Updated to deal with Gradio 4.xx versions

Set API Local to use float as Transformers demands it

.github/ISSUE_TEMPLATE/bug-report-support-request.md

@@ -1,6 +1,6 @@
 ---
-name: Support request OR Bug report
+name: Support Request/Issue
-about: Create a report to help us improve
+about: Use this to create a support request or report a bug/issue
 title: ''
 labels: ''
 assignees: ''

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,6 +1,6 @@
 ---
 name: Feature request
-about: Suggest an idea for this project
+about: Use this to create a Feature request
 title: ''
 labels: ''
 assignees: ''

Dockerfile

@@ -1,12 +1,19 @@
-FROM ubuntu:22.04
+FROM nvidia/cuda:12.1.1-devel-ubuntu22.04
 ENV HOST 0.0.0.0
 RUN apt-get update && apt-get upgrade -y \
     && apt-get install -y git build-essential portaudio19-dev \
     python3 python3-pip gcc wget \
+    ocl-icd-opencl-dev opencl-headers clinfo \
+    libclblast-dev libopenblas-dev libaio-dev \
+    && mkdir -p /etc/OpenCL/vendors && echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd \
     && ln -s /usr/bin/python3 /usr/bin/python
+
 WORKDIR /app
 COPY . .
-RUN pip install --no-cache-dir -r requirements_other.txt
+ENV CUDA_DOCKER_ARCH=all
-EXPOSE 7851
+RUN pip install --no-cache-dir --no-deps -r system/requirements/requirements_docker.txt && \
+    pip install --no-cache-dir deepspeed
+
+EXPOSE 7851 7852
 RUN chmod +x launch.sh
 ENTRYPOINT ["sh", "-c", "./launch.sh"]

atsetup.bat

@@ -1,7 +1,7 @@
 @echo off
 cd /D "%~dp0"
-
+setlocal enabledelayedexpansion
-set PATH=%PATH%;%SystemRoot%\system32
+@set "PATH=%PATH%;%SystemRoot%\system32"
 
 :: Generate the ESC character
 for /F %%a in ('echo prompt $E ^| cmd') do set "ESC=%%a"
@@ -25,6 +25,15 @@ set "L_CYAN=%ESC%[96m"
 set "L_WHITE=%ESC%[97m"
 set "RESET=%ESC%[0m"
 
+echo "%CD%"| findstr /C:" " >nul &&  echo. && echo    %L_BLUE%ALLTALK WINDOWS SETUP UTILITY%RESET% && echo. &&  echo. &&  echo    You are trying to install AllTalk in a folder that has a space in the folder path e.g. && echo. && echo       C:\%L_RED%program files%RESET%\alltalk_tts && echo.	&& echo    This causes errors with Conda and Python scripts. Please follow this link for reference: &&  echo. && echo      %L_CYAN%https://docs.anaconda.com/free/working-with-conda/reference/faq/#installing-anaconda%RESET% && echo. && echo    Please use a folder path that has no spaces in it e.g. && echo. && echo       C:\myfiles\alltalk_tts\ && echo. && echo. && pause && goto :end
+
+@rem Check for special characters in installation path
+set "SPCHARMESSAGE="WARNING: Special characters were detected in the installation path!" "         This can cause the installation to fail!""
+echo "%CD%"| findstr /R /C:"[!#\$%&()\*+,;<=>?@\[\]\^`{|}~]" >nul && (
+	call :PrintBigMessage %SPCHARMESSAGE%
+)
+set SPCHARMESSAGE=
+
 @rem Check if curl is available
 curl --version >nul 2>&1
 if "%ERRORLEVEL%" NEQ "0" (
@@ -120,7 +129,7 @@ if "%StandaloneOption%"=="3" goto STReapplyrequirements
 if "%StandaloneOption%"=="4" goto STDeleteCustomStandalone
 if "%StandaloneOption%"=="5" goto STPurgepipcache
 if "%StandaloneOption%"=="8" goto GenerateDiagsStandalone
-if "%StandaloneOption%"=="9" goto End
+if "%StandaloneOption%"=="9" goto EndStandalone
 goto StandaloneMenu
 
 :InstallNvidiaTextGen
@@ -128,6 +137,8 @@ pip install -r system\requirements\requirements_textgen.txt
 if %ERRORLEVEL% neq 0 (
     echo.
     echo    There was an error installing the requirements.
+    echo    Have you started your Text-gen-webui Python environment
+    echo    with cmd_{yourOS} before running atsetup.bat?
     echo    Press any key to return to the menu.
     echo.
     pause
@@ -383,14 +394,14 @@ echo     Downloading and installing PyTorch. This step can take a long time
 echo     depending on your internet connection and hard drive speed. Please
 echo     be patient.
 echo.
-pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121
+pip install torch==2.2.2+cu121 torchaudio>=2.2.2+cu121 --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
 echo Installing other requirements.
 echo.
 pip install -r system\requirements\requirements_standalone.txt
-curl -LO https://github.com/erew123/alltalk_tts/releases/download/DeepSpeed-12.7/deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
+curl -LO https://github.com/erew123/alltalk_tts/releases/download/DeepSpeed-14.0/deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
 echo Installing DeepSpeed...
-pip install deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
+pip install deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
-del deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
+del deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
 
 
 @rem Create start_environment.bat to run AllTalk environment
@@ -524,14 +535,14 @@ echo     Downloading and installing PyTorch. This step can take a long time
 echo     depending on your internet connection and hard drive speed. Please
 echo     be patient.
 echo.
-pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121
+pip install torch==2.2.2+cu121 torchaudio>=2.2.2+cu121 --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
 echo Installing other requirements.
 echo.
 pip install -r system\requirements\requirements_standalone.txt
-curl -LO https://github.com/erew123/alltalk_tts/releases/download/DeepSpeed-12.7/deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
+C:/Windows/system32/curl.exe -LO https://github.com/erew123/alltalk_tts/releases/download/DeepSpeed-14.0/deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
 echo Installing DeepSpeed...
-pip install deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
+pip install deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
-del deepspeed-0.12.7+d058d4b-cp311-cp311-win_amd64.whl
+del deepspeed-0.14.0+ce78a63-cp311-cp311-win_amd64.whl
 if %ERRORLEVEL% neq 0 (
     echo.
     echo    There was an error.
@@ -633,6 +644,16 @@ Echo.
 pause
 goto StandaloneMenu
 
+:EndStandalone
+echo Exiting AllTalk Setup Utility...
+echo.
+Echo    Remember, after installation you can....
+Echo    Run %L_YELLOW%start_alltalk.bat%RESET% to start AllTalk.
+Echo    Run %L_YELLOW%start_finetune.bat%RESET% to start Finetuning.
+Echo    Run %L_YELLOW%start_environment.bat%RESET% to start the AllTalk Python environment.
+Echo.
+exit /b
+
 :End
 echo Exiting AllTalk Setup Utility...
 exit /b

atsetup.sh

@@ -28,6 +28,40 @@ check_curl() {
     fi
 }
 
+# Check if the current directory path contains a space
+containsSpace=false
+currentPath=$(pwd)
+if echo "$currentPath" | grep -q ' '; then
+    containsSpace=true
+fi
+
+if [ "$containsSpace" = true ]; then
+    echo
+    echo -e "    ${L_BLUE}ALLTALK LINUX SETUP UTILITY${NC}"
+    echo
+    echo
+    echo -e "    You are trying to install AllTalk in a folder that has a space in the"
+    echo -e "    folder path e.g."
+    echo 
+    echo -e "       /home/${L_RED}program files${NC}/alltalk_tts"
+    echo 
+    echo -e "    This causes errors with Conda and Python scripts. Please follow this"
+    echo -e "    link for reference:"
+    echo 
+    echo -e "      ${L_CYAN}https://docs.anaconda.com/free/working-with-conda/reference/faq/#installing-anaconda${NC}"
+    echo 
+    echo -e "    Please use a folder path that has no spaces in it e.g." 
+    echo 
+    echo -e "       /home/myfiles/alltalk_tts/"
+    echo 
+    echo
+    read -p "Press Enter to continue..." 
+    exit 1
+else
+    # Continue with the main menu
+    echo "Continue with the main menu."
+fi
+
 # Main Menu
 main_menu() {
     while true; do
@@ -291,7 +325,7 @@ install_custom_standalone() {
     echo "    Downloading and installing PyTorch. This step can take a long time"
     echo "    depending on your internet connection and hard drive speed. Please"
     echo "    be patient."
-    pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121
+    pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
     echo
     echo "    Installing additional requirements."
     echo
@@ -469,7 +503,7 @@ reapply_standalone() {
     echo "    Downloading and installing PyTorch. This step can take a long time"
     echo "    depending on your internet connection and hard drive speed. Please"
     echo "    be patient."
-    pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121
+    pip install torch>=2.2.1+cu121 torchaudio>=2.2.1+cu121 --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
     echo
     echo "    Installing additional requirements."
     echo

docker-compose.yml

@@ -1,13 +1,21 @@
 version: '3.8'
 services:
   alltalk-tts:
-    image: erew123/alltalk_tts:cpu
+    image: alltalk:v1-9c
     restart: unless-stopped
     ports:
       - "7851:7851"
+      - "7852:7852"
     volumes:
       - ./outputs:/app/outputs/
       - ./models:/app/models/
       - ./voices:/app/voices/
       - ./finetune/put-voice-samples-in-here:/app/finetune/put-voice-samples-in-here
       - ./dockerconfig.json:/app/confignew.json
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [ gpu ]

finetune.py

@@ -291,12 +291,12 @@ def create_temporary_file(folder, suffix=".wav"):
     unique_filename = f"custom_tempfile_{int(time.time())}_{random.randint(1, 1000)}{suffix}"
     return os.path.join(folder, unique_filename)
 
-def format_audio_list(target_language, whisper_model, out_path, eval_split_number, gradio_progress=progress):
+def format_audio_list(target_language, whisper_model, out_path, eval_split_number, speaker_name_input, gradio_progress=progress):
     pfc_check_fail()
     audio_files = [os.path.join(audio_folder, file) for file in os.listdir(audio_folder) if file.endswith(('.mp3', '.flac', '.wav'))]
     buffer=0.2
     eval_percentage = eval_split_number / 100.0
-    speaker_name="coqui"
+    speaker_name=speaker_name_input
     audio_total_size = 0
     os.makedirs(out_path, exist_ok=True)
     temp_folder = os.path.join(out_path, "temp")  # Update with your folder name
@@ -533,7 +533,7 @@ def basemodel_or_finetunedmodel_choice(value):
     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):
+def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, learning_rate, output_path, max_audio_length=255995):
     pfc_check_fail()
     #  Logging parameters
     RUN_NAME = "XTTS_FT"
@@ -657,7 +657,7 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
         optimizer="AdamW",
         optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
         optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2},
-        lr=5e-06,  # learning rate
+        lr=learning_rate,  # learning rate
         lr_scheduler="MultiStepLR",
         # it was adjusted accordly for the new step scheme
         lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1},
@@ -735,6 +735,9 @@ def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
     config.load_json(xtts_config)
     XTTS_MODEL = Xtts.init_from_config(config)
     print("[FINETUNE] \033[94mStarting Step 3\033[0m Loading XTTS model!")
+    print(xtts_checkpoint)
+    print(xtts_vocab)
+    print(xtts_speakers_pth)
     XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False, speaker_file_path=xtts_speakers_pth)
     if torch.cuda.is_available():
         XTTS_MODEL.cuda()
@@ -773,18 +776,22 @@ def get_available_voices(minimum_size_kb=1200):
     ]
     return sorted([str(file) for file in voice_files])  # Return full path as string
 
+def find_best_models(directory):
+    """Find files named 'best_model.pth' in the given directory."""
+    return [str(file) for file in Path(directory).rglob("best_model.pth")]
+
 def find_models(directory, extension):
     """Find files with a specific extension in the given directory."""
-    return [file for file in Path(directory).rglob(f"*.{extension}")]
+    return [str(file) for file in Path(directory).rglob(f"*.{extension}")]
 
 def find_jsons(directory, filename):
     """Find files with a specific filename in the given directory."""
-    return list(Path(directory).rglob(filename))
+    return [str(file) for file in Path(directory).rglob(filename)]
 
 # Your main directory
 main_directory = Path(this_dir) / "finetune" / "tmp-trn"
-# XTTS checkpoint files (*.pth)
+# XTTS checkpoint files (best_model.pth)
-xtts_checkpoint_files = find_models(main_directory, "pth")
+xtts_checkpoint_files = find_best_models(main_directory)
 # XTTS config files (config.json)
 xtts_config_files = find_jsons(main_directory, "config.json")
 # XTTS vocab files (vocab.json)
@@ -800,68 +807,70 @@ def find_latest_best_model(folder_path):
     latest_file = max(files, key=os.path.getctime, default=None)
     return latest_file
 
-def compact_model():
+def compact_model(xtts_checkpoint_copy):
     this_dir = Path(__file__).parent.resolve()
-
+    print("THIS DIR:", this_dir)
-    best_model_path_str = find_latest_best_model(this_dir / "finetune" / "tmp-trn" / "training")
+    best_model_path_str = str(xtts_checkpoint_copy)  # Convert to string
-
+    print("best_model_path_str", best_model_path_str)
+    
     # Check if the best model file exists
-    if best_model_path_str is None:
+    if not best_model_path_str:
         print("[FINETUNE] No trained model was found.")
         return "No trained model was found."
-
+    
-    # Convert model_path_str to Path
+    print(f"[FINETUNE] Best model path: {best_model_path_str}")
-    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"))
+        checkpoint = torch.load(best_model_path_str, map_location=torch.device("cpu"))
+        print(f"[FINETUNE] Checkpoint loaded: {best_model_path_str}")
     except Exception as e:
         print("[FINETUNE] Error loading checkpoint:", e)
         raise
+    
+    # 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)
 
     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")
+    torch.save(checkpoint, str(target_dir / "model.pth"))  # Convert to string
 
     # Specify the files you want to copy
-    files_to_copy = ["vocab.json", "config.json", "dvae.pth", "mel_stats.pth","speakers_xtts.pth", ]
+    files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth", "dvae.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))
+        shutil.copy(str(src_path), str(dest_path))  # Convert to string
-
+    
     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))
+            shutil.copy(str(file_path), str(target_wavs_dir / file_path.name))  # Convert to string
     
     print("[FINETUNE] Model copied to '/models/trainedmodel/'")
     return "Model copied to '/models/trainedmodel/'"
 
 
-def compact_lastfinetuned_model():
+def compact_lastfinetuned_model(xtts_checkpoint_copy):
     this_dir = Path(__file__).parent.resolve()
 
-    best_model_path_str = find_latest_best_model(this_dir / "finetune" / "tmp-trn" / "training")
+    best_model_path_str = xtts_checkpoint_copy
+    print(f"[FINETUNE] Best model path: {best_model_path_str}")
 
     # Check if the best model file exists
     if best_model_path_str is None:
@@ -880,21 +889,21 @@ def compact_lastfinetuned_model():
 
     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)
 
+    for key in list(checkpoint["model"].keys()):
+        if "dvae" in key:
+            del checkpoint["model"][key]
+
     # 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", "speakers_xtts.pth",]
+    files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth", "dvae.pth",]
 
     for file_name in files_to_copy:
         src_path = this_dir / base_path / base_model_path / file_name
@@ -916,15 +925,16 @@ def compact_lastfinetuned_model():
     return "Model copied to '/models/lastfinetuned/'"
 
 
-def compact_legacy_model():
+def compact_custom_model(xtts_checkpoint_copy, folder_path):
     this_dir = Path(__file__).parent.resolve()
 
-    best_model_path_str = os.path.join(this_dir, "finetune", "best_model.pth")
+    best_model_path_str = xtts_checkpoint_copy
+    print(f"[FINETUNE] Best model path: {best_model_path_str}")
 
     # Check if the best model file exists
-    if not os.path.exists(best_model_path_str):
+    if best_model_path_str is None:
-        print("[FINETUNE] No model called best_model.pth was found in /finetune/")
+        print("[FINETUNE] No trained model was found.")
-        return "No model called best_model.pth was found in /finetune/"
+        return "No trained model was found."
 
     # Convert model_path_str to Path
     best_model_path = Path(best_model_path_str)
@@ -938,21 +948,41 @@ def compact_legacy_model():
 
     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"
+    target_dir = this_dir / "models" / folder_path
 
     # Create the target directory if it doesn't exist
     target_dir.mkdir(parents=True, exist_ok=True)
 
+    for key in list(checkpoint["model"].keys()):
+        if "dvae" in key:
+            del checkpoint["model"][key]
+
     # Save the modified checkpoint in the target directory
     torch.save(checkpoint, target_dir / "model.pth")
 
-    print("[FINETUNE] model.pth created in '/finetune/'")
+    # Specify the files you want to copy
-    return "model.pth created in '/finetune/'"
+    files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth", "dvae.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/",folder_path,"/")
+    return f"Model copied to '/models/{folder_path}/'"
+
 
 def delete_training_data():
     # Define the folder to be deleted
@@ -1264,6 +1294,9 @@ if __name__ == "__main__":
                 ### 🟥 <u>Important Note - Windows - "UserWarning: huggingface_hub cache-system uses symlinks."</u>
                 ◽ This error is caused by Huggingfaces download software downloading the Whisper model. If you get this error, please restart your Windows command prompt with "Run as Administrator" and restart finetuning.<br>
                 ◽ This should only occur the 1st time it downloads the Whisper model. [Huggingface Reference here](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache#limitations)
+                ### 🟥 <u>Important Note - Language support.</u>
+                ◽ Although I have done my best to help automate this step, the Whisper model is not great with all languages and may fail to build your training data correctly. The Large-v3 may be better at certain languages.<br>
+                ◽ You can find information about the Whisper model [here](https://github.com/openai/whisper?tab=readme-ov-file#available-models-and-languages) and you can find data about manually building training data [here](https://docs.coqui.ai/en/latest/formatting_your_dataset.html), as well as details below about the file structure this step performs.<br>
                 ### 🟦 <u>What you need to do</u>
                 ◽ 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)<br>
                 ◽ Place your audio files in <span style="color: #3366ff;">{str(audio_folder)}</span>          
@@ -1276,6 +1309,7 @@ if __name__ == "__main__":
                 ◽ Whisper is making a best effort to find spoken audio and break it down into smaller audio files. The content of these audio files is then transcribed into CSV fles (which you can edit in Excel or similar).<br>
                 ◽ These files (audio and CSV) are used at the next step to train the model "this is what the audio sounds like and these are the words being spoken".<br>
                 ◽ If you wish to manually look at the CSV files before running the next step, you are welcome to do so and edit them as necessary. The greater the accuracy of the text the better the training will be.<br>
+                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ◽ <span style="color: #3366ff;">/alltalk_tts/finetune/tmp-trn/lang.txt</span> Contains a two digit langauage code e.g. `en`, `fr` etc.<br>
                 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ◽ <span style="color: #3366ff;">/alltalk_tts/finetune/tmp-trn/metadata_train.csv</span> Contains the list of wavs to text that will be used. This is what the model is trained with.<br>
                 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ◽ <span style="color: #3366ff;">/alltalk_tts/finetune/tmp-trn/metadata_eval.csv</span> Lists the evaluation wav to text, used to generate TTS while training and evaluate the quality of generated TTS to the original sample.<br>
                 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ◽ <span style="color: #3366ff;">/alltalk_tts/finetune/tmp-trn/wavs/</span> These are the wavs split out from your original training samples and match the contents of the CSV files.<br>
@@ -1334,6 +1368,11 @@ if __name__ == "__main__":
                     maximum=95,  # Maximum value
                     step=1,  # Increment step
                 )
+                speaker_name_input = gr.Textbox(
+                label="The name of the speaker/person you are training",
+                value="personsname",
+                visible=True,
+            )
             progress_data = gr.Label(
                 label="Progress:"
             )
@@ -1345,14 +1384,15 @@ if __name__ == "__main__":
 
             prompt_compute_btn = gr.Button(value="Step 1 - Create dataset")
         
-            def preprocess_dataset(language,  whisper_model, out_path, eval_split_number, progress=gr.Progress(track_tqdm=True)):
+            def preprocess_dataset(language, whisper_model, out_path, eval_split_number, speaker_name_input, progress=gr.Progress(track_tqdm=True)):
                 clear_gpu_cache()
                 test_for_audio_files = [file for file in os.listdir(audio_folder) if any(file.lower().endswith(ext) for ext in ['.wav', '.mp3', '.flac'])]
                 if not test_for_audio_files:
                     return "I cannot find any mp3, wav or flac files in the folder called 'put-voice-samples-in-here'", "", ""
                 else:
                     try:
-                        train_meta, eval_meta, audio_total_size = format_audio_list(target_language=language, whisper_model=whisper_model, out_path=out_path, eval_split_number=eval_split_number, gradio_progress=progress)
+
+                        train_meta, eval_meta, audio_total_size = format_audio_list(target_language=language, whisper_model=whisper_model, out_path=out_path, eval_split_number=eval_split_number, speaker_name_input=speaker_name_input, gradio_progress=progress)
                     except:
                         traceback.print_exc()
                         error = traceback.format_exc()
@@ -1373,111 +1413,206 @@ if __name__ == "__main__":
 #### GRADIO STEP 2 ####
 #######################
         with gr.Tab("💻 Step 2 - Training"):
-
+            with gr.Tab("Training"):
-            gr.Markdown(
-                f"""
-                ### 💻 <u>Training</u><br>                
-                ### 🟦 <u>What you need to do</u>
-                ◽ 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 "Create Dataset" again.<br>
-                ◽ The default settings below are the suggested settings for most purposes, however you may choose to alter them depending on your specific use case.<br>
-                ◽ There are no absolute correct settings for training. It will vary based on:<br>
-                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- What you are training (A human voice, A cartoon voice, A new language entirely etc)<br>
-                &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- How much audio you have (you may want less or more eval or epochs)<br>
-                ◽ The key indicator of sufficient model training is whether it sounds right to you. Coqui suggests beginning with the base settings for training. If the resulting audio doesn't meet your expectations, additional training sessions may be necessary.<br>
-                ◽ **Number of epochs:** Defines how many times the entire dataset is passed through the neural network; more epochs can improve learning but increase the risk of memorizing the training data.<br>
-                ◽ **Batch size:** Sets how many training examples are used in one iteration. Larger sizes speed up training but require more computational memory.<br>
-                ◽ **Grad(ient) accumulation steps:** Allows larger batches by spreading the computation across multiple steps, enabling training with limited hardware resources.<br>
-                ◽ **Max permitted audio size in seconds:** Limits the length of audio that the model processes. Any audio exceeding this duration will be truncated to fit the model's input constraints.<br>
-                ### 🟨 <u>What this step is doing</u><br>
-                ◽ 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 e.g. listen to this audio file and this is what is being said in it, so learn to reproduce it.<br>
-                ### 🟩 <u>How long will this take?</u><br>
-                ◽ 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.<br>
-                ◽ Look at your command prompt/terminal window if you want to see what it is doing.<br>
-                """
-            )
-            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"""
+                    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.
+                    ### 💻 <u>Training</u><br>
-                """
+                    ### 🟥 <u>Important Note - Language support.</u>
+                    ◽ If this step is failing/erroring you may wish to check your training data was created correctly (Detailed in Step 1), confirming that wav files have been generated and your `metadata_train.csv` and `metadata_eval.csv` files have been populated.<br>             
+                    ### 🟦 <u>What you need to do</u>
+                    ◽ 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 "Create Dataset" again.<br>
+                    ◽ The default settings below are the suggested settings for most purposes, however you may choose to alter them depending on your specific use case.<br>
+                    ◽ There are no absolute correct settings for training. It will vary based on:<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- What you are training (A human voice, A cartoon voice, A new language entirely etc)<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- How much audio you have (you may want less or more eval or epochs)<br>
+                    ◽ The key indicator of sufficient model training is whether it sounds right to you. Coqui suggests beginning with the base settings for training. If the resulting audio doesn't meet your expectations, additional training sessions may be necessary.<br>
+                    ### 🟨 <u>What this step is doing</u><br>
+                    ◽ 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 e.g. listen to this audio file and this is what is being said in it, so learn to reproduce it.<br>
+                    ### 🟩 <u>How long will this take?</u><br>
+                    ◽ 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.<br>
+                    ◽ Look at your command prompt/terminal window if you want to see what it is doing.<br>
+                    """
+                )
+                with gr.Row():
+                    train_csv = gr.Textbox(
+                        label="Train CSV:",
+                        scale=2,
+                    )
+                    eval_csv = gr.Textbox(
+                        label="Eval CSV:",
+                        scale=2,
+                    )
+                    learning_rates = gr.Dropdown(
+                        value=5e-6,
+                        label="Learning Rate",
+                        choices=[
+                            ("1e-6", 1e-6),
+                            ("5e-6", 5e-6),
+                            ("1e-5", 1e-5),
+                            ("5e-5", 5e-5),
+                            ("1e-4", 1e-4),
+                            ("5e-4", 5e-4),
+                            ("1e-3", 1e-3),
+                        ],
+                        type="value",
+                        allow_custom_value=True,
+                        scale=0,
+                    )
+                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=1,
+                        maximum=32,
+                        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.
+                    """
+                    )
+
+                progress_train = gr.Label(
+                    label="Progress:"
+                )
+                logs_tts_train = gr.Textbox(
+                    label="Logs:",
+                    interactive=False,
+                )
+                demo.load(read_logs, None, logs_tts_train, every=1)
+                train_btn = gr.Button(value="Step 2 - Run the training")
+
+                def train_model(language, train_csv, eval_csv, learning_rates, num_epochs, batch_size, grad_acumm, output_path, max_audio_length):
+                    clear_gpu_cache()
+                    if not train_csv or not eval_csv:
+                        return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
+                    try:
+                        # convert seconds to waveform frames
+                        max_audio_length = int(max_audio_length * 22050)
+                        learning_rate = float(learning_rates)  # Convert the learning rate value to a float
+                        config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, learning_rate, output_path=str(output_path), max_audio_length=max_audio_length)
+                    except:
+                        traceback.print_exc()
+                        error = traceback.format_exc()
+                        return f"The training was interrupted due an error !! Please check the console to check the full error message! \n Error summary: {error}", "", "", "", ""
+
+                    # copy original files to avoid parameters changes issues
+                    shutil.copy(config_path, exp_path)
+                    shutil.copy(vocab_file, exp_path)
+
+                    ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
+                    print("[FINETUNE] Model training done. Move to Step 3")
+                    clear_gpu_cache()
+                    return "Model training done. Move to Step 3", config_path, vocab_file, ft_xtts_checkpoint, speaker_wav
+            with gr.Tab("Info - Grad Accumulation"):
+                                gr.Markdown(
+                    f"""
+                    ### 🟩 <u>Grad Accumulation</u><br>
+                    ◽ Gradient accumulation is a technique that allows you to simulate a larger batch size without actually increasing the memory consumption. Here's how it works:<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ Instead of updating the model's parameters after every batch, gradient accumulation enables you to process multiple batches and accumulate their gradients.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ By setting steps greater than 1, you can process multiple batches before a single optimization step e.g if steps is set to 4, the gradients will be accumulated over 4 batches before updating the model's parameters.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ This means that you can effectively use a larger batch size while keeping the actual batch size per iteration smaller, thereby reducing the memory footprint.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ Increasing the steps allows you to find a balance between memory consumption and computational efficiency. You can process more examples per optimization step without exceeding the available memory.<br><br>
+                    ◽ However, it's important to note that increasing the gradient accumulation steps does have an impact on the training process:<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ Since the model's parameters are updated less frequently (every grad_accum_steps batches), the training dynamics may be slightly different compared to updating the model after every batch.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ You may need to adjust the learning rate and other hyperparameters accordingly to compensate for the less frequent updates. Typically, you can increase the learning rate slightly when using gradient accumulation.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ The training progress will be slower in terms of the number of optimization steps per epoch, as the model updates occur less frequently.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ However, the overall training time may still be reduced compared to using a smaller batch size without gradient accumulation, as it allows for better utilization of GPU resources.<br>
+                    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◽ To start, you can try setting steps to a value like 4 and see if it resolves the OOM error. If the error persists, you can experiment with higher values until you find a balance that works for your specific setup.
+                    """
+                )
+            with gr.Tab("Info - Batch Size"):
+                                gr.Markdown(
+                    f"""
+                    ### 🟩 <u>Batch Size</u><br>
+                    ◽ Batch size refers to the number of training examples processed in a single iteration (forward and backward pass) before updating the model's parameters.<br>
+                    ◽ A larger batch size allows for more efficient computation and utilization of hardware resources, especially when using GPUs.<br>
+                    ◽ However, increasing the batch size also increases the memory consumption during training, as more examples need to be stored in memory.<br>
+                    ◽ If the batch size is too large, it can lead to out-of-memory (OOM) errors, especially when training on GPUs with limited memory.<br>
+                    ◽ On the other hand, using a smaller batch size can result in slower training and may require more iterations to converge.<br>
+                    ◽ The optimal batch size depends on various factors, such as the model architecture, available hardware resources, and the specific problem being solved.<br>
+                    ◽ It's common to experiment with different batch sizes to find a balance between training speed and memory efficiency.<br>
+                    ◽ If you encounter OOM errors, try reducing the batch size until the training can proceed without memory issues.<br>
+                    ◽ Keep in mind that the batch size also affects the model's generalization and convergence behavior, so it's important to monitor the model's performance while adjusting the batch size.
+                    """
+                )
+            with gr.Tab("Info - Learning Rate"):
+                                gr.Markdown(
+                    f"""
+                    ### 🟩 <u>Learning Rate</u><br>
+                    ◽ 1e-6: Very small learning rate, slow but stable learning.<br>
+                    ◽ 5e-6: Small learning rate, slow but stable learning.<br>
+                    ◽ 1e-5: Moderate learning rate, balanced between stability and convergence speed.<br>
+                    ◽ 5e-5: Higher learning rate, faster convergence but potential instability.<br>
+                    ◽ 1e-4: High learning rate, faster convergence but increased risk of instability.<br>
+                    ◽ 5e-4: Very high learning rate, fast convergence but higher risk of instability.<br>
+                    ◽ 1e-3: Extremely high learning rate, very fast convergence but high risk of instability.<br><br>
+                    The optimal learning rate depends on the model architecture, dataset, and other hyperparameters.
+                    """
+                )
+            with gr.Tab("Info - Epochs"):
+                                gr.Markdown(
+                    f"""
+                    ### 🟩 <u>Epochs</u><br>
+                    ◽ An epoch represents a single pass through the entire training dataset during the training process.<br>
+                    ◽ In each epoch, the model sees and learns from all the training examples once.<br>
+                    ◽ The number of epochs determines how many times the model will iterate over the complete training dataset.<br>
+                    ◽ Increasing the number of epochs allows the model to learn more from the data and potentially improve its performance.<br>
+                    ◽ However, training for too many epochs can lead to overfitting, where the model becomes too specialized to the training data and fails to generalize well to unseen data.<br>
+                    ◽ The optimal number of epochs depends on factors such as the complexity of the problem, the size of the dataset, and the model's capacity.<br>
+                    ◽ It's common to use techniques like early stopping or validation monitoring to determine the appropriate number of epochs.<br>
+                    ◽ Early stopping involves monitoring the model's performance on a validation set and stopping the training when the performance starts to degrade, indicating potential overfitting.<br>
+                    ◽ Validation monitoring involves evaluating the model's performance on a separate validation set after each epoch and selecting the model checkpoint that performs best on the validation set.<br>
+                    ◽ It's recommended to start with a reasonable number of epochs and monitor the model's performance to determine if more epochs are needed or if early stopping should be applied
+                    """
+                )
+            with gr.Tab("Info - Max Audio"):
+                                gr.Markdown(
+                    f"""
+                    ### 🟩 <u>Max Permitted Audio Size (in seconds)</u><br>
+                    ◽ The max permitted audio size determines the maximum duration of audio files that can be used as input to the model during training and inference.<br>
+                    ◽ It is specified in seconds and represents the longest audio clip that the model can process.<br>
+                    ◽ Limiting the audio size helps to control the memory usage and computational requirements of the model.<br>
+                    ◽ If the audio files in your dataset vary in length, it's important to consider the max permitted audio size to ensure that all files can be processed effectively.<br>
+                    ◽ Audio files longer than the max permitted size will typically be truncated or split into smaller segments before being fed into the model.<br>
+                    ◽ The choice of max permitted audio size depends on the specific requirements of your application and the available hardware resources.<br>
+                    ◽ Increasing the max permitted audio size allows the model to handle longer audio clips but also increases the memory consumption and computational burden.<br>
+                    ◽ If you encounter memory issues or slow processing times, you may need to reduce the max permitted audio size to find a suitable balance.<br>
+                    ◽ It's important to consider the nature of your audio data and choose a max permitted audio size that captures the relevant information while being computationally feasible.
+                    """
                 )
 
-            progress_train = gr.Label(
+                
-                label="Progress:"
-            )
-            logs_tts_train = gr.Textbox(
-                label="Logs:",
-                interactive=False,
-            )
-            demo.load(read_logs, None, logs_tts_train, every=1)
-            train_btn = gr.Button(value="Step 2 - Run the training")
-
-            def train_model(language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path, max_audio_length):
-                clear_gpu_cache()
-                if not train_csv or not eval_csv:
-                    return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
-                try:
-                    # convert seconds to waveform frames
-                    max_audio_length = int(max_audio_length * 22050)
-                    config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path=str(output_path), max_audio_length=max_audio_length)
-                except:
-                    traceback.print_exc()
-                    error = traceback.format_exc()
-                    return f"The training was interrupted due an error !! Please check the console to check the full error message! \n Error summary: {error}", "", "", "", ""
-
-                # copy original files to avoid parameters changes issues
-                shutil.copy(config_path, exp_path)
-                shutil.copy(vocab_file, exp_path)
-
-                ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
-                print("[FINETUNE] Model training done. Move to Step 3")
-                clear_gpu_cache()
-                return "Model training done. Move to Step 3", config_path, vocab_file, ft_xtts_checkpoint, speaker_wav
-            
 #######################
 #### GRADIO STEP 3 ####
 #######################
@@ -1572,7 +1707,7 @@ if __name__ == "__main__":
                         refresh_button = create_refresh_button(
                             [xtts_checkpoint, xtts_config, xtts_vocab, speaker_reference_audio],
                             [
-                                lambda: {"choices": find_models(main_directory, "pth"), "value": ""},
+                                lambda: {"choices": find_best_models(main_directory), "value": ""},
                                 lambda: {"choices": find_jsons(main_directory, "config.json"), "value": ""},
                                 lambda: {"choices": find_jsons(main_directory, "vocab.json"), "value": ""},
                                 lambda: {"choices": get_available_voices(), "value": ""},
@@ -1605,7 +1740,8 @@ if __name__ == "__main__":
                 ◽ **Note:** Sample wav files will be copied into the model folder. You will need to MANUALLY copy/move the WAV file(s) you want to use to the AllTalk voices folder, to make them available within the interface.<br>
                 ◽ **Note:** Text-generation-webui users, <span style="color: #3366ff;">OPTION A - Compact and move model to '/trainedmodel/'</span>. will make the model available for use within Text-gen-webui.<br>
                 ### 🟩 <u>Using your Finetuned model as you main model</u>
-                ◽ If you wish to use the model you have just fintuned as your main model, you will need to MANUALLY copy it over the base model in <span style="color: #3366ff;">/models/trainedmodel/</span> after you have used Option A or B.<br>
+                ◽ **Currently** only Text-gen-webui and SillyTavern have loaders built for loading models in the <span style="color: #3366ff;">'/finetuned/'</span> folder. So if you wish to use your finetuned model as elsewhere in AllTalk please copy it over your base model e.g. copy your model over the top of <span style="color: #3366ff;">/models/xttsv2_2.0.2/</span><br>
+                ◽ If you wish to use the model you have just fintuned as your main model, you will need to MANUALLY copy it over the base model e.g copy it over <span style="color: #3366ff;">/models/xttsv2_2.0.2/</span> after you have used Option A or B.<br>
                 ◽ Should you ever wish to revert your model back to the base model, you can delete the files in <span style="color: #3366ff;">/models/xttsv2_2.0.2/</span> and new copies will be re-downloaded when AllTalk starts up.<br>
                 ### 🟨 <u>Clearing up disk space</u>
                 ◽ If you are not going to train anything again, you can delete the whisper model from inside of your huggingface cache (3GB approx) <br>
@@ -1617,6 +1753,21 @@ if __name__ == "__main__":
             final_progress_data = gr.Label(
                 label="Progress:"
             )
+            with gr.Row():
+                xtts_checkpoint_copy = gr.Dropdown(
+                    [str(file) for file in xtts_checkpoint_files],
+                    label="XTTS checkpoint path (Select the model you want to copy over):",
+                    value="",
+                    allow_custom_value=True,
+                    scale=3,
+                )
+                # Create refresh button
+                refresh_button = create_refresh_button(
+                    [xtts_checkpoint_copy,],
+                    [
+                        lambda: {"choices": find_best_models(main_directory), "value": ""},
+                    ],
+                    elem_class="refresh-button-class")
             with gr.Row():
                 compact_btn = gr.Button(value="OPTION A - Compact and move model to '/trainedmodel/'")
                 gr.Markdown(
@@ -1631,6 +1782,9 @@ if __name__ == "__main__":
                 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():
+                compact_custom_btn = gr.Button(value="OPTION C - Compact and move model to a folder name of your choosing")
+                folder_path = gr.Textbox(label="Enter a new folder name (will be sub the models folder)", lines=1, value="mycustomfolder")
             with gr.Row():
                 delete_training_btn = gr.Button(value="Delete generated training data")
                 gr.Markdown(
@@ -1653,6 +1807,7 @@ if __name__ == "__main__":
                     whisper_model,
                     out_path,
                     eval_split_number,
+                    speaker_name_input,
                 ],
                 outputs=[
                     progress_data,
@@ -1668,6 +1823,7 @@ if __name__ == "__main__":
                     lang,
                     train_csv,
                     eval_csv,
+                    learning_rates,
                     num_epochs,
                     batch_size,
                     grad_acumm,
@@ -1695,14 +1851,20 @@ if __name__ == "__main__":
                     speaker_reference_audio,
                 ],
                 outputs=[progress_gen, tts_output_audio, reference_audio],
-            )
+            )    
-            
             compact_btn.click(
                 fn=compact_model,
+                inputs=[xtts_checkpoint_copy],
                 outputs=[final_progress_data],
             )
             compact_lastfinetuned_btn.click(
                 fn=compact_lastfinetuned_model,
+                inputs=[xtts_checkpoint_copy],
+                outputs=[final_progress_data],
+            )
+            compact_custom_btn.click(
+                fn=compact_custom_model,
+                inputs=[xtts_checkpoint_copy, folder_path],
                 outputs=[final_progress_data],
             )
             delete_training_btn.click(

modeldownload.py

@@ -1,10 +1,13 @@
 import os
+import sys
 from pathlib import Path
 import requests
+from requests.exceptions import ConnectionError
 from tqdm import tqdm
 import importlib.metadata as metadata
 import json
 from packaging import version
+from datetime import datetime
 
 #################################################################
 #### LOAD PARAMS FROM confignew.json - REQUIRED FOR BRANDING ####
@@ -12,14 +15,12 @@ from packaging import version
 # STARTUP VARIABLE - Create "this_dir" variable as the current script directory
 this_dir = Path(__file__).parent.resolve()
 
-
 # load standard config file in and get settings
 def load_config(file_path):
     with open(file_path, "r") as config_file:
         config = json.load(config_file)
     return config
 
-
 config_file_path = this_dir / "confignew.json"
 params = load_config(config_file_path)
 
@@ -37,12 +38,8 @@ if modeldownload_config_file_path.exists():
     files_to_download = settings.get("files_to_download", {})
 else:
     # Default settings if the JSON file doesn't exist or is empty
-    print(
+    print(f"[{params['branding']}Startup] \033[91mWarning\033[0m modeldownload.json is missing so please re-download it and save it in the coquii_tts main folder.")
-        f"[{params['branding']}Startup] \033[91mWarning\033[0m modeldownload.json is missing so please re-download it and save it in the coquii_tts main folder."
+    print(f"[{params['branding']}Startup] \033[91mWarning\033[0m API Local and XTTSv2 Local will error unless this is corrected.")
-    )
-    print(
-        f"[{params['branding']}Startup] \033[91mWarning\033[0m API Local and XTTSv2 Local will error unless this is corrected."
-    )
 
 # Read the version specifier from requirements_nvidia.txt
 with open(this_dir / "system" / "requirements" / "requirements_standalone.txt", "r") as req_file:
@@ -55,16 +52,12 @@ for req in requirements:
         break
 
 if tts_version_required is None:
-    raise ValueError(
+    raise ValueError(f"[{params['branding']}Startup] \033[91mWarning\033[0m Could not find TTS version specifier in requirements file")
-        f"[{params['branding']}Startup] \033[91mWarning\033[0m Could not find TTS version specifier in requirements file"
-    )
-
 
 def create_directory_if_not_exists(directory):
     if not directory.exists():
         directory.mkdir(parents=True)
 
-
 def download_file(url, destination):
     response = requests.get(url, stream=True)
     total_size_in_bytes = int(response.headers.get("content-length", 0))
@@ -83,63 +76,118 @@ def download_file(url, destination):
 def check_tts_version():
     try:
         tts_version = metadata.version("tts")
-        print(
+        print(f"[{params['branding']}Startup] \033[92mCurrent TTS Version    : \033[93m"+tts_version+"\033[0m")
-            f"[{params['branding']}Startup] TTS version installed: \033[93m"
-            + tts_version
-            + "\033[0m"
-        )
 
         if version.parse(tts_version) < version.parse(tts_version_required):
-            print(
+            print(f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS version is too old. Please upgrade to version \033[93m"+tts_version_required+"\033[0m or later.\033[0m")
-                f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS version is too old. Please upgrade to version \033[93m"
+            print(f"[{params['branding']}Startup] \033[91mWarning\033[0m At your terminal/command prompt \033[94mpip install --upgrade tts\033[0m")
-                + tts_version_required
-                + "\033[0m or later.\033[0m"
-            )
-            print(
-                f"[{params['branding']}Startup] \033[91mWarning\033[0m At your terminal/command prompt \033[94mpip install --upgrade tts\033[0m"
-            )
         else:
-            print(f"[{params['branding']}Startup] TTS version is up to date.")
+            print(f"[{params['branding']}Startup] \033[92mCurrent TTS Version is :\033[93m Up to date\033[0m")
     except metadata.PackageNotFoundError:
-        print(
+        print(f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS is not installed")
-            f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS is not installed."
-        )
 
+def check_torch_version():
+    import torch
+    pytorch_version = torch.__version__
+    cuda_version = torch.version.cuda
+    major, minor, micro = sys.version_info[:3]
+    python_version = f"{major}.{minor}.{micro}"
+    print(f"[{params['branding']}Startup] \033[92mCurrent Python Version :\033[93m {python_version}\033[0m")
+    print(f"[{params['branding']}Startup] \033[92mCurrent PyTorch Version:\033[93m {pytorch_version}\033[0m")
+    if cuda_version is None:
+        print(f"[{params['branding']}Startup] \033[92mCurrent CUDA Version   :\033[91m CUDA is not available\033[0m")
+    else:
+        print(f"[{params['branding']}Startup] \033[92mCurrent CUDA Version   :\033[93m {cuda_version}\033[0m")
+
+def ordinal(n):
+    return "%d%s" % (n, "th" if 4 <= n % 100 <= 20 else {1: "st", 2: "nd", 3: "rd"}.get(n % 10, "th"))
+
+def format_datetime(iso_str):
+    # Parse the ISO 8601 string to datetime
+    dt = datetime.strptime(iso_str, "%Y-%m-%dT%H:%M:%SZ")
+    # Format the datetime into a more readable string
+    return dt.strftime(f"{ordinal(dt.day)} %B %Y at %H:%M")
+
+def fetch_latest_commit_sha_and_date(owner, repo):
+    url = f"https://api.github.com/repos/{owner}/{repo}/commits"
+    try:
+        response = requests.get(url)
+        if response.status_code == 200:
+            latest_commit = response.json()[0]
+            latest_commit_sha = latest_commit['sha']
+            latest_commit_date = latest_commit['commit']['committer']['date']
+            return latest_commit_sha, latest_commit_date
+        else:
+            print(f"[{params['branding']}Startup] \033[92m{params['branding']}Github updated :\033[91m Failed to fetch the latest commits due to an unexpected response from GitHub")
+            return None, None
+    except ConnectionError:
+        # This block is executed when a connection error occurs
+        print(f"[{params['branding']}Startup] \033[92m{params['branding']}Github updated :\033[91m Could not reach GitHub to check for updates\033[0m")
+        return None, None
+
+def read_or_initialize_sha(file_path):
+    if os.path.exists(file_path):
+        with open(file_path, 'r') as file:
+            data = json.load(file)
+            return data.get("last_known_commit_sha")
+    else:
+        # File doesn't exist, fetch the latest SHA and create the file
+        latest_commit_sha = fetch_latest_commit_sha_and_date(github_site, github_repository)
+        if latest_commit_sha:
+            with open(file_path, 'w') as file:
+                json.dump({"last_known_commit_sha": latest_commit_sha}, file)
+            return latest_commit_sha
+        else:
+            # Handle the case where GitHub couldn't be reached
+            return None
+
+def update_sha_file(file_path, new_sha):
+    with open(file_path, 'w') as file:
+        json.dump({"last_known_commit_sha": new_sha}, file)
 
 # Check and create directories
 if str(base_path) == "models":
     create_directory_if_not_exists(this_dir / base_path / model_path)
 else:
     create_directory_if_not_exists(base_path / model_path)
-    print(
+    print(f"[{params['branding']}Startup] \033[94mInfo\033[0m Custom path set in \033[93mmodeldownload.json\033[0m. Using the following settings:")
-        f"[{params['branding']}Startup] \033[94mInfo\033[0m Custom path set in \033[93mmodeldownload.json\033[0m. Using the following settings:"
+    print(f"[{params['branding']}Startup] \033[94mInfo\033[0m Base folder Path:\033[93m",base_path,"\033[0m",)
-    )
+    print(f"[{params['branding']}Startup] \033[94mInfo\033[0m Model folder Path:\033[93m",model_path,"\033[0m",)
-    print(
+    print(f"[{params['branding']}Startup] \033[94mInfo\033[0m Full Path:\033[93m",base_path / model_path,"\033[0m",)
-        f"[{params['branding']}Startup] \033[94mInfo\033[0m Base folder Path:\033[93m",
-        base_path,
-        "\033[0m",
-    )
-    print(
-        f"[{params['branding']}Startup] \033[94mInfo\033[0m Model folder Path:\033[93m",
-        model_path,
-        "\033[0m",
-    )
-    print(
-        f"[{params['branding']}Startup] \033[94mInfo\033[0m Full Path:\033[93m",
-        base_path / model_path,
-        "\033[0m",
-    )
 
 # Download files if they don't exist
-print("[" + params["branding"] + "Startup] Checking Model is Downloaded.")
+print(f"[{params['branding']}Startup] \033[92mModel is available     :\033[93m Checking\033[0m")
 for filename, url in files_to_download.items():
     if str(base_path) == "models":
         destination = this_dir / base_path / model_path / filename
     else:
         destination = Path(base_path) / model_path / filename
-
     if not destination.exists():
-        print(f"[{params['branding']}Startup] Downloading " + filename + "...")
+        print(f"[{params['branding']}Startup] \033[92mModel Downloading file : \033[93m" + filename + "\033[0m")
         download_file(url, destination)
+print(f"[{params['branding']}Startup] \033[92mModel is available     :\033[93m Checked\033[0m")
 
+github_site = "erew123"
+github_repository = "alltalk_tts"
+
+check_torch_version()
 check_tts_version()
+# Define the file path based on your directory structure
+sha_file_path = this_dir / "system" / "config" / "at_github_sha.json"
+
+# Read or initialize the SHA (adjusted for handling both SHA and date)
+last_known_commit_sha = read_or_initialize_sha(sha_file_path)  # Assuming adjustment for date
+
+# Assuming you have fetched the latest commit SHA and date
+latest_commit_sha, latest_commit_date = fetch_latest_commit_sha_and_date(github_site, github_repository)
+
+formatted_date = format_datetime(latest_commit_date) if latest_commit_date else "an unknown date"
+
+if latest_commit_sha and latest_commit_sha != last_known_commit_sha:
+    #print(f"There's an update available for alltalk_tts.")
+    print(f"[{params['branding']}Startup] \033[92m{params['branding']}Github updated :\033[93m {formatted_date}\033[0m")
+    # Update the file with the new SHA
+    update_sha_file(sha_file_path, latest_commit_sha)
+elif latest_commit_sha == last_known_commit_sha:
+    #print(f"Your alltalk_tts software is up to date.")
+    print(f"[{params['branding']}Startup] \033[92m{params['branding']}Github updated :\033[93m {formatted_date}\033[0m")

nvidia.Dockerfile

@@ -1,20 +1,22 @@
-FROM nvidia/cuda:12.1.1-devel-ubuntu22.04
+FROM nvidia/cuda:12.1.1-devel-ubuntu22.04
-ENV HOST 0.0.0.0
+ENV HOST 0.0.0.0
-RUN apt-get update && apt-get upgrade -y \
+RUN apt-get update && apt-get upgrade -y \
-    && apt-get install -y git build-essential portaudio19-dev \
+    && apt-get install -y git build-essential portaudio19-dev \
-    python3 python3-pip gcc wget \
+    python3 python3-pip gcc wget \
-    ocl-icd-opencl-dev opencl-headers clinfo \
+    ocl-icd-opencl-dev opencl-headers clinfo \
-    libclblast-dev libopenblas-dev \
+    libclblast-dev libopenblas-dev \
-    && mkdir -p /etc/OpenCL/vendors && echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd \
+    && mkdir -p /etc/OpenCL/vendors && echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd \
-    && ln -s /usr/bin/python3 /usr/bin/python
+    && ln -s /usr/bin/python3 /usr/bin/python
-WORKDIR /app
+
-COPY . .
+RUN mkdir /app/pip_cache
-ENV CUDA_DOCKER_ARCH=all
+ENV PIP_CACHE_DIR=/app/pip_cache
-
+
-RUN pip install --no-cache-dir -r requirements_nvidia.txt && \
+WORKDIR /app
-    pip install --no-cache-dir -r requirements_finetune.txt && \
+COPY . .
-    pip install --no-cache-dir -r requirements_other.txt && \
+ENV CUDA_DOCKER_ARCH=all
-    pip install --no-cache-dir deepspeed
+RUN pip install --no-cache-dir --no-deps -r system/requirements/requirements_standalone.txt --cache-dir=/app/pip_cache && \
-EXPOSE 7851
+    pip install --no-cache-dir deepspeed --cache-dir=/app/pip_cache
-RUN chmod +x launch.sh
+
+EXPOSE 7851
+RUN chmod +x launch.sh
 ENTRYPOINT ["sh", "-c", "./launch.sh"]

script.py

@@ -9,12 +9,16 @@ import threading
 import signal
 import sys
 import atexit
+import shutil
 from pathlib import Path
 from datetime import datetime, timedelta
 import re
 import numpy as np
 import soundfile as sf
 import uuid
+import logging
+# Store the current disable level
+current_disable_level = logging.getLogger().manager.disable
 
 ######################################
 #### ALLTALK ALLOWED STARTUP TIME ####
@@ -30,43 +34,109 @@ startup_wait_time = 120
 # STARTUP VARIABLE - Create "this_dir" variable as the current script directory
 this_dir = Path(__file__).parent.resolve()
 
-
 # load config file in and get settings
 def load_config(file_path):
     with open(file_path, "r") as config_file:
         config = json.load(config_file)
     return config
-
+                               
-
 config_file_path = this_dir / "confignew.json"
 # Load the params dictionary from the confignew.json file
 params = load_config(config_file_path)
 
+print(f"[{params['branding']}Startup]\033[94m     _    _ _ \033[1;35m_____     _ _     \033[0m  _____ _____ ____  ")
+print(f"[{params['branding']}Startup]\033[94m    / \  | | |\033[1;35m_   _|_ _| | | __ \033[0m |_   _|_   _/ ___| ")
+print(f"[{params['branding']}Startup]\033[94m   / _ \ | | |\033[1;35m | |/ _` | | |/ / \033[0m   | |   | | \___ \ ")
+print(f"[{params['branding']}Startup]\033[94m  / ___ \| | |\033[1;35m | | (_| | |   <  \033[0m   | |   | |  ___) |")
+print(f"[{params['branding']}Startup]\033[94m /_/   \_\_|_|\033[1;35m |_|\__,_|_|_|\_\ \033[0m   |_|   |_| |____/ ")
+print(f"[{params['branding']}Startup]")
+
+##############################################
+#### Update any changes to confignew.json ####
+##############################################
+
+update_config_path = this_dir / "system" / "config" / "at_configupdate.json"
+downgrade_config_path = this_dir / "system" / "config" / "at_configdowngrade.json"
+
+def changes_needed(main_config, update_config, downgrade_config):
+    """Check if there are any changes to be made to the main configuration."""
+    for key in downgrade_config.keys():
+        if key in main_config:
+            return True
+    for key, value in update_config.items():
+        if key not in main_config:
+            return True
+    return False
+
+def update_config(config_file_path, update_config_path, downgrade_config_path):
+    try:
+        with open(config_file_path, 'r') as file:
+            main_config = json.load(file)
+        with open(update_config_path, 'r') as file:
+            update_config = json.load(file)
+        with open(downgrade_config_path, 'r') as file:
+            downgrade_config = json.load(file)
+
+        # Determine if changes are needed
+        if changes_needed(main_config, update_config, downgrade_config):
+            # Backup with timestamp to avoid overwriting
+            timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
+            backup_path = config_file_path.with_suffix(f".{timestamp}.bak")
+            logging.info(f"Creating backup of the main config to {backup_path}")
+            shutil.copy(config_file_path, backup_path)
+
+            # Proceed with updates and downgrades
+            for key, value in update_config.items():
+                if key not in main_config:
+                    main_config[key] = value
+            for key in downgrade_config.keys():
+                if key in main_config:
+                    del main_config[key]
+
+            # Save the updated configuration
+            with open(config_file_path, 'w') as file:
+                json.dump(main_config, file, indent=4)
+
+            print(f"[{params['branding']}Startup] \033[92mConfig file check      : \033[91mUpdates applied\033[0m")
+        else:
+            print(f"[{params['branding']}Startup] \033[92mConfig file check      : \033[93mNo Updates required\033[0m")
+
+    except Exception as e:
+        print(f"[{params['branding']}Startup] \033[92mConfig file check      : \033[91mError updating\033[0m")
+
+# Update the configuration
+update_config(config_file_path, update_config_path, downgrade_config_path)
+# Re-Load the params dictionary from the confignew.json file
+params = load_config(config_file_path)
+
+#########################################
+#### Continue on with Startup Checks ####
+#########################################
+            
 # Required for sentence splitting
 try:
     from TTS.api import TTS
     from TTS.utils.synthesizer import Synthesizer
 except ModuleNotFoundError:
-    logger.error(
+    # Inform the user about the missing module and suggest next steps
-        f"[{params['branding']}]\033[91mWarning\033[0m Could not find the TTS module. Make sure to install the requirements for the {params['branding']} extension."
+    print(f"[{params['branding']}]\033[91mWarning\033[0m Could not find the TTS module. Make sure to install the requirements for the {params['branding']} extension.")
-        f"[{params['branding']}]\033[91mWarning\033[0m Linux / Mac:\npip install -r /alltalk_tts/requirements.txt\n"
+    print(f"[{params['branding']}]\033[91mWarning\033[0m Please use the ATSetup utility or check the Github installation instructions.")
-        f"[{params['branding']}]\033[91mWarning\033[0m Windows:\npip install -r \\alltalk_tts\\requirements.txt\n"
+    # Re-raise the ModuleNotFoundError to stop the program and print the traceback
-        f"[{params['branding']}]\033[91mWarning\033[0m If you used the one-click installer, paste the command above in the terminal window launched after running the cmd_ script. On Windows, that's cmd_windows.bat."
+    raise 
-    )
-    raise
 
-# IMPORT - Attempt Importing DeepSpeed (required for displaying Deepspeed checkbox in gradio)
+# Suppress logging
+logging.disable(logging.ERROR)
 try:
     import deepspeed
-
     deepspeed_installed = True
 except ImportError:
     deepspeed_installed = False
+# Restore previous logging level
+logging.disable(current_disable_level)
 
 # Import gradio if being used within text generation webUI
 try:
     import gradio as gr
-
     from modules import chat, shared, ui_chat
     from modules.logging_colors import logger
     from modules.ui import create_refresh_button
@@ -75,9 +145,10 @@ try:
     # This is set to check if the script is being run within text generation webui or as a standalone script. False is running as part of text gen web ui or a gradio interface
     running_in_standalone = False
     output_folder_wav = params["output_folder_wav"]
+    print(f"[{params['branding']}Startup] \033[92m{params['branding']}startup Mode   : \033[93mText-Gen-webui mode\033[0m")
 except ModuleNotFoundError:
     output_folder_wav = params["output_folder_wav_standalone"]
-    print(f"[{params['branding']}Startup] Running script.py in standalone mode")
+    print(f"[{params['branding']}Startup] \033[92m{params['branding']}startup Mode   : \033[93mStandalone mode\033[0m")
     # This is set to check if the script is being run within text generation webui or as a standalone script. true means standalone
     running_in_standalone = True
 
@@ -98,23 +169,12 @@ def get_available_voices():
     return sorted([voice.name for voice in Path(f"{this_dir}/voices").glob("*.wav")])
 
 
-#########################
-#### LICENSE DISPLAY ####
-#########################
-# STARTUP Display Licence Information
-print(f"[{params['branding']}Startup] \033[94mCoqui Public Model License\033[0m")
-print(f"[{params['branding']}Startup] \033[94mhttps://coqui.ai/cpml.txt\033[0m")
-
-
 ############################################
 #### DELETE OLD OUTPUT WAV FILES IF SET ####
 ############################################
 def delete_old_files(folder_path, days_to_keep):
     current_time = datetime.now()
-    print(
+    print(f"[{params['branding']}Startup] \033[92mWAV file deletion      :\033[93m", delete_output_wavs_setting,"\033[0m")
-        f"[{params['branding']}Startup] Deletion of old output folder WAV files is currently enabled and set at",
-        delete_output_wavs_setting,
-    )
     for file_name in os.listdir(folder_path):
         file_path = os.path.join(folder_path, file_name)
         if os.path.isfile(file_path):
@@ -130,20 +190,18 @@ output_folder_wav = os.path.normpath(output_folder_wav)
 
 # Check and perform file deletion
 if delete_output_wavs_setting.lower() == "disabled":
-    print(
+    print("["+ params["branding"]+"Startup] \033[92mWAV file deletion      :\033[93m Disabled\033[0m")
-        "["
-        + params["branding"]
-        + "Startup] Old output wav file deletion is set to disabled."
-    )
 else:
     try:
         days_to_keep = int(delete_output_wavs_setting.split()[0])
         delete_old_files(output_folder_wav, days_to_keep)
     except ValueError:
-        print(
+        print(f"[{params['branding']}Startup] \033[92mWAV file deletion      :\033[93m Invalid setting for deleting old wav files. Please use 'Disabled' or 'X Days' format\033[0m")
-            f"[{params['branding']}Startup] Invalid setting for deleting old wav files. Please use 'Disabled' or 'X Days' format."
-        )
 
+if deepspeed_installed:
+    print(f"[{params['branding']}Startup] \033[92mDeepSpeed version      :\033[93m",deepspeed.__version__,"\033[0m")
+else:
+    print(f"[{params['branding']}Startup] \033[92mDeepSpeed version      :\033[91m Not Detected\033[0m")
 
 ########################
 #### STARTUP CHECKS ####
@@ -153,8 +211,6 @@ def check_required_files():
     this_dir = Path(__file__).parent.resolve()
     download_script_path = this_dir / "modeldownload.py"
     subprocess.run(["python", str(download_script_path)])
-    print(f"[{params['branding']}Startup] All required files are present.")
-
 
 # STARTUP Call Check routine
 check_required_files()
@@ -174,7 +230,7 @@ if finetuned_model:
         (trained_model_directory / file).exists() for file in required_files
     )
 if finetuned_model:
-    print(f"[{params['branding']}Startup] Finetuned model \033[93mDetected\033[0m")
+    print(f"[{params['branding']}Startup] \033[92mFinetuned model        :\033[93m Detected\033[0m")
 
 ####################################################
 #### SET GRADIO BUTTONS BASED ON confignew.json ####
@@ -190,7 +246,6 @@ elif params["tts_method_xtts_local"] == True:
 # Set the default for Narrated text without asterisk or quotes to be Narrator
 non_quoted_text_is = True
 
-
 ######################
 #### GRADIO STUFF ####
 ######################
@@ -237,9 +292,7 @@ script_path = this_dir / "tts_server.py"
 
 
 def signal_handler(sig, frame):
-    print(
+    print(f"[{params['branding']}Shutdown] \033[94mReceived Ctrl+C, terminating subprocess\033[92m")
-        f"[{params['branding']}Shutdown] \033[94mReceived Ctrl+C, terminating subprocess\033[92m"
-    )
     if process.poll() is None:
         process.terminate()
         process.wait()  # Wait for the subprocess to finish
@@ -258,33 +311,20 @@ else:
     process = subprocess.Popen(["python", script_path])
     # Check if the subprocess has started successfully
     if process.poll() is None:
-        print(f"[{params['branding']}Startup] TTS Subprocess starting")
+        print(f"[{params['branding']}Startup] \033[92mTTS Subprocess         :\033[93m Starting up\033[0m")
         print(f"[{params['branding']}Startup]")
         print(
-            f"[{params['branding']}Startup] \033[94m {params['branding']}Settings & Documentation:\033[00m",
+            f"[{params['branding']}Startup] \033[94m{params['branding']}Settings & Documentation:\033[00m",
             f"\033[92mhttp://{params['ip_address']}:{params['port_number']}\033[00m",
         )
         print(f"[{params['branding']}Startup]")
     else:
-        print(
+        print(f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess Webserver failing to start process")
-            f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess Webserver failing to start process"
+        print(f"[{params['branding']}Startup] \033[91mWarning\033[0m It could be that you have something on port:",params["port_number"],)
-        )
+        print(f"[{params['branding']}Startup] \033[91mWarning\033[0m Or you have not started in a Python environement with all the necesssary bits installed")
-        print(
+        print(f"[{params['branding']}Startup] \033[91mWarning\033[0m Check you are starting Text-generation-webui with either the start_xxxxx file or the Python environment with cmd_xxxxx file.")
-            f"[{params['branding']}Startup] \033[91mWarning\033[0m It could be that you have something on port:",
+        print(f"[{params['branding']}Startup] \033[91mWarning\033[0m xxxxx is the type of OS you are on e.g. windows, linux or mac.")
-            params["port_number"],
+        print(f"[{params['branding']}Startup] \033[91mWarning\033[0m Alternatively, you could check no other Python processes are running that shouldnt be e.g. Restart your computer is the simple way.")
-        )
-        print(
-            f"[{params['branding']}Startup] \033[91mWarning\033[0m Or you have not started in a Python environement with all the necesssary bits installed"
-        )
-        print(
-            f"[{params['branding']}Startup] \033[91mWarning\033[0m Check you are starting Text-generation-webui with either the start_xxxxx file or the Python environment with cmd_xxxxx file."
-        )
-        print(
-            f"[{params['branding']}Startup] \033[91mWarning\033[0m xxxxx is the type of OS you are on e.g. windows, linux or mac."
-        )
-        print(
-            f"[{params['branding']}Startup] \033[91mWarning\033[0m Alternatively, you could check no other Python processes are running that shouldnt be e.g. Restart your computer is the simple way."
-        )
         # Cleanly kill off this script, but allow text-generation-webui to keep running, albeit without this alltalk_tts
         sys.exit(1)
 
@@ -300,26 +340,14 @@ else:
                 break
         except requests.RequestException as e:
             # Print the exception for debugging purposes
-            print(
+            print(f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess has NOT started up yet, Will keep trying for {timeout} seconds maximum. Please wait.")
-                f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess has NOT started up yet, Will keep trying for {timeout} seconds maximum. Please wait."
-            )
         time.sleep(5)
     else:
-        print(
+        print(f"\n[{params['branding']}Startup] Startup timed out. Full help available here \033[92mhttps://github.com/erew123/alltalk_tts#-help-with-problems\033[0m")
-            f"\n[{params['branding']}Startup] Startup timed out. Full help available here \033[92mhttps://github.com/erew123/alltalk_tts#-help-with-problems\033[0m"
+        print(f"[{params['branding']}Startup] On older system you may wish to open and edit \033[94mscript.py\033[0m with a text editor and changing the")
-        )
+        print(f"[{params['branding']}Startup] \033[94mstartup_wait_time = 120\033[0m setting to something like \033[94mstartup_wait_time = 240\033[0m as this will allow")
-        print(
+        print(f"[{params['branding']}Startup] AllTalk more time to try load the model into your VRAM. Otherise please visit the Github for")
-            f"[{params['branding']}Startup] On older system you may wish to open and edit \033[94mscript.py\033[0m with a text editor and changing the"
+        print(f"[{params['branding']}Startup] a list of other possible troubleshooting options.")
-        )
-        print(
-            f"[{params['branding']}Startup] \033[94mstartup_wait_time = 120\033[0m setting to something like \033[94mstartup_wait_time = 240\033[0m as this will allow"
-        )
-        print(
-            f"[{params['branding']}Startup] AllTalk more time to try load the model into your VRAM. Otherise please visit the Github for"
-        )
-        print(
-            f"[{params['branding']}Startup] a list of other possible troubleshooting options."
-        )
         # Cleanly kill off this script, but allow text-generation-webui to keep running, albeit without this alltalk_tts
         sys.exit(1)
 
@@ -371,9 +399,7 @@ def send_reload_request(tts_method):
         return f'<audio src="file/{audio_path}" controls autoplay></audio>'
     except requests.exceptions.RequestException as e:
         # Handle the HTTP request error
-        print(
+        print(f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}")
-            f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}"
-        )
         return {"status": "error", "message": str(e)}
 
 
@@ -400,9 +426,7 @@ def send_lowvram_request(low_vram):
         return f'<audio src="file/{audio_path}" controls autoplay></audio>'
     except requests.exceptions.RequestException as e:
         # Handle the HTTP request error
-        print(
+        print(f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}")
-            f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}"
-        )
         return {"status": "error", "message": str(e)}
 
 
@@ -417,7 +441,6 @@ def send_deepspeed_request(deepspeed_param):
             audio_path = this_dir / "system" / "at_sounds" / "deepspeedenabled.wav"
         else:
             audio_path = this_dir / "system" / "at_sounds" / "deepspeeddisabled.wav"
-
         url = f"{base_url}/api/deepspeed?new_deepspeed_value={deepspeed_param}"
         headers = {"Content-Type": "application/json"}
         response = requests.post(url, headers=headers)
@@ -430,9 +453,7 @@ def send_deepspeed_request(deepspeed_param):
         return f'<audio src="file/{audio_path}" controls autoplay></audio>'
     except requests.exceptions.RequestException as e:
         # Handle the HTTP request error
-        print(
+        print(f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}")
-            f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}"
-        )
         return {"status": "error", "message": str(e)}
 
 
@@ -465,9 +486,7 @@ Synthesizer.split_into_sentences = new_split_into_sentences
 def before_audio_generation(string, params):
     # Check Model is loaded into cuda or cpu and error if not
     if not params["tts_model_loaded"]:
-        print(
+        print(f"[{params['branding']}Model] \033[91mWarning\033[0m Model is still loading, please wait before trying to generate TTS")
-            f"[{params['branding']}Model] \033[91mWarning\033[0m Model is still loading, please wait before trying to generate TTS"
-        )
         return
     string = html.unescape(string) or random_sentence()
     if string == "":
@@ -491,13 +510,9 @@ def combine(audio_files, output_folder, state):
 
     # Save the combined audio to a file with a specified sample rate
     if "character_menu" in state:
-        output_file_path = os.path.join(
+        output_file_path = os.path.join(output_folder, f'{state["character_menu"]}_{int(time.time())}_combined.wav')
-            output_folder, f'{state["character_menu"]}_{int(time.time())}_combined.wav'
-        )
     else:
-        output_file_path = os.path.join(
+        output_file_path = os.path.join(output_folder, f"TTSOUT_{int(time.time())}_combined.wav")
-            output_folder, f"TTSOUT_{int(time.time())}_combined.wav"
-        )
     sf.write(output_file_path, audio, samplerate=sample_rate)
     # Clean up unnecessary files
     for audio_file in audio_files:
@@ -544,9 +559,7 @@ def voice_preview(string):
         )
     # Check if lock is already acquired
     if process_lock.locked():
-        print(
+        print(f"[{params['branding']}Model] \033[91mWarning\033[0m Audio generation is already in progress. Please wait.")
-            f"[{params['branding']}Model] \033[91mWarning\033[0m Audio generation is already in progress. Please wait."
-        )
         return
     if generate_response.get("status") == "generate-success":
         # Handle Gradio and playback
@@ -666,14 +679,10 @@ def output_modifier(string, state):
                     # Determine the voice to use based on the part type
                     if part_type == "narrator":
                         voice_to_use = params["narrator_voice"]
-                        print(
+                        print(f"[{params['branding']}TTSGen] \033[92mNarrator\033[0m")  # Green
-                            f"[{params['branding']}TTSGen] \033[92mNarrator\033[0m"
-                        )  # Green
                     elif part_type == "character":
                         voice_to_use = params["voice"]
-                        print(
+                        print(f"[{params['branding']}TTSGen] \033[36mCharacter\033[0m")  # Yellow
-                            f"[{params['branding']}TTSGen] \033[36mCharacter\033[0m"
-                        )  # Yellow
                     else:
                         # Handle ambiguous parts based on user preference
                         voice_to_use = (
@@ -689,9 +698,11 @@ def output_modifier(string, state):
                         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)
+                    cleaned_part = re.sub(r"([!?.\u3002\uFF1F\uFF01\uFF0C])\1+", r"\1", part)
+                    # Replace "Chinese ellipsis" with a single dot
+                    cleaned_part = re.sub(r"\u2026{1,2}", ". ", cleaned_part)
                     # Further clean to remove any other unwanted characters
-                    cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0100-\u017F\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\u4E00-\u9FFF\u3400-\u4DBF\u20000-\u2A6DF\u3000-\u303F\$]', '', cleaned_part)
+                    cleaned_part = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', cleaned_part)
                     # Remove all newline characters (single or multiple)
                     cleaned_part = re.sub(r"\n+", " ", cleaned_part)
 
@@ -716,9 +727,11 @@ def output_modifier(string, state):
                 # Decode HTML entities first
                 cleaned_part = html.unescape(cleaned_string)
                 # Replace multiple instances of certain punctuation marks with a single instance
-                cleaned_part = re.sub(r"([!?.])\1+", r"\1", cleaned_part)
+                cleaned_part = re.sub(r"([!?.\u3002\uFF1F\uFF01\uFF0C])\1+", r"\1", cleaned_part)
+                # Replace "Chinese ellipsis" with a single dot
+                cleaned_part = re.sub(r"\u2026{1,2}", ". ", cleaned_part)
                 # Further clean to remove any other unwanted characters
-                cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0100-\u017F\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\u4E00-\u9FFF\u3400-\u4DBF\u20000-\u2A6DF\u3000-\u303F\$]', '', cleaned_part)
+                cleaned_part = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', 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.

system/admin.html

@@ -304,6 +304,7 @@
                 <option value="nl">Dutch</option>
                 <option value="fr">French</option>
                 <option value="de">German</option>
+                <option value="hi">Hindi</option>
                 <option value="hu">Hungarian</option>
                 <option value="it">Italian</option>
                 <option value="ja">Japanese</option>
@@ -474,7 +475,7 @@
         <span style="color: #3366ff;">22050Hz</span>, <span style="color: #3366ff;">Mono</span>, <span
             style="color: #3366ff;">16 bit</span> and between 6 to 30 seconds long, though 8 to 10 seconds is usually
         good enough. The model can handle up to 30 second samples, however I've not noticed any improvement in voice
-        output from a much longer clips.
+        output from much longer clips.
     </p>
     <p>You want to find a nice clear selection of audio, so lets say
         you wanted to clone your favourite celebrity. You may go looking for an interview where they are talking. Pay
@@ -1357,6 +1358,7 @@
     <h3 id="🟨-help-with-problems">🟨 Help with problems</h3>
     <h4 id="🔄-minor-updatesbug-fixes-list-can-be-found-here">&nbsp;&nbsp;&nbsp;&nbsp; 🔄 <strong>Minor updates/bug
             fixes list</strong> can be found <a href="https://github.com/erew123/alltalk_tts/issues/25">here</a></h4>
+    <h4>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Please check Github for the most up to date help list <a href="https://github.com/erew123/alltalk_tts#-help-with-problems">here</a></h4>
     <details>
         <summary>🟨 How to make a diagnostics report file</summary>
 
@@ -2499,6 +2501,7 @@
         <code>en</code> English<br>
         <code>fr</code> French<br>
         <code>de</code> German<br>
+        <code>hi</code> Hindi<br>
         <code>hu</code> Hungarian<br>
         <code>it</code> Italian<br>
         <code>ja</code> Japanese<br>

system/at_admin/at_settings.html

@@ -124,6 +124,7 @@
             <option value="English" {% if data.language=='English' %}selected{% endif %}>English</option>
             <option value="French" {% if data.language=='French' %}selected{% endif %}>French</option>
             <option value="German" {% if data.language=='German' %}selected{% endif %}>German</option>
+            <option value="Hindi" {% if data.language=='Hindi' %}selected{% endif %}>Hindi</option>
             <option value="Hungarian" {% if data.language=='Hungarian' %}selected{% endif %}>Hungarian</option>
             <option value="Italian" {% if data.language=='Italian' %}selected{% endif %}>Italian</option>
             <option value="Japanese" {% if data.language=='Japanese' %}selected{% endif %}>Japanese</option>

system/config/at_configdowngrade.json

@@ -0,0 +1,2 @@
+{
+}

system/config/at_configupdate.json

@@ -0,0 +1,2 @@
+{
+}

system/config/languages.json

@@ -6,6 +6,7 @@
   "English": "en",
   "French": "fr",
   "German": "de",
+  "Hindi": "hi",
   "Hungarian": "hu",
   "Italian": "it",
   "Japanese": "ja",

system/requirements/requirements_docker.txt

@@ -0,0 +1,173 @@
+absl-py==2.1.0
+aiofiles==23.2.1
+aiohttp==3.9.3
+aiosignal==1.3.1
+altair==5.2.0
+annotated-types==0.6.0
+anyascii==0.3.2
+anyio==4.3.0
+attrs==23.2.0
+audioread==3.0.1
+av==11.0.0
+Babel==2.14.0
+bangla==0.0.2
+blinker==1.7.0
+blis==0.7.11
+bnnumerizer==0.0.2
+bnunicodenormalizer==0.1.6
+catalogue==2.0.10
+certifi==2024.2.2
+cffi==1.16.0
+charset-normalizer==3.3.2
+click==8.1.7
+cloudpathlib==0.16.0
+colorama==0.4.6
+coloredlogs==15.0.1
+confection==0.1.4
+contourpy==1.2.0
+coqpit==0.0.17
+ctranslate2==4.1.0
+cutlet==0.4.0
+cycler==0.12.1
+cymem==2.0.8
+Cython==3.0.9
+dateparser==1.1.8
+decorator==5.1.1
+docopt==0.6.2
+einops==0.7.0
+encodec==0.1.1
+exceptiongroup
+fastapi==0.110.0
+faster-whisper==1.0.1
+ffmpy==0.3.2
+filelock==3.13.3
+Flask==3.0.2
+flatbuffers==24.3.25
+fonttools==4.50.0
+frozenlist==1.4.1
+fsspec==2024.3.1
+fugashi==1.3.1
+fuzzywuzzy>=0.18.0
+g2pkk==0.1.2
+gradio==3.50.2
+gradio_client==0.6.1
+grpcio==1.62.1
+gruut==2.2.3
+gruut-ipa==0.13.0
+gruut-lang-de==2.0.0
+gruut-lang-en==2.0.0
+gruut-lang-es==2.0.0
+gruut-lang-fr==2.0.2
+h11==0.14.0
+hangul-romanize==0.1.0
+httpcore==1.0.4
+httpx==0.27.0
+huggingface-hub==0.22.1
+humanfriendly==10.0
+idna==3.6
+importlib_metadata==7.1.0
+importlib_resources==6.4.0
+inflect==7.0.0
+itsdangerous==2.1.2
+jaconv==0.3.4
+jamo==0.4.1
+jieba==0.42.1
+Jinja2==3.1.3
+joblib==1.3.2
+jsonlines==1.2.0
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+kiwisolver==1.4.5
+langcodes==3.3.0
+lazy_loader==0.3
+librosa==0.10.1
+llvmlite==0.42.0
+Markdown==3.6
+MarkupSafe==2.1.5
+matplotlib==3.8.3
+mojimoji==0.0.13
+mpmath==1.3.0
+msgpack==1.0.8
+multidict==6.0.5
+murmurhash==1.0.10
+networkx==2.8.8
+nltk==3.8.1
+num2words==0.5.13
+numba==0.59.1
+numpy==1.26.4
+nvidia-cublas-cu11>=11.11.3.6
+nvidia-cudnn-cu11>=9.0.0.312
+onnxruntime==1.17.1
+orjson==3.9.15
+packaging==24.0
+pandas==1.5.3
+pillow==10.2.0
+platformdirs==4.2.0
+pooch==1.8.1
+preshed==3.0.9
+protobuf==5.26.0
+psutil==5.9.8
+pycparser==2.21
+pydantic==2.6.4
+pydantic_core==2.16.3
+pydub==0.25.1
+pynndescent==0.5.11
+pyparsing==3.1.2
+pypinyin==0.51.0
+pyreadline3==3.4.1
+pysbd==0.3.4
+python-crfsuite==0.9.10
+python-dateutil==2.9.0.post0
+python-Levenshtein>=0.25.0
+python-multipart==0.0.9
+pytz==2024.1
+PyYAML==6.0.1
+referencing==0.34.0
+regex==2023.12.25
+requests==2.31.0
+rpds-py==0.18.0
+safetensors==0.4.2
+scikit-learn==1.4.1.post1
+scipy==1.12.0
+semantic-version==2.10.0
+six==1.16.0
+smart-open==6.4.0
+sniffio==1.3.1
+sounddevice==0.4.6
+soundfile==0.12.1
+soxr==0.3.7
+spacy==3.7.4
+spacy-legacy==3.0.12
+spacy-loggers==1.0.5
+srsly==2.4.8
+starlette==0.36.3
+SudachiDict-core==20240109
+SudachiPy==0.6.8
+sympy==1.12
+tensorboard==2.16.2
+tensorboard-data-server==0.7.2
+thinc==8.2.3
+threadpoolctl==3.4.0
+tokenizers==0.15.2
+toolz==0.12.1
+torch>=2.2.0
+torchaudio>=2.2.0
+tqdm==4.66.2
+trainer==0.0.36
+transformers==4.39.1
+TTS==0.22.0
+typer==0.9.4
+typing_extensions==4.10.0
+tzdata==2024.1
+tzlocal==5.2
+umap-learn==0.5.5
+Unidecode==1.3.8
+unidic-lite==1.0.8
+urllib3==2.2.1
+uvicorn==0.29.0
+wasabi==1.1.2
+weasel==0.3.4
+websockets==11.0.3
+Werkzeug==3.0.1
+yarl==1.9.4
+zipp==3.18.1

system/requirements/requirements_googlecolab.txt

@@ -0,0 +1,171 @@
+absl-py==2.1.0
+aiofiles==23.2.1
+aiohttp==3.9.3
+aiosignal==1.3.1
+altair==5.2.0
+annotated-types==0.6.0
+anyascii==0.3.2
+anyio==4.3.0
+attrs==23.2.0
+audioread==3.0.1
+av==11.0.0
+Babel==2.14.0
+bangla==0.0.2
+blinker==1.7.0
+blis==0.7.11
+bnnumerizer==0.0.2
+bnunicodenormalizer==0.1.6
+catalogue==2.0.10
+certifi==2024.2.2
+cffi==1.16.0
+charset-normalizer==3.3.2
+click==8.1.7
+cloudpathlib==0.16.0
+colorama==0.4.6
+coloredlogs==15.0.1
+confection==0.1.4
+contourpy==1.2.0
+coqpit==0.0.17
+ctranslate2==4.1.0
+cutlet==0.4.0
+cycler==0.12.1
+cymem==2.0.8
+Cython==3.0.9
+dateparser==1.1.8
+decorator==5.1.1
+docopt==0.6.2
+einops==0.7.0
+encodec==0.1.1
+fastapi==0.110.0
+faster-whisper==1.0.1
+ffmpy==0.3.2
+filelock==3.13.3
+Flask==3.0.2
+flatbuffers==24.3.25
+fonttools==4.50.0
+frozenlist==1.4.1
+fsspec==2024.3.1
+fugashi==1.3.1
+fuzzywuzzy>=0.18.0
+g2pkk==0.1.2
+gradio==3.50.2
+gradio_client==0.6.1
+grpcio==1.62.1
+gruut==2.2.3
+gruut-ipa==0.13.0
+gruut-lang-de==2.0.0
+gruut-lang-en==2.0.0
+gruut-lang-es==2.0.0
+gruut-lang-fr==2.0.2
+h11==0.14.0
+hangul-romanize==0.1.0
+httpcore==1.0.4
+httpx==0.27.0
+huggingface-hub==0.22.1
+humanfriendly==10.0
+idna==3.6
+importlib_metadata==7.1.0
+importlib_resources==6.4.0
+inflect==7.0.0
+itsdangerous==2.1.2
+jaconv==0.3.4
+jamo==0.4.1
+jieba==0.42.1
+Jinja2==3.1.3
+joblib==1.3.2
+jsonlines==1.2.0
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+kiwisolver==1.4.5
+langcodes==3.3.0
+lazy_loader==0.3
+librosa==0.10.1
+llvmlite==0.42.0
+Markdown==3.6
+MarkupSafe==2.1.5
+matplotlib==3.8.3
+mojimoji==0.0.13
+mpmath==1.3.0
+msgpack==1.0.8
+multidict==6.0.5
+murmurhash==1.0.10
+networkx==2.8.8
+nltk==3.8.1
+num2words==0.5.13
+numba==0.59.1
+numpy>=1.17.0,<1.29.0
+nvidia-cublas-cu11>=11.11.3.6
+nvidia-cudnn-cu11>=9.0.0.312
+onnxruntime==1.17.1
+orjson==3.9.15
+packaging==24.0
+pandas==1.5.3
+pillow==10.2.0
+platformdirs==4.2.0
+pooch==1.8.1
+preshed==3.0.9
+protobuf==5.26.0
+psutil==5.9.8
+pycparser==2.21
+pydantic==2.6.4
+pydantic_core==2.16.3
+pydub==0.25.1
+pynndescent==0.5.11
+pyparsing==3.1.2
+pypinyin==0.51.0
+pyreadline3==3.4.1
+pysbd==0.3.4
+python-crfsuite==0.9.10
+python-dateutil==2.9.0.post0
+python-Levenshtein>=0.25.0
+python-multipart==0.0.9
+pytz==2024.1
+PyYAML==6.0.1
+referencing==0.34.0
+regex==2023.12.25
+requests==2.31.0
+rpds-py==0.18.0
+safetensors==0.4.2
+scikit-learn==1.4.1.post1
+scipy==1.12.0
+semantic-version==2.10.0
+six==1.16.0
+smart-open==6.4.0
+sniffio==1.3.1
+sounddevice==0.4.6
+soundfile==0.12.1
+soxr==0.3.7
+spacy==3.7.4
+spacy-legacy==3.0.12
+spacy-loggers==1.0.5
+srsly==2.4.8
+starlette==0.36.3
+SudachiDict-core==20240109
+SudachiPy==0.6.8
+sympy==1.12
+tensorboard==2.16.2
+tensorboard-data-server==0.7.2
+thinc==8.2.3
+threadpoolctl==3.4.0
+tokenizers==0.15.2
+toolz==0.12.1
+torch>=2.2.0
+torchaudio>=2.2.0
+tqdm==4.66.2
+trainer==0.0.36
+transformers==4.39.1
+typer==0.9.4
+typing_extensions==4.10.0
+tzdata==2024.1
+tzlocal==5.2
+umap-learn==0.5.5
+Unidecode==1.3.8
+unidic-lite==1.0.8
+urllib3==2.2.1
+uvicorn==0.29.0
+wasabi==1.1.2
+weasel==0.3.4
+websockets==11.0.3
+Werkzeug==3.0.1
+yarl==1.9.4
+zipp==3.18.1

system/requirements/requirements_textgen.txt

@@ -22,3 +22,4 @@ nvidia-cublas-cu11>=11.11.3.6
 nvidia-cudnn-cu11>=9.0.0.312
 fuzzywuzzy>=0.18.0
 python-Levenshtein>=0.25.0
+spacy>=3.7.2

system/st_files/alltalk.js

@@ -281,7 +281,7 @@ class AllTalkTtsProvider {
             if (narratorVoiceSelect) narratorVoiceSelect.disabled = !this.settings.narrator_enabled;
         }
         const modelSelect = document.getElementById('switch_model');
-        if (this.settings.finetuned_model === 'true') {
+        if (this.settings.finetuned_model === true) {
             const ftOption = document.createElement('option');
             ftOption.value = 'XTTSv2 FT';
             ftOption.textContent = 'XTTSv2 FT';
@@ -830,4 +830,4 @@ function updateStatus(message) {
                 break;
         }
     }
-}
+}

system/tts_generator/crunker.d.ts

@@ -176,4 +176,4 @@ export default class Crunker {
      * @internal
      */
     private _renderURL;
-}
+}

system/tts_generator/crunker.esm.js

@@ -1,2 +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
+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

system/tts_generator/crunker.esm.js.map

@@ -1 +1 @@
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truncated
+{"version":3,"file":"crunker.esm.js","mappings":"AACA,IAAIA,EAAsB,CCA1BA,EAAwB,CAACC,EAASC,KACjC,IAAI,IAAIC,KAAOD,EACXF,EAAoBI,EAAEF,EAAYC,KAASH,EAAoBI,EAAEH,EAASE,IAC5EE,OAAOC,eAAeL,EAASE,EAAK,CAAEI,YAAY,EAAMC,IAAKN,EAAWC,IAE1E,ECNDH,EAAwB,CAACS,EAAKC,IAAUL,OAAOM,UAAUC,eAAeC,KAAKJ,EAAKC,I,8jDCoBlF,IAGqBI,EAAO,WAU1B,aAAqE,6DAAJ,CAAC,EAApDC,EAAU,EAAVA,WAAU,UACtBC,KAAKC,SAAWD,KAAKE,eAAeH,GAEpCA,IAAAA,EAAeC,KAAKC,SAASF,YAE7BC,KAAKG,YAAcJ,CACrB,C,UAoZC,O,EAlZD,E,EAAA,6BAKA,WAAkE,IAA3CA,EAAqB,UAAH,6CAAG,MAE1C,OADAK,OAAOC,aAAeD,OAAOC,cAAiBD,OAAeE,oBAAuBF,OAAeG,gBAC5F,IAAIF,aAAa,CAAEN,WAAAA,GAC5B,GAEA,mBAIA,WACE,OAAOC,KAAKC,QACd,GAEA,wBAGAO,iBAA4E,kCAAxDC,EAAS,yBAATA,EAAS,gBAC3B,aAAaC,QAAQC,IACnBF,EAAUG,KAAIJ,eAAOK,GACnB,IAAIC,EAkBJ,OAfEA,EADED,aAAoBE,MAAQF,aAAoBG,WACnCH,EAASI,oBAETC,MAAML,GAAUM,MAAK,SAACC,GASnC,OARIA,EAASC,QAAQC,IAAI,kBAAoBF,EAASC,QAAQ7B,IAAI,gBAAiB+B,SAAS,WAC1FC,QAAQC,KAAK,oEAAD,OAERL,EAASC,QAAQ7B,IAAI,gBAAiBkC,MAAM,KAAK,GAAE,sDACPb,EAAQ,OAInDO,EAASH,aAClB,UAGW,EAAKhB,SAAS0B,gBAAgBb,EAC7C,IAEJ,GAEA,wBAOA,SAAWc,GACT,IAAMC,EAAS7B,KAAKC,SAAS6B,aAC3B9B,KAAK+B,qBAAqBH,GAC1B5B,KAAKG,YAAcH,KAAKgC,aAAaJ,GACrC5B,KAAKG,aAgBP,OAbAyB,EAAQK,SAAQ,SAACnB,GACf,IAAK,IAAIoB,EAAgB,EAAGA,EAAgBpB,EAAOqB,iBAAkBD,IAAiB,CAIpF,IAHA,IAAME,EAAaP,EAAOQ,eAAeH,GACnCI,EAAaxB,EAAOuB,eAAeH,GAEhCK,EAAIzB,EAAOuB,eAAeH,GAAeM,OAAS,EAAGD,GAAK,EAAGA,IACpEH,EAAWG,IAAMD,EAAWC,GAG9BV,EAAOQ,eAAeH,GAAeO,IAAIL,EAC3C,CACF,IAEOP,CACT,GAEA,yBAOA,SAAYD,GACV,IAAMC,EAAS7B,KAAKC,SAAS6B,aAC3B9B,KAAK+B,qBAAqBH,GAC1B5B,KAAK0C,aAAad,GAClB5B,KAAKG,aAEHwC,EAAS,EAUb,OARAf,EAAQK,SAAQ,SAACnB,GACf,IAAK,IAAIoB,EAAgB,EAAGA,EAAgBpB,EAAOqB,iBAAkBD,IACnEL,EAAOQ,eAAeH,GAAeO,IAAI3B,EAAOuB,eAAeH,GAAgBS,GAGjFA,GAAU7B,EAAO0B,MACnB,IAEOX,CACT,GAEA,sBAUA,SAASf,GAA6E,IAAxD8B,EAAmB,UAAH,6CAAG,EAAGC,EAAkB,UAAH,6CAAG,EACpE,GAAgB,IAAZA,EAAe,OAAO/B,EAE1B,GAAI8B,EAAW,EAAG,MAAM,IAAIE,MAAM,8DAClC,GAAID,EAAU,EAAG,MAAM,IAAIC,MAAM,6DAQjC,IANA,IAAMC,EAAgB/C,KAAKC,SAAS6B,aAClChB,EAAOqB,iBACPa,KAAKC,KAAKnC,EAAO0B,OAASK,EAAU/B,EAAOf,YAC3Ce,EAAOf,YAGAmC,EAAgB,EAAGA,EAAgBpB,EAAOqB,iBAAkBD,IAAiB,CACpF,IAAMgB,EAAcpC,EAAOuB,eAAeH,GAC1Ca,EACGV,eAAeH,GACfO,IAAIS,EAAYC,SAAS,EAAGH,KAAKC,KAAKL,EAAW9B,EAAOf,YAAc,GAAI,GAE7EgD,EACGV,eAAeH,GACfO,IACCS,EAAYC,SAASH,KAAKC,KAAKL,EAAW9B,EAAOf,YAAc,EAAGgD,EAAcP,OAAS,GACzFQ,KAAKC,MAAML,EAAWC,GAAW/B,EAAOf,YAE9C,CAEA,OAAOgD,CACT,GAEA,wBASA,SAAWjC,EAAqBsC,EAAeC,GAAmE,IAAtDC,EAAiB,UAAH,6CAAG,EAAGC,EAAkB,UAAH,6CAAG,EAChG,GAAIH,GAASC,EAAK,MAAM,IAAIP,MAAM,6EAMlC,IAJA,IAAMN,EAASQ,KAAKQ,OAAOH,EAAMD,GAASpD,KAAKG,aACzCwC,EAASK,KAAKQ,MAAMJ,EAAQpD,KAAKG,aACjCsD,EAAYzD,KAAKC,SAAS6B,aAAahB,EAAOqB,iBAAkBK,EAAQxC,KAAKG,aAE1EuD,EAAU,EAAGA,EAAU5C,EAAOqB,iBAAkBuB,IAIvD,IAHA,IAAMC,EAAY7C,EAAOuB,eAAeqB,GAClCtB,EAAaqB,EAAUpB,eAAeqB,GAEnCnB,EAAI,EAAGA,EAAIC,EAAQD,IAC1BH,EAAWG,GAAKoB,EAAUhB,EAASJ,GAG/BA,EAAIe,EAAStD,KAAKG,cACpBiC,EAAWG,IAAMA,GAAKe,EAAStD,KAAKG,cAIlCoC,EAAIC,EAASe,EAAUvD,KAAKG,cAC9BiC,EAAWG,KAAOC,EAASD,IAAMgB,EAAUvD,KAAKG,cAKtD,OAAOsD,CACT,GAEA,kBAGA,SAAK3C,GACH,IAAM8C,EAAS5D,KAAKC,SAAS4D,qBAM7B,OAJAD,EAAO9C,OAASA,EAChB8C,EAAOE,QAAQ9D,KAAKC,SAAS8D,aAC7BH,EAAOR,QAEAQ,CACT,GAEA,oBAUA,SAAO9C,GAAuE,IAAlDkD,EAAe,UAAH,6CAAG,YACnCC,EAAWjE,KAAKkE,YAAYpD,GAC5BqD,EAAWnE,KAAKoE,cAAcH,EAAUnD,EAAOqB,iBAAkBrB,EAAOf,YACxEsE,EAAY,IAAIrD,KAAK,CAACmD,GAAW,CAAEH,KAAAA,IAEzC,MAAO,CACLM,KAAMD,EACNE,IAAKvE,KAAKwE,WAAWH,GACrBI,QAASzE,KAAK0E,oBAAoBL,GAEtC,GAEA,sBAMA,SAASC,GAA6D,IAAjDK,EAAmB,UAAH,6CAAG,UAChCC,EAAIC,SAASC,cAAc,KAOjC,OALAF,EAAEG,MAAMC,QAAU,OAClBJ,EAAEK,KAAOjF,KAAKwE,WAAWF,GACzBM,EAAEM,SAAW,GAAH,OAAMP,EAAQ,YAAIL,EAAKN,KAAKtC,MAAM,KAAK,IACjDkD,EAAEO,QAEKP,CACT,GAEA,0BAOA,SAAgBQ,GACd,OAAOpF,KAAKqF,oBAAiBC,EAAYF,GAC3C,GAEA,mBAGA,WAEE,OADApF,KAAKC,SAASsF,QACPvF,IACT,GAEA,0BAKA,SAAqB4B,GACnB,OAAOoB,KAAKwC,IAAG,MAARxC,KAAI,EAAQpB,EAAQhB,KAAI,SAACE,GAAM,OAAKA,EAAO2E,QAAQ,KAC5D,GAEA,kCAKA,SAA6B7D,GAC3B,OAAOoB,KAAKwC,IAAG,MAARxC,KAAI,EAAQpB,EAAQhB,KAAI,SAACE,GAAM,OAAKA,EAAOqB,gBAAgB,KACpE,GAEA,0BAKA,SAAqBP,GACnB,OAAOA,EAAQhB,KAAI,SAACE,GAAM,OAAKA,EAAO0B,MAAM,IAAEkD,QAAO,SAACd,EAAGe,GAAC,OAAKf,EAAIe,CAAC,GAAE,EACxE,GAEA,0BAKA,WACE,MAAO,iBAAkBvF,QAAU,uBAAwBA,QAAU,oBAAqBA,MAC5F,GAEA,2BAOA,SAAsBU,EAAsB8E,EAAuB7F,GACjE,IAEM8F,EADiBC,EACJF,EAIbG,EALiBD,EAKDhF,EAAO0B,OACvBwD,EAFkB,GAEiBD,EAEnC9E,EAAc,IAAIgF,YALD,EAK8BD,GAC/CE,EAAO,IAAIC,SAASlF,GAgB1B,OAdAjB,KAAKoG,aAAaF,EAAM,EAAG,QAC3BA,EAAKG,UAAU,EAAGL,GAAgB,GAClChG,KAAKoG,aAAaF,EAAM,EAAG,QAC3BlG,KAAKoG,aAAaF,EAAM,GAAI,QAC5BA,EAAKG,UAAU,GAAI,IAAI,GACvBH,EAAKI,UAAU,GAAI,GAAG,GACtBJ,EAAKI,UAAU,GAAIV,GAAe,GAClCM,EAAKG,UAAU,GAAItG,GAAY,GAC/BmG,EAAKG,UAAU,GAAItG,EAAa8F,GAAY,GAC5CK,EAAKI,UAAU,GAAIT,GAAY,GAC/BK,EAAKI,UAAU,GAtBE,IAsBY,GAC7BtG,KAAKoG,aAAaF,EAAM,GAAI,QAC5BA,EAAKG,UAAU,GAAIN,GAAe,GAE3B/F,KAAKuG,iBAAiBL,EAAMpF,EAAQ0F,GAC7C,GAEA,8BAKA,SAAyBrC,EAAoBrD,EAAsB6B,GACjE,IAAK,IAAIJ,EAAI,EAAGA,EAAIzB,EAAO0B,OAAQD,IAAKI,GAAU,EAAG,CACnD,IAAM8D,EAAMzD,KAAKwC,KAAK,EAAGxC,KAAK0D,IAAI,EAAG5F,EAAOyB,KAC5C4B,EAASwC,SAAShE,EAAQ8D,EAAM,EAAU,MAANA,EAAqB,MAANA,GAAc,EACnE,CAEA,OAAOtC,CACT,GAEA,0BAKA,SAAqBA,EAAoBxB,EAAgBiE,GACvD,IAAK,IAAIrE,EAAI,EAAGA,EAA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system/tts_generator/tts_generator.html

@@ -532,6 +532,7 @@
                 <option value="en" selected>English</option>
                 <option value="fr">French</option>
                 <option value="de">German</option>
+                <option value="hi">Hindi</option>
                 <option value="hu">Hungarian</option>
                 <option value="it">Italian</option>
                 <option value="ja">Japanese</option>
@@ -921,18 +922,21 @@
             const playType = document.getElementById('playType').value; // Play type (chunk or stream)
             playModeValue = document.getElementById('playMode').value;
 
-            let cleanedText = textInput.replace(/ \- | \– /g, ' ');
+            let cleanedText = textInput.replace(/ \\- | \\– /g, ' ');
             cleanedText = cleanedText.replace(/%/g, " percent");
-            cleanedText = cleanedText.replace(/\.\s'/g, ".'");
+            cleanedText = cleanedText.replace(/\\.\\s'/g, ".'");
-            cleanedText = cleanedText.replace(/(?<!\w)[']+(?!\w)/g, '');
+            cleanedText = cleanedText.replace(/(?<!\\w)\['\]+(?!\\w)/g, '');
             cleanedText = cleanedText.replace(/"/g, "'");
-            cleanedText = cleanedText.replace(/[^a-zA-Z0-9\s\.,;:!?\-\'"À-ÿ\u0400-\u04FF\u0150\u0151\u0170\u0171]\$/g, '');
+            cleanedText = cleanedText.replace(/[^a-zA-Z0-9\s.,;:!?\-'"$À-ÿ\u0400-\u04FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u2026\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]/g, '');
-            // Replace dash followed by space with comma and space
+            cleanedText = cleanedText.replace(/-\\s/g, '; ');
-            cleanedText = cleanedText.replace(/-\s/g, '; ');
-            // Replace double dash "--" with semicolon "; "
             cleanedText = cleanedText.replace(/--/g, '; ');
-
+            // Split by Fullwidth/CJK punctuation regardless of if there are spaces behind them
-            let sentences = cleanedText.split(/(?<=[.!?:])\s+/);
+            // Please test if it works properly. In my test they it should be 
+            // (?<=[.!?:])\s+ instead of 
+            // (?<=\[.!?:\])\\s+ 
+            // so I don't know whats going on here
+            // let sentences = cleanedText.split(/(?<=[\[.!?:\]])\s+|(?<=[\u3002\uFF01\uFF1A\uFF1F])/);
+            let sentences = cleanedText.split(/(?<=[\[.!?:\]]['"\u2018\u2019\u201C\u201D]*)\s+|(?<=[\u3002\uFF01\uFF1A\uFF1F])/);
 
             if (!sentences) {
                 console.error("No sentences found in the input text.");
@@ -2208,4 +2212,4 @@
 </script>
 </body>
 
-</html>
+</html>

tts_server.py

@@ -8,7 +8,8 @@ from TTS.tts.configs.xtts_config import XttsConfig
 from TTS.tts.models.xtts import Xtts
 import io
 import wave
-
+import logging
+logging.disable(logging.WARNING)
 ##########################
 #### Webserver Imports####
 ##########################
@@ -120,12 +121,6 @@ try:
     deepspeed_available = True
 except ImportError:
     pass
-if deepspeed_available:
-    print(f"[{params['branding']}Startup] DeepSpeed \033[93mDetected\033[0m")
-    print(f"[{params['branding']}Startup] Activate DeepSpeed in {params['branding']}settings")
-else:
-    print(f"[{params['branding']}Startup] DeepSpeed \033[93mNot Detected\033[0m. See https://github.com/microsoft/DeepSpeed")
-
 
 @asynccontextmanager
 async def startup_shutdown(no_actual_value_it_demanded_something_be_here):
@@ -187,9 +182,8 @@ async def setup():
     # Calculate start time minus end time
     generate_elapsed_time = generate_end_time - generate_start_time
     # Print out the result of the load time
-    print(
+    print(f"[{params['branding']}Model] \033[94mModel Loaded in \033[93m{generate_elapsed_time:.2f} seconds.\033[0m")
-        f"[{params['branding']}Model] \033[94mModel Loaded in \033[93m{generate_elapsed_time:.2f} seconds.\033[0m"
+    print(f"[{params['branding']}Model] Ready")
-    )
     # Set "tts_model_loaded" to true
     params["tts_model_loaded"] = True
     # Set the output path for wav files
@@ -223,6 +217,8 @@ async def api_manual_load_model():
             model_path=modeldownload_base_path / modeldownload_model_path,
             config_path=modeldownload_base_path / modeldownload_model_path / "config.json",
         ).to(device)
+        print(f"[{params['branding']}Model] \033[94mCoqui Public Model License\033[0m")
+        print(f"[{params['branding']}Model] \033[94mhttps://coqui.ai/cpml.txt\033[0m")
     return model
 
 
@@ -252,6 +248,8 @@ async def xtts_manual_load_model():
         use_deepspeed=params["deepspeed_activate"],
     )
     model.to(device)
+    print(f"[{params['branding']}Model] \033[94mCoqui Public Model License\033[0m")
+    print(f"[{params['branding']}Model] \033[94mhttps://coqui.ai/cpml.txt\033[0m")
     return model
 
 # MODEL LOADER For "XTTSv2 FT"
@@ -270,6 +268,8 @@ async def xtts_ft_manual_load_model():
         use_deepspeed=params["deepspeed_activate"],
     )
     model.to(device)
+    print(f"[{params['branding']}Model] \033[94mCoqui Public Model License\033[0m")
+    print(f"[{params['branding']}Model] \033[94mhttps://coqui.ai/cpml.txt\033[0m")
     return model
 
 # MODEL UNLOADER
@@ -476,6 +476,11 @@ async def deepspeed(request: Request, new_deepspeed_value: bool):
 ########################
 #### TTS GENERATION ####
 ########################
+debug_generate_audio = False
+tts_stop_generation = False # Called to stop generation of the current text at whatever stage its at. currently only set for streaming.
+tts_generation_lock = False # Tracks locking of the generaetion process.
+tts_narrator_generatingtts = False # Tracks if the current tts processes are narrator based, to avoid moving model on each chunk of text generated.
+# Not in use tts_queue_behaviour = False # True is to queue the current generation request. False is to cancel the current generation and start a new one.
 
 # TTS VOICE GENERATION METHODS (called from voice_preview and output_modifer)
 async def generate_audio(text, voice, language, temperature, repetition_penalty, output_file, streaming=False):
@@ -486,9 +491,11 @@ async def generate_audio(text, voice, language, temperature, repetition_penalty,
         return response
     async for _ in response:
         pass
-    
+
+
 async def generate_audio_internal(text, voice, language, temperature, repetition_penalty, output_file, streaming):
-    global model
+    global model, tts_stop_generation, tts_generation_lock
+    tts_generation_lock = True
     if params["low_vram"] and device == "cpu":
         await switch_device()
     generate_start_time = time.time()  # Record the start time of generating TTS
@@ -539,6 +546,12 @@ async def generate_audio_internal(text, voice, language, temperature, repetition
             yield wav_buf.read()
 
             for i, chunk in enumerate(output):
+                if tts_stop_generation:
+                    print(f"[{params['branding']}TTSGen] Stopping audio generation.")
+                    file_chunks.clear()  # Clear the file_chunks list
+                    tts_stop_generation = False
+                    tts_generation_lock = False
+                    break
                 file_chunks.append(chunk)
                 if isinstance(chunk, list):
                     chunk = torch.cat(chunk, dim=0)
@@ -547,6 +560,7 @@ async def generate_audio_internal(text, voice, language, temperature, repetition
                 chunk = np.clip(chunk, -1, 1)
                 chunk = (chunk * 32767).astype(np.int16)
                 yield chunk.tobytes()
+                print(f"[{params['branding']}Debug] Stream audio generation: Yielded audio chunk {i}.") if debug_generate_audio else None   
         else:
             # Non-streaming-specific operation
             torchaudio.save(output_file, torch.tensor(output["wav"]).unsqueeze(0), 24000)
@@ -565,9 +579,9 @@ async def generate_audio_internal(text, voice, language, temperature, repetition
             file_path=output_file,
             speaker_wav=[f"{this_dir}/voices/{voice}"],
             language=language,
-            temperature=temperature,
+            temperature=float(temperature),
-            length_penalty=model.config.length_penalty,
+            length_penalty=float(model.config.length_penalty),
-            repetition_penalty=repetition_penalty,
+            repetition_penalty=float(repetition_penalty),
             top_k=model.config.top_k,
             top_p=model.config.top_p,
         )
@@ -589,12 +603,11 @@ async def generate_audio_internal(text, voice, language, temperature, repetition
     # Print Generation time and settings
     generate_end_time = time.time()  # Record the end time to generate TTS
     generate_elapsed_time = generate_end_time - generate_start_time
-    print(
+    print(f"[{params['branding']}TTSGen] \033[93m{generate_elapsed_time:.2f} seconds. \033[94mLowVRAM: \033[33m{params['low_vram']} \033[94mDeepSpeed: \033[33m{params['deepspeed_activate']}\033[0m")
-        f"[{params['branding']}TTSGen] \033[93m{generate_elapsed_time:.2f} seconds. \033[94mLowVRAM: \033[33m{params['low_vram']} \033[94mDeepSpeed: \033[33m{params['deepspeed_activate']}\033[0m"
-    )
     # Move model back to cpu system ram if needed.
-    if params["low_vram"] and device == "cuda":
+    if params["low_vram"] and device == "cuda" and tts_narrator_generatingtts == False:
         await switch_device()
+    tts_generation_lock = False
     return
 
 
@@ -854,6 +867,13 @@ async def tts_generate_streaming(request: Request, text: str = Form(...), voice:
         print(f"An error occurred: {e}")
         return JSONResponse(content={"error": "An error occurred"}, status_code=500)
 
+@app.put("/api/stop-generation")
+async def stop_generation_endpoint():
+    global tts_stop_generation, tts_generation_lock
+    if tts_generation_lock and not tts_stop_generation:
+        tts_stop_generation = True
+    return {"message": "Generation stopped"}
+
 ##############################
 #### Standard Generation ####
 ##############################
@@ -890,7 +910,7 @@ class JSONInput(BaseModel):
     narrator_enabled: bool = Field(..., description="narrator_enabled needs to be true or false.")
     narrator_voice_gen: str = Field(..., pattern="^.*\.wav$", description="narrator_voice_gen needs to be the name of a wav file e.g. mysample.wav.")
     text_not_inside: str = Field(..., pattern="^(character|narrator)$", description="text_not_inside needs to be 'character' or 'narrator'.")
-    language: str = Field(..., pattern="^(ar|zh-cn|cs|nl|en|fr|de|hu|it|ja|ko|pl|pt|ru|es|tr)$", description="language needs to be one of the following ar|zh-cn|cs|nl|en|fr|de|hu|it|ja|ko|pl|pt|ru|es|tr.")
+    language: str = Field(..., pattern="^(ar|zh-cn|cs|nl|en|fr|de|hu|hi|it|ja|ko|pl|pt|ru|es|tr)$", description="language needs to be one of the following ar|zh-cn|cs|nl|en|fr|de|hu|hi|it|ja|ko|pl|pt|ru|es|tr.")
     output_file_name: str = Field(..., pattern="^[a-zA-Z0-9_]+$", description="output_file_name needs to be the name without any special characters or file extension e.g. 'filename'")
     output_file_timestamp: bool = Field(..., description="output_file_timestamp needs to be true or false.")
     autoplay: bool = Field(..., description="autoplay needs to be a true or false value.")
@@ -1029,12 +1049,17 @@ async def tts_generate(
             "autoplay_volume": autoplay_volume,
             "streaming": streaming,
         }
+        global tts_narrator_generatingtts
         JSONresult = TTSGenerator.validate_json_input(json_input_data)
         if JSONresult is None:
             pass
         else:
+            print(f"[{params['branding']}API] \033[91mError with API request:\033[0m", JSONresult)
             return JSONResponse(content={"error": JSONresult}, status_code=400)
         if narrator_enabled:
+            if params["low_vram"] and device == "cpu":
+                await switch_device()
+            tts_narrator_generatingtts = True
             processed_parts = process_text(text_input)
             audio_files_all_paragraphs = []
             for part_type, part in processed_parts:
@@ -1054,9 +1079,11 @@ async def tts_generate(
                     voice_description = "\033[36mCharacter (Text-not-inside)\033[0m" if text_not_inside == "character" 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)
+                cleaned_part = re.sub(r'([!?.\u3002\uFF1F\uFF01\uFF0C])\1+', r'\1', part)
+                # Replace "Chinese ellipsis" with a single dot
+                cleaned_part = re.sub(r"\u2026{1,2}", ". ", 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)
+                cleaned_part = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', cleaned_part)
                 # Remove all newline characters (single or multiple)
                 cleaned_part = re.sub(r'\n+', ' ', cleaned_part)
                 output_file = this_dir / "outputs" / f"{output_file_name}_{uuid.uuid4()}_{int(time.time())}.wav"
@@ -1066,6 +1093,10 @@ async def tts_generate(
                 audio_files_all_paragraphs.append(audio_path)
             # Combine audio files across paragraphs
             output_file_path, output_file_url, output_cache_url = combine(output_file_timestamp, output_file_name, audio_files_all_paragraphs)
+            tts_narrator_generatingtts = False
+            # Move model back to cpu system ram if needed.
+            if params["low_vram"] and device == "cuda":
+                await switch_device()
         else:
             if output_file_timestamp:
                 timestamp = int(time.time())
@@ -1085,15 +1116,19 @@ async def tts_generate(
                 output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}.wav'
             if text_filtering == "html":
                 cleaned_string = html.unescape(standard_filtering(text_input))
-                cleaned_string = re.sub(r'([!?.])\1+', r'\1', text_input)
+                cleaned_string = re.sub(r'([!?.\u3002\uFF1F\uFF01\uFF0C])\1+', r'\1', text_input)
+                # Replace "Chinese ellipsis" with a single dot
+                cleaned_string = re.sub(r"\u2026{1,2}", ". ", cleaned_string)
                 # Further clean to remove any other unwanted characters
-                cleaned_string = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_string)
+                cleaned_string = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', cleaned_string)
                 # Remove all newline characters (single or multiple)
                 cleaned_string = re.sub(r'\n+', ' ', cleaned_string)
             elif text_filtering == "standard":
-                cleaned_string = re.sub(r'([!?.])\1+', r'\1', text_input)
+                cleaned_string = re.sub(r'([!?.\u3002\uFF1F\uFF01\uFF0C])\1+', r'\1', text_input)
+                # Replace "Chinese ellipsis" with a single dot
+                cleaned_string = re.sub(r"\u2026{1,2}", ". ", cleaned_string)
                 # Further clean to remove any other unwanted characters
-                cleaned_string = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_string)
+                cleaned_string = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', cleaned_string)
                 # Remove all newline characters (single or multiple)
                 cleaned_string = re.sub(r'\n+', ' ', cleaned_string)
             else: