Code for How to Convert Text to Speech in Python Tutorial

tts_google.py

import gtts
from playsound import playsound

# make request to google to get synthesis
tts = gtts.gTTS("Hello world")
# save the audio file
tts.save("hello.mp3")
# play the audio file
playsound("hello.mp3")

# in spanish
tts = gtts.gTTS("Hola Mundo", lang="es")
tts.save("hola.mp3")
playsound("hola.mp3")

# all available languages along with their IETF tag
print(gtts.lang.tts_langs())

tts_pyttsx3.py

import pyttsx3

# initialize Text-to-speech engine
engine = pyttsx3.init()

# convert this text to speech
text = "Python is a great programming language"
engine.say(text)
# play the speech
engine.runAndWait()

# get details of speaking rate
rate = engine.getProperty("rate")
print(rate)

# setting new voice rate (faster)
engine.setProperty("rate", 300)
engine.say(text)
engine.runAndWait()

# slower
engine.setProperty("rate", 100)
engine.say(text)
engine.runAndWait()

# get details of all voices available
voices = engine.getProperty("voices")
print(voices)
# set another voice
engine.setProperty("voice", voices[1].id)
engine.say(text)
engine.runAndWait()

# saving speech audio into a file
engine.save_to_file(text, "python.mp3")
engine.runAndWait()

tts_openai.py

from openai import OpenAI

# initialize the OpenAI API client
api_key = "YOUR_OPENAI_API_KEY"
client = OpenAI(api_key=api_key)

# sample text to generate speech from
text = """In his miracle year, he published four groundbreaking papers. 
These outlined the theory of the photoelectric effect, explained Brownian motion, 
introduced special relativity, and demonstrated mass-energy equivalence."""

# generate speech from the text
response = client.audio.speech.create(
    model="tts-1", # the model to use, there is tts-1 and tts-1-hd
    voice="nova", # the voice to use, there is alloy, echo, fable, onyx, nova, and shimmer
    input=text, # the text to generate speech from
    speed=1.0, # the speed of the generated speech, ranging from 0.25 to 4.0
)
# save the generated speech to a file
response.stream_to_file("openai-output.mp3")

tts_transformers.py

from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset
import torch
import random
import string
import soundfile as sf

device = "cuda" if torch.cuda.is_available() else "cpu"
# load the processor
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
# load the model
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
# load the vocoder, that is the voice encoder
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
# we load this dataset to get the speaker embeddings
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")

# speaker ids from the embeddings dataset
speakers = {
    'awb': 0,     # Scottish male
    'bdl': 1138,  # US male
    'clb': 2271,  # US female
    'jmk': 3403,  # Canadian male
    'ksp': 4535,  # Indian male
    'rms': 5667,  # US male
    'slt': 6799   # US female
}

def save_text_to_speech(text, speaker=None):
    # preprocess text
    inputs = processor(text=text, return_tensors="pt").to(device)
    if speaker is not None:
        # load xvector containing speaker's voice characteristics from a dataset
        speaker_embeddings = torch.tensor(embeddings_dataset[speaker]["xvector"]).unsqueeze(0).to(device)
    else:
        # random vector, meaning a random voice
        speaker_embeddings = torch.randn((1, 512)).to(device)
    # generate speech with the models
    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
    if speaker is not None:
        # if we have a speaker, we use the speaker's ID in the filename
        output_filename = f"{speaker}-{'-'.join(text.split()[:6])}.mp3"
    else:
        # if we don't have a speaker, we use a random string in the filename
        random_str = ''.join(random.sample(string.ascii_letters+string.digits, k=5))
        output_filename = f"{random_str}-{'-'.join(text.split()[:6])}.mp3"
    # save the generated speech to a file with 16KHz sampling rate
    sf.write(output_filename, speech.cpu().numpy(), samplerate=16000)
    # return the filename for reference
    return output_filename

# generate speech with a US female voice
save_text_to_speech("Python is my favorite programming language", speaker=speakers["slt"])
# generate speech with a random voice
save_text_to_speech("Python is my favorite programming language")

# a challenging text with all speakers
text = """In his miracle year, he published four groundbreaking papers. 
These outlined the theory of the photoelectric effect, explained Brownian motion, 
introduced special relativity, and demonstrated mass-energy equivalence."""

for speaker_name, speaker in speakers.items():
    output_filename = save_text_to_speech(text, speaker)
    print(f"Saved {output_filename}")
# random speaker
output_filename = save_text_to_speech(text)
print(f"Saved {output_filename}")