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 = "PHBJT/french_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 = "La voix humaine est un instrument de musique au-dessus de tous les autres."
default_description = "A male voice speaks slowly with a very noisy background, displaying a touch of expressiveness and animation. The sound is very distant, adding an air of intrigue."
examples = [
[
"La voix humaine est un instrument de musique au-dessus de tous les autres.",
"A male voice speaks slowly with a very noisy background, displaying a touch of expressiveness and animation. The sound is very distant, adding an air of intrigue.",
None,
],
[
"Tout ce qu'un homme est capable d'imaginer, d'autres hommes seront capables de le réaliser.",
"A male voice delivers a slightly expressive and animated speech with a moderate speed. The recording features a low-pitch voice, creating a close-sounding audio experience.",
None,
],
[
"La machine elle-même, si perfectionnée qu'on la suppose, n'est qu'un outil.",
"A male voice provides a monotone yet slightly fast delivery, with a very close recording that almost has no background noise.",
None,
],
[
"Le progrès fait naître plus de besoins qu'il n'en satisfait.",
"A female voice, in a very poor recording quality, delivers slightly expressive and animated words with a fast pace. There's a high level of background noise and a very distant-sounding reverberation. The voice is slightly higher pitched than average.",
None,
],
]
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.strip(), 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, attention_mask=inputs.attention_mask, prompt_attention_mask=prompt.attention_mask, 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, French Parler-TTS Mini v0.1 French,
has been fine-tuned on a French dataset. 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).
Due to limitations on the dataset, this model might underperform for female voices (we recommend using male voices only).
By default, Parler-TTS generates 🎲 random male voice characteristics. To ensure 🎯 speaker consistency across generations, try to use consistent descriptions in your prompts.
Note: do NOT specify the nationnality of the speaker it will cause inconsistent audio generation (do: "a male speaker", don't: "a french male speaker")
Important note: this model does NOT work in english, it will generate incoherent audios. But you can still use the original Parler TTS model for that.
"""
)
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=default_description, 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", autoplay = True)
inputs = [input_text, description]
outputs = [audio_out]
run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True)
gr.Examples(examples=examples, fn=gen_tts, inputs=inputs, outputs=outputs, cache_examples=True)
gr.HTML(
"""
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
"""
)
block.queue()
block.launch(share=True)