import os import gradio as gr import numpy as np import librosa from huggingface_hub import snapshot_download # ------------------------------ # Model bootstrap # ------------------------------ MODEL_DIR = os.path.join(os.getcwd(), "models") OPENVOICE_REPO = "myshell-ai/OpenVoiceV2" os.makedirs(MODEL_DIR, exist_ok=True) # Lazy import to speed up Space boot _openvoice_loaded = False _tone_converter = None _content_extractor = None _demucs_model = None def _ensure_openvoice(): global _openvoice_loaded, _tone_converter, _content_extractor if _openvoice_loaded: return # Download model snapshots into ./models/openvoice local_dir = snapshot_download(repo_id=OPENVOICE_REPO, local_dir=os.path.join(MODEL_DIR, "openvoice"), local_dir_use_symlinks=False) # OpenVoice v2 layout ships python modules; import after download import sys if local_dir not in sys.path: sys.path.append(local_dir) # Import OpenVoice components try: from openvoice import se_extractor from openvoice.api import ToneColorConverter, ContentVec except Exception: # Fallback to module paths used in some snapshots from tone_color_converter.api import ToneColorConverter from contentvec.api import ContentVec from se_extractor import se_extractor # Init content extractor (HuBERT-like) content_ckpt = os.path.join(local_dir, "checkpoints", "contentvec", "checkpoint.pth") _content_extractor = ContentVec(content_ckpt) # Init tone color converter tcc_ckpt = os.path.join(local_dir, "checkpoints", "tone_color_converter", "checkpoint.pth") _tone_converter = ToneColorConverter(tcc_ckpt, device=os.environ.get("DEVICE", "cuda" if gr.cuda.is_available() else "cpu")) _openvoice_loaded = True def _ensure_demucs(): global _demucs_model if _demucs_model is not None: return from demucs.apply import apply_model from demucs.pretrained import get_model from demucs.audio import AudioFile _demucs_model = { "apply_model": apply_model, "get_model": get_model, "AudioFile": AudioFile, } def separate_vocals(wav_path, stem="vocals"): """Return path to separated vocals and accompaniment using htdemucs.""" _ensure_demucs() apply_model = _demucs_model["apply_model"] get_model = _demucs_model["get_model"] AudioFile = _demucs_model["AudioFile"] model = get_model(name="htdemucs") model.cpu() with AudioFile(wav_path).read(streams=0, samplerate=44100, channels=2) as mix: ref = mix out = apply_model(model, ref, shifts=1, split=True, overlap=0.25) sources = {name: out[idx] for idx, name in enumerate(model.sources)} # Save stems base = os.path.splitext(os.path.basename(wav_path))[0] out_dir = tempfile.mkdtemp(prefix="stems_") vocal_path = os.path.join(out_dir, f"{base}_vocals.wav") inst_path = os.path.join(out_dir, f"{base}_inst.wav") sf.write(vocal_path, sources["vocals"].T, 44100) # Combine other stems for instrumental inst = sum([v for k, v in sources.items() if k != "vocals"]) / (len(model.sources) - 1) sf.write(inst_path, inst.T, 44100) return vocal_path, inst_path def load_audio(x, sr=44100, mono=True): y, _sr = librosa.load(x, sr=sr, mono=mono) return y, sr def save_audio(y, sr): path = tempfile.mktemp(suffix=".wav") sf.write(path, y, sr) return path def match_length(a, b): # Pad/trim a to match length of b if len(a) < len(b): a = np.pad(a, (0, len(b)-len(a))) else: a = a[:len(b)] return a def convert_voice(reference_wav, source_vocal_wav, style_strength=0.8, pitch_shift=0.0, formant_shift=0.0): _ensure_openvoice() # Load audio ref, sr = load_audio(reference_wav, sr=16000, mono=True) src, _ = load_audio(source_vocal_wav, sr=16000, mono=True) # Extract content features from source content = _content_extractor.extract(src, sr) # Extract speaker embedding / tone color from reference # OpenVoice ships an SE (speaker encoder) util; we mimic via API if exposed. try: from openvoice import se_extractor se = se_extractor.get_se(reference_wav, device=_tone_converter.device) except Exception: # Some snapshots provide a function name get_se_wav from se_extractor import get_se se = get_se(reference_wav) # Run tone color conversion converted = _tone_converter.convert(content, se, style_strength=style_strength) y = converted # Optional pitch & formant adjustments (light touch) if abs(pitch_shift) > 1e-3: y = librosa.effects.pitch_shift(y.astype(np.float32), 16000, n_steps=pitch_shift) if abs(formant_shift) > 1e-3: # crude formant-esque EQ tilt using shelving filter via librosa import scipy.signal as sps w = 2 * np.pi * 1500 / 16000 b, a = sps.iirfilter(2, Wn=w/np.pi, btype='high', ftype='butter') if formant_shift > 0 else sps.iirfilter(2, Wn=w/np.pi, btype='low', ftype='butter') y = sps.filtfilt(b, a, y) out_path = save_audio(y, 16000) return out_path def process(reference, track, acapella=None, separate=False, style_strength=0.8, pitch_shift=0.0, formant_shift=0.0, remix=False, vocal_gain_db=0.0, inst_gain_db=0.0): if reference is None: raise gr.Error("Загрузите референс голоса (reference_wav)") # Prepare vocals & instrumental vocals_path = None instrumental_path = None if acapella is not None: vocals_path = acapella elif separate and track is not None: vocals_path, instrumental_path = separate_vocals(track) elif track is not None: vocals_path = track else: raise gr.Error("Загрузите либо полный трек, либо акапеллу") # Convert vocal converted_vocal = convert_voice(reference, vocals_path, style_strength, pitch_shift, formant_shift) if not remix: return converted_vocal, None # Remix back to instrumental (if missing, make silence) if instrumental_path is None and track is not None and separate: _, instrumental_path = separate_vocals(track) if instrumental_path is None: # create silent instrumental length matched to converted vocal y, sr = load_audio(converted_vocal) inst = np.zeros_like(y) instrumental_path = save_audio(inst, sr) cv, sr = load_audio(converted_vocal) inst, isr = load_audio(instrumental_path) if isr != sr: inst = librosa.resample(inst, orig_sr=isr, target_sr=sr) cv = match_length(cv, inst) # apply gains cv = cv * (10 ** (vocal_gain_db / 20.0)) inst = inst * (10 ** (inst_gain_db / 20.0)) mix = cv + inst mix_path = save_audio(mix, sr) return converted_vocal, mix_path with gr.Blocks(theme=gr.themes.Soft()) as demo: gr.Markdown(""" # 🎙️ Reference Voice Conversion Загрузите **референс** голоса и **трек/акапеллу** — получайте конвертированный вокал под тембр референса. Опционально: разделение вокала (Demucs) и ремикс в инструментал. """) with gr.Row(): with gr.Column(): ref = gr.Audio(label="Reference Voice (clean, 5–20s)", type="filepath") track = gr.Audio(label="Source Track (full mix)", type="filepath") acap = gr.Audio(label="Source Acapella (optional)", type="filepath") separate = gr.Checkbox(label="Разделить вокал Demucs", value=True) remix = gr.Checkbox(label="Сделать финальный микс (вокал + инструментал)", value=True) with gr.Column(): style = gr.Slider(0.0, 1.0, value=0.85, step=0.01, label="Сила стиля (тембр)") pitch = gr.Slider(-6, 6, value=0, step=0.5, label="Pitch shift (полутонов)") formant = gr.Slider(-1.0, 1.0, value=0.0, step=0.1, label="Formant tilt (экспериментально)") vgain = gr.Slider(-12, 12, value=0, step=0.5, label="Гейн вокала (dB)") igain = gr.Slider(-12, 12, value=0, step=0.5, label="Гейн инструментала (dB)") btn = gr.Button("Convert") with gr.Row(): out_vocal = gr.Audio(label="Converted Vocal", type="filepath") out_mix = gr.Audio(label="Remix (Vocal + Instrumental)", type="filepath") btn.click( fn=process, inputs=[ref, track, acap, separate, style, pitch, formant, remix, vgain, igain], outputs=[out_vocal, out_mix] ) if __name__ == "__main__": demo.launch()