move into the folder where you want to download AllTalk to and then git clone this repository. For simplicity I am going to assume that location is `c:\myfiles\`. So you will:<br><br>`cd myfiles`<br><br>`git clone https://github.com/erew123/alltalk_tts`<br><br>
+
+4) Now we will create our custom Conda Python 3.11.5 environment and give it the name alltalkenv by typing the following at the prompt:<br><br>`conda create --name alltalkenv python=3.11.5`<br><br>You will be prompted if you want to continue and you say yes.<br><br>Once that process has completed, we now need to change from the `(base)` Conda Python environment, to our newly created `(alltalkenv)` environment. So type the following:<br><br>`conda activate alltalkenv`<br><br>Your prompt should now change from `(base) C:\myfiles\>` to `(alltalkenv) C:\myfiles\>`<br><br>
+
+5) **If you are NOT using an Nvidia card and CUDA, skip to step 6**. To force PyTorch to install with CUDA, perform the following:<br><br>`pip cache purge` (Clear the current Pip cache)<br><br>`pip uninstall torch torchaudio` (Uninstall Torch and Torchaudio from the `alltalkenv` environment)<br><br>
+You can now either install the CUDA 11.8 or 12.1 version of Torch and Torchaudio with **one** of the following<br><br>**CUDA 11.8** > `pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118`<br><br>**CUDA 12.1** > `pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121`<br><br>
+
+6) Move into the alltalk_tts folder and install one of the two requirements files. Whichever one of the two is correct for your machine type:<br><br>`cd alltalk_tts`<br><br>
+**Nvidia graphics card machines** - `pip install -r requirements_nvidia.txt`<br><br>
+**Other machines (mac, amd etc)** - `pip install -r requirements_other.txt`<br><br>
+
+7) Start AllTalk with `python script.py`<br><br>
+
+🔹Anytime you wish to use AllTalk, update its requirements, install some other requirements such as DeepSpeed, you will need to start its Conda Python Environment in the terminal/prompt, which you will do with `conda activate alltalkenv` as long as the Conda executable is accessible at the command prompt/terminal you are in.<br><br>It is fully possible to create a batch file or script file that you can use as a launcher from your Start Menu/Application Launcher that will start the environment and AllTalk for you. 
+
+Deepspeed and other such things can be installed. Please read the relevant instructions for those items, however, make the relevant changes to load your correct Python environment when installing any requirements files and starting AllTalk.<br><br>
+
+## ⬜ AllTalk TTS Generator
+AllTalk TTS Generator is the solution for converting large volumes of text into speech using the voice of your choice. Whether you're creating audio content or just want to hear text read aloud, the TTS Generator is equipped to handle it all efficiently.<br><br>The link to open the TTS generator can be found on the built-in Settings and Documentation page. 
+
+#### ⬜ Quick Start
+- **Text Input:** Enter the text you wish to convert into speech in the 'Text Input' box.
+- **Generate TTS:** Hit this to start the text-to-speech conversion.
+- **Pause/Resume:** Used to pause and resume the playback of the initial generation of wavs or the stream.
+- **Stop Playback:** This will stop the current audio playing back. It does not stop the text from being generated however. 
+Once you have sent text off to be generated, either as a stream or wav file generation, the TTS server will remain busy until this process has competed. As such, think carefully as to how much you want to send to the server. 
+If you are generating wav files and populating the queue, you can generate one lot of text to speech, then input your next lot of text and it will continue adding to the list.
+#### ⬜ TTS Generation Modes
+- **Wav Chunks:** Perfect for creating audio books, or anything you want to keep long term. Breaks down your text into manageable wav files and queues them up. Generation begins automatically, and playback will start after a few chunks have been prepared ahead. You can set the volume to 0 if you don’t want to hear playback. With Wav chunks, you can edit and/or regenerate portions of the TTS as needed.
+- **Streaming:** For immediate playback without the ability to save. Ideal for on-the-fly speech generation and listening. This will not generate wav files and it will play back through your browser. You cannot stop the server generating the TTS once it has been sent.
+With wav chunks you can either playback “In Browser” which is the web page you are on, or “On Server” which is through the console/terminal where AllTalk is running from. Only generation “In Browser” can play back smoothly and populate the Generated TTS List. Setting the Volume will affect the volume level played back both “In Browser” and “On Server”.
+#### ⬜ Playback and List Management
+- **Playback Controls:** Utilize 'Play List' to start from the beginning or 'Stop Playback' to halt at any time.
+- **Custom Start:** Jump into your list at a specific ID to hear a particular section.
+- **Regeneration and Editing:** If a chunk isn't quite right, you can opt to regenerate it or edit the text directly. Click off the text to save changes and hit regenerate for the specific line.
+- **Export/Import List:** Save your TTS list as a JSON file or import one. Note: Existing wav files are needed for playback. Exporting is handy if you want to take your files away into another program and have a list of which wav is which, or if you keep your audio files, but want to come back at a later date, edit one or two lines, regenerate the speech and re-combine the wav’s into one new long wav.
+#### ⬜ Exporting Your Audio
+- **Export to WAV:** Combine all generated TTS from the list, into one single WAV file for easy download and distribution.
+#### ⬜ Customization and Preferences
+- **Character Voice:** Choose the voice that will read your text.
+- **Language:** Select the language of your text.
+- **Chunk Sizes:** Decide the size of text chunks for generation. Smaller sizes are recommended for better TTS quality.
+#### ⬜ Interface and Accessibility
+- **Dark/Light Mode:** Switch between themes for your visual comfort.
+- **Word Count and Generation Queue:** Keep track of the word count and the generation progress.
+#### ⬜ Notes on Usage
+- For seamless TTS generation, it's advised to keep text chunks under 250 characters, which you can control with the Chunk sizes.
+- Generated audio can be played back from the list, which also highlights the currently playing chunk.
+- The TTS Generator remembers your settings, so you can pick up where you left off even after refreshing the page.
+
 ## 🟠 API Suite and JSON-CURL
 ### 🟠Overview
 The Text-to-Speech (TTS) Generation API allows you to generate speech from text input using various configuration options. This API supports both character and narrator voices, providing flexibility for creating dynamic and engaging audio content.

- URL: `http://127.0.0.1:7851/api/tts-generate`<br>
- Method: `POST`<br>
- Content-Type: `application/x-www-form-urlencoded`<br>
+#### 🟠 Ready Endpoint<br>
+Check if the Text-to-Speech (TTS) service is ready to accept requests.
+
+- URL: `http://127.0.0.1:7851/api/ready`<br> - Method: `GET`<br> 
+
+   `curl -X GET "http://127.0.0.1:7851/api/ready"`
+
+  Response: `Ready`
+
+#### 🟠 Voices List Endpoint<br>
+Retrieve a list of available voices for generating speech.
+
+- URL: `http://127.0.0.1:7851/api/voices`<br> - Method: `GET`<br>
+
+   `curl -X GET "http://127.0.0.1:7851/api/voices"`
+
+   JSON return: `{"voices": ["voice1.wav", "voice2.wav", "voice3.wav"]}`
+
+#### 🟠 Preview Voice Endpoint
+Generate a preview of a specified voice with hardcoded settings.
+
+- URL: `http://127.0.0.1:7851/api/previewvoice/`<br> - Method: `POST`<br> - Content-Type: `application/x-www-form-urlencoded`<br>
+
+   `curl -X POST "http://127.0.0.1:7851/api/previewvoice/" -F "voice=female_01.wav"`
+
+   Replace `female_01.wav` with the name of the voice sample you want to hear.
+
+   JSON return: `{"status": "generate-success", "output_file_path": "/path/to/outputs/api_preview_voice.wav", "output_file_url": "http://127.0.0.1:7851/audio/api_preview_voice.wav"}`
+
+#### 🟠 Switching Model Endpoint<br>
+
+- URL: `http://127.0.0.1:7851/api/reload`<br> - Method: `POST`<br><br>
+   `curl -X POST "http://127.0.0.1:7851/api/reload?tts_method=API%20Local"`<br>
+   `curl -X POST "http://127.0.0.1:7851/api/reload?tts_method=API%20TTS"`<br>
+   `curl -X POST "http://127.0.0.1:7851/api/reload?tts_method=XTTSv2%20Local"`<br>
+
+   Switch between the 3 models respectively.
+
+   `curl -X POST "http://127.0.0.1:7851/api/reload?tts_method=XTTSv2%20FT"`<br>
+
+   If you have a finetuned model in `/models/trainedmodel/` (will error otherwise)
+
+   JSON return `{"status": "model-success"}`
+
+#### 🟠 Switch DeepSpeed Endpoint<br>
+
+- URL: `http://127.0.0.1:7851/api/deepspeed`<br> - Method: `POST`<br><br>
+   `curl -X POST "http://127.0.0.1:7851/api/deepspeed?new_deepspeed_value=True"`
+
+   Replace True with False to disable DeepSpeed mode.
+
+   JSON return `{"status": "deepspeed-success"}`
+
+#### 🟠 Switching Low VRAM Endpoint<br>
+
+- URL: `http://127.0.0.1:7851/api/lowvramsetting`<br> - Method: `POST`<br><br>
+   `curl -X POST "http://127.0.0.1:7851/api/lowvramsetting?new_low_vram_value=True"`
+
+   Replace True with False to disable Low VRAM mode.
+
+   JSON return `{"status": "lowvram-success"}`
+
+### 🟠 TTS Generation Endpoint
+
+- URL: `http://127.0.0.1:7851/api/tts-generate`<br> - Method: `POST`<br> - Content-Type: `application/x-www-form-urlencoded`<br>

 ### 🟠 Example command lines
 Standard TTS speech Example (standard text) generating a time-stamped file<br>
@@ -526,22 +699,22 @@ Example:

 🟠 **language**: Choose the language for TTS. Options:

-`ar Arabic`<br>
-`zh-cn Chinese (Simplified)`<br>
-`cs Czech`<br>
-`nl Dutch`<br>
-`en English`<br>
-`fr French`<br>
-`de German`<br>
-`hu Hungarian`<br>
-`it Italian`<br>
-`ja Japanese`<br>
-`ko Korean`<br>
-`pl Polish`<br>
-`pt Portuguese`<br>
-`ru Russian`<br>
-`es Spanish`<br>
-`tr Turkish`<br>
+`ar` Arabic<br>
+`zh-cn` Chinese (Simplified)<br>
+`cs` Czech<br>
+`nl` Dutch<br>
+`en` English<br>
+`fr` French<br>
+`de` German<br>
+`hu` Hungarian<br>
+`it` Italian<br>
+`ja` Japanese<br>
+`ko` Korean<br>
+`pl` Polish<br>
+`pt` Portuguese<br>
+`ru` Russian<br>
+`es` Spanish<br>
+`tr` Turkish<br>

 `-d "language=en"`<br>

@@ -554,12 +727,12 @@ Example:
 `-d "output_file_timestamp=true"`<br>
 `-d "output_file_timestamp=false"`

-🟠 **autoplay**: Feature not yet available. Enable or disable autoplay. Still needs to be specified in the JSON request.
+🟠 **autoplay**: Enable or disable playing the generated TTS to your standard sound output device at time of TTS generation.

 `-d "autoplay=true"`<br>
 `-d "autoplay=false"`

-🟠 **autoplay_volume**: Feature not yet available. Set the autoplay volume. Should be between 0.1 and 1.0. Still needs to be specified in the JSON request.
+🟠 **autoplay_volume**: Set the autoplay volume. Should be between 0.1 and 1.0. Needs to be specified in the JSON request even if autoplay is false.

 `-d "autoplay_volume=0.8"`

@@ -568,37 +741,14 @@ The API returns a JSON object with the following properties:

 - **status** Indicates whether the generation was successful (generate-success) or failed (generate-failure).<br>
 - **output_file_path** The on-disk location of the generated WAV file.<br>
- **output_file_url** The HTTP location for accessing the generated WAV file.<br>
+- **output_file_url** The HTTP location for accessing the generated WAV file for browser playback.<br>
+- **output_cache_url** The HTTP location for accessing the generated WAV file as a pushed download.<br>

 Example JSON TTS Generation Response:

-`{"status": "generate-success", "output_file_path": "C:\text-generation-webui\extensions\alltalk_tts\outputs\myoutputfile_1703149973.wav", "output_file_url": "http://127.0.0.1:7851/audio/myoutputfile_1703149973.wav"}`
-
-🟠 **Switching Model**<br><br>
-`curl -X POST "http://127.0.0.1:7851/api/reload?tts_method=API%20Local"`<br>
-`curl -X POST "http://127.0.0.1:7851/api/reload?tts_method=API%20TTS"`<br>
-`curl -X POST "http://127.0.0.1:7851/api/reload?tts_method=XTTSv2%20Local"`<br>
-
-Switch between the 3 models respectively.
-
-JSON return `{"status": "model-success"}`
-
-🟠 **Switch DeepSpeed**<br><br>
-`curl -X POST "http://127.0.0.1:7851/api/deepspeed?new_deepspeed_value=True"`
-
-Replace True with False to disable DeepSpeed mode.
-
-JSON return `{"status": "deepspeed-success"}`
-
-🟠 **Switching Low VRAM**<br><br>
-`curl -X POST "http://127.0.0.1:7851/api/lowvramsetting?new_low_vram_value=True"`
-
-Replace True with False to disable Low VRAM mode.
-
-JSON return `{"status": "lowvram-success"}`
+`{"status":"generate-success","output_file_path":"C:\\text-generation-webui\\extensions\\alltalk_tts\\outputs\\myoutputfile_1704141936.wav","output_file_url":"http://127.0.0.1:7851/audio/myoutputfile_1704141936.wav","output_cache_url":"http://127.0.0.1:7851/audiocache/myoutputfile_1704141936.wav"}`

 ### 🔴 Future to-do list
- Voice output within the command prompt/terminal (TBD).
- Correct a few spelling mistakes in the documentation.
+- SillyTavern Support
 - Possibly add some additional TTS engines (TBD).
 - Have a break!
@@ -6,6 +6,7 @@ try:
    import torch
    import os  # Import the os module
    import re
+    import sys
    import glob
    import textwrap
    import packaging.version
@@ -17,7 +18,7 @@ except ImportError as e:
    print(f"\033[91mError importing module: {e}\033[0m\n")
    print("\033[94mPlease ensure you started the Text-generation-webUI Python environment with either\033[0m")
    print("\033[92mcmd_linux.sh\033[0m, \033[92mcmd_windows.bat\033[0m, \033[92mcmd_macos.sh\033[0m, or \033[92mcmd_wsl.bat\033[0m")
-    print("\033[94mand then try running the diagnostics again.\033[0m")
+    print("\033[94mfrom the text-generation-webui directory, and then try running the diagnostics again.\033[0m")
    exit(1)

 try:
@@ -110,6 +111,18 @@ def log_system_info():
    # Package versions using importlib_metadata
    package_versions = {d.metadata['Name']: d.version for d in distributions()}

+    # Python environment information
+    python_executable = sys.executable
+    python_version_info = sys.version_info
+    python_virtual_env = os.environ.get('VIRTUAL_ENV', 'N/A')
+
+    # Conda environment information
+    conda_env = os.environ.get('CONDA_DEFAULT_ENV', 'N/A')
+
+    # Get Path environment information
+    search_path = sys.path
+    path_env = os.environ.get('PATH', 'N/A')
+
    # Compare with requirements file
    requirements_file = get_requirements_file()
    if requirements_file:
@@ -134,14 +147,24 @@ def log_system_info():

    # Log and print information
    logging.info(f"OS Version: {os_version}")
-    logging.info(f"Note: Windows 11 build is 10.x.22xxx")
-    logging.info(f"Python Version: {python_version}")
+    logging.info(f"Note: Windows 11 will list as build is 10.x.22xxx")
    logging.info(f"Torch Version: {torch_version}")
    logging.info(f"System RAM: {system_ram}")
    logging.info(f"CUDA_HOME: {cuda_home}")
    logging.info(f"Port Status: {port_status}")
+    logging.info(f"Python Version: {platform.python_version()}")
+    logging.info(f"Python Version Info: {python_version_info}")
+    logging.info(f"Python Executable: {python_executable}")
+    logging.info(f"Python Virtual Environment: {python_virtual_env} (Should be N/A when in Text-generation-webui Conda Python environment)")
+    logging.info(f"Conda Environment: {conda_env}")
+    logging.info("\nPython Search Path:")
+    for path in search_path:
+        logging.info(f"  {path}")
+    logging.info("\nOS PATH Environment Variable:")
+    for path in path_env.split(';'):
+        logging.info(f"  {path}")
    if required_packages:  # Check if the dictionary is not empty
-        logging.info("Package Versions:")
+        logging.info("\nPackage Versions:")
        max_package_length = max(len(package) for package in required_packages.keys())
        for package_name, (operator, required_version) in required_packages.items():
            installed_version = installed_packages.get(package_name, 'Not installed')
@@ -153,12 +176,22 @@ def log_system_info():

    # Print to screen
    print(f"\n\033[94mOS Version:\033[0m \033[92m{os_version}\033[0m")
-    print(f"\033[94mOS Ver note:\033[0m \033[92mWindows 11 build is 10.x.22xxx\033[0m")
+    print(f"\033[94mOS Ver note:\033[0m \033[92m(Windows 11 will say build is 10.x.22xxx)\033[0m")
    print(f"\033[94mCUDA_HOME:\033[0m \033[92m{cuda_home}\033[0m")
    print(f"\033[94mSystem RAM:\033[0m \033[92m{system_ram}\033[0m")
    print(f"\033[94mPort Status:\033[0m \033[92m{port_status}\033[0m")
    print(f"\033[94mTorch Version:\033[0m \033[92m{torch_version}\033[0m")
-    print(f"\033[94mPython Version:\033[0m \033[92m{python_version}\033[0m")
+    print(f"\033[94mPython Version:\033[0m \033[92m{platform.python_version()}\033[0m")
+    print(f"\033[94mPython Version Info:\033[0m \033[92m{python_version_info}\033[0m")
+    print(f"\033[94mPython Executable:\033[0m \033[92m{python_executable}\033[0m")
+    print(f"\033[94mPython Virtual Environment:\033[0m \033[92m{python_virtual_env}\033[0m (Should be N/A when in Text-generation-webui Conda Python environment)")
+    print(f"\033[94mConda Environment:\033[0m \033[92m{conda_env}\033[0m")
+    print(f"\n\033[94mPython Search Path:\033[0m")
+    for path in search_path:
+        print(f"  {path}")
+    print(f"\n\033[94mOS Search PATH Environment Variable:\033[0m")
+    for path in path_env.split(';'):
+        print(f"  {path}")
    if required_packages:  # Check if the dictionary is not empty
        print("\033[94m\nRequirements file package comparison:\033[0m")
        max_package_length = max(len(package) for package in required_packages.keys())
@@ -14,6 +14,7 @@ import gc
 import time
 import shutil
 import pandas
+import glob
 import json
 from pathlib import Path
 from tqdm import tqdm
@@ -40,12 +41,27 @@ if modeldownload_config_file_path.exists():
    # Extract settings from the loaded JSON
    base_path = Path(settings.get("base_path", ""))
    model_path = Path(settings.get("model_path", ""))
+    base_model_path = Path(settings.get("model_path", ""))
    files_to_download = settings.get("files_to_download", {})
 else:
    # Default settings if the JSON file doesn't exist or is empty
    print("[FINETUNE]  \033[91mWarning\033[0m modeldownload.json is missing. Please run this script in the /alltalk_tts/ folder")
    sys.exit(1)

+##################################################
+#### Check to see if a finetuned model exists ####
+##################################################
+# Set the path to the directory
+trained_model_directory = this_dir / "models" / "trainedmodel"
+# Check if the directory "trainedmodel" exists
+finetuned_model = trained_model_directory.exists()
+# If the directory exists, check for the existence of the required files
+# If true, this will add a extra option in the Gradio interface for loading Xttsv2 FT
+if finetuned_model:
+    required_files = ["model.pth", "config.json", "vocab.json", "mel_stats.pth", "dvae.pth"]
+    finetuned_model = all((trained_model_directory / file).exists() for file in required_files)
+basemodel_or_finetunedmodel = True
+
 #####################
 #### STEP 1 BITS ####
 #####################
@@ -270,6 +286,8 @@ def format_audio_list(target_language, whisper_model, out_path, gradio_progress=
    # deallocate VRAM and RAM
    del asr_model, final_eval_set, final_training_set, new_data_df, existing_metadata
    gc.collect()
+    existing_train_df = None
+    existing_eval_df = None
    print("[FINETUNE] Train CSV:", train_metadata_path)
    print("[FINETUNE] Eval CSV:", eval_metadata_path)
    print("[FINETUNE] Audio Total:", audio_total_size)
@@ -287,6 +305,13 @@ from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrai
 from TTS.utils.manage import ModelManager


+def basemodel_or_finetunedmodel_choice(value):
+    global basemodel_or_finetunedmodel 
+    if value == "Base Model":
+        basemodel_or_finetunedmodel = True
+    elif value == "Existing finetuned model":
+        basemodel_or_finetunedmodel = False
+
 def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path, max_audio_length=255995):
    #  Logging parameters
    RUN_NAME = "XTTS_FT"
@@ -319,12 +344,22 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
    # Add here the configs of the datasets
    DATASETS_CONFIG_LIST = [config_dataset]

-    # XTTS model checkpoints for fine-tuning.
-    TOKENIZER_FILE = str(this_dir / base_path / model_path / "vocab.json")  # vocab.json file
-    XTTS_CHECKPOINT = str(this_dir / base_path / model_path / "model.pth")  # model.pth file
-    XTTS_CONFIG_FILE = str(this_dir / base_path / model_path / "config.json")  # config.json file
-    DVAE_CHECKPOINT = str(this_dir / base_path / model_path / "dvae.pth")
-    MEL_NORM_FILE = str(this_dir / base_path / model_path / "mel_stats.pth")
+    if basemodel_or_finetunedmodel:
+        # BASE XTTS model checkpoints for fine-tuning.
+        print("[FINETUNE] Starting finetuning on Base Model")
+        TOKENIZER_FILE = str(this_dir / base_path / model_path / "vocab.json")
+        XTTS_CHECKPOINT = str(this_dir / base_path / model_path / "model.pth")
+        XTTS_CONFIG_FILE = str(this_dir / base_path / model_path / "config.json")
+        DVAE_CHECKPOINT = str(this_dir / base_path / model_path / "dvae.pth")
+        MEL_NORM_FILE = str(this_dir / base_path / model_path / "mel_stats.pth")
+    else:
+        # FINETUNED XTTS model checkpoints for fine-tuning.
+        print("[FINETUNE] Starting finetuning on Existing Finetuned Model")
+        TOKENIZER_FILE = str(this_dir / base_path / "trainedmodel" / "vocab.json")
+        XTTS_CHECKPOINT = str(this_dir / base_path / "trainedmodel" / "model.pth")
+        XTTS_CONFIG_FILE = str(this_dir / base_path / "trainedmodel" / "config.json")
+        DVAE_CHECKPOINT = str(this_dir / base_path / "trainedmodel" / "dvae.pth")
+        MEL_NORM_FILE = str(this_dir / base_path / "trainedmodel" / "mel_stats.pth")

    # init args and config
    model_args = GPTArgs(
@@ -415,9 +450,14 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
    trainer_out_path = trainer.output_path

    # deallocate VRAM and RAM
-    del model, trainer, train_samples, eval_samples
+    del model, trainer, train_samples, eval_samples, config, model_args, config_dataset
    gc.collect()
-
+    train_samples = None
+    eval_samples = None
+    config_dataset = None
+    trainer = None
+    model = None
+    model_args = None
    return XTTS_CONFIG_FILE, XTTS_CHECKPOINT, TOKENIZER_FILE, trainer_out_path, speaker_ref

 ##########################
@@ -495,7 +535,222 @@ xtts_config_files = find_jsons(main_directory, "config.json")
 # XTTS vocab files (vocab.json)
 xtts_vocab_files = find_jsons(main_directory, "vocab.json")

+##########################
+#### STEP 4 AND OTHER ####
+##########################

+def find_latest_best_model(folder_path):
+    search_path = folder_path / "XTTS_FT-*" / "best_model.pth"
+    files = glob.glob(str(search_path), recursive=True)
+    latest_file = max(files, key=os.path.getctime, default=None)
+    return latest_file
+
+def compact_model():
+    this_dir = Path(__file__).parent.resolve()
+
+    best_model_path_str = find_latest_best_model(this_dir / "finetune" / "tmp-trn" / "training")
+
+    # Check if the best model file exists
+    if best_model_path_str is None:
+        print("[FINETUNE] No trained model was found.")
+        return "No trained model was found."
+
+    # Convert model_path_str to Path
+    best_model_path = Path(best_model_path_str)
+
+    # Attempt to load the model
+    try:
+        checkpoint = torch.load(best_model_path, map_location=torch.device("cpu"))
+    except Exception as e:
+        print("[FINETUNE] Error loading checkpoint:", e)
+        raise
+
+    del checkpoint["optimizer"]
+
+    for key in list(checkpoint["model"].keys()):
+        if "dvae" in key:
+            del checkpoint["model"][key]
+
+    # Define the target directory
+    target_dir = this_dir / "models" / "trainedmodel"
+
+    # Create the target directory if it doesn't exist
+    target_dir.mkdir(parents=True, exist_ok=True)
+
+    # Save the modified checkpoint in the target directory
+    torch.save(checkpoint, target_dir / "model.pth")
+
+    # Specify the files you want to copy
+    files_to_copy = ["vocab.json", "config.json", "dvae.pth", "mel_stats.pth"]
+
+    for file_name in files_to_copy:
+        src_path = this_dir / base_path / base_model_path / file_name
+        dest_path = target_dir / file_name
+        shutil.copy(str(src_path), str(dest_path))
+
+    source_wavs_dir = this_dir / "finetune" / "tmp-trn" / "wavs"
+    target_wavs_dir = target_dir / "wavs"
+    target_wavs_dir.mkdir(parents=True, exist_ok=True)
+
+    # Iterate through files in the source directory
+    for file_path in source_wavs_dir.iterdir():
+        # Check if it's a file and larger than 1000 KB
+        if file_path.is_file() and file_path.stat().st_size > 1000 * 1024:
+            # Copy the file to the target directory
+            shutil.copy(str(file_path), str(target_wavs_dir / file_path.name))
+    
+    print("[FINETUNE] Model copied to '/models/trainedmodel/'")
+    return "Model copied to '/models/trainedmodel/'"
+
+
+def compact_lastfinetuned_model():
+    this_dir = Path(__file__).parent.resolve()
+
+    best_model_path_str = find_latest_best_model(this_dir / "finetune" / "tmp-trn" / "training")
+
+    # Check if the best model file exists
+    if best_model_path_str is None:
+        print("[FINETUNE] No trained model was found.")
+        return "No trained model was found."
+
+    # Convert model_path_str to Path
+    best_model_path = Path(best_model_path_str)
+
+    # Attempt to load the model
+    try:
+        checkpoint = torch.load(best_model_path, map_location=torch.device("cpu"))
+    except Exception as e:
+        print("[FINETUNE] Error loading checkpoint:", e)
+        raise
+
+    del checkpoint["optimizer"]
+
+    for key in list(checkpoint["model"].keys()):
+        if "dvae" in key:
+            del checkpoint["model"][key]
+
+    # Define the target directory
+    target_dir = this_dir / "models" / "lastfinetuned"
+
+    # Create the target directory if it doesn't exist
+    target_dir.mkdir(parents=True, exist_ok=True)
+
+    # Save the modified checkpoint in the target directory
+    torch.save(checkpoint, target_dir / "model.pth")
+
+    # Specify the files you want to copy
+    files_to_copy = ["vocab.json", "config.json", "dvae.pth", "mel_stats.pth"]
+
+    for file_name in files_to_copy:
+        src_path = this_dir / base_path / base_model_path / file_name
+        dest_path = target_dir / file_name
+        shutil.copy(str(src_path), str(dest_path))
+
+    source_wavs_dir = this_dir / "finetune" / "tmp-trn" / "wavs"
+    target_wavs_dir = target_dir / "wavs"
+    target_wavs_dir.mkdir(parents=True, exist_ok=True)
+
+    # Iterate through files in the source directory
+    for file_path in source_wavs_dir.iterdir():
+        # Check if it's a file and larger than 1000 KB
+        if file_path.is_file() and file_path.stat().st_size > 1000 * 1024:
+            # Copy the file to the target directory
+            shutil.copy(str(file_path), str(target_wavs_dir / file_path.name))
+    
+    print("[FINETUNE] Model copied to '/models/lastfinetuned/'")
+    return "Model copied to '/models/lastfinetuned/'"
+
+
+def compact_legacy_model():
+    this_dir = Path(__file__).parent.resolve()
+
+    best_model_path_str = os.path.join(this_dir, "finetune", "best_model.pth")
+
+    # Check if the best model file exists
+    if not os.path.exists(best_model_path_str):
+        print("[FINETUNE] No model called best_model.pth was found in /finetune/")
+        return "No model called best_model.pth was found in /finetune/"
+
+    # Convert model_path_str to Path
+    best_model_path = Path(best_model_path_str)
+
+    # Attempt to load the model
+    try:
+        checkpoint = torch.load(best_model_path, map_location=torch.device("cpu"))
+    except Exception as e:
+        print("[FINETUNE] Error loading checkpoint:", e)
+        raise
+
+    del checkpoint["optimizer"]
+
+    for key in list(checkpoint["model"].keys()):
+        if "dvae" in key:
+            del checkpoint["model"][key]
+
+    # Define the target directory
+    target_dir = this_dir / "finetune"
+
+    # Create the target directory if it doesn't exist
+    target_dir.mkdir(parents=True, exist_ok=True)
+
+    # Save the modified checkpoint in the target directory
+    torch.save(checkpoint, target_dir / "model.pth")
+
+    print("[FINETUNE] model.pth created in '/finetune/'")
+    return "model.pth created in '/finetune/'"
+
+
+def delete_training_data():
+    # Define the folder to be deleted
+    folder_to_delete = Path(this_dir / "finetune" / "tmp-trn")
+
+    # Check if the folder exists before deleting
+    if folder_to_delete.exists():
+        # Iterate over all files and subdirectories
+        for item in folder_to_delete.iterdir():
+            # Exclude trainer_0_log.txt from deletion
+            if item.name != "trainer_0_log.txt":
+                try:
+                    if item.is_file():
+                        item.unlink()
+                    elif item.is_dir():
+                        shutil.rmtree(item)
+                except PermissionError:
+                    print(f"[FINETUNE] PermissionError: Could not delete {item}. Skipping.")
+
+        print(f"[FINETUNE] Folder {folder_to_delete} contents (excluding trainer_0_log.txt) deleted successfully.")
+        return "Folder '/finetune/tmp-trn/' contents (excluding trainer_0_log.txt) deleted successfully."
+    else:
+        print(f"[FINETUNE] Folder {folder_to_delete} does not exist.")
+        return "Folder '/finetune/tmp-trn/' does not exist."
+
+
+def delete_voice_sample_contents():
+    # Define the folder to be cleared
+    folder_to_clear = Path(this_dir / "finetune" / "put-voice-samples-in-here")
+
+    # Check if the folder exists before clearing its contents
+    if folder_to_clear.exists() and folder_to_clear.is_dir():
+        # List all files and subdirectories in the folder
+        for item in os.listdir(folder_to_clear):
+            item_path = folder_to_clear / item
+            if item_path.is_file():
+                # If it's a file, remove it
+                os.remove(item_path)
+            elif item_path.is_dir():
+                # If it's a subdirectory, remove it recursively
+                shutil.rmtree(item_path)
+
+        print(f"[FINETUNE] Contents of {folder_to_clear} deleted successfully.")
+        return f"Contents of 'put-voice-samples-in-here deleted' successfully."
+    else:
+        print(f"[FINETUNE] Folder {folder_to_clear} does not exist.")
+        return f"Folder 'put-voice-samples-in-here' does not exist."
+
+
+#######################
+#### OTHER Generic ####
+#######################
 # define a logger to redirect 
 class Logger:
    def __init__(self, filename="finetune.log"):
@@ -620,20 +875,21 @@ if __name__ == "__main__":
                f"""
                ## <u>Finetuning Information</u><br>
                ### 🟥 <u>Important Note</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟥 <span style="color: #3366ff;">finetune.py</span> needs to be run from the <span style="color: #3366ff;">/alltalk_tts/</span> folder. Don't move the location of this script.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟥 Have you run AllTalk at least once? It needs to have downloaded+updated the voice model, before we can finetune it.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - <span style="color: #3366ff;">finetune.py</span> needs to be run from the <span style="color: #3366ff;">/alltalk_tts/</span> folder. Don't move the location of this script.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Have you run AllTalk at least once? It needs to have downloaded+updated the voice model, before we can finetune it.
                ### 🟦 <u>What you need to run finetuning</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 An Nvidia GPU. Tested on Windows with extended shared VRAM and training used about 16GB's total (which worked on a 12GB card).
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 I have not been able to test this on a GPU with less than 12GB of VRAM, so cannot say if it will work or how that would affect performance.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 <span style="color: red;">Version 11.8</span> of Nvidia cuBLAS and cuDNN (guide below). Only 11.8 of cuBLAS and cuDNN work for this process currently.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 Minimum <span style="color: red;">18GB</span> free disk space (most of it is used temporarily).
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 Some decent quality audio, multiple files if you like. Minimum of 2 minutes and Ive tested up to 20 minutes of audio.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 There is no major need to chop down your audio files into small slices as Step 1 will do that for you automatically and prepare the training set. Ive been testing with 5 minute long clips.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 This process will need access to all your GPU and VRAM, so close any other software that's using your GPU currently.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - An Nvidia GPU. Tested on Windows with extended shared VRAM and training used about 16GB's total (which worked on a 12GB card).
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - If you have multiple Nvidia GPU's in your system, please see this [important note](https://github.com/erew123/alltalk_tts#-i-have-multiple-gpus-and-i-have-problems-running-finetuning).
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - I have not been able to test this on a GPU with less than 12GB of VRAM, so cannot say if it will work or how that would affect performance.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - <span style="color: red;">Version 11.8</span> of Nvidia cuBLAS and cuDNN (guide below). Only 11.8 of cuBLAS and cuDNN work for this process currently.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Minimum <span style="color: red;">18GB</span> free disk space (most of it is used temporarily).
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Some decent quality audio, multiple files if you like. Minimum of 2 minutes and Ive tested up to 20 minutes of audio.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - There is no major need to chop down your audio files into small slices as Step 1 will do that for you automatically and prepare the training set. Ive been testing with 5 minute long clips.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - This process will need access to all your GPU and VRAM, so close any other software that's using your GPU currently.
                ### 🟨 <u>Setting up cuBLAS and cuDNN <span style="color: red;">Version 11.8</span></u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟨 If you have the <span style="color: #3366ff;;">Nvidia CUDA Toolkit Version 11.8</span> installed and can type <span style="color: #3366ff;;">nvcc --version</span> at the command prompt/terminal and it reports <span style="color: #00a000;">Cuda compilation tools, release 11.8</span> you should be good to go. 
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟨 If you dont have the toolkit installed, the idea is just to install the smallest bit possible and this will not affect or impact other things on your system.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟨 You will need to download the Nvidia Cuda Toolkit 11.8<span style="color: #3366ff;"> network</span> install from [here](https://developer.nvidia.com/cuda-11-8-0-download-archive) 
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - If you have the <span style="color: #3366ff;;">Nvidia CUDA Toolkit Version 11.8</span> installed and can type <span style="color: #3366ff;;">nvcc --version</span> at the command prompt/terminal and it reports <span style="color: #00a000;">Cuda compilation tools, release 11.8</span> you should be good to go. 
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - If you dont have the toolkit installed, the idea is just to install the smallest bit possible and this will not affect or impact other things on your system.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - You will need to download the Nvidia Cuda Toolkit 11.8<span style="color: #3366ff;"> network</span> install from [here](https://developer.nvidia.com/cuda-11-8-0-download-archive) 
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1) Run the installer and select <span style="color: #3366ff;">Custom Advanced</span> Uncheck <span style="color: #3366ff;">everything</span> at the top then expand <span style="color: #3366ff;">CUDA</span>, <span style="color: #3366ff;">Development</span> > <span style="color: #3366ff;">Compiler</span> > and select <span style="color: #3366ff;;">nvcc</span> then expand <span style="color: #3366ff;;">Libraries</span> and select <span style="color: #3366ff;;">CUBLAS</span>.
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2) Back at the top of <span style="color: #3366ff;">CUDA</span>, expand <span style="color: #3366ff;">Runtime</span> > <span style="color: #3366ff;">Libraries</span> and select <span style="color: #3366ff;">CUBLAS</span>. Click <span style="color: #3366ff;;">Next</span>, accept the default path (taking a note of its location) and let the install run. 
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3) You should be able to drop to your terminal or command prompt and type <span style="color: #3366ff;">nvcc --version</span> and have it report <span style="color: #00a000;">Cuda compilation tools, release 11.8</span>. If it does you are good to go. If it doesn't > Step 4.
@@ -657,16 +913,16 @@ if __name__ == "__main__":
                f"""
                ## <u>STEP 1 - Preparing Audio/Generating the dataset</u><br>
                ### 🟦 <u>What you need to do</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 Please read Coqui's guide on what makes a good dataset [here](https://docs.coqui.ai/en/latest/what_makes_a_good_dataset.html#what-makes-a-good-dataset)
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 Place your audio files in <span style="color: #3366ff;">{str(audio_folder)}</span>                
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 Your audio samples can be in the format <span style="color: #3366ff;">mp3, wav,</span> or <span style="color: #3366ff;">flac.</span>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 You will need a minimum of <span style="color: #3366ff;">2 minutes</span> of audio in either one or multiple audio files. 
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 When you have completed Steps 1, 2, and 3, you are welcome to delete your samples from "put-voice-samples-in-here".<br>
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Please read Coqui's guide on what makes a good dataset [here](https://docs.coqui.ai/en/latest/what_makes_a_good_dataset.html#what-makes-a-good-dataset)
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Place your audio files in <span style="color: #3366ff;">{str(audio_folder)}</span>                
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Your audio samples can be in the format <span style="color: #3366ff;">mp3, wav,</span> or <span style="color: #3366ff;">flac.</span>
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - You will need a minimum of <span style="color: #3366ff;">2 minutes</span> of audio in either one or multiple audio files. Very small sample files cause errors, so I would suggest 30 seconds and longer samples. 
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - When you have completed Steps 1, 2, and 3, you are welcome to delete your samples from "put-voice-samples-in-here".<br>
                ### 🟨 <u>What this step is doing</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟨 With step one, we are going to be stripping your audio file(s) into smaller files, using Whisper to find spoken words/sentences, compile that into excel sheets of training data, ready for finetuning the model on Step 2.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟨 Whilst you can choose multiple Whisper models, its best only to use the 1 model as each one is about 3GB in size and will download to your local huggingface cache on first-time use. If and when you have completed training, you wish to delete this 3GB model from your system, you are welcome to do so.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - With step one, we are going to be stripping your audio file(s) into smaller files, using Whisper to find spoken words/sentences, compile that into excel sheets of training data, ready for finetuning the model on Step 2.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Whilst you can choose multiple Whisper models, its best only to use the 1 model as each one is about 3GB in size and will download to your local huggingface cache on first-time use. If and when you have completed training, you wish to delete this 3GB model from your system, you are welcome to do so.
                ### 🟩 <u>How long will this take?</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟩 First time, it needs to download the Whisper model which is 3GB. After that a few minutes on an average 3-4 year old system.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - First time, it needs to download the Whisper model which is 3GB. After that a few minutes on an average 3-4 year old system.
                """
            )
                 
@@ -675,41 +931,41 @@ if __name__ == "__main__":
                value=out_path,
                visible=False,
            )
+            with gr.Row():
+                whisper_model = gr.Dropdown(
+                    label="Whisper Model",
+                    value="large-v3",
+                    choices=[
+                        "large-v3",
+                        "large-v2",
+                        "large",
+                        "medium",
+                        "small"
+                    ],
+                )

-            whisper_model = gr.Dropdown(
-                label="Whisper Model",
-                value="large-v3",
-                choices=[
-                    "large-v3",
-                    "large-v2",
-                    "large",
-                    "medium",
-                    "small"
-                ],
-            )
-
-            lang = gr.Dropdown(
-                label="Dataset Language",
-                value="en",
-                choices=[
-                    "en",
-                    "es",
-                    "fr",
-                    "de",
-                    "it",
-                    "pt",
-                    "pl",
-                    "tr",
-                    "ru",
-                    "nl",
-                    "cs",
-                    "ar",
-                    "zh",
-                    "hu",
-                    "ko",
-                    "ja"
-                ],
-            )
+                lang = gr.Dropdown(
+                    label="Dataset Language",
+                    value="en",
+                    choices=[
+                        "en",
+                        "es",
+                        "fr",
+                        "de",
+                        "it",
+                        "pt",
+                        "pl",
+                        "tr",
+                        "ru",
+                        "nl",
+                        "cs",
+                        "ar",
+                        "zh",
+                        "hu",
+                        "ko",
+                        "ja"
+                    ],
+                )
            progress_data = gr.Label(
                label="Progress:"
            )
@@ -744,6 +1000,7 @@ if __name__ == "__main__":

                print("[FINETUNE] Dataset Generated. Move to Step 2")
                return "Dataset Generated. Move to Step 2", train_meta, eval_meta
+            
 #######################
 #### GRADIO STEP 2 ####
 #######################
@@ -753,51 +1010,67 @@ if __name__ == "__main__":
                f"""
                ## <u>STEP 2 - Fine-tuning the XTTS Encoder</u><br>
                ### 🟦 <u>What you need to do</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 The <span style="color: #3366ff;">Train CSV</span> and <span style="color: #3366ff;">Eval CSV</span> should already be populated. If not, just go back to Step 1 and click the button again.             
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 If for any reason it crashed, close the app and re-start the finetuning and kick it off again, clicking through setp 1 (it doesnt need to regenerate the data)
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 The settings below are factory and should be ok.<br>
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - The <span style="color: #3366ff;">Train CSV</span> and <span style="color: #3366ff;">Eval CSV</span> should already be populated. If not, just go back to Step 1 and click the button again.             
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - If for any reason it crashed, close the app and re-start the finetuning and kick it off again, clicking through setp 1 (it doesnt need to regenerate the data)
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - The settings below are factory and should be ok.<br>
                ### 🟨 <u>What this step is doing</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟨 Very simply put, it's taking all our wav files generated in Step 1, along with our recorded speech in out excel documents and its training the model on that voice.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Very simply put, it's taking all our wav files generated in Step 1, along with our recorded speech in out excel documents and its training the model on that voice.
                ### 🟩 <u>How long will this take?</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟩 On a RTX 4070 with factory settings (as below) and 20 minutes of audio, this took 20 minutes. Again, it will vary by system and how much audio you are throwing at it. You have time to go make a coffee.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟩 Look at your command prompt/terminal window if you want to see what it is doing.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - On a RTX 4070 with factory settings (as below) and 20 minutes of audio, this took 20 minutes. Again, it will vary by system and how much audio you are throwing at it. You have time to go make a coffee.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Look at your command prompt/terminal window if you want to see what it is doing.
                """
            )
+            with gr.Row():
+                train_csv = gr.Textbox(
+                    label="Train CSV:",
+                )
+                eval_csv = gr.Textbox(
+                    label="Eval CSV:",
+                )
+            with gr.Row():
+                num_epochs =  gr.Slider(
+                    label="Number of epochs:",
+                    minimum=1,
+                    maximum=100,
+                    step=1,
+                    value=args.num_epochs,
+                )
+                batch_size = gr.Slider(
+                    label="Batch size:",
+                    minimum=2,
+                    maximum=512,
+                    step=1,
+                    value=args.batch_size,
+                )
+                grad_acumm = gr.Slider(
+                    label="Grad accumulation steps:",
+                    minimum=2,
+                    maximum=128,
+                    step=1,
+                    value=args.grad_acumm,
+                )
+                max_audio_length = gr.Slider(
+                    label="Max permitted audio size in seconds:",
+                    minimum=2,
+                    maximum=20,
+                    step=1,
+                    value=args.max_audio_length,
+                )
+            with gr.Row():
+                model_to_train_choices = ["Base Model"]
+                if finetuned_model:
+                    model_to_train_choices.append("Existing finetuned model")
+                model_to_train = gr.Radio(
+                    choices=model_to_train_choices,
+                    label="Which model do you want to train?",
+                    value="Base Model"
+                )
+                gr.Markdown(
+                f"""
+                ##### If you have an existing finetuned model in <span style="color: #3366ff;">/models/trainedmodel/</span> you will have an option to select <span style="color: #3366ff;">Existing finetuned model</span>, allowing you to further train it. Otherwise, you will only have Base Model as an option.
+                """
+                )

-            train_csv = gr.Textbox(
-                label="Train CSV:",
-            )
-            eval_csv = gr.Textbox(
-                label="Eval CSV:",
-            )
-            num_epochs =  gr.Slider(
-                label="Number of epochs:",
-                minimum=1,
-                maximum=100,
-                step=1,
-                value=args.num_epochs,
-            )
-            batch_size = gr.Slider(
-                label="Batch size:",
-                minimum=2,
-                maximum=512,
-                step=1,
-                value=args.batch_size,
-            )
-            grad_acumm = gr.Slider(
-                label="Grad accumulation steps:",
-                minimum=2,
-                maximum=128,
-                step=1,
-                value=args.grad_acumm,
-            )
-            max_audio_length = gr.Slider(
-                label="Max permitted audio size in seconds:",
-                minimum=2,
-                maximum=20,
-                step=1,
-                value=args.max_audio_length,
-            )
            progress_train = gr.Label(
                label="Progress:"
            )
@@ -839,16 +1112,16 @@ if __name__ == "__main__":
                f"""
                ## <u>STEP 3 -  Inference/Testing</u><br>
                ### 🟥 <u>Important Note</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟥 This step will error if you are using TTS version 0.22.0. Please re-run  <span style="color: #3366ff;">pip install -r requirements_finetune.txt</span> if it errors loading the model.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟥 If you dont see multiple speaker reference files and you used more than 3 minutes of speech, try refreshing the page as it may not have loaded them correctly.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - This step will error if you are using TTS version 0.22.0. Please re-run  <span style="color: #3366ff;">pip install -r requirements_finetune.txt</span> if it errors loading the model.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - If you dont see multiple speaker reference files and you used more than 3 minutes of speech, try refreshing the page as it may not have loaded them correctly.
                ### 🟦 <u>What you need to do</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 The model is now trained and you are at the testing stage. Hopefully all the dropdowns should be pre-populated now.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 You need to <span style="color: #3366ff;">Load Fine-tuned XTTS model</span> and then select your <span style="color: #3366ff;">Speaker Reference Audio</span>. You can choose various <span style="color: #3366ff;">Speaker Reference Audios</span> to see which works best.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 All the <span style="color: #3366ff;">Speaker Reference Audios</span> in the dropdown are ones that are <span style="color: #3366ff;">8 seconds</span> long or more. You can use one of these later for your voice sample in All Talk, so remember the one you like.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 Some <span style="color: #3366ff;">Speaker Reference Audios</span> are a bit long and may cause issues generating the TTS, so try different ones.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 Once you are happy, move to the next tab for instruction on cleaning out the training data and how to copy your newly trained model over your current model.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - The model is now trained and you are at the testing stage. Hopefully all the dropdowns should be pre-populated now.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - You need to <span style="color: #3366ff;">Load Fine-tuned XTTS model</span> and then select your <span style="color: #3366ff;">Speaker Reference Audio</span>. You can choose various <span style="color: #3366ff;">Speaker Reference Audios</span> to see which works best.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - All the <span style="color: #3366ff;">Speaker Reference Audios</span> in the dropdown are ones that are <span style="color: #3366ff;">8 seconds</span> long or more. You can use one of these later for your voice sample in All Talk, so remember the one you like.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Some <span style="color: #3366ff;">Speaker Reference Audios</span> are a bit long and may cause issues generating the TTS, so try different ones.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Once you are happy, move to the next tab for instruction on cleaning out the training data and how to copy your newly trained model over your current model.
                ### 🟨 <u>What this step is doing</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟨 Its loading the finetuned model into memory, loading a voice sample and generating TTS, so that you can test out how well fine tuning worked.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Its loading the finetuned model into memory, loading a voice sample and generating TTS, so that you can test out how well fine tuning worked.
                """
            )

@@ -941,22 +1214,57 @@ if __name__ == "__main__":
                f"""
                ## <u>What to do next</u><br>
                ### 🟦 <u>What you need to do</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 The wav(s) you liked from <span style="color: #3366ff;">Speaker Reference Audios</span> are in <span style="color: #3366ff;">/finetune/tmp-trn/wavs/</span> so copy the one(s) you selected to your <span style="color: #3366ff;">/alltalk_tts/voices/</span> samples folder.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 You can delete your original samples from inside of the <span style="color: #3366ff;">/finetune/put-voice-samples-in-here/</span> folder.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 In the folder <span style="color: #3366ff;">/finetune/tmp-trn/training/XTTS_FT(date here)/</span> you will find your <span style="color: #3366ff;">best_model.pth</span>, <span style="color: #3366ff;">config.json</span> and <span style="color: #3366ff;">vocab.json</span> (also the last epoch training best_model_xxx.pth)
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 You can delete the last epoch training model file <span style="color: #3366ff;">best_model_xxx.pth</span> (the one with the numbers).
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 You will need to rename <span style="color: #3366ff;">best_model.pth</span> to <span style="color: #3366ff;">model.pth</span>.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 You will close the Finetuning application and copy your 3 files listed above over the top of the existing model files inside your <span style="color: #3366ff;">/alltalk_tts/models/xttsv2_2.0.2/</span> folder. 
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟦 Start up Alltalk and you should be off and running. Preferably use the <span style="color: #3366ff;">XTTS Local</span> method for best results. 
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - You have a few options below:
+                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 🔹 Compact &amp; move model. This will compress the raw finetuned model and move it, along with any large enough wav files created to one of two locations (please read text next to the button).<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; You can use the wav files in that location as sample voices with your model. Models moved to <span style="color: #3366ff;">/models/trainedmodel/</span> will be loadable within Text-gen-webui on the <span style="color: #3366ff;">XTTSv2 FT</span> loader.
+                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 🔹 Delete Generated Training data. This will delete the finetuned model and any other training data generated during this process. You will more than likely want to do this after you have Compacted &amp; moved the model.
+                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 🔹 Delete original voice samples. This will delete the voice samples you placed in put-voice-samples-in-here (if you no longer have a use for them).
+                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 🔹 Compress a legacy finetuned model. For any models that were generated pre the current version of finetuning, you can still compact them down.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - Instructions on using the finetuned model in Text-generation-webui and also further tuning this model you just created are on <span style="color: #3366ff;">OPTION A - Compact and move model to '/trainedmodel/'</span>.
                ### 🟨 <u>Clearing up disk space</u>
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟨 When you are happy with everything, you can delete the <span style="color: #3366ff;">/alltalk_tts/finetune/tmp-trn/</span> folder. This will delete all training data that was generated.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟨 If you are not going to train anything again, you can delete the whisper model from inside of your huggingface cache (3GB approx) 
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - If you are not going to train anything again, you can delete the whisper model from inside of your huggingface cache (3GB approx) 
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Linux <span style="color: #3366ff;">~/.cache/huggingface/hub/(folder-here)</span>
                #### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Windows <span style="color: #3366ff;">C:&bsol;users&lpar;your-username&rpar;&bsol;.cache&bsol;huggingface&bsol;hub&bsol;(folder-here)</span>.
-                #### &nbsp;&nbsp;&nbsp;&nbsp; 🟨 You can also uninstall the Nvidia CUDA 11.8 Toolkit if you wish and remove your environment variable entries.
+                #### &nbsp;&nbsp;&nbsp;&nbsp; - You can also uninstall the Nvidia CUDA 11.8 Toolkit if you wish and remove your environment variable entries.
                """
            )
-
+            final_progress_data = gr.Label(
+                label="Progress:"
+            )
+            with gr.Row():
+                compact_btn = gr.Button(value="OPTION A - Compact and move model to '/trainedmodel/'")
+                gr.Markdown(
+                f"""
+                ##### This will compact the model and move it, along with the reference wav's to <span style="color: #3366ff;">/models/trainedmodel/</span> and will <span style="color: red;">OVERWRITE</span> any model in that location. This model will become available to load in Text-gen-webui with the <span style="color: #3366ff;">XTTSv2 FT</span> loader. It will also be detected by Finetune as a model that can be further trained (Reload finetune.py).
+                """
+                )
+            with gr.Row():
+                compact_lastfinetuned_btn = gr.Button(value="OPTION B - Compact and move model to '/lastfinetuned/'")
+                gr.Markdown(
+                f"""
+                ##### This will compact the model and move it, along with the reference wav's to <span style="color: #3366ff;">/models/lastfinetuned/</span> and will <span style="color: red;">OVERWRITE</span> any model in that location. This will just leave you a finetuned model that you can do with as you please. It will <span style="color: red;">NOT</span> be available in Text-gen-webui unless you copy it over one of the other model loader locations.
+                """
+                )
+            with gr.Row():
+                delete_training_btn = gr.Button(value="Delete generated training data")
+                gr.Markdown(
+                f"""
+                ##### This will <span style="color: red;">DELETE</span> your training data and the raw finetuned model from <span style="color: #3366ff;">/finetune/tmp-trn/</span>.
+                """
+                )
+            with gr.Row():
+                delete_voicesamples_btn = gr.Button(value="Delete original voice samples")
+                gr.Markdown(
+                f"""
+                ##### This will <span style="color: red;">DELETE</span> your original voice samples from <span style="color: #3366ff;">/finetune/put-voice-samples-in-here/</span>.
+                """
+                )
+            with gr.Row():
+                compact_legacy_btn = gr.Button(value="Compact a legacy finetuned model")
+                gr.Markdown(
+                f"""
+                ##### This will compact your old finetuned models from 5GB to 1.9GB. Place your 5GB <span style="color: #3366ff;">base_model.pth</span> file in <span style="color: #3366ff;">/finetune/</span> (rename your file to <span style="color: #3366ff;">base_model.pth</span> if necessary). This will generate you a <span style="color: #3366ff;">model.pth</span> file.
+                """
+                )

            prompt_compute_btn.click(
                fn=preprocess_dataset,
@@ -1008,6 +1316,28 @@ if __name__ == "__main__":
                outputs=[progress_gen, tts_output_audio, reference_audio],
            )
            
+            compact_btn.click(
+                fn=compact_model,
+                outputs=[final_progress_data],
+            )
+            compact_lastfinetuned_btn.click(
+                fn=compact_lastfinetuned_model,
+                outputs=[final_progress_data],
+            )
+            delete_training_btn.click(
+                fn=delete_training_data,
+                outputs=[final_progress_data],
+            )
+            delete_voicesamples_btn.click(
+                fn=delete_voice_sample_contents,
+                outputs=[final_progress_data],
+            )
+            compact_legacy_btn.click(
+                fn=compact_legacy_model,
+                outputs=[final_progress_data],
+            )
+            model_to_train.change(basemodel_or_finetunedmodel_choice, model_to_train, None)
+
    demo.queue().launch(
        show_api=False,
        inbrowser=True,
@@ -0,0 +1,26 @@
+import torch
+import os
+MODEL_PATH = "best_model.pth"
+# Check if best_model.pth exists
+if not os.path.exists(MODEL_PATH):
+    print(f"\nCannot find \033[94m{MODEL_PATH}\033[0m in the current folder.")
+    
+    # Check if model.pth exists as an alternative
+    ALTERNATIVE_PATH = "model.pth"
+    if os.path.exists(ALTERNATIVE_PATH):
+        print(f"\nIf your model is currently called \033[93m{ALTERNATIVE_PATH}\033[0m please rename it to \033[94mbest_model.pth\033[0m.")
+    else:
+        print(f"Please put \033[91mbest_model.pth\033[0m in the current folder or rename your model file to \033[94mbest_model.pth\033[0m.")
+    exit(1)
+# Attempt to load the model
+try:
+    checkpoint = torch.load(MODEL_PATH, map_location=torch.device("cpu"))
+except Exception as e:
+    print("Error loading checkpoint:", e)
+    raise
+del checkpoint["optimizer"]
+for key in list(checkpoint["model"].keys()):
+    if "dvae" in key:
+        del checkpoint["model"][key]
+torch.save(checkpoint, "model.pth")
+print(f"\nYour compacted model is created \033[93mmodel.pth\033[0m.")
@@ -8,6 +8,7 @@
        "model.pth": "https://huggingface.co/coqui/XTTS-v2/resolve/v2.0.2/model.pth?download=true",
        "dvae.pth": "https://huggingface.co/coqui/XTTS-v2/resolve/v2.0.2/dvae.pth?download=true",
        "mel_stats.pth": "https://huggingface.co/coqui/XTTS-v2/resolve/v2.0.2/mel_stats.pth?download=true",
+        "speakers_xtts.pth": "https://huggingface.co/coqui/XTTS-v2/resolve/v2.0.2/speakers_xtts.pth?download=true",
        "vocab.json": "https://huggingface.co/coqui/XTTS-v2/resolve/v2.0.2/vocab.json?download=true"
    }
 }
@@ -11,3 +11,5 @@ tqdm>=4.66.1
 importlib-metadata>=4.8.1
 packaging>=23.2
 pydantic>=1.10.13
+sounddevice>=0.4.6
+python-multipart>=0.0.6
@@ -11,3 +11,5 @@ tqdm>=4.66.1
 importlib-metadata>=4.8.1
 packaging>=23.2
 pydantic>=1.10.13
+sounddevice>=0.4.6
+python-multipart>=0.0.6
@@ -77,6 +77,8 @@ with open(this_dir / "languages.json", encoding="utf8") as f:
    languages = json.load(f)
 # Create a global lock
 process_lock = threading.Lock()
+# Base setting for a possible FineTuned model existing and the loader being available
+tts_method_xtts_ft = False

 # Gather the voice files
 def get_available_voices():
@@ -139,10 +141,23 @@ def check_required_files():
    subprocess.run(["python", str(download_script_path)])
    print(f"[{params['branding']}Startup] All required files are present.")

-
 # STARTUP Call Check routine
 check_required_files()

+##################################################
+#### Check to see if a finetuned model exists ####
+##################################################
+# Set the path to the directory
+trained_model_directory = this_dir / "models" / "trainedmodel"
+# Check if the directory "trainedmodel" exists
+finetuned_model = trained_model_directory.exists()
+# If the directory exists, check for the existence of the required files
+# If true, this will add a extra option in the Gradio interface for loading Xttsv2 FT
+if finetuned_model:
+    required_files = ["model.pth", "config.json", "vocab.json"]
+    finetuned_model = all((trained_model_directory / file).exists() for file in required_files)
+if finetuned_model:
+    print(f"[{params['branding']}Startup] Finetuned model \033[93mDetected\033[0m")

 ####################################################
 #### SET GRADIO BUTTONS BASED ON confignew.json ####
@@ -252,7 +267,7 @@ else:
 timeout = 120  # Adjust the timeout as needed

 # Introduce a delay before starting the check loop
-time.sleep(25)  # Wait 25 secs before checking if the tts_server.py has started up.
+time.sleep(26)  # Wait 26 secs before checking if the tts_server.py has started up.
 start_time = time.time()
 while time.time() - start_time < timeout:
    try:
@@ -264,7 +279,7 @@ while time.time() - start_time < timeout:
        print(
            f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess has NOT started up yet, Will keep trying for 120 seconds maximum. Please wait."
        )
-    time.sleep(1)
+    time.sleep(4)
 else:
    print(
        f"[{params['branding']}Startup] Startup timed out. Check the server logs for more information."
@@ -272,11 +287,13 @@ else:
    # Cleanly kill off this script, but allow text-generation-webui to keep running, albeit without this alltalk_tts
    sys.exit(1)

+
 #####################################
 #### MODEL LOADING AND UNLOADING ####
 #####################################
 # MODEL - Swap model based on Gradio selection API TTS, API Local, XTTSv2 Local
 def send_reload_request(tts_method):
+    global tts_method_xtts_ft
    try:
        params["tts_model_loaded"] = False
        url = f"{base_url}/api/reload"
@@ -295,17 +312,26 @@ def send_reload_request(tts_method):
                params["tts_method_api_tts"] = True
                params["deepspeed_activate"] = False
                audio_path = this_dir / "templates" / "apitts.wav"
+                tts_method_xtts_ft = False
            elif tts_method == "API Local":
                params["tts_method_api_tts"] = False
                params["tts_method_xtts_local"] = False
                params["tts_method_api_local"] = True
                params["deepspeed_activate"] = False
                audio_path = this_dir / "templates" / "apilocal.wav"
+                tts_method_xtts_ft = False
            elif tts_method == "XTTSv2 Local":
                params["tts_method_api_tts"] = False
                params["tts_method_api_local"] = False
                params["tts_method_xtts_local"] = True
                audio_path = this_dir / "templates" / "xttslocal.wav"
+                tts_method_xtts_ft = False
+            elif tts_method == "XTTSv2 FT":
+                params["tts_method_api_tts"] = False
+                params["tts_method_api_local"] = False
+                params["tts_method_xtts_local"] = False
+                audio_path = this_dir / "templates" / "xttsfinetuned.wav"
+                tts_method_xtts_ft = True
        return f'<audio src="file/{audio_path}" controls autoplay></audio>'
    except requests.exceptions.RequestException as e:
        # Handle the HTTP request error
@@ -483,6 +509,54 @@ def voice_preview(string):
        return f"[{params['branding']}Server] Audio generation failed. Status: {generate_response.get('status')}"


+def process_text(text):
+    # Normalize HTML encoded quotes
+    text = html.unescape(text)
+
+    # Replace ellipsis with a single dot
+    text = re.sub(r'\.{3,}', '.', text)
+
+    # Pattern to identify combined narrator and character speech
+    combined_pattern = r'(\*[^*"]+\*|"[^"*]+")'
+
+    # List to hold parts of speech along with their type
+    ordered_parts = []
+
+    # Track the start of the next segment
+    start = 0
+
+    # Find all matches
+    for match in re.finditer(combined_pattern, text):
+        # Add the text before the match, if any, as ambiguous
+        if start < match.start():
+            ambiguous_text = text[start:match.start()].strip()
+            if ambiguous_text:
+                ordered_parts.append(('ambiguous', ambiguous_text))
+
+        # Add the matched part as either narrator or character
+        matched_text = match.group(0)
+        if matched_text.startswith('*') and matched_text.endswith('*'):
+            ordered_parts.append(('narrator', matched_text.strip('*').strip()))
+        elif matched_text.startswith('"') and matched_text.endswith('"'):
+            ordered_parts.append(('character', matched_text.strip('"').strip()))
+        else:
+            # In case of mixed or improperly formatted parts
+            if '*' in matched_text:
+                ordered_parts.append(('narrator', matched_text.strip('*').strip('"')))
+            else:
+                ordered_parts.append(('character', matched_text.strip('"').strip('*')))
+
+        # Update the start of the next segment
+        start = match.end()
+
+    # Add any remaining text after the last match as ambiguous
+    if start < len(text):
+        ambiguous_text = text[start:].strip()
+        if ambiguous_text:
+            ordered_parts.append(('ambiguous', ambiguous_text))
+
+    return ordered_parts
+
 #################################
 #### TTS STANDARD GENERATION ####
 #################################
@@ -491,6 +565,7 @@ def output_modifier(string, state):
    if not params["activate"]:
        return string
    original_string = string
+    #print("ORIGINAL STRING IS:", original_string)
    cleaned_string = before_audio_generation(string, params)
    if cleaned_string is None:
        return
@@ -501,80 +576,55 @@ def output_modifier(string, state):
    if process_lock.acquire(blocking=False):
        try:
            if params["narrator_enabled"]:
-                #print(original_string)
-                # Do Some basic stripping and remove any double CR's
-                processed_string = (
-                    original_string
-                    .replace("***", "*")
-                    .replace("**", "*")
-                    .replace("\n\n", "\n")
-                )
-                # Clean up two asterisks (narrators) being made between a newline, making it one sentence.
-                processed_string = re.sub(r'\.\*\n\*', '. ', processed_string)
-                # Clean up a few other bits.
-                processed_string = (
-                    processed_string
-                    .replace("&#x27;", "'")
-                    .replace("\n", " ")
-                    # Add special characters to the quote so that we can use it to identify things later after its been split
-                    .replace('&quot;', '&quot;<')
-                    # Capture new conversations which wont have things like &quote in them
-                    .replace('"', '&quot;<')
-                )
-                #capturing another outlier in inital character paragraph
-                #print("processed string 1 is:", processed_string)
-                processed_string = processed_string.replace('&quot;<. *', '&quot;< *"')
-                processed_string = processed_string.replace('< *"', '< *')
-                processed_string = processed_string.replace('. *', '< *')
-                #print("processed string 2 is:", processed_string)
-                # Set up a tracking of the individual wav files.
+                processed_parts = process_text(original_string)
+
                audio_files_all_paragraphs = []
-                # Split the line using &quot; and ".* " (so end of sentences, leaving special characters added to the start of all OTHER sentences, bar possibly the first one if its starting with a *
-                parts = re.split(r'&quot;|\.\*', processed_string)
-                audio_files_paragraph = []
-                for i, part in enumerate(parts):
+                for part_type, part in processed_parts:
                    # Skip parts that are too short
-                    if len(part.strip()) <= 1:
+                    if len(part.strip()) <= 3:
                        continue
-                    # Figure out which type of line it is, then replace characters as necessary to avoid TTS trying to pronunce them, htmlunescape after. 
-                    # Character will always be a < with a letter immediately after it
-                    if '<' in part and '< *' not in part and '<*' not in part and '<  *' not in part and '< ' not in part and '<  ' not in part:
-                        cleaned_part = html.unescape(part.replace('<', ''))
-                        voice_to_use = params["voice"]
-                        #Narrator will always be an * or < with an * a position or two after it.
-                    elif '<*' in part or '< *' in part or '<  *' in part or '*' in part:
-                        cleaned_part = html.unescape(part.replace('<*', '').replace('< *', '').replace('<  *', '').replace('*', ''))
+
+                    # Determine the voice to use based on the part type
+                    if part_type == 'narrator':
                        voice_to_use = params["narrator_voice"]
-                        #If the other two dont capture it, aka, the AI gave no * or &quot; on the line, use non_quoted_text_is aka user interface, user can choose Char or Narrator
-                    elif non_quoted_text_is:
-                        cleaned_part = html.unescape(part.replace('< ', '').replace('<  ', '').replace('<  ', ''))
+                        print(f"[{params['branding']}TTSGen] \033[92mNarrator\033[0m")  # Green
+                    elif part_type == 'character':
                        voice_to_use = params["voice"]
+                        print(f"[{params['branding']}TTSGen] \033[36mCharacter\033[0m")  # Yellow
                    else:
-                        cleaned_part = html.unescape(part.replace('< ', '').replace('<  ', '').replace('<  ', ''))
-                        voice_to_use = params["narrator_voice"]
-                    # Generate that TTS and output to a file
+                        # Handle ambiguous parts based on user preference
+                        voice_to_use = params["voice"] if non_quoted_text_is else params["narrator_voice"]
+                        voice_description = "\033[36mCharacter (Text-not-inside)\033[0m" if non_quoted_text_is else "\033[92mNarrator (Text-not-inside)\033[0m"
+                        print(f"[{params['branding']}TTSGen] {voice_description}")
+
+                    # Replace multiple exclamation marks, question marks, or other punctuation with a single instance
+                    cleaned_part = re.sub(r'([!?.])\1+', r'\1', part)
+                    # Further clean to remove any other unwanted characters
+                    cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\$]', '', cleaned_part)
+                    # Remove all newline characters (single or multiple)
+                    cleaned_part = re.sub(r'\n+', ' ', cleaned_part)
+
+                    # Generate TTS and output to a file
                    output_filename = get_output_filename(state)
                    generate_response = send_generate_request(
                        cleaned_part, voice_to_use, language_code, output_filename
                    )
                    audio_path = generate_response.get("data", {}).get("audio_path")
-                    audio_files_paragraph.append(audio_path)
-                # Accumulate audio files within the paragraph
-                audio_files_all_paragraphs.extend(audio_files_paragraph)
+                    audio_files_all_paragraphs.append(audio_path)
+
                # Combine audio files across paragraphs
                final_output_file = combine(
                    audio_files_all_paragraphs, params["output_folder_wav"], state
                )
            else:
-                processed_string = (
-                    original_string
-                    .replace("***", "")
-                    .replace("**", "")
-                    .replace("*", "")
-                    .replace("\n\n", "\n")
-                    .replace("&#x27;", "'")
-                )
-                cleaned_part = html.unescape(processed_string)                
+                # Decode HTML entities first
+                cleaned_part = html.unescape(original_string)
+                # Replace multiple instances of certain punctuation marks with a single instance
+                cleaned_part = re.sub(r'([!?.])\1+', r'\1', cleaned_part)
+                # Further clean to remove any other unwanted characters
+                cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\$]', '', cleaned_part)
+                # Remove all newline characters (single or multiple)
+                cleaned_part = re.sub(r'\n+', ' ', cleaned_part)
                # Process the part and give it a non-character name if being used vai API or standalone.
                if "character_menu" in state:
                    output_file = Path(f'{params["output_folder_wav"]}/{state["character_menu"]}_{int(time.time())}.wav')
@@ -682,10 +732,6 @@ def input_modifier(string, state):
    shared.processing_message = "*Is recording a voice message...*"
    return string

-def custom_css():
-    path_to_css = Path(f"{this_dir}/style.css")
-    return open(path_to_css, "r").read()
-

 def ui():
    with gr.Accordion(params["branding"] + " TTS (XTTSv2)"):
@@ -779,14 +825,17 @@ def ui():
            deepspeed_checkbox_play = gr.HTML(visible=False)

        with gr.Row():
+            model_loader_choices=["API TTS", "API Local", "XTTSv2 Local"]
+            if finetuned_model:
+                model_loader_choices.append("XTTSv2 FT")
            tts_radio_buttons = gr.Radio(
-                choices=["API TTS", "API Local", "XTTSv2 Local"],
+                choices=model_loader_choices,
                label="Select TTS Generation Method (Read NOTE)",
                value=gr_modelchoice,  # Set the default value
            )
            tts_radio_buttons_play = gr.HTML(visible=False)
            explanation_text = gr.HTML(
-                f"<p>NOTE: Switching Model Type, Low VRAM & DeepSpeed, each takes 15 seconds. Each TTS generation method has a slightly different sound. DeepSpeed checkbox is only visible if DeepSpeed is present. Readme & Settings: <a href='http://{params['ip_address']}:{params['port_number']}'>http://{params['ip_address']}:{params['port_number']}</a>"
+                f"<p>⚙️ <a href='http://{params['ip_address']}:{params['port_number']}'>Click here for the Settings page, help & instructions</a><a href='http://{params['ip_address']}:{params['port_number']}'></a>⚙️<br>NOTE: Switching Model Type, Low VRAM & DeepSpeed, each takes 15 seconds. Each TTS generation method has a slightly different sound. DeepSpeed checkbox is only visible if DeepSpeed is installed on your system."
            )

        with gr.Row():
@@ -1,8 +0,0 @@
-.SDAP .hires_opts input[type="number"] {
-    width: 6em !important;
-}
-
-/* silero_tts preview */
-.form:has(> #silero_preview_text) {
-    min-width: 75%
-}
@@ -0,0 +1,179 @@
+export interface CrunkerConstructorOptions {
+    /**
+     * Sample rate for Crunker's internal audio context.
+     *
+     * @default 44100
+     */
+    sampleRate: number;
+}
+export type CrunkerInputTypes = string | File | Blob;
+/**
+ * An exported Crunker audio object.
+ */
+export interface ExportedCrunkerAudio {
+    blob: Blob;
+    url: string;
+    element: HTMLAudioElement;
+}
+/**
+ * Crunker is the simple way to merge, concatenate, play, export and download audio files using the Web Audio API.
+ */
+export default class Crunker {
+    private readonly _sampleRate;
+    private readonly _context;
+    /**
+     * Creates a new instance of Crunker with the provided options.
+     *
+     * If `sampleRate` is not defined, it will auto-select an appropriate sample rate
+     * for the device being used.
+     */
+    constructor({ sampleRate }?: Partial<CrunkerConstructorOptions>);
+    /**
+     * Creates Crunker's internal AudioContext.
+     *
+     * @internal
+     */
+    private _createContext;
+    /**
+     *
+     * The internal AudioContext used by Crunker.
+     */
+    get context(): AudioContext;
+    /**
+     * Asynchronously fetches multiple audio files and returns an array of AudioBuffers.
+     */
+    fetchAudio(...filepaths: CrunkerInputTypes[]): Promise<AudioBuffer[]>;
+    /**
+     * Merges (layers) multiple AudioBuffers into a single AudioBuffer.
+     *
+     * **Visual representation:**
+     *
+     * ![](https://user-images.githubusercontent.com/12958674/88806278-968f0680-d186-11ea-9cb5-8ef2606ffcc7.png)
+     */
+    mergeAudio(buffers: AudioBuffer[]): AudioBuffer;
+    /**
+     * Concatenates multiple AudioBuffers into a single AudioBuffer.
+     *
+     * **Visual representation:**
+     *
+     * ![](https://user-images.githubusercontent.com/12958674/88806297-9d1d7e00-d186-11ea-8cd2-c64cb0324845.png)
+     */
+    concatAudio(buffers: AudioBuffer[]): AudioBuffer;
+    /**
+     * Pads a specified AudioBuffer with silence from a specified start time,
+     * for a specified length of time.
+     *
+     * Accepts float values as well as whole integers.
+     *
+     * @param buffer AudioBuffer to pad
+     * @param padStart Time to start padding (in seconds)
+     * @param seconds Duration to pad for (in seconds)
+     */
+    padAudio(buffer: AudioBuffer, padStart?: number, seconds?: number): AudioBuffer;
+    /**
+     * Slices an AudioBuffer from the specified start time to the end time, with optional fade in and out.
+     *
+     * @param buffer AudioBuffer to slice
+     * @param start Start time (in seconds)
+     * @param end End time (in seconds)
+     * @param fadeIn Fade in duration (in seconds, default is 0)
+     * @param fadeOut Fade out duration (in seconds, default is 0)
+     */
+    sliceAudio(buffer: AudioBuffer, start: number, end: number, fadeIn?: number, fadeOut?: number): AudioBuffer;
+    /**
+     * Plays the provided AudioBuffer in an AudioBufferSourceNode.
+     */
+    play(buffer: AudioBuffer): AudioBufferSourceNode;
+    /**
+     * Exports the specified AudioBuffer to a Blob, Object URI and HTMLAudioElement.
+     *
+     * Note that changing the MIME type does not change the actual file format. The
+     * file format will **always** be a WAVE file due to how audio is stored in the
+     * browser.
+     *
+     * @param buffer Buffer to export
+     * @param type MIME type (default: `audio/wav`)
+     */
+    export(buffer: AudioBuffer, type?: string): ExportedCrunkerAudio;
+    /**
+     * Downloads the provided Blob.
+     *
+     * @param blob Blob to download
+     * @param filename An optional file name to use for the download (default: `crunker`)
+     */
+    download(blob: Blob, filename?: string): HTMLAnchorElement;
+    /**
+     * Executes a callback if the browser does not support the Web Audio API.
+     *
+     * Returns the result of the callback, or `undefined` if the Web Audio API is supported.
+     *
+     * @param callback callback to run if the browser does not support the Web Audio API
+     */
+    notSupported<T>(callback: () => T): T | undefined;
+    /**
+     * Closes Crunker's internal AudioContext.
+     */
+    close(): this;
+    /**
+     * Returns the largest duration of the longest AudioBuffer.
+     *
+     * @internal
+     */
+    private _maxDuration;
+    /**
+     * Returns the largest number of channels in an array of AudioBuffers.
+     *
+     * @internal
+     */
+    private _maxNumberOfChannels;
+    /**
+     * Returns the sum of the lengths of an array of AudioBuffers.
+     *
+     * @internal
+     */
+    private _totalLength;
+    /**
+     * Returns whether the browser supports the Web Audio API.
+     *
+     * @internal
+     */
+    private _isSupported;
+    /**
+     * Writes the WAV headers for the specified Float32Array.
+     *
+     * Returns a DataView containing the WAV headers and file content.
+     *
+     * @internal
+     */
+    private _writeHeaders;
+    /**
+     * Converts a Float32Array to 16-bit PCM.
+     *
+     * @internal
+     */
+    private _floatTo16BitPCM;
+    /**
+     * Writes a string to a DataView at the specified offset.
+     *
+     * @internal
+     */
+    private _writeString;
+    /**
+     * Converts an AudioBuffer to a Float32Array.
+     *
+     * @internal
+     */
+    private _interleave;
+    /**
+     * Creates an HTMLAudioElement whose source is the specified Blob.
+     *
+     * @internal
+     */
+    private _renderAudioElement;
+    /**
+     * Creates an Object URL for the specified Blob.
+     *
+     * @internal
+     */
+    private _renderURL;
+}
@@ -0,0 +1,2 @@
+var e={d:(t,n)=>{for(var r in n)e.o(n,r)&&!e.o(t,r)&&Object.defineProperty(t,r,{enumerable:!0,get:n[r]})},o:(e,t)=>Object.prototype.hasOwnProperty.call(e,t)},t={};function n(e){return n="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},n(e)}function r(e){return function(e){if(Array.isArray(e))return a(e)}(e)||function(e){if("undefined"!=typeof Symbol&&null!=e[Symbol.iterator]||null!=e["@@iterator"])return Array.from(e)}(e)||function(e,t){if(!e)return;if("string"==typeof e)return a(e,t);var n=Object.prototype.toString.call(e).slice(8,-1);"Object"===n&&e.constructor&&(n=e.constructor.name);if("Map"===n||"Set"===n)return Array.from(e);if("Arguments"===n||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(n))return a(e,t)}(e)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function a(e,t){(null==t||t>e.length)&&(t=e.length);for(var n=0,r=new Array(t);n<t;n++)r[n]=e[n];return r}function o(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function i(e,t){for(var r=0;r<t.length;r++){var a=t[r];a.enumerable=a.enumerable||!1,a.configurable=!0,"value"in a&&(a.writable=!0),Object.defineProperty(e,(o=a.key,i=void 0,i=function(e,t){if("object"!==n(e)||null===e)return e;var r=e[Symbol.toPrimitive];if(void 0!==r){var a=r.call(e,t||"default");if("object"!==n(a))return a;throw new TypeError("@@toPrimitive must return a primitive value.")}return("string"===t?String:Number)(e)}(o,"string"),"symbol"===n(i)?i:String(i)),a)}var o,i}e.d(t,{Z:()=>u});var u=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},n=t.sampleRate;o(this,e),this._context=this._createContext(n),n||(n=this._context.sampleRate),this._sampleRate=n}var t,n,a;return t=e,n=[{key:"_createContext",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:44100;return window.AudioContext=window.AudioContext||window.webkitAudioContext||window.mozAudioContext,new AudioContext({sampleRate:e})}},{key:"context",get:function(){return this._context}},{key:"fetchAudio",value:async function(){for(var e=this,t=arguments.length,n=new Array(t),r=0;r<t;r++)n[r]=arguments[r];return await Promise.all(n.map((async function(t){var n;return n=t instanceof File||t instanceof Blob?await t.arrayBuffer():await fetch(t).then((function(e){return e.headers.has("Content-Type")&&!e.headers.get("Content-Type").includes("audio/")&&console.warn("Crunker: Attempted to fetch an audio file, but its MIME type is `".concat(e.headers.get("Content-Type").split(";")[0],"`. We'll try and continue anyway. (file: \"").concat(t,'")')),e.arrayBuffer()})),await e._context.decodeAudioData(n)})))}},{key:"mergeAudio",value:function(e){var t=this._context.createBuffer(this._maxNumberOfChannels(e),this._sampleRate*this._maxDuration(e),this._sampleRate);return e.forEach((function(e){for(var n=0;n<e.numberOfChannels;n++){for(var r=t.getChannelData(n),a=e.getChannelData(n),o=e.getChannelData(n).length-1;o>=0;o--)r[o]+=a[o];t.getChannelData(n).set(r)}})),t}},{key:"concatAudio",value:function(e){var t=this._context.createBuffer(this._maxNumberOfChannels(e),this._totalLength(e),this._sampleRate),n=0;return e.forEach((function(e){for(var r=0;r<e.numberOfChannels;r++)t.getChannelData(r).set(e.getChannelData(r),n);n+=e.length})),t}},{key:"padAudio",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0;if(0===n)return e;if(t<0)throw new Error('Crunker: Parameter "padStart" in padAudio must be positive');if(n<0)throw new Error('Crunker: Parameter "seconds" in padAudio must be positive');for(var r=this._context.createBuffer(e.numberOfChannels,Math.ceil(e.length+n*e.sampleRate),e.sampleRate),a=0;a<e.numberOfChannels;a++){var o=e.getChannelData(a);r.getChannelData(a).set(o.subarray(0,Math.ceil(t*e.sampleRate)+1),0),r.getChannelData(a).set(o.subarray(Math.ceil(t*e.sampleRate)+2,r.length+1),Math.ceil((t+n)*e.sampleRate))}return r}},{key:"sliceAudio",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:0,a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:0;if(t>=n)throw new Error('Crunker: "start" time should be less than "end" time in sliceAudio method');for(var o=Math.round((n-t)*this._sampleRate),i=Math.round(t*this._sampleRate),u=this._context.createBuffer(e.numberOfChannels,o,this._sampleRate),l=0;l<e.numberOfChannels;l++)for(var s=e.getChannelData(l),c=u.getChannelData(l),f=0;f<o;f++)c[f]=s[i+f],f<r*this._sampleRate&&(c[f]*=f/(r*this._sampleRate)),f>o-a*this._sampleRate&&(c[f]*=(o-f)/(a*this._sampleRate));return u}},{key:"play",value:function(e){var t=this._context.createBufferSource();return t.buffer=e,t.connect(this._context.destination),t.start(),Line truncated
+//# sourceMappingURL=crunker.esm.js.map
@@ -0,0 +1 @@
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truncated
@@ -0,0 +1,4 @@
+/*! howler.js v2.2.4 | (c) 2013-2020, James Simpson of GoldFire Studios | MIT License | howlerjs.com */
+!function(){"use strict";var e=function(){this.init()};e.prototype={init:function(){var e=this||n;return e._counter=1e3,e._html5AudioPool=[],e.html5PoolSize=10,e._codecs={},e._howls=[],e._muted=!1,e._volume=1,e._canPlayEvent="canplaythrough",e._navigator="undefined"!=typeof window&&window.navigator?window.navigator:null,e.masterGain=null,e.noAudio=!1,e.usingWebAudio=!0,e.autoSuspend=!0,e.ctx=null,e.autoUnlock=!0,e._setup(),e},volume:function(e){var o=this||n;if(e=parseFloat(e),o.ctx||_(),void 0!==e&&e>=0&&e<=1){if(o._volume=e,o._muted)return o;o.usingWebAudio&&o.masterGain.gain.setValueAtTime(e,n.ctx.currentTime);for(var t=0;t<o._howls.length;t++)if(!o._howls[t]._webAudio)for(var r=o._howls[t]._getSoundIds(),a=0;a<r.length;a++){var u=o._howls[t]._soundById(r[a]);u&&u._node&&(u._node.volume=u._volume*e)}return o}return o._volume},mute:function(e){var o=this||n;o.ctx||_(),o._muted=e,o.usingWebAudio&&o.masterGain.gain.setValueAtTime(e?0:o._volume,n.ctx.currentTime);for(var t=0;t<o._howls.length;t++)if(!o._howls[t]._webAudio)for(var r=o._howls[t]._getSoundIds(),a=0;a<r.length;a++){var u=o._howls[t]._soundById(r[a]);u&&u._node&&(u._node.muted=!!e||u._muted)}return o},stop:function(){for(var e=this||n,o=0;o<e._howls.length;o++)e._howls[o].stop();return e},unload:function(){for(var e=this||n,o=e._howls.length-1;o>=0;o--)e._howls[o].unload();return e.usingWebAudio&&e.ctx&&void 0!==e.ctx.close&&(e.ctx.close(),e.ctx=null,_()),e},codecs:function(e){return(this||n)._codecs[e.replace(/^x-/,"")]},_setup:function(){var e=this||n;if(e.state=e.ctx?e.ctx.state||"suspended":"suspended",e._autoSuspend(),!e.usingWebAudio)if("undefined"!=typeof Audio)try{var o=new Audio;void 0===o.oncanplaythrough&&(e._canPlayEvent="canplay")}catch(n){e.noAudio=!0}else e.noAudio=!0;try{var o=new Audio;o.muted&&(e.noAudio=!0)}catch(e){}return e.noAudio||e._setupCodecs(),e},_setupCodecs:function(){var e=this||n,o=null;try{o="undefined"!=typeof Audio?new Audio:null}catch(n){return e}if(!o||"function"!=typeof o.canPlayType)return e;var t=o.canPlayType("audio/mpeg;").replace(/^no$/,""),r=e._navigator?e._navigator.userAgent:"",a=r.match(/OPR\/(\d+)/g),u=a&&parseInt(a[0].split("/")[1],10)<33,d=-1!==r.indexOf("Safari")&&-1===r.indexOf("Chrome"),i=r.match(/Version\/(.*?) /),_=d&&i&&parseInt(i[1],10)<15;return e._codecs={mp3:!(u||!t&&!o.canPlayType("audio/mp3;").replace(/^no$/,"")),mpeg:!!t,opus:!!o.canPlayType('audio/ogg; codecs="opus"').replace(/^no$/,""),ogg:!!o.canPlayType('audio/ogg; codecs="vorbis"').replace(/^no$/,""),oga:!!o.canPlayType('audio/ogg; codecs="vorbis"').replace(/^no$/,""),wav:!!(o.canPlayType('audio/wav; codecs="1"')||o.canPlayType("audio/wav")).replace(/^no$/,""),aac:!!o.canPlayType("audio/aac;").replace(/^no$/,""),caf:!!o.canPlayType("audio/x-caf;").replace(/^no$/,""),m4a:!!(o.canPlayType("audio/x-m4a;")||o.canPlayType("audio/m4a;")||o.canPlayType("audio/aac;")).replace(/^no$/,""),m4b:!!(o.canPlayType("audio/x-m4b;")||o.canPlayType("audio/m4b;")||o.canPlayType("audio/aac;")).replace(/^no$/,""),mp4:!!(o.canPlayType("audio/x-mp4;")||o.canPlayType("audio/mp4;")||o.canPlayType("audio/aac;")).replace(/^no$/,""),weba:!(_||!o.canPlayType('audio/webm; codecs="vorbis"').replace(/^no$/,"")),webm:!(_||!o.canPlayType('audio/webm; codecs="vorbis"').replace(/^no$/,"")),dolby:!!o.canPlayType('audio/mp4; codecs="ec-3"').replace(/^no$/,""),flac:!!(o.canPlayType("audio/x-flac;")||o.canPlayType("audio/flac;")).replace(/^no$/,"")},e},_unlockAudio:function(){var e=this||n;if(!e._audioUnlocked&&e.ctx){e._audioUnlocked=!1,e.autoUnlock=!1,e._mobileUnloaded||44100===e.ctx.sampleRate||(e._mobileUnloaded=!0,e.unload()),e._scratchBuffer=e.ctx.createBuffer(1,1,22050);var o=function(n){for(;e._html5AudioPool.length<e.html5PoolSize;)try{var t=new Audio;t._unlocked=!0,e._releaseHtml5Audio(t)}catch(n){e.noAudio=!0;break}for(var r=0;r<e._howls.length;r++)if(!e._howls[r]._webAudio)for(var a=e._howls[r]._getSoundIds(),u=0;u<a.length;u++){var d=e._howls[r]._soundById(a[u]);d&&d._node&&!d._node._unlocked&&(d._node._unlocked=!0,d._node.load())}e._autoResume();var i=e.ctx.createBufferSource();i.buffer=e._scratchBuffer,i.connect(e.ctx.destination),void 0===i.start?i.noteOn(0):i.start(0),"function"==typeof e.ctx.resume&&e.ctx.resume(),i.onended=function(){i.disconnect(0),e._audioUnlocked=!0,document.removeEventListener("touchstart",o,!0),document.removeEventListener("touchend",o,!0),document.removeEventListener("click",o,!0),document.removeEventListener("keydown",o,!0);for(var n=0;n<e._howls.length;n++)e._howls[n]._emit("unlock")}};return document.addEventListener("touchstart",o,!0),document.addEventListener("touchend",o,!0),document.addEventListener("click",o,!0),document.addEventListener("keydown",o,!0),e}},_obtainHtml5Audio:function(){var e=this||n;if(e._html5AudioPool.length)return e._html5AudioPool.pop();var o=(new Audio).play();return o&&"undefined"!=typeof Promise&&(o instanceof Promise||"function"==typeof o.then)&&o.catch(function(){console.warn("HTML5 Audio pool eLine truncated
+/*! Spatial Plugin */
+!function(){"use strict";HowlerGlobal.prototype._pos=[0,0,0],HowlerGlobal.prototype._orientation=[0,0,-1,0,1,0],HowlerGlobal.prototype.stereo=function(e){var n=this;if(!n.ctx||!n.ctx.listener)return n;for(var t=n._howls.length-1;t>=0;t--)n._howls[t].stereo(e);return n},HowlerGlobal.prototype.pos=function(e,n,t){var r=this;return r.ctx&&r.ctx.listener?(n="number"!=typeof n?r._pos[1]:n,t="number"!=typeof t?r._pos[2]:t,"number"!=typeof e?r._pos:(r._pos=[e,n,t],void 0!==r.ctx.listener.positionX?(r.ctx.listener.positionX.setTargetAtTime(r._pos[0],Howler.ctx.currentTime,.1),r.ctx.listener.positionY.setTargetAtTime(r._pos[1],Howler.ctx.currentTime,.1),r.ctx.listener.positionZ.setTargetAtTime(r._pos[2],Howler.ctx.currentTime,.1)):r.ctx.listener.setPosition(r._pos[0],r._pos[1],r._pos[2]),r)):r},HowlerGlobal.prototype.orientation=function(e,n,t,r,o,i){var a=this;if(!a.ctx||!a.ctx.listener)return a;var p=a._orientation;return n="number"!=typeof n?p[1]:n,t="number"!=typeof t?p[2]:t,r="number"!=typeof r?p[3]:r,o="number"!=typeof o?p[4]:o,i="number"!=typeof i?p[5]:i,"number"!=typeof e?p:(a._orientation=[e,n,t,r,o,i],void 0!==a.ctx.listener.forwardX?(a.ctx.listener.forwardX.setTargetAtTime(e,Howler.ctx.currentTime,.1),a.ctx.listener.forwardY.setTargetAtTime(n,Howler.ctx.currentTime,.1),a.ctx.listener.forwardZ.setTargetAtTime(t,Howler.ctx.currentTime,.1),a.ctx.listener.upX.setTargetAtTime(r,Howler.ctx.currentTime,.1),a.ctx.listener.upY.setTargetAtTime(o,Howler.ctx.currentTime,.1),a.ctx.listener.upZ.setTargetAtTime(i,Howler.ctx.currentTime,.1)):a.ctx.listener.setOrientation(e,n,t,r,o,i),a)},Howl.prototype.init=function(e){return function(n){var t=this;return t._orientation=n.orientation||[1,0,0],t._stereo=n.stereo||null,t._pos=n.pos||null,t._pannerAttr={coneInnerAngle:void 0!==n.coneInnerAngle?n.coneInnerAngle:360,coneOuterAngle:void 0!==n.coneOuterAngle?n.coneOuterAngle:360,coneOuterGain:void 0!==n.coneOuterGain?n.coneOuterGain:0,distanceModel:void 0!==n.distanceModel?n.distanceModel:"inverse",maxDistance:void 0!==n.maxDistance?n.maxDistance:1e4,panningModel:void 0!==n.panningModel?n.panningModel:"HRTF",refDistance:void 0!==n.refDistance?n.refDistance:1,rolloffFactor:void 0!==n.rolloffFactor?n.rolloffFactor:1},t._onstereo=n.onstereo?[{fn:n.onstereo}]:[],t._onpos=n.onpos?[{fn:n.onpos}]:[],t._onorientation=n.onorientation?[{fn:n.onorientation}]:[],e.call(this,n)}}(Howl.prototype.init),Howl.prototype.stereo=function(n,t){var r=this;if(!r._webAudio)return r;if("loaded"!==r._state)return r._queue.push({event:"stereo",action:function(){r.stereo(n,t)}}),r;var o=void 0===Howler.ctx.createStereoPanner?"spatial":"stereo";if(void 0===t){if("number"!=typeof n)return r._stereo;r._stereo=n,r._pos=[n,0,0]}for(var i=r._getSoundIds(t),a=0;a<i.length;a++){var p=r._soundById(i[a]);if(p){if("number"!=typeof n)return p._stereo;p._stereo=n,p._pos=[n,0,0],p._node&&(p._pannerAttr.panningModel="equalpower",p._panner&&p._panner.pan||e(p,o),"spatial"===o?void 0!==p._panner.positionX?(p._panner.positionX.setValueAtTime(n,Howler.ctx.currentTime),p._panner.positionY.setValueAtTime(0,Howler.ctx.currentTime),p._panner.positionZ.setValueAtTime(0,Howler.ctx.currentTime)):p._panner.setPosition(n,0,0):p._panner.pan.setValueAtTime(n,Howler.ctx.currentTime)),r._emit("stereo",p._id)}}return r},Howl.prototype.pos=function(n,t,r,o){var i=this;if(!i._webAudio)return i;if("loaded"!==i._state)return i._queue.push({event:"pos",action:function(){i.pos(n,t,r,o)}}),i;if(t="number"!=typeof t?0:t,r="number"!=typeof r?-.5:r,void 0===o){if("number"!=typeof n)return i._pos;i._pos=[n,t,r]}for(var a=i._getSoundIds(o),p=0;p<a.length;p++){var s=i._soundById(a[p]);if(s){if("number"!=typeof n)return s._pos;s._pos=[n,t,r],s._node&&(s._panner&&!s._panner.pan||e(s,"spatial"),void 0!==s._panner.positionX?(s._panner.positionX.setValueAtTime(n,Howler.ctx.currentTime),s._panner.positionY.setValueAtTime(t,Howler.ctx.currentTime),s._panner.positionZ.setValueAtTime(r,Howler.ctx.currentTime)):s._panner.setPosition(n,t,r)),i._emit("pos",s._id)}}return i},Howl.prototype.orientation=function(n,t,r,o){var i=this;if(!i._webAudio)return i;if("loaded"!==i._state)return i._queue.push({event:"orientation",action:function(){i.orientation(n,t,r,o)}}),i;if(t="number"!=typeof t?i._orientation[1]:t,r="number"!=typeof r?i._orientation[2]:r,void 0===o){if("number"!=typeof n)return i._orientation;i._orientation=[n,t,r]}for(var a=i._getSoundIds(o),p=0;p<a.length;p++){var s=i._soundById(a[p]);if(s){if("number"!=typeof n)return s._orientation;s._orientation=[n,t,r],s._node&&(s._panner||(s._pos||(s._pos=i._pos||[0,0,-.5]),e(s,"spatial")),void 0!==s._panner.orientationX?(s._panner.orientationX.setValueAtTime(n,Howler.ctx.currentTime),s._panner.orientationY.setValueAtTime(t,Howler.ctx.currentTime),s._panner.orientationZ.setValueAtTime(r,Howler.ctx.currentTime)):s._panner.setOrientation(n,t,r)),i._emit("orientation",s._id)}}return i},Howl.prototype.pannerAttr=function(){var n,t,r,o=this,i=arguments;if(!o._webAudioLine truncated
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+/*! For license information please see 1995fcd6d39cade7d871.js.LICENSE.txt */
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+     display: -webkit-flex;
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+<!doctype html><html><head><meta charset="UTF-8"/><meta http-equiv="X-UA-Compatible" content="IE=Edge"/><script src="https://appsforoffice.microsoft.com/lib/1.1/hosted/office.js"></script><script defer="defer" src="polyfill.js"></script><script defer="defer" src="commands.js"></script></head><body></body></html>
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+!function(){var e={};e.g=function(){if("object"==typeof globalThis)return globalThis;try{return this||new Function("return this")()}catch(e){if("object"==typeof window)return window}}(),Office.onReady((function(){})),("undefined"!=typeof self?self:"undefined"!=typeof window?window:void 0!==e.g?e.g:void 0).action=function(e){var n={type:Office.MailboxEnums.ItemNotificationMessageType.InformationalMessage,message:"Performed action.",icon:"Icon.80x80",persistent:!0};Office.context.mailbox.item.notificationMessages.replaceAsync("action",n),e.completed()}}();
+//# sourceMappingURL=commands.js.map
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+<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
+<OfficeApp xmlns="http://schemas.microsoft.com/office/appforoffice/1.1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:bt="http://schemas.microsoft.com/office/officeappbasictypes/1.0" xmlns:ov="http://schemas.microsoft.com/office/taskpaneappversionoverrides" xsi:type="TaskPaneApp">
+  <Id>68b6ad53-4f72-42a8-a971-a9643c2f409a</Id>
+  <Version>1.0.0.0</Version>
+  <ProviderName>AllTalk</ProviderName>
+  <DefaultLocale>en-US</DefaultLocale>
+  <DisplayName DefaultValue="AllTalk TTS"/>
+  <Description DefaultValue="Enhance your document creation with AllTalk TTS's advanced text-to-voice functionality. Requires connection to AllTalk server."/>
+  <IconUrl DefaultValue="http://127.0.0.1:7851/api/word_addin/assets/icon-32.png"/>
+  <HighResolutionIconUrl DefaultValue="http://127.0.0.1:7851/api/word_addin/assets/icon-64.png"/>
+  <SupportUrl DefaultValue="https://github.com/erew123/alltalk_tts/issues"/>
+  <AppDomains>
+    <AppDomain>https://www.contoso.com</AppDomain>
+  </AppDomains>
+  <Hosts>
+    <Host Name="Document"/>
+  </Hosts>
+  <DefaultSettings>
+    <SourceLocation DefaultValue="http://127.0.0.1:7851/api/word_addin/taskpane.html"/>
+  </DefaultSettings>
+  <Permissions>ReadWriteDocument</Permissions>
+  <VersionOverrides xmlns="http://schemas.microsoft.com/office/taskpaneappversionoverrides" xsi:type="VersionOverridesV1_0">
+    <Hosts>
+      <Host xsi:type="Document">
+        <DesktopFormFactor>
+          <!-- You can add GetStarted information here if needed -->
+          <FunctionFile resid="Commands.Url"/>
+          <ExtensionPoint xsi:type="PrimaryCommandSurface">
+            <OfficeTab id="TabHome">
+              <Group id="AllTalkGroup">
+                <Label resid="AllTalkGroup.Label"/>
+                <Icon>
+                  <bt:Image size="16" resid="Icon.16x16"/>
+                  <bt:Image size="32" resid="Icon.32x32"/>
+                  <bt:Image size="80" resid="Icon.80x80"/>
+                </Icon>
+                <Control xsi:type="Button" id="AllTalkTaskpaneButton">
+                  <Label resid="AllTalkTaskpaneButton.Label"/>
+                  <Supertip>
+                    <Title resid="AllTalkTaskpaneButton.Label"/>
+                    <Description resid="AllTalkTaskpaneButton.Tooltip"/>
+                  </Supertip>
+                  <Icon>
+                    <bt:Image size="16" resid="Icon.16x16"/>
+                    <bt:Image size="32" resid="Icon.32x32"/>
+                    <bt:Image size="80" resid="Icon.80x80"/>
+                  </Icon>
+                  <Action xsi:type="ShowTaskpane">
+                    <TaskpaneId>ButtonId1</TaskpaneId>
+                    <SourceLocation resid="Taskpane.Url"/>
+                  </Action>
+                </Control>
+              </Group>
+            </OfficeTab>
+          </ExtensionPoint>
+        </DesktopFormFactor>
+      </Host>
+    </Hosts>
+    <Resources>
+      <bt:Images>
+        <bt:Image id="Icon.16x16" DefaultValue="http://127.0.0.1:7851/api/word_addin/assets/icon-16.png"/>
+        <bt:Image id="Icon.32x32" DefaultValue="http://127.0.0.1:7851/api/word_addin/assets/icon-32.png"/>
+        <bt:Image id="Icon.80x80" DefaultValue="http://127.0.0.1:7851/api/word_addin/assets/icon-80.png"/>
+      </bt:Images>
+      <bt:Urls>
+        <!-- Ensure that these URLs are pointing to valid locations -->
+        <bt:Url id="Commands.Url" DefaultValue="http://127.0.0.1:7851/api/word_addin/commands.html"/>
+        <bt:Url id="Taskpane.Url" DefaultValue="http://127.0.0.1:7851/api/word_addin/taskpane.html"/>
+      </bt:Urls>
+      <bt:ShortStrings>
+        <bt:String id="AllTalkGroup.Label" DefaultValue="AllTalk Controls"/>
+        <bt:String id="AllTalkTaskpaneButton.Label" DefaultValue="Open AllTalk"/>
+      </bt:ShortStrings>
+      <bt:LongStrings>
+        <bt:String id="AllTalkTaskpaneButton.Tooltip" DefaultValue="Open the AllTalk task pane to start using the add-in."/>
+      </bt:LongStrings>
+    </Resources>
+  </VersionOverrides>
+</OfficeApp>
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+<!doctype html>
+<html>
+
+<head>
+    <meta charset="UTF-8" />
+    <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
+    <meta name="viewport" content="width=device-width,initial-scale=1">
+    <title>AllTalk TTS Add-in</title>
+    <script src="https://appsforoffice.microsoft.com/lib/1.1/hosted/office.js"></script>
+    <link rel="stylesheet"
+        href="https://static2.sharepointonline.com/files/fabric/office-ui-fabric-core/11.0.0/css/fabric.min.css" />
+    <link href="1fda685b81e1123773f6.css" rel="stylesheet" />
+    <style>
+        label {
+            font-weight: 700;
+            display: block;
+            margin-top: 10px
+        }
+
+        select {
+            width: 100%;
+            padding: 5px
+        }
+
+        .nice-button {
+            background-color: #0078d4;
+            color: #fff;
+            padding: 10px 20px;
+            border: none;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 16px;
+            transition: background-color .3s ease
+        }
+
+        .nice-button:hover {
+            background-color: #005a9e
+        }
+
+        #audioSource {
+            width: 300px;
+            height: 50px
+        }
+
+        .input-container {
+            display: flex;
+            flex-direction: column;
+            margin-right: 10px
+        }
+
+        .input-group {
+            display: flex;
+            justify-content: start;
+            align-items: flex-end
+        }
+
+        .input-group>.input-container {
+            flex: 1
+        }
+
+        input {
+            width: 100%
+        }
+    </style>
+    <script defer="defer" src="polyfill.js"></script>
+    <script defer="defer" src="taskpane.js"></script>
+</head>
+
+<body class="ms-font-m ms-welcome ms-Fabric">
+    <header class="ms-welcome__header ms-bgColor-neutralLighter"><img width="90" height="90"
+            src="http://127.0.0.1:7851/api/word_addin/assets/logo-filled.png" alt="AllTalk TTS" title="AllTalk TTS" />
+        <h1 class="ms-font-su">AllTalk TTS</h1>
+    </header>
+    <main class="ms-welcome__main"><textarea id="text" rows="4" placeholder="Enter text here"
+            style="display:none"></textarea> <label for="voice">Voice:</label> <select id="voice" name="voice"
+            required></select> <label for="language">Language:</label> <select id="language" name="language"
+            value="English" required>
+            <option value="en" selected="selected">English</option>
+            <option value="ar">Arabic</option>
+            <option value="zh-cn">Chinese</option>
+            <option value="cs">Czech</option>
+            <option value="nl">Dutch</option>
+            <option value="fr">French</option>
+            <option value="de">German</option>
+            <option value="hu">Hungarian</option>
+            <option value="it">Italian</option>
+            <option value="ja">Japanese</option>
+            <option value="ko">Korean</option>
+            <option value="pl">Polish</option>
+            <option value="pt">Portuguese</option>
+            <option value="ru">Russian</option>
+            <option value="es">Spanish</option>
+            <option value="tr">Turkish</option>
+        </select><br><audio id="audioSource" controls autoplay></audio><br><button id="sendTextButton"
+            class="nice-button">Send Text to TTS</button><br><br><br><br>
+        <div class="input-group">
+            <div class="input-container"><label for="serverIP">Server IP:</label> <input id="serverIP" name="serverIP"
+                    value="127.0.0.1" required></div>
+            <div class="input-container"><label for="portNumber">Port Number:</label> <input type="number"
+                    id="portNumber" name="portNumber" value="7851" required></div>
+        </div>
+    </main>
+    <script src="1995fcd6d39cade7d871.js"></script>
+</body>
+
+</html>
@@ -0,0 +1,3 @@
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truncated
Author	SHA1	Message	Date
erew123	4f80b39cd0	Update README.md	2024-01-06 20:49:58 +00:00
erew123	332582baea	AllTalk TTS Generator	2024-01-06 20:26:05 +00:00
erew123	2bd7ebc514	Delete templates/TTS_Input_Proof_of_concept.html	2024-01-06 20:24:20 +00:00
erew123	7718bfd3ea	Update admin.html Added Hungarian characters	2024-01-05 13:27:49 +00:00
erew123	002c5ad9db	Update tts_server.py Added Hungarian characters	2024-01-05 13:26:15 +00:00
erew123	2c14847708	Update script.py added Hungarian characters	2024-01-05 13:24:20 +00:00
erew123	3647d69007	Update README.md	2024-01-05 13:18:02 +00:00
erew123	abaa56e8f4	Update admin.html Additional European and Cyrillic characters	2024-01-04 12:17:23 +00:00
erew123	d0dc9f3212	Update tts_server.py Additional European language & Cyrillic characters	2024-01-04 12:16:21 +00:00
erew123	67dcc411ad	Update script.py Extend allowed characters, additional European & Cyrillic characters	2024-01-04 12:13:38 +00:00
erew123	b11a087dc8	Update tts_server.py # Start Uvicorn Webserver host_parameter = params["ip_address"] port_parameter = int(params["port_number"]) Corrected format	2024-01-03 20:57:21 +00:00
erew123	e068115eef	Merge pull request #43 from nicobubulle/improvement Regex improvement for text_filtering	2024-01-03 17:54:15 +00:00
nicobubulle	0ec580539a	Regex improvement Add greedy option to avoid apostrophe being removed Add accentuated character for foreign language	2024-01-03 16:54:25 +01:00
erew123	e9feae12ad	path corrected for word_addin	2024-01-03 10:50:22 +00:00
erew123	e183e3a7bb	Filtering Demo page removed some of the demo filtering as it was stripping too much from the text.	2024-01-03 09:36:33 +00:00
erew123	67d2b1c5ea	Word-Add-in Proof of concept	2024-01-03 01:29:57 +00:00
erew123	f3b44424ef	Update for word add-in	2024-01-03 01:28:56 +00:00
erew123	3b5c4b9b68	added an extra replace for % to percent	2024-01-02 20:37:27 +00:00
erew123	e452a926f2	adding params to demo generate params.ip_address & params.port_number	2024-01-02 20:30:16 +00:00
erew123	b28ecaf43e	added pause button	2024-01-01 21:43:23 +00:00
erew123	ba1328311c	Update README.md	2024-01-01 20:47:32 +00:00
erew123	9e6b9396bc	Corrected Spelling mistakes	2024-01-01 20:30:18 +00:00
erew123	2e40db5fe5	Merge pull request #39 from erew123/dev Dev	2024-01-01 20:05:09 +00:00
erew123	720bf8697f	Merge branch 'main' into dev	2024-01-01 20:05:00 +00:00
erew123	1893d1a130	remove unused style.css file	2024-01-01 19:55:38 +00:00
erew123	8472baf031	Delete style.css	2024-01-01 19:51:23 +00:00
erew123	6cab618706	Update README.md	2024-01-01 18:45:41 +00:00
erew123	ebf3b362f7	Add files via upload	2024-01-01 18:20:44 +00:00
erew123	2dd303f53c	Add files via upload	2024-01-01 18:10:14 +00:00
erew123	df69fb0b07	Merge pull request #36 from rbruels/feature/add-streaming-tts Feature: Add streaming TTS, using the new inference_stream() function from CoquiTTS	2024-01-01 16:58:33 +00:00
erew123	a0d9771fa6	Add files via upload	2024-01-01 16:20:58 +00:00
erew123	a71c46bb98	Merge pull request #35 from rbruels/enhancement/templatize-tts-server-html Enhancement: Templatize tts-server html for ease of editing	2024-01-01 12:41:08 +00:00
erew123	995f317b4f	Merge pull request #37 from erew123/main sync main to dev	2024-01-01 12:36:36 +00:00
Ryan	6ef2de2923	Support streaming of model inference to lower time to first token for long passages. Uses the new CoquiTTS inference_stream() function	2024-01-01 00:27:11 -08:00
Ryan	37c4e86566	Merge branch 'main' into enhancement/templatize-tts-server-html	2023-12-31 15:09:11 -08:00
Ryan	11071b279b	Templatize the tts-server HTML for ease of editing	2023-12-31 15:06:39 -08:00
erew123	5ab7463f35	More narrator shenanigans	2023-12-31 21:35:23 +00:00
erew123	74c9238e3a	Update README.md	2023-12-30 22:15:30 +00:00
erew123	e874e46304	Update README.md	2023-12-30 15:26:02 +00:00
erew123	b32df6ac15	Additional note in finetune re multiple GPU support	2023-12-30 14:45:42 +00:00
erew123	b76dd3561f	Merge pull request #34 from erew123/dev Dev	2023-12-30 14:23:22 +00:00
erew123	60bfbdcabd	Update README.md	2023-12-30 14:12:41 +00:00
erew123	c128d0851b	Updated Finetune	2023-12-30 14:00:36 +00:00
erew123	2f3b7762e4	Update README.md	2023-12-30 13:31:41 +00:00
erew123	0e594f5098	Update .gitignore	2023-12-30 13:30:16 +00:00
erew123	14151d4dda	Update README.md	2023-12-30 11:27:20 +00:00
erew123	94ab21fb15	Update README.md	2023-12-30 11:26:29 +00:00
erew123	1acac3d6c6	Update README.md	2023-12-30 11:23:59 +00:00
erew123	95ec2dc9d2	Merge pull request #32 from erew123/main merge main to dev	2023-12-30 11:11:11 +00:00
erew123	6b93eb6c1e	Update README.md	2023-12-29 23:38:38 +00:00
erew123	838578a042	Update README.md	2023-12-29 23:28:47 +00:00
erew123	2f689f0438	Model loader for Finetuned models Adds a detection routine and additional loader for a finetuned model. Model has to be in /models/trainedmodel/	2023-12-29 23:15:15 +00:00
erew123	022025ded6	Update README.md	2023-12-29 20:57:28 +00:00
erew123	320a7a6aba	Update README.md	2023-12-29 20:51:45 +00:00
erew123	fa82870883	Merge pull request #31 from erew123/dev 3x new API endpoints & Play audio at command prompt/terminal	2023-12-29 20:37:20 +00:00
erew123	a094d4f9da	Completed autoplay at command line Added additional requirement. Lowered the repetition on the start-up warning when its taking a while for the model to load.	2023-12-29 20:31:49 +00:00
erew123	bb028af694	Update README.md	2023-12-29 10:54:02 +00:00
erew123	aeb225ec7c	Update README.md	2023-12-29 10:51:06 +00:00
erew123	06a632459e	Update README.md	2023-12-29 10:44:39 +00:00
erew123	221efbddb4	Supports playback at the command line prompt Added sounddevice as its currently well supported. Allows playback at the terminal/command prompt. Added speakers_xtts.pth to the modeldownload.json as it will be needed for finetuning in future updates of TTS 0.22.0+	2023-12-29 10:42:35 +00:00
erew123	e81a88938a	Completed built-in documentation update For the new API endpoints	2023-12-29 10:11:01 +00:00
erew123	65e257077c	3x new API endpoints /api/voices/ /api/previewvoice/ /api/ready/	2023-12-29 09:56:51 +00:00
erew123	95659d5b7a	Update README.md	2023-12-29 09:55:23 +00:00
erew123	ff57270286	Update README.md	2023-12-29 09:53:02 +00:00
erew123	4fc61ba99a	Merge pull request #30 from erew123/main merge main to dev	2023-12-29 09:08:07 +00:00
erew123	3cd22081f4	Update script.py	2023-12-28 21:05:59 +00:00
erew123	d20ee95e67	Delete finetune/put-voice-samples-in-here/compact.py	2023-12-28 20:38:33 +00:00
erew123	cd47f6b123	Add files via upload	2023-12-28 20:38:12 +00:00
erew123	53a2d0a65e	Update README.md	2023-12-28 19:25:59 +00:00
erew123	13318243e4	Merge pull request #27 from rbruels/patch-1 A few changes/clarifications in the fine-tuning sections	2023-12-28 18:41:06 +00:00
erew123	acc457436a	updated narrator splitting function Improved over what was before. Still more work to do in future though.	2023-12-28 18:39:01 +00:00
erew123	8d80151889	New version of Finetune Simplified the final stages of finetuning. Added model compaction. Added cleaning routines to tidy up after training.	2023-12-28 18:36:51 +00:00
erew123	8b0ade5b9f	compact routine - separate to finetune a mini compact routine for anyone who wants to compact their existing model. This is built into the new version of finetune anyway, but is here just to capture those outlier instances.	2023-12-28 18:34:49 +00:00
Ryan Bruels	9f033e4654	A few changes/clarifications in the fine-tuning sections	2023-12-28 10:28:20 -08:00
erew123	1a2b8571d0	Updated diagnostics to include Path Now includes both Python and environment variables PATH search locations.	2023-12-28 12:05:46 +00:00
erew123	9ab20be294	Update README.md updated a screenshot	2023-12-27 06:49:46 +00:00
erew123	a34cbfdd55	Made Settings link more visble Updated the visibility of the settings & documentation page within Text-gen-webui interface. Minor built in documentation update.	2023-12-27 06:43:28 +00:00
erew123	94f5b781cc	Update Standalone API - race condition fix Applies the same race condition fix for narrator/character that potentially existed, but for the standalone mode API. Will have zero effect for anyone not using the API	2023-12-27 06:08:10 +00:00
erew123	f83e7c3b4e	Update README.md	2023-12-27 06:00:30 +00:00
erew123	607dc89be5	Update README.md Added a section on Running AllTalk as a standalone	2023-12-27 04:44:44 +00:00
erew123	47758c4aeb	Updated diagnostics Pulls Conda and Python environment details.	2023-12-26 18:58:06 +00:00
erew123	26414d4925	Update README.md	2023-12-26 17:30:40 +00:00
erew123	7ad92e7f5d	Update README.md	2023-12-26 17:28:28 +00:00
erew123	e775cef5bd	Update README.md	2023-12-26 17:19:21 +00:00
erew123	ac9ce144a0	Update README.md	2023-12-26 16:47:24 +00:00
erew123	f181c06f89	Update README.md Added a video link to show people how to install AllTalk.	2023-12-26 10:02:17 +00:00
erew123	9fae4692e3	Update README.md	2023-12-26 08:03:24 +00:00
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				`/! regenerator-runtime -- Copyright (c) 2014-present, Facebook, Inc. -- license (MIT): https://github.com/facebook/regenerator/blob/main/LICENSE /`
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				`<!doctype html><html><head><meta charset="UTF-8"/><meta http-equiv="X-UA-Compatible" content="IE=Edge"/><script src="https://appsforoffice.microsoft.com/lib/1.1/hosted/office.js"></script><script defer="defer" src="polyfill.js"></script><script defer="defer" src="commands.js"></script></head><body></body></html>`
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