In this tutorial, we will learn how to use Tacotron2 for TTS Voice Models in order to create voice clones. Tacotron2 is a deep learning model that is able to generate human-like speech from text input. By following the steps outlined in this tutorial, you will be able to create your own voice clones quickly and easily.
Step 1: Set up your environment
Before we can start using Tacotron2, we need to set up our development environment. Ensure that you have Python installed on your system, as well as the necessary libraries such as TensorFlow and PyTorch. You can install these libraries using pip:
pip install tensorflow
pip install torchIt is also recommended to set up a virtual environment to keep your project dependencies separate from other projects. You can create a virtual environment using the following commands:
pip install virtualenv
virtualenv venv
source venv/bin/activateStep 2: Download the Tacotron2 model
The Tacotron2 model is freely available online and can be downloaded from various sources. You can find the model on the official Tacotron2 GitHub repository or from other online repositories. Once you have downloaded the model, extract it to a folder on your system.
Step 3: Preprocess your text data
Before we can use the Tacotron2 model, we need to preprocess our text data. This involves tokenizing the text and converting it into a format that the model can understand. You can use libraries such as NLTK or SpaCy to tokenize your text data. Once you have tokenized the text, save it to a file in a format that the model can read.
Step 4: Load the Tacotron2 model
Now that we have our text data preprocessed, we can load the Tacotron2 model into our Python script. You can do this by importing the necessary libraries and loading the model file using PyTorch. Here is an example code snippet to load the Tacotron2 model:
import torch
from tacotron2 import Tacotron2
model = Tacotron2()
model.load_state_dict(torch.load('path/to/model.pth'))
model.eval()Step 5: Generate voice clones
With the Tacotron2 model loaded, we can now generate voice clones from our text data. To do this, simply pass the tokenized text data through the model and decode the output into speech. Here is an example code snippet to generate voice clones using the Tacotron2 model:
text = "Hello, how are you?"
text_tensor = torch.tensor(tokenized_text_data)
with torch.no_grad():
mel_outputs, mel_outputs_postnet, _, alignments = model(text_tensor)
# Decode the mel outputs into speechStep 6: Save and export your voice clones
Once you have generated your voice clones, you can save and export them as audio files. You can use libraries such as librosa to save the mel outputs as audio files in WAV format. Here is an example code snippet to save your voice clones as audio files:
import librosa
wav_output = mel2wav(mel_outputs_postnet)
librosa.output.write_wav('output.wav', wav_output, sr=22050)And that’s it! By following these steps, you can easily create voice clones using Tacotron2 for TTS Voice Models. Experiment with different text inputs and parameters to create unique voice clones for your projects.
Hi sir there are some problem in begin traning. Some error found of code. How to resolve those?
this looks complicated
Have you tried Coqui TTS. Its a gold mine, you can clown your a voice with just a single recorded memo. The output is great
Hello, I had a question, it gave me an error. I am doing it for the Uzbek language. How can I fix it? I was running FP16: False
Dynamic Loss Scaling: True
Distributed Run: False
cuDNN Enabled: True
cuDNN Benchmark: False
—————————————————– ————————-
ValueError Traceback (most recent call last)
<ipython-input-26-0b097e06e1b2> in <cell line: 17>()
15 print('cuDNN Enabled:', hparams.cudnn_enabled)
16 print('cuDNN Benchmark:', hparams.cudnn_benchmark)
—> 17 train(output_directory, log_directory, checkpoint_path,
18 warm_start, n_gpus, rank, group_name, hparams, log_directory2,
19 save_interval, backup_interval)
3 frames
/usr/local/lib/python3.10/dist-packages/torch/utils/data/sampler.py in __init__(self, data_source, replacement, num_samples, generator)
141
142 if not isinstance(self.num_samples, int) or self.num_samples <= 0:
–> 143 raise ValueError(f"num_samples should be a positive integer value, but got num_samples={self.num_samples}")
144
145 @property
ValueError: num_samples should be a positive integer value, but got num_samples=0
Omg, this video waffling drove me crazy all fir a terrible output 😂😂😂
Did you intentionally type every prompt's sentence incorrectly?
ModuleNotFoundError: No module named 'taglib' -> pip install pytaglib
Great video, used this and it worked excellently but every prompt you used in the video had typos in it hahaha
This is the most chaotic programming how-to I've ever seen. You automated like 80% of the process, but you still need users to do trivial things like rename folders and copy/paste paths at each step. And you read the phrases from a text script, but then use speech to text recreate the script, which inherently adds errors?
I can't tell if you actually know how the process works, ir you're jyst mimicking what someone else told you to do.
buenas, me falta el ultimo paso con los archivos BAT de tu web, pero no lo encuentro. Muchas gracias por ese pedazo de tutorial
19:09 this doesn't work for me. the error message is "ValueError: num_samples should be a positive integer value, but got num_samples=0"
Creating a Text-to-Talk Model of Your Voice
This video outlines the process of creating a text-to-talk model of your own voice using the Tacotron 2 model. The model will be able to read out any text you type, sounding like your own voice.
Requirements:
* Microphone: A good quality microphone is recommended for optimal results.
* ChatGPT: Used to generate sentences for recording and training the model.
* Visual Studio Code: Used to run the provided code for renaming and transcribing audio files.
* Python: Required for running the pre-processing and metadata updating scripts.
* Google Drive: Used to store and access the training data.
* Google Colab: Online platform for training and synthesizing the model.
Process:
1. Recording Sentences (0:30):
* Use ChatGPT to generate 50 sentences for training the model.
* Record yourself reading each sentence, saving each as a separate audio clip.
* More audio clips will result in a better sounding model.
2. Organizing Audio Files (1:21):
* Create a folder named "wavs" to store your audio clips.
* Rename the audio files from "1.wav" to "25.wav" (or however many clips you have).
* A provided script can automate this renaming process.
3. Transcribing Audio (2:16):
* Use the provided "transcribe_wav_to_rec.py" script to generate rough transcripts of your recordings.
* Edit the generated transcripts for accuracy.
* Ensure each line ends with a period and avoid using capitals or commas.
4. Pre-processing Audio (6:24):
* Use the "tacatron2_preprocessing_wav_files.py" script to convert the audio files to the format required by Tacotron 2.
* This script changes the audio format, including sample rate and channels.
5. Updating Metadata (9:24):
* Use the provided script to update the title of each WAV file to match its corresponding number.
6. Uploading Data to Google Drive (13:00):
* Create a compressed zip folder of your "wavs" folder.
* Upload the zipped folder to your Google Drive.
7. Training the Model in Google Colab (14:00):
* Open the provided "training_notebook.ipynb" file in Google Colab.
* Follow the steps in the notebook to:
* Check your GPU.
* Mount your Google Drive.
* Install Tacotron 2.
* Load your dataset.
* Train the model.
* Monitor the loss values and stop the training when it reaches an acceptable level (below 0.30).
8. Synthesizing Speech (20:00):
* Open the provided "synthesize_notebook.ipynb" file in Google Colab.
* Share the trained model file from your Google Drive and paste the link in the notebook.
* Enter a phrase you want the model to say and run the script.
* Play the generated audio file to hear your text read out in your own voice. (25:39) is the best model (still not usable)
I used gemini 1.5 pro to summarize the transcript
Token count
11,117 / 1,048,576
How come new startups like HeyGen can generate good quality audio from just 30 seconds of audio? And here from 10 files the output is so bad?
They have better pre-trained models?
Thanks for the info. Do you know if is possible to download the generated cloned voice for being able to define that voice to be used with the mozilla TTS browser API? I know that you can define a custom voice, I don't know exactly the file format for that also, is this exportable?
What can I do to implement other languages
I just wanted to say I have same wallpaper 🙂
Hello love the tutorial. i wanted to ask if there is a way to run this on local pc without using google coab.
FileNotFoundError: '/content/TTS-TT2/wavs/1.npy|escuchame john vos tenes armado el video del cierre primario de coledoco del otro dia o tenes armado algo para un ateneo para hacerlo ya' no existe. Compruebe su transcripción y sus audios.
What's the point of setting up the metadata when the "Load Dataset" step of the trainer says it removes all metadata?
can you please make video on waveglow?