Implement text-to-speech TT with the Transferes library embracing Face in Google Colab environment
Text-to-speech (TTS) technology has undergone tremendous developments in recent years, from robotic sound to highly natural speech synthesis. Bark is an impressive open source TTS model developed by Suno that can produce very human-like voices in multiple languages with nonverbal voices such as laughing, sighing, and crying.
In this tutorial, we will implement bark using Hugging Face’s Transformers library in Google Colab environment. By the end, you will be able to:
- Set up and run bark in Colab
- Generate voice from text input
- Try different voices and speaking styles
- Create a practical TTS application
The bark is fascinating because it is a fully generated text with the original model that produces natural speech, music, background noise and simple sound effects. Unlike many other TTS systems that rely on extensive audio preprocessing and voice cloning, bark can produce multiple sounds without speaker-specific training.
Let’s get started!
Implementation steps
Step 1: Set up the environment
First, we need to install the necessary libraries. Bark requires Transformers to hug the face, among other dependencies:
# Install the required libraries
!pip install transformers==4.31.0
!pip install accelerate
!pip install scipy
!pip install torch
!pip install torchaudioNext, we will import the library we will use:
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}")Step 2: Load the bark model
Now, let’s load the bark model and processor from the hug face:
# 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)Bark is a relatively large model, so this step may take a minute or two to complete the model weight.
Step 3: Generate basic voice
Let’s start with a simple example to generate speech from text:
# 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")Output: To listen to the audio, please refer to the notebook (please find the attached link at the end
Step 4: Use different speaker presets
Bark comes with several predefined speaker presets in different languages. Let’s explore how to 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))Step 5: Generate a multilingual speech
Bark is available in several languages out of the box. Let’s generate speeches 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")Step 6: Create a Practical Application – Audio Book Generator
Let’s build a simple audiobook generator that converts text paragraphs 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 Rabbit with pink eyes ran close by her.
"""
# Generate audiobook
audiobook = generate_audiobook(book_excerpt)
# Play the audio
ipd.display(ipd.Audio(audiobook, rate=sampling_rate))
# Save the audio file
write("alice_audiobook.wav", sampling_rate, audiobook)
print("Audiobook saved to alice_audiobook.wav")In this tutorial, we successfully implemented the Bark Toxech Model using the Transfereers library embracing Face in Google Colab. In this tutorial, we learned how to:
- Set up and load the bark model in the Colab environment
- Generate basic voice from text input
- Use different speaker presets as variety
- Create multilingual voice
- Build a practical audiobook generator application
Bark represents an impressive advancement in text-to-voice technology, providing high-quality expressive speech generation without extensive training or fine-tuning.
Future experiments you can try
Some potential next steps to further explore and expand your bark work:
- Voice cloning: Experiment using voice cloning techniques to produce speech that mimics a specific speaker.
- Integrate with other systems: Combining tree bark with other AI models, such as language models for dynamic personalized voice assistants for restaurants and reception, content generation, translation systems, etc.
- Web Applications: Build a web interface for your TTS system to make it easier to access.
- Custom fine tune: Explore techniques for fine-tuning bark for specific areas or speaking styles.
- Performance optimization: Investigate methods to optimize the inference speed of real-time applications. This is an important aspect for any application in production, as these megamo models are even the reasoning time that processes a small portion of text, as they generalize in a large number of use cases all take a lot of time.
- Quality Assessment: Implement objective and subjective evaluation indicators to evaluate the quality of the voice produced.
The text-to-voice field is growing rapidly, and projects like Bark are pushing the boundaries of possible. As you continue to explore this technology, you will find more exciting applications and improvements.
This is COLAB notebook. Also, don’t forget to follow us twitter And join us Telegram Channel and LinkedIn GrOUP. Don’t forget to join us 80k+ ml subcolumn count.
Meet Parlant: LLM-first conversational AI framework designed to provide developers with the control and accuracy they need for AI customer service agents, leveraging behavioral guidelines and runtime supervision. 
It is operated using an easy-to-use CLI 
and local client SDK in Python and Typescript 
.
First on Marktechpost, use the Transformers library to embrace Face to implement text-to-speech TT posts in Google Colab environment using the Transformers library for embrace Face.
