import spaces
import gradio as gr
import torch
from transformers.models.speecht5.number_normalizer import EnglishNumberNormalizer
from string import punctuation
import re
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed
device = "cuda:0" if torch.cuda.is_available() else "cpu"
repo_id = "parler-tts/parler_tts_mini_v0.1"
model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).to(device)
tokenizer = AutoTokenizer.from_pretrained(repo_id)
feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
SAMPLE_RATE = feature_extractor.sampling_rate
SEED = 42
default_text = "Please surprise me and speak in whatever voice you enjoy."
examples = [
[
"Remember - this is only the first iteration of the model! To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data by a factor of five times.",
"A male speaker with a low-pitched voice delivering his words at a fast pace in a small, confined space with a very clear audio and an animated tone."
],
[
"'This is the best time of my life, Bartley,' she said happily.",
"A female speaker with a slightly low-pitched, quite monotone voice delivers her words at a slightly faster-than-average pace in a confined space with very clear audio.",
],
[
"Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.",
"A male speaker with a slightly high-pitched voice delivering his words at a slightly slow pace in a small, confined space with a touch of background noise and a quite monotone tone.",
],
[
"Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.",
"A male speaker with a low-pitched voice delivers his words at a fast pace and an animated tone, in a very spacious environment, accompanied by noticeable background noise.",
],
]
number_normalizer = EnglishNumberNormalizer()
def preprocess(text):
text = number_normalizer(text).strip()
text = text.replace("-", " ")
if text[-1] not in punctuation:
text = f"{text}."
abbreviations_pattern = r'\b[A-Z][A-Z\.]+\b'
def separate_abb(chunk):
chunk = chunk.replace(".","")
print(chunk)
return " ".join(chunk)
abbreviations = re.findall(abbreviations_pattern, text)
for abv in abbreviations:
if abv in text:
text = text.replace(abv, separate_abb(abv))
return text
@spaces.GPU
def gen_tts(text, description):
inputs = tokenizer(description, return_tensors="pt").to(device)
prompt = tokenizer(preprocess(text), return_tensors="pt").to(device)
set_seed(SEED)
generation = model.generate(
input_ids=inputs.input_ids, prompt_input_ids=prompt.input_ids, do_sample=True, temperature=1.0
)
audio_arr = generation.cpu().numpy().squeeze()
return SAMPLE_RATE, audio_arr
css = """
#share-btn-container {
display: flex;
padding-left: 0.5rem !important;
padding-right: 0.5rem !important;
background-color: #000000;
justify-content: center;
align-items: center;
border-radius: 9999px !important;
width: 13rem;
margin-top: 10px;
margin-left: auto;
flex: unset !important;
}
#share-btn {
all: initial;
color: #ffffff;
font-weight: 600;
cursor: pointer;
font-family: 'IBM Plex Sans', sans-serif;
margin-left: 0.5rem !important;
padding-top: 0.25rem !important;
padding-bottom: 0.25rem !important;
right:0;
}
#share-btn * {
all: unset !important;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
"""
with gr.Blocks(css=css) as block:
gr.HTML(
"""
"""
)
gr.HTML(
f"""
Parler-TTS is a training and inference library for
high-fidelity text-to-speech (TTS) models. The model demonstrated here, Parler-TTS Mini v0.1,
is the first iteration model trained using 10k hours of narrated audiobooks. It generates high-quality speech
with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).
Tips for ensuring good generation:
- Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise
- Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech
- The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt
"""
)
with gr.Row():
with gr.Column():
input_text = gr.Textbox(label="Input Text", lines=2, value=default_text, elem_id="input_text")
description = gr.Textbox(label="Description", lines=2, value="", elem_id="input_description")
run_button = gr.Button("Generate Audio", variant="primary")
with gr.Column():
audio_out = gr.Audio(label="Parler-TTS generation", type="numpy", elem_id="audio_out")
inputs = [input_text, description]
outputs = [audio_out]
gr.Examples(examples=examples, fn=gen_tts, inputs=inputs, outputs=outputs, cache_examples=True)
run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True)
gr.HTML(
"""
To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data to 50k hours of speech.
The v1 release of the model will be trained on this data, as well as inference optimisations, such as flash attention
and torch compile, that will improve the latency by 2-4x. If you want to find out more about how this model was trained and even fine-tune it yourself, check-out the
Parler-TTS repository on GitHub.
The Parler-TTS codebase and its associated checkpoints are licensed under Apache 2.0.
"""
)
block.queue()
block.launch(share=True)