Implementation of text-to-speech TTS with bark using Hugging Face’s Transformers Library in a Google Colab environment

# Install the required libraries
!pip install transformers==4.31.0
!pip install accelerate
!pip install scipy
!pip install torch
!pip install torchaudio

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}")

# 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)

# 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")

# 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))

# 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")

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")