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.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tortoise/voices/angie/2.wav filter=lfs diff=lfs merge=lfs -text
+tortoise/voices/deniro/2.wav filter=lfs diff=lfs merge=lfs -text
+tortoise/voices/train_lescault/lescault_new4.wav filter=lfs diff=lfs merge=lfs -text

CITATION.cff

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+cff-version: 1.3.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Betker"
+  given-names: "James"
+  orcid: "https://orcid.org/my-orcid?orcid=0000-0003-3259-4862"
+title: "TorToiSe text-to-speech"
+version: 2.0
+date-released: 2022-04-28
+url: "https://github.com/neonbjb/tortoise-tts"

Dockerfile

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+FROM nvidia/cuda:12.2.0-base-ubuntu22.04
+
+COPY . /app
+
+RUN apt-get update && \
+    apt-get install -y --allow-unauthenticated --no-install-recommends \
+    wget \
+    git \
+    && apt-get autoremove -y \
+    && apt-get clean -y \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV HOME "/root"
+ENV CONDA_DIR "${HOME}/miniconda"
+ENV PATH="$CONDA_DIR/bin":$PATH
+ENV CONDA_AUTO_UPDATE_CONDA=false
+ENV PIP_DOWNLOAD_CACHE="$HOME/.pip/cache"
+ENV TORTOISE_MODELS_DIR
+
+RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/miniconda3.sh \
+    && bash /tmp/miniconda3.sh -b -p "${CONDA_DIR}" -f -u \
+    && "${CONDA_DIR}/bin/conda" init bash \
+    && rm -f /tmp/miniconda3.sh \
+    && echo ". '${CONDA_DIR}/etc/profile.d/conda.sh'" >> "${HOME}/.profile"
+
+# --login option used to source bashrc (thus activating conda env) at every RUN statement
+SHELL ["/bin/bash", "--login", "-c"]
+
+RUN conda create --name tortoise python=3.9 numba inflect \
+    && conda activate tortoise \
+    && conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia \
+    && conda install transformers=4.29.2 \
+    && cd /app \
+    && python setup.py install

LICENSE

@@ -0,0 +1,201 @@
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MANIFEST.in

@@ -0,0 +1,2 @@
+recursive-include tortoise/data *
+recursive-include tortoise/voices *

README2.md

@@ -0,0 +1,53 @@
+Check out original repo for more details and usage: [Original Repo](https://github.com/neonbjb/tortoise-tts)
+
+# Windows Install
+1. Create and activate new conda environment.
+```
+conda create --name tortoise-WebUI python=3.9 -y && conda activate tortoise-WebUI
+```
+
+2. Install pytorch.
+```
+conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia -y
+```
+
+3. Confirm CUDA is available
+```
+python
+import torch
+torch.cuda.is_available()
+exit()
+
+```
+
+4. Clone the repository and cd into the new folder
+```
+git clone https://github.com/natlamir/tortoise-WebUI.git && cd tortoise-WebUI
+```
+
+5. Install dependencies
+```
+pip install -r requirements.txt
+```
+
+6. Install pysoundfile
+```
+conda install -c conda-forge pysoundfile -y
+```
+
+7. Run tortoise setup file
+```
+python setup.py install
+```
+
+# Usage
+Using the example command from original repo
+```
+python tortoise/do_tts.py --text "we have now re-installed tortoise. enjoy!" --voice random --preset fast
+```
+
+Or use this command to locally run the gradio web UI (same as the one from [huggingface space](https://huggingface.co/spaces/Manmay/tortoise-tts)) with some modifications to be able to use locally
+```
+python app.py
+```
+

app.py

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+import os
+import torch
+import gradio as gr
+import torchaudio
+import time
+import numpy as np
+from datetime import datetime
+from tortoise.api import TextToSpeech, MODELS_DIR
+from tortoise.utils.text import split_and_recombine_text
+from tortoise.utils.audio import load_audio, load_voice, load_voices
+
+VOICE_OPTIONS = []
+
+def inference(
+    text,
+    script,
+    voice,
+    voice_b,
+    seed,
+    split_by_newline,
+):
+    if text is None or text.strip() == "":
+        with open(script.name) as f:
+            text = f.read()
+        if text.strip() == "":
+            raise gr.Error("Please provide either text or script file with content.")
+
+    if split_by_newline == "Yes":
+        texts = list(filter(lambda x: x.strip() != "", text.split("\n")))
+    else:
+        texts = split_and_recombine_text(text)
+
+    voices = [voice]
+    if voice_b != "disabled":
+        voices.append(voice_b)
+
+    if len(voices) == 1:
+        voice_samples, conditioning_latents = load_voice(voice)
+    else:
+        voice_samples, conditioning_latents = load_voices(voices)
+
+    start_time = time.time()
+
+    gen, dbg_state = tts.tts_with_preset(text=text, k=1, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
+                                  preset='fast', use_deterministic_seed=None, return_deterministic_state=True, cvvp_amount=.0)
+
+    sep_segment = gen.squeeze(0).squeeze(0).data.cpu().numpy()
+    return 24000, np.round(sep_segment * 32767).astype(np.int16)
+
+def main():
+    title = "Tortoise TTS 🐢"
+    description = """
+    A text-to-speech system which powers lot of organizations in Speech synthesis domain.
+    <br/>
+    a model with strong multi-voice capabilities, highly realistic prosody and intonation.
+    <br/>
+    for faster inference, use the 'ultra_fast' preset and duplicate space if you don't want to wait in a queue.
+    <br/>
+    """
+    
+    for root, dirs, files in os.walk("tortoise/voices"):
+        for folder in dirs:
+            VOICE_OPTIONS.append(folder)
+    
+    text = gr.Textbox(
+        lines=4,
+        label="Text (Provide either text, or upload a newline separated text file below):",
+    )
+    script = gr.File(label="Upload a text file")
+
+    voice = gr.Dropdown(
+        VOICE_OPTIONS, value="jane_eyre", label="Select voice:", type="value"
+    )
+    voice_b = gr.Dropdown(
+        VOICE_OPTIONS,
+        value="disabled",
+        label="(Optional) Select second voice:",
+        type="value",
+    )
+    split_by_newline = gr.Radio(
+        ["Yes", "No"],
+        label="Split by newline (If [No], it will automatically try to find relevant splits):",
+        type="value",
+        value="No",
+    )
+
+    output_audio = gr.Audio(label="streaming audio:", scale=10)
+    # download_audio = gr.Audio(label="dowanload audio:")
+    interface = gr.Interface(
+        fn=inference,
+        inputs=[
+            text,
+            script,
+            voice,
+            voice_b,
+            split_by_newline,
+        ],
+        title=title,
+        description=description,
+        outputs=[output_audio],
+    )
+    interface.queue().launch(inbrowser=True)
+
+if __name__ == "__main__":
+    #tts = TextToSpeech(kv_cache=True, use_deepspeed=False, half=True)
+    tts = TextToSpeech(models_dir=MODELS_DIR, use_deepspeed=False, kv_cache=True, half=True)
+
+    with open("Tortoise_TTS_Runs_Scripts.log", "a") as f:
+        f.write(
+            f"\n\n-------------------------Tortoise TTS Scripts Logs, {datetime.now()}-------------------------\n"
+        )
+
+    main()