Implementing Text-to-Speech with BARK in Google Colab using Hugging Face

Text-to-Speech Technology Overview

Text-to-Speech (TTS) technology has significantly advanced, evolving from robotic voices to highly natural speech synthesis. BARK, developed by Suno, is an open-source TTS model that generates human-like speech in multiple languages, including non-verbal sounds like laughter and sighs.

Implementation Objectives

In this tutorial, you will learn to:

• Set up and run BARK in Google Colab
• Generate speech from text input
• Experiment with different voices and speaking styles
• Create practical TTS applications

Why BARK is Unique

BARK is a fully generative text-to-audio model capable of producing natural-sounding speech, music, background noise, and sound effects without the need for extensive audio preprocessing or speaker-specific training.

Implementation Steps

Step 1: Setting Up the Environment

Begin by installing the necessary libraries:

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

Next, import the required libraries:

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

Check if a GPU is available:

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

Step 2: Loading the BARK Model

Load the BARK model and processor:

model = BarkModel.from_pretrained("suno/bark")
processor = BarkProcessor.from_pretrained("suno/bark")
model = model.to(device)

Step 3: Generating Basic Speech

Generate speech from a simple text example:

text = "Hello! My name is BARK. I'm an AI text to speech model. It's nice to meet you!"
inputs = processor(text, return_tensors="pt").to(device)
speech_output = model.generate(**inputs)
sampling_rate = model.generation_config.sample_rate
audio_array = speech_output.cpu().numpy().squeeze()
ipd.display(ipd.Audio(audio_array, rate=sampling_rate))

Step 4: Using Different Speaker Presets

Explore predefined 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"]
speaker = english_speakers[3]
text = "BARK can generate speech in different voices."
inputs = processor(text, return_tensors="pt", voice_preset=speaker).to(device)
speech_output = model.generate(**inputs)
audio_array = speech_output.cpu().numpy().squeeze()
ipd.display(ipd.Audio(audio_array, rate=sampling_rate))

Step 5: Generating Multilingual Speech

Generate speech in various 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？"
}
for language, text in texts.items():
   voice_preset = None
   if language == "English":
       voice_preset = "v2/en_speaker_1"
   # Additional language presets...
   inputs = processor(text, return_tensors="pt", voice_preset=voice_preset).to(device)
   speech_output = model.generate(**inputs)
   audio_array = speech_output.cpu().numpy().squeeze()
   ipd.display(ipd.Audio(audio_array, rate=sampling_rate))

Step 6: Creating a Practical Application – Audio Book Generator

Build an audiobook generator that converts text into speech:

def generate_audiobook(text, speaker_preset="v2/en_speaker_2", chunk_size=250):
   # Function implementation...
   return full_audio
book_excerpt = "Alice was beginning to get very tired..."
audiobook = generate_audiobook(book_excerpt)
ipd.display(ipd.Audio(audiobook, rate=sampling_rate))

Conclusion

In this tutorial, we have successfully implemented the BARK TTS model using Hugging Face’s Transformers library in Google Colab. Key takeaways include:

• Setting up and loading the BARK model
• Generating basic speech from text
• Using different speaker presets
• Creating multilingual speech
• Building an audiobook generator

Future Experimentation

Consider exploring the following:

• Voice Cloning
• Integration with Other Systems
• Web Application Development
• Custom Fine-tuning
• Performance Optimization
• Quality Evaluation

As you delve deeper into TTS technology, you will uncover more innovative applications and enhancements.

For further assistance or inquiries, please contact us at hello@itinai.ru.

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