import datetime import logging import os import time import traceback import tempfile import edge_tts import librosa import torch from fairseq import checkpoint_utils import uuid 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 tts_voice_list = 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 get_unique_filename(extension): return f"{uuid.uuid4()}.{extension}" 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 get_model_names(): model_root = "weights" # Assuming this is where your models are stored return [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")] def tts( model_name, tts_text, tts_voice, index_rate, use_uploaded_voice, uploaded_voice, ): # Default values for parameters used in EdgeTTS 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 edge_time = 0 # Initialize edge_time edge_output_filename = get_unique_filename("mp3") try: if use_uploaded_voice: if uploaded_voice is None: return "No voice file uploaded.", None, None # Process the uploaded voice file with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: tmp_file.write(uploaded_voice) uploaded_file_path = tmp_file.name #uploaded_file_path = uploaded_voice.name audio, sr = librosa.load(uploaded_file_path, sr=16000, mono=True) else: # EdgeTTS processing if limitation and len(tts_text) > 4000: return ( f"Text characters should be at most 280 in this huggingface space, but got {len(tts_text)} characters.", None, None, ) # Invoke Edge TTS t0 = time.time() speed_str = f"+{speed}%" if speed >= 0 else f"{speed}%" 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) # Common processing after loading the audio duration = len(audio) / sr print(f"Audio duration: {duration}s") if limitation and duration >= 20: return ( f"Audio should be less than 20 seconds in this huggingface space, but got {duration}s.", None, None, ) f0_up_key = int(f0_up_key) tgt_sr, net_g, vc, version, index_file, if_f0 = model_data(model_name) # Setup for RMVPE or other pitch extraction methods if f0_method == "rmvpe": vc.model_rmvpe = rmvpe_model # Perform voice conversion pipeline times = [0, 0, 0] audio_opt = vc.pipeline( hubert_model, net_g, 0, audio, edge_output_filename if not use_uploaded_voice else uploaded_file_path, times, f0_up_key, f0_method, index_file, index_rate, if_f0, filter_radius, tgt_sr, resample_sr, rms_mix_rate, version, protect, None, ) if tgt_sr != resample_sr and resample_sr >= 16000: tgt_sr = resample_sr info = f"Success. Time: tts: {edge_time}s, npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s" print(info) return ( info, edge_output_filename if not use_uploaded_voice else None, (tgt_sr, audio_opt), edge_output_filename ) except EOFError: info = ( "output not valid. This may occur when input text and speaker do not match." ) print(info) return info, None, None except Exception as e: traceback_info = traceback.format_exc() print(traceback_info) return str(e), None, None voice_mapping = { "Mongolian Male": "mn-MN-BataaNeural", "Mongolian Female": "mn-MN-YesuiNeural" } hubert_model = load_hubert() rmvpe_model = RMVPE("rmvpe.pt", config.is_half, config.device)