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Configuration error
Configuration error
| from typing import Any, Tuple | |
| from time import sleep | |
| import scipy | |
| import numpy | |
| import onnxruntime | |
| import facefusion.globals | |
| from facefusion import process_manager | |
| from facefusion.thread_helper import thread_lock, thread_semaphore | |
| from facefusion.typing import ModelSet, AudioChunk, Audio | |
| from facefusion.execution import apply_execution_provider_options | |
| from facefusion.filesystem import resolve_relative_path, is_file | |
| from facefusion.download import conditional_download | |
| VOICE_EXTRACTOR = None | |
| MODELS : ModelSet =\ | |
| { | |
| 'voice_extractor': | |
| { | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/voice_extractor.onnx', | |
| 'path': resolve_relative_path('../.assets/models/voice_extractor.onnx') | |
| } | |
| } | |
| def get_voice_extractor() -> Any: | |
| global VOICE_EXTRACTOR | |
| with thread_lock(): | |
| while process_manager.is_checking(): | |
| sleep(0.5) | |
| if VOICE_EXTRACTOR is None: | |
| model_path = MODELS.get('voice_extractor').get('path') | |
| VOICE_EXTRACTOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers)) | |
| return VOICE_EXTRACTOR | |
| def clear_voice_extractor() -> None: | |
| global VOICE_EXTRACTOR | |
| VOICE_EXTRACTOR = None | |
| def pre_check() -> bool: | |
| download_directory_path = resolve_relative_path('../.assets/models') | |
| model_url = MODELS.get('voice_extractor').get('url') | |
| model_path = MODELS.get('voice_extractor').get('path') | |
| if not facefusion.globals.skip_download: | |
| process_manager.check() | |
| conditional_download(download_directory_path, [ model_url ]) | |
| process_manager.end() | |
| return is_file(model_path) | |
| def batch_extract_voice(audio : Audio, chunk_size : int, step_size : int) -> Audio: | |
| temp_audio = numpy.zeros((audio.shape[0], 2)).astype(numpy.float32) | |
| temp_chunk = numpy.zeros((audio.shape[0], 2)).astype(numpy.float32) | |
| for start in range(0, audio.shape[0], step_size): | |
| end = min(start + chunk_size, audio.shape[0]) | |
| temp_audio[start:end, ...] += extract_voice(audio[start:end, ...]) | |
| temp_chunk[start:end, ...] += 1 | |
| audio = temp_audio / temp_chunk | |
| return audio | |
| def extract_voice(temp_audio_chunk : AudioChunk) -> AudioChunk: | |
| voice_extractor = get_voice_extractor() | |
| chunk_size = 1024 * (voice_extractor.get_inputs()[0].shape[3] - 1) | |
| trim_size = 3840 | |
| temp_audio_chunk, pad_size = prepare_audio_chunk(temp_audio_chunk.T, chunk_size, trim_size) | |
| temp_audio_chunk = decompose_audio_chunk(temp_audio_chunk, trim_size) | |
| with thread_semaphore(): | |
| temp_audio_chunk = voice_extractor.run(None, | |
| { | |
| voice_extractor.get_inputs()[0].name: temp_audio_chunk | |
| })[0] | |
| temp_audio_chunk = compose_audio_chunk(temp_audio_chunk, trim_size) | |
| temp_audio_chunk = normalize_audio_chunk(temp_audio_chunk, chunk_size, trim_size, pad_size) | |
| return temp_audio_chunk | |
| def prepare_audio_chunk(temp_audio_chunk : AudioChunk, chunk_size : int, trim_size : int) -> Tuple[AudioChunk, int]: | |
| step_size = chunk_size - 2 * trim_size | |
| pad_size = step_size - temp_audio_chunk.shape[1] % step_size | |
| audio_chunk_size = temp_audio_chunk.shape[1] + pad_size | |
| temp_audio_chunk = temp_audio_chunk.astype(numpy.float32) / numpy.iinfo(numpy.int16).max | |
| temp_audio_chunk = numpy.pad(temp_audio_chunk, ((0, 0), (trim_size, trim_size + pad_size))) | |
| temp_audio_chunks = [] | |
| for index in range(0, audio_chunk_size, step_size): | |
| temp_audio_chunks.append(temp_audio_chunk[:, index:index + chunk_size]) | |
| temp_audio_chunk = numpy.concatenate(temp_audio_chunks, axis = 0) | |
| temp_audio_chunk = temp_audio_chunk.reshape((-1, chunk_size)) | |
| return temp_audio_chunk, pad_size | |
| def decompose_audio_chunk(temp_audio_chunk : AudioChunk, trim_size : int) -> AudioChunk: | |
| frame_size = 7680 | |
| frame_overlap = 6656 | |
| voice_extractor_shape = get_voice_extractor().get_inputs()[0].shape | |
| window = scipy.signal.windows.hann(frame_size) | |
| temp_audio_chunk = scipy.signal.stft(temp_audio_chunk, nperseg = frame_size, noverlap = frame_overlap, window = window)[2] | |
| temp_audio_chunk = numpy.stack((numpy.real(temp_audio_chunk), numpy.imag(temp_audio_chunk)), axis = -1).transpose((0, 3, 1, 2)) | |
| temp_audio_chunk = temp_audio_chunk.reshape(-1, 2, 2, trim_size + 1, voice_extractor_shape[3]).reshape(-1, voice_extractor_shape[1], trim_size + 1, voice_extractor_shape[3]) | |
| temp_audio_chunk = temp_audio_chunk[:, :, :voice_extractor_shape[2]] | |
| temp_audio_chunk /= numpy.sqrt(1.0 / window.sum() ** 2) | |
| return temp_audio_chunk | |
| def compose_audio_chunk(temp_audio_chunk : AudioChunk, trim_size : int) -> AudioChunk: | |
| frame_size = 7680 | |
| frame_overlap = 6656 | |
| voice_extractor_shape = get_voice_extractor().get_inputs()[0].shape | |
| window = scipy.signal.windows.hann(frame_size) | |
| temp_audio_chunk = numpy.pad(temp_audio_chunk, ((0, 0), (0, 0), (0, trim_size + 1 - voice_extractor_shape[2]), (0, 0))) | |
| temp_audio_chunk = temp_audio_chunk.reshape(-1, 2, trim_size + 1, voice_extractor_shape[3]).transpose((0, 2, 3, 1)) | |
| temp_audio_chunk = temp_audio_chunk[:, :, :, 0] + 1j * temp_audio_chunk[:, :, :, 1] | |
| temp_audio_chunk = scipy.signal.istft(temp_audio_chunk, nperseg = frame_size, noverlap = frame_overlap, window = window)[1] | |
| temp_audio_chunk *= numpy.sqrt(1.0 / window.sum() ** 2) | |
| return temp_audio_chunk | |
| def normalize_audio_chunk(temp_audio_chunk : AudioChunk, chunk_size : int, trim_size : int, pad_size : int) -> AudioChunk: | |
| temp_audio_chunk = temp_audio_chunk.reshape((-1, 2, chunk_size)) | |
| temp_audio_chunk = temp_audio_chunk[:, :, trim_size:-trim_size].transpose(1, 0, 2) | |
| temp_audio_chunk = temp_audio_chunk.reshape(2, -1)[:, :-pad_size].T | |
| return temp_audio_chunk | |