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| import gradio as gr | |
| import os | |
| import torch | |
| from huggingface_hub import InferenceClient | |
| # Khurram | |
| # from fastapi import FastAPI, Query | |
| # from pydantic import BaseModel | |
| # import uvicorn | |
| # from fastapi.responses import JSONResponse | |
| ################# | |
| # Import eSpeak TTS pipeline | |
| from tts_cli import ( | |
| build_model as build_model_espeak, | |
| generate_long_form_tts as generate_long_form_tts_espeak, | |
| ) | |
| # Import OpenPhonemizer TTS pipeline | |
| from tts_cli_op import ( | |
| build_model as build_model_open, | |
| generate_long_form_tts as generate_long_form_tts_open, | |
| ) | |
| from pretrained_models import Kokoro | |
| # | |
| # --------------------------------------------------------------------- | |
| # Path to models and voicepacks | |
| # --------------------------------------------------------------------- | |
| MODELS_DIR = "pretrained_models/Kokoro" | |
| VOICES_DIR = "pretrained_models/Kokoro/voices" | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| client = InferenceClient(api_key=HF_TOKEN) | |
| # --------------------------------------------------------------------- | |
| # List the models (.pth) and voices (.pt) | |
| # --------------------------------------------------------------------- | |
| def get_models(): | |
| return sorted([f for f in os.listdir(MODELS_DIR) if f.endswith(".pth")]) | |
| def get_voices(): | |
| return sorted([f for f in os.listdir(VOICES_DIR) if f.endswith(".pt")]) | |
| # --------------------------------------------------------------------- | |
| # We'll map engine selection -> (build_model_func, generate_func) | |
| # --------------------------------------------------------------------- | |
| ENGINES = { | |
| "espeak": (build_model_espeak, generate_long_form_tts_espeak), | |
| "openphonemizer": (build_model_open, generate_long_form_tts_open), | |
| } | |
| # --------------------------------------------------------------------- | |
| # The main inference function called by Gradio | |
| # --------------------------------------------------------------------- | |
| def tts_inference(text, engine, model_file, voice_file, speed=1.0): | |
| """ | |
| text: Input string | |
| engine: "espeak" or "openphonemizer" | |
| model_file: Selected .pth from the models folder | |
| voice_file: Selected .pt from the voices folder | |
| speed: Speech speed | |
| """ | |
| # 0) Get the response of user query from LLAMA | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": text + str('describe in one line only') | |
| } #, | |
| # { | |
| # "type": "image_url", | |
| # "image_url": { | |
| # "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" | |
| # } | |
| # } | |
| ] | |
| } | |
| ] | |
| response_from_llama = client.chat.completions.create( | |
| model="meta-llama/Llama-3.2-11B-Vision-Instruct", | |
| messages=messages, | |
| max_tokens=500) | |
| # 1) Map engine to the correct build_model + generate_long_form_tts | |
| build_fn, gen_fn = ENGINES[engine] | |
| # 2) Prepare paths | |
| model_path = os.path.join(MODELS_DIR, model_file) | |
| voice_path = os.path.join(VOICES_DIR, voice_file) | |
| # 3) Decide device | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # 4) Load model | |
| model = build_fn(model_path, device=device) | |
| # Set submodules eval | |
| for k, subm in model.items(): | |
| if hasattr(subm, "eval"): | |
| subm.eval() | |
| # 5) Load voicepack | |
| voicepack = torch.load(voice_path, map_location=device) | |
| if hasattr(voicepack, "eval"): | |
| voicepack.eval() | |
| # 6) Generate TTS | |
| audio, phonemes = gen_fn(model, response_from_llama.choices[0].message['content'], voicepack, speed=speed) | |
| sr = 22050 # or your actual sample rate | |
| return (sr, audio) # Gradio expects (sample_rate, np_array) | |
| #------------------------------------------ | |
| # FAST API | |
| #--------------- | |
| # app = FastAPI() | |
| # class TTSRequest(BaseModel): | |
| # text: str | |
| # engine: str | |
| # model_file: str | |
| # voice_file: str | |
| # speed: float = 1.0 | |
| # @app.post("/tts") | |
| # def generate_tts(request: TTSRequest): | |
| # try: | |
| # sr, audio = tts_inference( | |
| # text="What is Deep SeEK? define in 2 lines", | |
| # engine="openphonemizer", | |
| # model_file="kokoro-v0_19.pth", | |
| # voice_file="af_bella.pt", | |
| # speed=1.0 | |
| # ) | |
| # return JSONResponse(content={ | |
| # "sample_rate": sr, | |
| # "audio_tensor": audio.tolist() | |
| # }) | |
| # except Exception as e: | |
| # return JSONResponse(content={"error": str(e)}, status_code=500) | |
| # if __name__ == "__main__": | |
| # uvicorn.run(app, host="0.0.0.0", port=8000) | |
| ############################### | |
| # --------------------------------------------------------------------- | |
| # Build Gradio App | |
| # --------------------------------------------------------------------- | |
| def create_gradio_app(): | |
| model_list = get_models() | |
| voice_list = get_voices() | |
| css = """ | |
| h4 { | |
| text-align: center; | |
| display:block; | |
| } | |
| h2 { | |
| text-align: center; | |
| display:block; | |
| } | |
| """ | |
| with gr.Blocks(theme=gr.themes.Ocean(), css=css) as demo: | |
| gr.Markdown("## LLAMA TTS DEMO - API - GRADIO VISUAL") | |
| # Row 1: Text input | |
| text_input = gr.Textbox( | |
| label="Enter your question", | |
| value="What is AI?", | |
| lines=2, | |
| ) | |
| # Row 2: Engine selection | |
| # engine_dropdown = gr.Dropdown( | |
| # choices=["espeak", "openphonemizer"], | |
| # value="openphonemizer", | |
| # label="Phonemizer", | |
| # ) | |
| # Row 3: Model dropdown | |
| # model_dropdown = gr.Dropdown( | |
| # choices=model_list, | |
| # value=model_list[0] if model_list else None, | |
| # label="Model (.pth)", | |
| # ) | |
| # Row 4: Voice dropdown | |
| # voice_dropdown = gr.Dropdown( | |
| # choices=voice_list, | |
| # value=voice_list[0] if voice_list else None, | |
| # label="Voice (.pt)", | |
| # ) | |
| # Row 5: Speed slider | |
| speed_slider = gr.Slider( | |
| minimum=0.5, maximum=2.0, value=1.0, step=0.1, label="Speech Speed" | |
| ) | |
| # Generate button + audio output | |
| generate_btn = gr.Button("Generate") | |
| tts_output = gr.Audio(label="TTS Output") | |
| # Connect the button to our inference function | |
| generate_btn.click( | |
| fn=tts_inference, | |
| inputs=[ | |
| text_input, | |
| gr.State("openphonemizer"), #engine_dropdown, | |
| gr.State("kokoro-v0_19.pth"), #model_dropdown, | |
| gr.State("af_bella.pt"), #voice_dropdown, | |
| speed_slider, | |
| ], | |
| outputs=tts_output, | |
| ) | |
| gr.Markdown( | |
| "#### LLAMA - TTS" | |
| ) | |
| return demo | |
| # --------------------------------------------------------------------- | |
| # Main | |
| # --------------------------------------------------------------------- | |
| if __name__ == "__main__": | |
| app = create_gradio_app() | |
| app.launch() | |