app.py

import gradio as gr
import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer, set_seed
import numpy as np

# Set device
device = "cuda:0" if torch.cuda.is_available() else "cpu"

# Load model and tokenizer
model = ParlerTTSForConditionalGeneration.from_pretrained("TArtx/parler-tts-mini-narrated-13").to(device)
tokenizer = AutoTokenizer.from_pretrained("TArtx/parler-tts-mini-narrated-13")

# Constants
SAMPLE_RATE = model.config.sampling_rate
SEED = 42

# Default inputs
default_text = "This is a demonstration of my ability to convert written words into spoken language, seamlessly and naturally. As a text-to-speech model, my goal is to sound as clear and engaging as a human, making sure every word I say leaves an impression."
default_description = "moderate speed, very clear, monotone, wonderful speech quality"

# TTS generation function
def gen_tts(text, description):
    try:
        # Set seed for reproducibility
        set_seed(SEED)

        # Prepare inputs
        input_ids = tokenizer(description.strip(), return_tensors="pt").input_ids.to(device)
        prompt_input_ids = tokenizer(text.strip(), return_tensors="pt").input_ids.to(device)

        # Generate audio
        generation = model.generate(
            input_ids=input_ids, 
            prompt_input_ids=prompt_input_ids,
            do_sample=True,
            temperature=0.7
        )

        # Convert to numpy array
        audio_arr = generation.cpu().numpy().squeeze()

        # Normalize audio
        if np.max(np.abs(audio_arr)) > 0:
            audio_arr = audio_arr / np.max(np.abs(audio_arr))
            audio_arr = (audio_arr * np.iinfo(np.int16).max).astype(np.int16)
        else:
            # Fallback to white noise if generation fails
            audio_arr = np.random.randint(-32768, 32767, SAMPLE_RATE * 10, dtype=np.int16)

        return SAMPLE_RATE, audio_arr

    except Exception as e:
        print(f"Error in TTS generation: {str(e)}")
        # Return white noise as fallback
        return SAMPLE_RATE, np.random.randint(-32768, 32767, SAMPLE_RATE * 10, dtype=np.int16)

# Gradio interface
with gr.Blocks() as block:
    gr.Markdown(
        """
        ## Parler-TTS 🗣️
        Parler-TTS is a training and inference library for high-fidelity text-to-speech (TTS) models. This demo uses the Mini v1 model.
        """
    )
    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)

# Launch the interface
block.launch(debug=True)