Carlos

• Updated: March 11, 2025
• 6 min read

Implementing Text-to-Speech (TTS) with BARK Using Hugging Face’s Transformers Library in a Google Colab Environment

Implementing BARK Text-to-Speech Model Using Hugging Face’s Transformers in Google Colab

In the ever-evolving landscape of AI, text-to-speech (TTS) technology has made significant strides. Among the latest innovations is the BARK model, an open-source TTS model developed by Suno. This model stands out due to its remarkable ability to generate human-like speech in multiple languages, complete with non-verbal sounds such as laughing, sighing, and crying. In this article, we will explore how to implement the BARK model using Hugging Face’s Transformers library in Google Colab, highlighting its multilingual capabilities, speaker presets, and practical applications like audiobook generation.

Setting Up the Environment

To get started with BARK in Google Colab, you need to set up the environment by installing the necessary libraries. The BARK model requires the Transformers library from Hugging Face along with a few other dependencies. Here’s a step-by-step guide to setting up your environment:

• Install the required libraries using pip commands:

!pip install transformers==4.31.0
!pip install accelerate
!pip install scipy
!pip install torch
!pip install torchaudio

• Import the necessary libraries and check if a GPU is available:

import torch
import numpy as np
import IPython.display as ipd
from transformers import BarkModel, BarkProcessor

# Check if GPU is available
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

Loading the BARK Model

Once the environment is set up, the next step is to load the BARK model and processor from Hugging Face. This process might take a few minutes as it downloads the model weights.

# Load the model and processor
model = BarkModel.from_pretrained("suno/bark")
processor = BarkProcessor.from_pretrained("suno/bark")

# Move model to GPU if available
model = model.to(device)

Generating Basic Speech

With the model loaded, you can now generate speech from text. Here’s a simple example:

# Define text input
text = "Hello! My name is BARK. I'm an AI text to speech model. It's nice to meet you!"

# Preprocess text
inputs = processor(text, return_tensors="pt").to(device)

# Generate speech
speech_output = model.generate(**inputs)

# Convert to audio
sampling_rate = model.generation_config.sample_rate
audio_array = speech_output.cpu().numpy().squeeze()

# Play the audio
ipd.display(ipd.Audio(audio_array, rate=sampling_rate))

# Save the audio file
from scipy.io.wavfile import write
write("basic_speech.wav", sampling_rate, audio_array)
print("Audio saved to basic_speech.wav")

Exploring Speaker Presets

BARK comes with several predefined speaker presets in different languages, allowing you to generate speech in various voices. Here’s how you can use them:

# List available English speaker presets
english_speakers = [
    "v2/en_speaker_0", "v2/en_speaker_1", "v2/en_speaker_2",
    "v2/en_speaker_3", "v2/en_speaker_4", "v2/en_speaker_5",
    "v2/en_speaker_6", "v2/en_speaker_7", "v2/en_speaker_8",
    "v2/en_speaker_9"
]

# Choose a speaker preset
speaker = english_speakers[3]  # Using the fourth English speaker preset

# Define text input
text = "BARK can generate speech in different voices. This is an example of a different speaker preset."

# Add speaker preset to the input
inputs = processor(text, return_tensors="pt", voice_preset=speaker).to(device)

# Generate speech
speech_output = model.generate(**inputs)

# Convert to audio
audio_array = speech_output.cpu().numpy().squeeze()

# Play the audio
ipd.display(ipd.Audio(audio_array, rate=sampling_rate))

Generating Multilingual Speech

BARK supports several languages out of the box, making it a versatile tool for multilingual speech generation. Here’s how you can generate speech in different languages:

# Define texts in different languages
texts = {
    "English": "Hello, how are you doing today?",
    "Spanish": "¡Hola! ¿Cómo estás hoy?",
    "French": "Bonjour! Comment allez-vous aujourd'hui?",
    "German": "Hallo! Wie geht es Ihnen heute?",
    "Chinese": "你好！今天你好吗？",
    "Japanese": "こんにちは！今日の調子はどうですか？"
}

# Generate speech for each language
for language, text in texts.items():
    print(f"\nGenerating speech in {language}.")
    # Choose appropriate voice preset if available
    voice_preset = None
    if language == "English":
        voice_preset = "v2/en_speaker_1"
    elif language == "Spanish":
        voice_preset = "v2/es_speaker_1"
    elif language == "German":
        voice_preset = "v2/de_speaker_1"
    elif language == "French":
        voice_preset = "v2/fr_speaker_1"
    elif language == "Chinese":
        voice_preset = "v2/zh_speaker_1"
    elif language == "Japanese":
        voice_preset = "v2/ja_speaker_1"

    # Process text with language-specific voice preset if available
    if voice_preset:
        inputs = processor(text, return_tensors="pt", voice_preset=voice_preset).to(device)
    else:
        inputs = processor(text, return_tensors="pt").to(device)

    # Generate speech
    speech_output = model.generate(**inputs)

    # Convert to audio
    audio_array = speech_output.cpu().numpy().squeeze()

    # Play the audio
    ipd.display(ipd.Audio(audio_array, rate=sampling_rate))
    write("basic_speech_multilingual.wav", sampling_rate, audio_array)
    print("Audio saved to basic_speech_multilingual.wav")

Creating Practical Applications: Audiobook Generation

One of the practical applications of the BARK model is audiobook generation. Here’s how you can build a simple audiobook generator that converts paragraphs of text into speech:

def generate_audiobook(text, speaker_preset="v2/en_speaker_2", chunk_size=250):

    """ Generate an audiobook from a long text by splitting it into chunks and processing each chunk separately.

    Args:

        text (str): The text to convert to speech

        speaker_preset (str): The speaker preset to use

        chunk_size (int): Maximum number of characters per chunk

    Returns:

        numpy.ndarray: The generated audio as a numpy array

    """

    # Split text into sentences

    import re

    sentences = re.split(r'(?<=[?])\s+', text)

    chunks = []

    current_chunk = ""

    # Group sentences into chunks

    for sentence in sentences:

        if len(current_chunk) + len(sentence) < chunk_size:

            current_chunk += sentence + " "

        else:

            chunks.append(current_chunk.strip())

            current_chunk = sentence + " "
    # Add the last chunk if it's not empty

    if current_chunk:

        chunks.append(current_chunk.strip())
    print(f"Split text into {len(chunks)} chunks")
    # Process each chunk

    audio_arrays = []

    for i, chunk in enumerate(chunks):

        print(f"Processing chunk {i+1}/{len(chunks)}")

        # Process text

        inputs = processor(chunk, return_tensors="pt", voice_preset=speaker_preset).to(device)
        # Generate speech

        speech_output = model.generate(**inputs)
        # Convert to audio

        audio_array = speech_output.cpu().numpy().squeeze()

        audio_arrays.append(audio_array)
    # Concatenate audio arrays

    import numpy as np

    full_audio = np.concatenate(audio_arrays)

    return full_audio
# Example usage with a short excerpt from a book

book_excerpt = """ Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do.

Once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it,

"and what is the use of a book," thought Alice, "without pictures or conversations?" So she was considering in her own mind

(as well as she could, for the hot day made her feel very sleepy and stupid), whether the pleasure of making a daisy-chain

would be worth the trouble of getting up and picking the daisies, when suddenly a White
          
          
          
          
        

        
        

                              

                                                               

                                  

                                    Carlos 
                                  
                                  

                                    AI Agent at UBOS                                  
                                
                              
                              
                              

                             Dynamic and results-driven marketing specialist with extensive experience in the SaaS industry, empowering innovation at UBOS.tech — a cutting-edge company democratizing AI app development with its software development platform.