AICover / src /mdx.py
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import gc
import hashlib
import os
import queue
import threading
import warnings
import librosa
import numpy as np
import onnxruntime as ort
import soundfile as sf
import torch
from tqdm import tqdm
warnings.filterwarnings("ignore")
stem_naming = {'Vocals': 'Instrumental', 'Other': 'Instruments', 'Instrumental': 'Vocals', 'Drums': 'Drumless', 'Bass': 'Bassless'}
class MDXModel:
def __init__(self, device, dim_f, dim_t, n_fft, hop=1024, stem_name=None, compensation=1.000):
self.dim_f = dim_f
self.dim_t = dim_t
self.dim_c = 4
self.n_fft = n_fft
self.hop = hop
self.stem_name = stem_name
self.compensation = compensation
self.n_bins = self.n_fft // 2 + 1
self.chunk_size = hop * (self.dim_t - 1)
self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)
out_c = self.dim_c
self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t]).to(device)
def stft(self, x):
x = x.reshape([-1, self.chunk_size])
x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True)
x = torch.view_as_real(x)
x = x.permute([0, 3, 1, 2])
x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 4, self.n_bins, self.dim_t])
return x[:, :, :self.dim_f]
def istft(self, x, freq_pad=None):
freq_pad = self.freq_pad.repeat([x.shape[0], 1, 1, 1]) if freq_pad is None else freq_pad
x = torch.cat([x, freq_pad], -2)
# c = 4*2 if self.target_name=='*' else 2
x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 2, self.n_bins, self.dim_t])
x = x.permute([0, 2, 3, 1])
x = x.contiguous()
x = torch.view_as_complex(x)
x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
return x.reshape([-1, 2, self.chunk_size])
class MDX:
DEFAULT_SR = 44100
# Unit: seconds
DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR
DEFAULT_PROCESSOR = 0
def __init__(self, model_path: str, params: MDXModel, processor=DEFAULT_PROCESSOR):
# Set the device and the provider (CPU or CUDA)
self.device = torch.device(f'cuda:{processor}') if processor >= 0 else torch.device('cpu')
self.provider = ['CUDAExecutionProvider'] if processor >= 0 else ['CPUExecutionProvider']
self.model = params
# Load the ONNX model using ONNX Runtime
self.ort = ort.InferenceSession(model_path, providers=self.provider)
# Preload the model for faster performance
self.ort.run(None, {'input': torch.rand(1, 4, params.dim_f, params.dim_t).numpy()})
self.process = lambda spec: self.ort.run(None, {'input': spec.cpu().numpy()})[0]
self.prog = None
@staticmethod
def get_hash(model_path):
try:
with open(model_path, 'rb') as f:
f.seek(- 10000 * 1024, 2)
model_hash = hashlib.md5(f.read()).hexdigest()
except:
model_hash = hashlib.md5(open(model_path, 'rb').read()).hexdigest()
return model_hash
@staticmethod
def segment(wave, combine=True, chunk_size=DEFAULT_CHUNK_SIZE, margin_size=DEFAULT_MARGIN_SIZE):
"""
Segment or join segmented wave array
Args:
wave: (np.array) Wave array to be segmented or joined
combine: (bool) If True, combines segmented wave array. If False, segments wave array.
chunk_size: (int) Size of each segment (in samples)
margin_size: (int) Size of margin between segments (in samples)
Returns:
numpy array: Segmented or joined wave array
"""
if combine:
processed_wave = None # Initializing as None instead of [] for later numpy array concatenation
for segment_count, segment in enumerate(wave):
start = 0 if segment_count == 0 else margin_size
end = None if segment_count == len(wave) - 1 else -margin_size
if margin_size == 0:
end = None
if processed_wave is None: # Create array for first segment
processed_wave = segment[:, start:end]
else: # Concatenate to existing array for subsequent segments
processed_wave = np.concatenate((processed_wave, segment[:, start:end]), axis=-1)
else:
processed_wave = []
sample_count = wave.shape[-1]
if chunk_size <= 0 or chunk_size > sample_count:
chunk_size = sample_count
if margin_size > chunk_size:
margin_size = chunk_size
for segment_count, skip in enumerate(range(0, sample_count, chunk_size)):
margin = 0 if segment_count == 0 else margin_size
end = min(skip + chunk_size + margin_size, sample_count)
start = skip - margin
cut = wave[:, start:end].copy()
processed_wave.append(cut)
if end == sample_count:
break
return processed_wave
def pad_wave(self, wave):
"""
Pad the wave array to match the required chunk size
Args:
wave: (np.array) Wave array to be padded
Returns:
tuple: (padded_wave, pad, trim)
- padded_wave: Padded wave array
- pad: Number of samples that were padded
- trim: Number of samples that were trimmed
"""
n_sample = wave.shape[1]
trim = self.model.n_fft // 2
gen_size = self.model.chunk_size - 2 * trim
pad = gen_size - n_sample % gen_size
# Padded wave
wave_p = np.concatenate((np.zeros((2, trim)), wave, np.zeros((2, pad)), np.zeros((2, trim))), 1)
mix_waves = []
for i in range(0, n_sample + pad, gen_size):
waves = np.array(wave_p[:, i:i + self.model.chunk_size])
mix_waves.append(waves)
mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device)
return mix_waves, pad, trim
def _process_wave(self, mix_waves, trim, pad, q: queue.Queue, _id: int):
"""
Process each wave segment in a multi-threaded environment
Args:
mix_waves: (torch.Tensor) Wave segments to be processed
trim: (int) Number of samples trimmed during padding
pad: (int) Number of samples padded during padding
q: (queue.Queue) Queue to hold the processed wave segments
_id: (int) Identifier of the processed wave segment
Returns:
numpy array: Processed wave segment
"""
mix_waves = mix_waves.split(1)
with torch.no_grad():
pw = []
for mix_wave in mix_waves:
self.prog.update()
spec = self.model.stft(mix_wave)
processed_spec = torch.tensor(self.process(spec))
processed_wav = self.model.istft(processed_spec.to(self.device))
processed_wav = processed_wav[:, :, trim:-trim].transpose(0, 1).reshape(2, -1).cpu().numpy()
pw.append(processed_wav)
processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
q.put({_id: processed_signal})
return processed_signal
def process_wave(self, wave: np.array, mt_threads=1):
"""
Process the wave array in a multi-threaded environment
Args:
wave: (np.array) Wave array to be processed
mt_threads: (int) Number of threads to be used for processing
Returns:
numpy array: Processed wave array
"""
self.prog = tqdm(total=0)
chunk = wave.shape[-1] // mt_threads
waves = self.segment(wave, False, chunk)
# Create a queue to hold the processed wave segments
q = queue.Queue()
threads = []
for c, batch in enumerate(waves):
mix_waves, pad, trim = self.pad_wave(batch)
self.prog.total = len(mix_waves) * mt_threads
thread = threading.Thread(target=self._process_wave, args=(mix_waves, trim, pad, q, c))
thread.start()
threads.append(thread)
for thread in threads:
thread.join()
self.prog.close()
processed_batches = []
while not q.empty():
processed_batches.append(q.get())
processed_batches = [list(wave.values())[0] for wave in
sorted(processed_batches, key=lambda d: list(d.keys())[0])]
assert len(processed_batches) == len(waves), 'Incomplete processed batches, please reduce batch size!'
return self.segment(processed_batches, True, chunk)
def run_mdx(model_params, output_dir, model_path, filename, exclude_main=False, exclude_inversion=False, suffix=None, invert_suffix=None, denoise=False, keep_orig=True, m_threads=2):
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
device_properties = torch.cuda.get_device_properties(device)
vram_gb = device_properties.total_memory / 1024**3
m_threads = 1 if vram_gb < 8 else 2
model_hash = MDX.get_hash(model_path)
mp = model_params.get(model_hash)
model = MDXModel(
device,
dim_f=mp["mdx_dim_f_set"],
dim_t=2 ** mp["mdx_dim_t_set"],
n_fft=mp["mdx_n_fft_scale_set"],
stem_name=mp["primary_stem"],
compensation=mp["compensate"]
)
mdx_sess = MDX(model_path, model)
wave, sr = librosa.load(filename, mono=False, sr=44100)
# normalizing input wave gives better output
peak = max(np.max(wave), abs(np.min(wave)))
wave /= peak
if denoise:
wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads))
wave_processed *= 0.5
else:
wave_processed = mdx_sess.process_wave(wave, m_threads)
# return to previous peak
wave_processed *= peak
stem_name = model.stem_name if suffix is None else suffix
main_filepath = None
if not exclude_main:
main_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
sf.write(main_filepath, wave_processed.T, sr)
invert_filepath = None
if not exclude_inversion:
diff_stem_name = stem_naming.get(stem_name) if invert_suffix is None else invert_suffix
stem_name = f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
invert_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
sf.write(invert_filepath, (-wave_processed.T * model.compensation) + wave.T, sr)
if not keep_orig:
os.remove(filename)
del mdx_sess, wave_processed, wave
gc.collect()
return main_filepath, invert_filepath