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 = "parler-tts/parler-tts-mini-expresso"

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 = "Hello This is created by Abhinay as a project assignment of SML hiring process"
default_description = "Thomas speaks with emphasis and excitement at a moderate pace with high quality."
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.",
        "Thomas speaks in a sad tone at a moderate pace with high quality."
    ],
    [
        "Did you know? You can reproduce this entire training recipe by following the steps outlined on the model card!",
        "Talia speaks quickly with excitement and high quality audio.",
    ],
    [
        "But that's no secret! The entire project is open source first, with all release artefacts on the Hub.",
        "Elisabeth speaks happily at a slightly slower than average pace with high quality audio.",
    ],
    [
        "Hey there! I'm Jerry. Or at least I think I am? I just need to check that quickly.",
        "Jerry speaks in a confused tone at a moderately slow pace with high quality audio.",
    ],
    [
        "<laugh> It can even laugh! Do you believe it ? I don't!",
        "Talia speaks with laughter with high quality.",
    ],
]

number_normalizer = EnglishNumberNormalizer()


def preprocess(text):
    text = number_normalizer(text).strip()
    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)
    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;">
                  SML Emotional Text to Speech Project
                </h1>
              </div>
            </div>
        """
    )
    gr.HTML(
        f"""
        
        <p>Tips for ensuring good generation:
        <ul>
            <li>Specify the name of a male speaker (Jerry, Thomas) or female speaker (Talia, Elisabeth) for consistent voices</li>
            <li>The model can generate in a range of emotions, including: "happy", "confused", "default" (meaning no particular emotion conveyed), "laughing", "sad", "whisper", "emphasis"</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>To emphasise particular words, wrap them in asterisk (e.g. *you* in the example above) and include "emphasis" in the prompt</li>
        </ul>
        </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]
    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)

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