app.py

import os
import tempfile
import gradio as gr
from TTS.api import TTS
from TTS.utils.synthesizer import Synthesizer
from huggingface_hub import hf_hub_download
import json

# Define constants
MODEL_INFO = [
    ["VITS Grapheme Multispeaker CV15(90K)", "best_model_56960.pth", "config.json", "saillab/multi_speaker"],

    ["VITS Grapheme Azure (61000)", "checkpoint_61000.pth", "config.json", "saillab/persian-tts-azure-grapheme-60K"],

    ["VITS Grapheme ARM24 Fine-Tuned on 1 (66651)", "best_model_66651.pth", "config.json",
     "saillab/persian-tts-grapheme-arm24-finetuned-on1"],
    ["VITS Grapheme ARM24 Fine-Tuned on 1 (120000)", "checkpoint_120000.pth", "config.json",
     "saillab/persian-tts-grapheme-arm24-finetuned-on1"],
]

# Extract model names from MODEL_INFO
MODEL_NAMES = [info[0] for info in MODEL_INFO]

MAX_TXT_LEN = 400
TOKEN = os.getenv('HUGGING_FACE_HUB_TOKEN')

model_files = {}
config_files = {}

# Create a dictionary to store synthesizer objects for each model
synthesizers = {}

   
# Download models and initialize synthesizers
for info in MODEL_INFO:
    model_name, model_file, config_file, repo_name = info[:4]

    print(f"|> Downloading: {model_name}")

    # Download model and config files
    model_files[model_name] = hf_hub_download(repo_id=repo_name, filename=model_file, use_auth_token=TOKEN)
    config_files[model_name] = hf_hub_download(repo_id=repo_name, filename=config_file, use_auth_token=TOKEN)

    # Initialize synthesizer for the model
    synthesizer = Synthesizer(tts_checkpoint=model_files[model_name],
                              tts_config_path=config_files[model_name],
                              use_cuda=False)
    synthesizers[model_name] = synthesizer

def synthesize(text: str, model_name: str) -> str:
  
    if len(text) > MAX_TXT_LEN:
        text = text[:MAX_TXT_LEN]
        print(f"Input text was cut off as it exceeded the {MAX_TXT_LEN} character limit.")

    synthesizer = synthesizers[model_name]
    if synthesizer is None:
        raise NameError("Model not found")

    wavs = synthesizer.tts(text)
  
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        synthesizer.save_wav(wavs, fp)
        return fp.name

iface = gr.Interface(
    fn=synthesize,
    inputs=[
        gr.Textbox(label="Enter Text to Synthesize:", value="زین همرهان سست عناصر، دلم گرفت."),
        gr.Radio(label="Pick a Model", choices=MODEL_NAMES, value=MODEL_NAMES[0], type="value"),
    ],
    outputs=gr.Audio(label="Output", type='filepath'),
    examples=[["زین همرهان سست عناصر، دلم گرفت.", MODEL_NAMES[0]]],  
    title='Persian TTS Playground',
    description="""
    ### Persian text to speech model demo. 
    """,
    article="",
    live=False
)

iface.launch()