import tempfile from argparse import Namespace from pathlib import Path import gradio as gr import soundfile as sf import torch from matcha.cli import ( MATCHA_URLS, VOCODER_URLS, assert_model_downloaded, get_device, load_matcha, load_vocoder, process_text, to_waveform, ) from matcha.utils.utils import get_user_data_dir, plot_tensor LOCATION = Path(get_user_data_dir()) args = Namespace( cpu=True, model="akyl_ai", vocoder="hifigan_T2_v1", ) CURRENTLY_LOADED_MODEL = args.model def MATCHA_TTS_LOC(x): return LOCATION / f"{x}.ckpt" def VOCODER_LOC(x): return LOCATION / f"{x}" LOGO_URL = "https://github.com/simonlobgromov/Matcha-TTS/blob/main/photo_2024-04-07_15-59-52.png" RADIO_OPTIONS = { "Akyl_AI": { "model": "akyl_ai", "vocoder": "hifigan_T2_v1", }, } # Ensure all the required models are downloaded assert_model_downloaded(MATCHA_TTS_LOC("akyl_ai"), MATCHA_URLS["akyl_ai"]) assert_model_downloaded(VOCODER_LOC("hifigan_T2_v1"), VOCODER_URLS["hifigan_T2_v1"]) device = get_device(args) # Load default model model = load_matcha(args.model, MATCHA_TTS_LOC(args.model), device) vocoder, denoiser = load_vocoder(args.vocoder, VOCODER_LOC(args.vocoder), device) def load_model(model_name, vocoder_name): model = load_matcha(model_name, MATCHA_TTS_LOC(model_name), device) vocoder, denoiser = load_vocoder(vocoder_name, VOCODER_LOC(vocoder_name), device) return model, vocoder, denoiser def load_model_ui(model_type, textbox): model_name, vocoder_name = RADIO_OPTIONS[model_type]["model"], RADIO_OPTIONS[model_type]["vocoder"] global model, vocoder, denoiser, CURRENTLY_LOADED_MODEL # pylint: disable=global-statement if CURRENTLY_LOADED_MODEL != model_name: model, vocoder, denoiser = load_model(model_name, vocoder_name) CURRENTLY_LOADED_MODEL = model_name if model_name == "akyl_ai": single_speaker_examples = gr.update(visible=True) multi_speaker_examples = gr.update(visible=False) length_scale = gr.update(value=0.95) else: single_speaker_examples = gr.update(visible=False) multi_speaker_examples = gr.update(visible=True) length_scale = gr.update(value=0.85) return ( textbox, gr.update(interactive=True), single_speaker_examples, multi_speaker_examples, length_scale, ) @torch.inference_mode() def process_text_gradio(text): output = process_text(1, text, device) return output["x_phones"][1::2], output["x"], output["x_lengths"] @torch.inference_mode() def synthesise_mel(text, text_length, n_timesteps, temperature, length_scale, spk=-1): spk = torch.tensor([spk], device=device, dtype=torch.long) if spk >= 0 else None output = model.synthesise( text, text_length, n_timesteps=n_timesteps, temperature=temperature, spks=spk, length_scale=length_scale, ) output["waveform"] = to_waveform(output["mel"], vocoder, denoiser) with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: sf.write(fp.name, output["waveform"], 22050, "PCM_24") return fp.name, plot_tensor(output["mel"].squeeze().cpu().numpy()) def ljspeech_example_cacher(text, n_timesteps, mel_temp, length_scale, spk=-1): global CURRENTLY_LOADED_MODEL # pylint: disable=global-statement if CURRENTLY_LOADED_MODEL == "akyl_ai": global model, vocoder, denoiser # pylint: disable=global-statement model, vocoder, denoiser = load_model("akyl_ai", "hifigan_T2_v1") CURRENTLY_LOADED_MODEL = "akyl_ai" phones, text, text_lengths = process_text_gradio(text) audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk) return phones, audio, mel_spectrogram def main(): description = """# AkylAI TTS Mini""" with gr.Blocks(title="AkylAI TTS") as demo: processed_text = gr.State(value=None) processed_text_len = gr.State(value=None) with gr.Box(): with gr.Row(): gr.Markdown(description, scale=3) with gr.Row(): image_url = "https://github.com/simonlobgromov/Matcha-TTS/blob/main/photo_2024-04-07_15-59-52.png?raw=true" gr.Image(image_url, label=None, width=660, height=315, show_label=False) with gr.Box(): radio_options = list(RADIO_OPTIONS.keys()) model_type = gr.Radio( radio_options, value=radio_options[0], label="Choose a Model", interactive=True, container=False, visible=False, ) with gr.Row(): gr.Markdown("## Текстти кыргыз тилинде жазыңыз\n### Text Input") with gr.Row(): text = gr.Textbox(value="", label=None, scale=3, show_label=False) with gr.Row(): gr.Markdown("## Сүйлөө ылдамдыгы\n### Speaking rate") # gr.Markdown("") with gr.Row(): n_timesteps = gr.Slider( label="Number of ODE steps", minimum=1, maximum=100, step=1, value=10, interactive=True, visible=False ) length_scale = gr.Slider( label=None, minimum=0.5, maximum=1, step=0.05, value=0.9, interactive=True, show_label=False ) mel_temp = gr.Slider( label="Sampling temperature", minimum=0.00, maximum=2.001, step=0.16675, value=0.667, interactive=True, visible=False ) synth_btn = gr.Button("БАШТОО | RUN") phonetised_text = gr.Textbox(interactive=False, scale=10, label=None, visible=False ) with gr.Box(): with gr.Row(): mel_spectrogram = gr.Image(interactive=False, label="mel spectrogram", visible=False) # with gr.Row(): audio = gr.Audio(interactive=False, label="Audio") with gr.Row(visible=True) as example_row_lj_speech: examples = gr.Examples( # pylint: disable=unused-variable examples=[ [ "Баарыңарга салам, менин атым Акылай. Мен бардыгын бул жерде Инновация борборунда көргөнүмө абдан кубанычтамын.", 50, 0.677, 0.95, ], [ "Мага колдоо көрсөтүп, мени тандагандарга ыраазымын. Айыл үчүн иштейбиз, жол курабыз, асфальт төшөйбүз”, — деген ал.", 2, 0.677, 0.95, ], ], fn=ljspeech_example_cacher, inputs=[text, n_timesteps, mel_temp, length_scale], outputs=[phonetised_text, audio, mel_spectrogram], cache_examples=True, ) model_type.change(lambda x: gr.update(interactive=False), inputs=[synth_btn], outputs=[synth_btn]).then( load_model_ui, inputs=[model_type, text], outputs=[text, synth_btn, example_row_lj_speech, length_scale], ) synth_btn.click( fn=process_text_gradio, inputs=[ text, ], outputs=[phonetised_text, processed_text, processed_text_len], api_name="AkylAI TTS Mini", queue=True, ).then( fn=synthesise_mel, inputs=[processed_text, processed_text_len, n_timesteps, mel_temp, length_scale], outputs=[audio, mel_spectrogram], ) demo.queue().launch() if __name__ == "__main__": main()