How to Use Hugging Face Transformers for Text-to-Speech Applications

Hugging Face provides powerful models for TTS. These models can convert written text into spoken words. In this article, we will explore how to use Hugging Face Transformers to create TTS applications. We will focus on popular models like Tacotron2 and FastSpeech2. These models are made to create a speech that sounds natural and human-like. You will learn to choose a model, load it, and generate speech from text.

What is Text-to-Speech?

 
Text-to-Speech (TTS) is a technology that changes written text into spoken words. It uses AI models to make the text sound like real speech. TTS is useful in many areas. It helps virtual assistants like Siri and Alexa talk. It can also be used for audiobooks or tools for people who can’t see well. TTS makes it easier for people to get information by listening instead of reading. The quality of the voice depends on the model. Some TTS voices sound very natural, like real humans. You can also change the speed or tone of the voice in some systems.

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Install the Necessary Libraries

 
First, install the Hugging Face Transformers library. You also need to install torch (PyTorch). Finally, install the TTS library for text-to-speech.

pip install transformers torch TTS

Choose a TTS Model

 
Hugging Face provides a variety of pre-trained models that can turn text into speech. For TTS applications, you can use models like Tacotron2 or FastSpeech2. These models have been trained to convert text into human-like speech. You can browse available models on Hugging Face’s Model Hub and search for models tagged with “text-to-speech”.

Example Model Names

• Tacotron2: tts_models/en/ljspeech/tacotron2
• FastSpeech2: tts_models/en/ljspeech/fastspeech2

Loading the Model and Tokenizer

Now, let’s load the chosen model. While Hugging Face’s transformers library is mainly used for text-processing models, we will use the TTS library to load TTS models.

# Import TTS
from TTS.api import TTS

# Initialize the TTS model (Tacotron2 + HiFi-GAN)
tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False, gpu=False)

Convert Text to Speech

 
Now, you can convert any text to speech using the loaded model. The text variable contains the text that we want to convert into speech. This can be any sentence or phrase. The TTS library makes it easy to convert the text into audio and save it as a file.

# Text to be converted to speech
text = "Hello! Welcome to the world of Text-to-Speech using the TTS library."

# Convert the text to speech and save it as an audio file
tts.tts_to_file(text=text, file_path="output.wav")

Play the Generated Audio

 
Once you have generated the audio file, you can use Python libraries like pydub to play the sound directly in your script or use a media player to listen to it.

pip install pydub
from pydub import AudioSegment
from pydub.playback import play

# Load and play the audio
audio = AudioSegment.from_wav("output.wav")
play(audio)

Using Different TTS Models

 
If you want to experiment with different models, you can easily switch by changing the model_name parameter in the TTS() function.

Example: Using FastSpeech 2 for TTS

# Load the FastSpeech 2 model instead of Tacotron 2
tts = TTS(model_name="tts_models/en/ljspeech/fastspeech2", progress_bar=False, gpu=False)

# Convert text to speech and save as audio
tts.tts_to_file(text="This is a demo of FastSpeech 2.", file_path="fastspeech_output.wav")

Conclusion

 
In this article, we learned how to use Hugging Face Transformers for Text-to-Speech (TTS) applications. We discussed popular models like Tacotron2 and FastSpeech2. These models help convert text into natural-sounding speech.

We discussed how to choose a model, load it, and generate speech from text. Now you have the tools to create your own TTS applications. You can make your projects more interactive and accessible. Thank you for following along!
 
 

Jayita Gulati is a machine learning enthusiast and technical writer driven by her passion for building machine learning models. She holds a Master’s degree in Computer Science from the University of Liverpool.