from pathlib import Path import argparse import soundfile as sf import torch import io import argparse from matcha.hifigan.config import v1 from matcha.hifigan.denoiser import Denoiser from matcha.hifigan.env import AttrDict from matcha.hifigan.models import Generator as HiFiGAN from matcha.models.matcha_tts import MatchaTTS from matcha.text import sequence_to_text, text_to_sequence from matcha.utils.utils import intersperse import gradio as gr import requests def download_file(url, save_path): response = requests.get(url) print(f'---Loading from URL: {url} ---') with open(save_path, 'wb') as file: file.write(response.content) url_checkpoint = 'https://github.com/simonlobgromov/AkylAI_Matcha_Checkpoint/releases/download/Akyl-AI-TTS-v2/checkpoint_epoch.669.ckpt' #'https://github.com/simonlobgromov/AkylAI_Matcha_Checkpoint/releases/download/Matcha-TTS/checkpoint_epoch.499.ckpt' save_checkpoint_path = './checkpoints/checkpoint.ckpt' url_generator = 'https://github.com/simonlobgromov/AkylAI_Matcha_HiFiGan/releases/download/Generator/generator_v1' save_generator_path = './checkpoints/generator' download_file(url_checkpoint, save_checkpoint_path) download_file(url_generator, save_generator_path) def log_event(input_text, log_file="usage_log.json"): event_data = {'timestamp': datetime.now().isoformat(), 'text': input_text} with open(log_file, "a") as file: file.write(json.dumps(event_data) + "\n") def load_matcha( checkpoint_path, device): model = MatchaTTS.load_from_checkpoint(checkpoint_path, map_location=device) _ = model.eval() return model def load_hifigan(checkpoint_path, device): h = AttrDict(v1) hifigan = HiFiGAN(h).to(device) hifigan.load_state_dict(torch.load(checkpoint_path, map_location=device)["generator"]) _ = hifigan.eval() hifigan.remove_weight_norm() return hifigan def load_vocoder(checkpoint_path, device): vocoder = None vocoder = load_hifigan(checkpoint_path, device) denoiser = Denoiser(vocoder, mode="zeros") return vocoder, denoiser def process_text(i: int, text: str, device: torch.device): print(f"[{i}] - Input text: {text}") # log_event(text) x = torch.tensor( intersperse(text_to_sequence(text, ["kyrgyz_cleaners"]), 0), dtype=torch.long, device=device, )[None] x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device) x_phones = sequence_to_text(x.squeeze(0).tolist()) print(f"[{i}] - Phonetised text: {x_phones}") return {"x_orig": text, "x": x, "x_lengths": x_lengths, "x_phones": x_phones.replace('_q_ˌ_o_l_o_n_q_ˈ_ɑ_', '_q_ˌ_o_l_ˈ_o_n_q_ɑ_')} def to_waveform(mel, vocoder, denoiser=None): audio = vocoder(mel).clamp(-1, 1) if denoiser is not None: audio = denoiser(audio.squeeze(), strength=0.00025).cpu().squeeze() return audio.cpu().squeeze() @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) return output["waveform"].numpy() def get_inference(text, n_timesteps=20, mel_temp = 0.667, length_scale=0.8, spk=-1): phones, text, text_lengths = process_text_gradio(text) print(type(synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk))) return synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_path = './checkpoints/checkpoint.ckpt' vocoder_path = './checkpoints/generator' model = load_matcha(model_path, device) vocoder, denoiser = load_vocoder(vocoder_path, device) def gen_tts(text, speaking_rate): return 22050, get_inference(text = text, length_scale = speaking_rate) default_text = "Баарыңарга салам, менин атым Акылай." 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; } } img { display: block; margin: 0 auto; width: 132px !important; height: 132px !important; } """ with gr.Blocks(css=css) as block: gr.HTML( """

Akyl-AI TTS

""" ) with gr.Row(): image_path = "./photo_2024-04-07_15-59-52.png" gr.Image(image_path, label=None, width=132, height=132, show_label=False) with gr.Row(): with gr.Column(): input_text = gr.Textbox(label="Input Text", lines=2, value=default_text, elem_id="input_text") speaking_rate = gr.Slider(label='Speaking rate', minimum=0.5, maximum=1, step=0.05, value=0.8, interactive=True, show_label=True, elem_id="speaking_rate") run_button = gr.Button("Generate Audio", variant="primary") with gr.Column(): audio_out = gr.Audio(label="AkylAi-TTS", type="numpy", elem_id="audio_out") inputs = [input_text, speaking_rate] outputs = [audio_out] run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True) block.queue() block.launch(share=True)