import asyncio import datetime import logging import os import time import traceback import edge_tts import gradio as gr import librosa import torch from fairseq import checkpoint_utils from config import Config from lib.infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono, ) from rmvpe import RMVPE from vc_infer_pipeline import VC # Set logging levels logging.getLogger("fairseq").setLevel(logging.WARNING) logging.getLogger("numba").setLevel(logging.WARNING) logging.getLogger("markdown_it").setLevel(logging.WARNING) logging.getLogger("urllib3").setLevel(logging.WARNING) logging.getLogger("matplotlib").setLevel(logging.WARNING) limitation = os.getenv("SYSTEM") == "spaces" config = Config() # Edge TTS edge_output_filename = "edge_output.mp3" tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) tts_voices = ["mn-MN-BataaNeural", "mn-MN-YesuiNeural"] # Specific voices # RVC models model_root = "weights" models = [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")] models.sort() def model_data(model_name): # global n_spk, tgt_sr, net_g, vc, cpt, version, index_file pth_path = [ f"{model_root}/{model_name}/{f}" for f in os.listdir(f"{model_root}/{model_name}") if f.endswith(".pth") ][0] print(f"Loading {pth_path}") cpt = torch.load(pth_path, map_location="cpu") tgt_sr = cpt["config"][-1] cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk if_f0 = cpt.get("f0", 1) version = cpt.get("version", "v1") if version == "v1": if if_f0 == 1: net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) else: net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) else: raise ValueError("Unknown version") del net_g.enc_q net_g.load_state_dict(cpt["weight"], strict=False) print("Model loaded") net_g.eval().to(config.device) if config.is_half: net_g = net_g.half() else: net_g = net_g.float() vc = VC(tgt_sr, config) # n_spk = cpt["config"][-3] index_files = [ f"{model_root}/{model_name}/{f}" for f in os.listdir(f"{model_root}/{model_name}") if f.endswith(".index") ] if len(index_files) == 0: print("No index file found") index_file = "" else: index_file = index_files[0] print(f"Index file found: {index_file}") return tgt_sr, net_g, vc, version, index_file, if_f0 def load_hubert(): # global hubert_model models, _, _ = checkpoint_utils.load_model_ensemble_and_task( ["hubert_base.pt"], suffix="", ) hubert_model = models[0] hubert_model = hubert_model.to(config.device) if config.is_half: hubert_model = hubert_model.half() else: hubert_model = hubert_model.float() return hubert_model.eval() def tts( model_name, tts_text, tts_voice, index_rate, ): # Default values for other parameters speed = 0 # Default speech speed f0_up_key = 0 # Default pitch adjustment f0_method = "rmvpe" # Default pitch extraction method protect = 0.33 # Default protect value filter_radius = 3 resample_sr = 0 rms_mix_rate = 0.25 print("------------------") print(datetime.datetime.now()) print("tts_text:") print(tts_text) print(f"tts_voice: {tts_voice}, speed: {speed}") print(f"Model name: {model_name}") print(f"F0: {f0_method}, Key: {f0_up_key}, Index: {index_rate}, Protect: {protect}") try: if limitation and len(tts_text) > 280: print("Error: Text too long") return ( f"Text characters should be at most 280 in this huggingface space, but got {len(tts_text)} characters.", None, None, ) t0 = time.time() if speed >= 0: speed_str = f"+{speed}%" else: speed_str = f"{speed}%" asyncio.run( edge_tts.Communicate( tts_text, tts_voice, rate=speed_str ).save(edge_output_filename) ) t1 = time.time() edge_time = t1 - t0 audio, sr = librosa.load(edge_output_filename, sr=16000, mono=True) duration = len(audio) / sr print(f"Audio duration: {duration}s") if limitation and duration >= 20: print("Error: Audio too long") return ( f"Audio should be less than 20 seconds in this huggingface space, but got {duration}s.", edge_output_filename, None, ) f0_up_key = int(f0_up_key) tgt_sr, net_g, vc, version, index_file, if_f0 = model_data(model_name) if f0_method == "rmvpe": vc.model_rmvpe = rmvpe_model times = [0, 0, 0] audio_opt = vc.pipeline( hubert_model, net_g, 0, audio, edge_output_filename, times, f0_up_key, f0_method, index_file, # file_big_npy, index_rate, if_f0, filter_radius, tgt_sr, resample_sr, rms_mix_rate, version, protect, None, ) if tgt_sr != resample_sr >= 16000: tgt_sr = resample_sr info = f"Success. Time: edge-tts: {edge_time}s, npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s" print(info) return ( info, edge_output_filename, (tgt_sr, audio_opt), ) except EOFError: info = ( "It seems that the edge-tts output is not valid. " "This may occur when the input text and the speaker do not match. " "For example, maybe you entered Japanese (without alphabets) text but chose non-Japanese speaker?" ) print(info) return info, None, None except: info = traceback.format_exc() print(info) return info, None, None print("Loading hubert model...") hubert_model = load_hubert() print("Hubert model loaded.") print("Loading rmvpe model...") rmvpe_model = RMVPE("rmvpe.pt", config.is_half, config.device) print("rmvpe model loaded.") app = gr.Blocks() with app: with gr.Row(): model_name = gr.Dropdown( label="Model", choices=models, value=models[0], ) tts_voice = gr.Dropdown( label="Edge-tts speaker (format: language-Country-Name-Gender)", choices=tts_voices, value="mn-MN-BataaNeural", ) slang_rate = gr.Slider( minimum=0, maximum=1, label="Slang rate", value=0.75, interactive=True, ) with gr.Row(): tts_text = gr.Textbox(label="Input Text", value="Текстыг оруулна уу.") but0 = gr.Button("Convert", variant="primary") with gr.Row(): info_text = gr.Textbox(label="Output info") edge_tts_output = gr.Audio(label="Edge Voice", type="filepath") tts_output = gr.Audio(label="Result") but0.click( tts, [model_name, tts_text, tts_voice, slang_rate], [info_text, edge_tts_output, tts_output], ) app.launch()