app.py

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 =  "ylacombe/p-m-e"

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 woman with a slightly low- pitched voice speaks slowly in a clear and close- sounding environment, but her delivery is quite monotone."
examples = [
    # French
    [
        "La voix humaine est un instrument de musique au-dessus de tous les autres.",
        "a woman with a slightly low- pitched voice speaks slowly in a clear and close- sounding environment, but her delivery is quite monotone.",
        None,
    ],
    # Spanish
    [
        "La voz es el reflejo del alma en el espejo del tiempo.",
        "a man with a moderate pitch voice speaks slowly with a slightly animated delivery in a very close- sounding environment with minimal background noise.",
        None,
    ],
    # Italian
    [
        "La voce umana è la più bella musica che esista al mondo.",
        "a man with a moderate pitch speaks slowly in a very noisy environment that sounds very distant, delivering his words in a monotone manner.",
        None,
    ],
    # Portuguese
    [
        "A voz é o espelho da alma e o som do coração.",
        "a man speaks slowly in a distant- sounding environment with a clean audio quality, delivering his message in a monotone voice at a moderate pitch. ",
        None,
    ],
    # Polish
    [
        "Głos ludzki jest najpiękniejszym instrumentem świata.",
        "a man with a moderate pitch speaks in a monotone manner at a slightly slow pace, but the recording is quite noisy and sounds very distant.",
        None,
    ],
    # German
    [
        "Die menschliche Stimme ist das schönste Instrument der Welt.",
        "a man with a moderate pitch speaks slowly in a noisy environment with a flat tone of voice, creating a slightly close- sounding effect.",
        None,
    ],
    # Dutch
    [
        "De menselijke stem is het mooiste instrument dat er bestaat.",
        "a man with a moderate pitch speaks slightly slowly with an expressive and animated delivery in a very close- sounding environment with a bit of background noise.",
        None,
    ],
    # English
    [
        "The human voice is nature's most perfect instrument.",
        "Aa woman with a slightly low- pitched voice speaks slowly in a very distant- sounding environment with a clean audio quality, delivering her message in a very monotone manner.",
        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(
        """
            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
              <div
                style="
                  display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;
                "
              >
                <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
                  Multi Parler-TTS 🗣️
                </h1>
              </div>
            </div>
        """
    )
    gr.HTML(
f"""
       <p><a href="https://github.com/huggingface/parler-tts">Parler-TTS</a> is a training and inference library for
high-fidelity text-to-speech (TTS) models.</p> 
<p>This multilingual model supports French, Spanish, Italian, Portuguese, Polish, German, Dutch, and English. 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). </p>

<p>By default, Parler-TTS generates 🎲 random voice characteristics. To ensure 🎯 <b>speaker consistency</b> across generations, try to use consistent descriptions in your prompts.</p>
<p><b>Note:</b> you do not need to specify the nationality of the speaker in the description (do: "a male speaker", don't: "a french male speaker") </p>
        """
    )
    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")

    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(
        """
        <p>Tips for ensuring good generation:
        <ul>
            <li>Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise</li>
            <li>Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech</li>
            <li>The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt</li>
        </ul>
        </p>

        """
    )


block.queue()
block.launch(share=True)