F5 tts brazilian LLM Model

Introduction

F5-TTS is a text-to-speech model designed for synthesizing speech in Brazilian Portuguese. It can generate speech using reference audio to mimic voice characteristics, allowing for personalized AI-driven audio content.

Architecture

The architecture of F5-TTS leverages sophisticated deep learning techniques to analyze a few seconds of audio input and generate text-to-speech outputs that reflect the voice characteristics of the input.

Training

Details on the specific training process of F5-TTS are not provided in the documentation. However, it typically involves training on vast datasets of paired text and audio to learn accurate speech synthesis.

Guide: Running Locally

To run the F5-TTS model locally, follow these steps:

Clone the Repository
Clone the repository to your local environment:
```
git clone https://github.com/SWivid/F5-TTS.git
cd F5-TTS
```

Download the Model Weights
Use the wget command to download the model weights:

wget https://hf.rst.im/ModelsLab/F5-tts-brazilian/resolve/main/Brazilian_Portuguese/model_2600000.pt -P ckpts/

Install CUDA
Install an appropriate CUDA version compatible with your PyTorch and torchaudio versions:

pip install torch==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
pip install torchaudio==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118

Install Required Python Packages
Install the dependencies from the requirements.txt file:
```
pip install -r requirements.txt
```
System Setup: APT Update and FFmpeg
Ensure your system is updated and has FFmpeg for audio processing:
```
apt update
apt install -y ffmpeg
```

Run Inference with the F5-TTS Model
Execute the inference script, adjusting paths as necessary:

python inference-cli.py \
  --model "F5-TTS" \
  --ckpt_file "path/to/model.pt" \
  --ref_audio "wavs/sample_audio.wav" \
  --ref_text "levantara a mão contra ele..." \
  --gen_text "O Brasil, oficialmente República Federativa do Brasil..."

Cloud GPU Suggestion

For optimal performance, consider using cloud GPUs such as those offered by AWS, Google Cloud, or Azure to handle the computational demands of running F5-TTS.

License

The licensing information for F5-TTS is not explicitly stated in the provided documentation. Users should refer to the repository or model card for detailed licensing terms.

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