Voice_cloinig

Text-Based Voice Cloning

This repository contains the Fine tunning code and tts code

Dataset Preparation

The original 20-minute audio file was processed as follows:

Clipping: The audio was divided into smaller clips, each ranging from 3 to 11 seconds in length. Resampling: All clips were converted to a sample rate of 22,050 Hz. Noise Reduction: Background noise was removed from the clips using Audacity. After processing, the clips were organized into two parts:

Training Data: Contains 103 clips. Evaluation Data: Contains 18 clips. For each clip, the following metadata was included:

Path: The file path to the audio clip. Transcription: The corresponding text transcription of the audio. Speaker Name: The name of the speaker. This dataset is now ready for use in training and evaluation. Dataset

Model Training

The model was fine-tuned using the following setup:

1. Training Configuration Language: en Number of Epochs: 30 Batch Size: 2 Gradient Accumulation Steps: 252 Maximum Audio Length: 255,995 samples (~11.6 seconds) Optimizer: AdamW Betas: [0.9, 0.96] Epsilon: 1e-8 Weight Decay: 1e-3 Learning Rate: 1e-5 Scheduler: MultiStepLR with milestones at [50000 * 18, 150000 * 18, 300000 * 18] and gamma = 0.5
2. Dataset Configuration Training Data: 103 audio clips Evaluation Data: 18 audio clips Sample Rate: 22,050 Hz for input, 22,050 Hz for output Max Conditioning Length: 132,300 samples (6 seconds) Min Conditioning Length: 66,150 samples (3 seconds) Max Text Length: 200 characters
3. Model Files The following model files were downloaded and used during training:

DVAE Checkpoint: dvae.pth Mel-Norm File: mel_stats.pth XTTS Checkpoint: model.pth Tokenizer File: vocab.json

Training Process

The model was trained using the fine-tuned parameters, with logs and checkpoints managed using TensorBoard. The training process involved loading the dataset, initializing the model with the GPTTrainerConfig, and using a customized Trainer class to fit the model.