Merge pull request 'Merge the most successful implementation of this crap' (#2) from rt-test into main
Reviewed-on: #2
This commit is contained in:
@@ -1,18 +1,41 @@
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import torch
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import torch.nn.functional as F
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def stereo_tensor_to_mono(waveform):
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if waveform.shape[0] > 1:
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# Average across channels
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mono_waveform = torch.mean(waveform, dim=0, keepdim=True)
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else:
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# Already mono
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mono_waveform = waveform
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return mono_waveform
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def stretch_tensor(tensor, target_length):
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scale_factor = target_length / tensor.size(1)
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def stereo_tensor_to_mono(waveform: torch.Tensor) -> torch.Tensor:
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mono_tensor = torch.mean(waveform, dim=0, keepdim=True)
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return mono_tensor
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tensor = F.interpolate(tensor, scale_factor=scale_factor, mode='linear', align_corners=False)
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return tensor
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def pad_tensor(audio_tensor: torch.Tensor, target_length: int = 512) -> torch.Tensor:
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padding_amount = target_length - audio_tensor.size(-1)
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if padding_amount <= 0:
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return audio_tensor
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padded_audio_tensor = F.pad(audio_tensor, (0, padding_amount))
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return padded_audio_tensor
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def split_audio(audio_tensor: torch.Tensor, chunk_size: int = 512, pad_last_tensor: bool = False) -> list[torch.Tensor]:
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chunks = list(torch.split(audio_tensor, chunk_size, dim=1))
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if pad_last_tensor:
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last_chunk = chunks[-1]
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if last_chunk.size(-1) < chunk_size:
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chunks[-1] = pad_tensor(last_chunk, chunk_size)
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return chunks
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def reconstruct_audio(chunks: list[torch.Tensor]) -> torch.Tensor:
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reconstructed_tensor = torch.cat(chunks, dim=-1)
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return reconstructed_tensor
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def normalize(audio_tensor: torch.Tensor, eps: float = 1e-8) -> torch.Tensor:
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max_val = torch.max(torch.abs(audio_tensor))
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if max_val < eps:
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return audio_tensor
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return audio_tensor / max_val
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0
__init__.py
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0
__init__.py
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128
app.py
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128
app.py
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@@ -0,0 +1,128 @@
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import argparse
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import torch
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import torchaudio
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import torchcodec
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import tqdm
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from accelerate import Accelerator
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import AudioUtils
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from generator import SISUGenerator
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# Init script argument parser
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parser = argparse.ArgumentParser(description="Training script")
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parser.add_argument("--device", type=str, default="cpu", help="Select device")
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parser.add_argument("--model", type=str, help="Model to use for upscaling")
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parser.add_argument(
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"--clip_length",
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type=int,
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default=8000,
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help="Internal clip length, leave unspecified if unsure",
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)
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parser.add_argument(
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"--sample_rate", type=int, default=44100, help="Output clip sample rate"
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)
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parser.add_argument(
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"--bitrate",
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type=int,
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default=192000,
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help="Output clip bitrate",
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)
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parser.add_argument("-i", "--input", type=str, help="Input audio file")
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parser.add_argument("-o", "--output", type=str, help="Output audio file")
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args = parser.parse_args()
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if args.sample_rate < 8000:
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print(
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"Sample rate cannot be lower than 8000! (44100 is recommended for base models)"
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)
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exit()
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# ---------------------------
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# Init accelerator
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# ---------------------------
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accelerator = Accelerator(mixed_precision="bf16")
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# ---------------------------
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# Models
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# ---------------------------
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generator = SISUGenerator()
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accelerator.print("🔨 | Compiling models...")
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generator = torch.compile(generator)
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accelerator.print("✅ | Compiling done!")
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# ---------------------------
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# Prepare accelerator
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# ---------------------------
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generator = accelerator.prepare(generator)
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# ---------------------------
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# Checkpoint helpers
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# ---------------------------
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models_dir = args.model
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clip_length = args.clip_length
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input_audio = args.input
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output_audio = args.output
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if models_dir:
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ckpt = torch.load(models_dir)
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accelerator.unwrap_model(generator).load_state_dict(ckpt["G"])
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accelerator.print("💾 | Loaded model!")
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else:
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print(
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"Generator model (--model) isn't specified. Do you have the trained model? If not, you need to train it OR acquire it from somewhere (DON'T ASK ME, YET!)"
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)
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def start():
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# To Mono!
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decoder = torchcodec.decoders.AudioDecoder(input_audio)
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decoded_samples = decoder.get_all_samples()
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audio = decoded_samples.data
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original_sample_rate = decoded_samples.sample_rate
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# Support for multichannel audio
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# audio = AudioUtils.stereo_tensor_to_mono(audio)
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audio = AudioUtils.normalize(audio)
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resample_transform = torchaudio.transforms.Resample(
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original_sample_rate, args.sample_rate
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)
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audio = resample_transform(audio)
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splitted_audio = AudioUtils.split_audio(audio, clip_length)
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splitted_audio_on_device = [t.view(1, t.shape[0], t.shape[-1]).to(accelerator.device) for t in splitted_audio]
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processed_audio = []
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with torch.no_grad():
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for clip in tqdm.tqdm(splitted_audio_on_device, desc="Processing..."):
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channels = []
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for audio_channel in torch.split(clip, 1, dim=1):
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output_piece = generator(audio_channel)
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channels.append(output_piece.detach().cpu())
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output_clip = torch.cat(channels, dim=1)
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processed_audio.append(output_clip)
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reconstructed_audio = AudioUtils.reconstruct_audio(processed_audio)
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reconstructed_audio = reconstructed_audio.squeeze(0)
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print(f"🔊 | Saving {output_audio}!")
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torchaudio.save_with_torchcodec(
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uri=output_audio,
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src=reconstructed_audio,
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sample_rate=args.sample_rate,
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channels_first=True,
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compression=args.bitrate,
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)
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start()
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98
data.py
98
data.py
@@ -1,53 +1,71 @@
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from torch.utils.data import Dataset
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import torch.nn.functional as F
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import torch
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import torchaudio
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import os
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import random
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import torchaudio.transforms as T
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import torch
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import torchaudio
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import torchcodec.decoders as decoders
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import tqdm
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from torch.utils.data import Dataset
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import AudioUtils
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class AudioDataset(Dataset):
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audio_sample_rates = [11025]
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MAX_LENGTH = 44100 # Define your desired maximum length here
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def __init__(self, input_dir, device):
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self.input_files = [os.path.join(root, f) for root, _, files in os.walk(input_dir) for f in files if f.endswith('.wav')]
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self.device = device
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class AudioDataset(Dataset):
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audio_sample_rates = [8000, 11025, 12000, 16000, 22050, 24000, 32000, 44100]
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def __init__(self, input_dir, clip_length: int = 512, normalize: bool = True):
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self.clip_length = clip_length
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self.normalize = normalize
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input_files = [
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os.path.join(input_dir, f)
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for f in os.listdir(input_dir)
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if os.path.isfile(os.path.join(input_dir, f))
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and f.lower().endswith((".wav", ".mp3", ".flac"))
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]
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data = []
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for audio_clip in tqdm.tqdm(
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input_files, desc=f"Processing {len(input_files)} audio file(s)"
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):
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decoder = decoders.AudioDecoder(audio_clip)
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decoded_samples = decoder.get_all_samples()
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audio = decoded_samples.data.float()
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original_sample_rate = decoded_samples.sample_rate
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if normalize:
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audio = AudioUtils.normalize(audio)
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splitted_high_quality_audio = AudioUtils.split_audio(audio, clip_length, True)
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if not splitted_high_quality_audio:
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continue
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for splitted_audio_clip in splitted_high_quality_audio:
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for audio_clip in torch.split(splitted_audio_clip, 1):
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data.append((audio_clip, original_sample_rate))
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self.audio_data = data
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def __len__(self):
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return len(self.input_files)
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return len(self.audio_data)
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def __getitem__(self, idx):
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# Load high-quality audio
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high_quality_audio, original_sample_rate = torchaudio.load(self.input_files[idx], normalize=True)
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# Generate low-quality audio with random downsampling
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audio_clip = self.audio_data[idx]
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mangled_sample_rate = random.choice(self.audio_sample_rates)
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resample_transform_low = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
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low_quality_audio = resample_transform_low(high_quality_audio)
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resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
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low_quality_audio = resample_transform_high(low_quality_audio)
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resample_transform_low = torchaudio.transforms.Resample(
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audio_clip[1], mangled_sample_rate
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)
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high_quality_audio = AudioUtils.stereo_tensor_to_mono(high_quality_audio)
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low_quality_audio = AudioUtils.stereo_tensor_to_mono(low_quality_audio)
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resample_transform_high = torchaudio.transforms.Resample(
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mangled_sample_rate, audio_clip[1]
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)
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# Pad or truncate high-quality audio
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if high_quality_audio.shape[1] < self.MAX_LENGTH:
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padding = self.MAX_LENGTH - high_quality_audio.shape[1]
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high_quality_audio = F.pad(high_quality_audio, (0, padding))
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elif high_quality_audio.shape[1] > self.MAX_LENGTH:
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high_quality_audio = high_quality_audio[:, :self.MAX_LENGTH]
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# Pad or truncate low-quality audio
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if low_quality_audio.shape[1] < self.MAX_LENGTH:
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padding = self.MAX_LENGTH - low_quality_audio.shape[1]
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low_quality_audio = F.pad(low_quality_audio, (0, padding))
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elif low_quality_audio.shape[1] > self.MAX_LENGTH:
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low_quality_audio = low_quality_audio[:, :self.MAX_LENGTH]
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high_quality_audio = high_quality_audio.to(self.device)
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low_quality_audio = low_quality_audio.to(self.device)
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return (high_quality_audio, original_sample_rate), (low_quality_audio, mangled_sample_rate)
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low_audio_clip = resample_transform_high(resample_transform_low(audio_clip[0]))
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if audio_clip[0].shape[1] < low_audio_clip.shape[1]:
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low_audio_clip = low_audio_clip[:, :audio_clip[0].shape[1]]
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elif audio_clip[0].shape[1] > low_audio_clip.shape[1]:
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low_audio_clip = AudioUtils.pad_tensor(low_audio_clip, self.clip_length)
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return ((audio_clip[0], low_audio_clip), (audio_clip[1], mangled_sample_rate))
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216
discriminator.py
216
discriminator.py
@@ -1,63 +1,179 @@
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import torch
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import torch.nn as nn
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import torch.nn.utils as utils
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import torch.nn.functional as F
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from torch.nn.utils.parametrizations import weight_norm, spectral_norm
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def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, spectral_norm=True, use_instance_norm=True):
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padding = (kernel_size // 2) * dilation
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conv_layer = nn.Conv1d(
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in_channels,
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out_channels,
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kernel_size=kernel_size,
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stride=stride,
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dilation=dilation,
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padding=padding
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)
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# -------------------------------------------------------------------
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# 1. Multi-Period Discriminator (MPD)
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# Captures periodic structures (pitch/timbre) by folding audio.
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# -------------------------------------------------------------------
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if spectral_norm:
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conv_layer = utils.spectral_norm(conv_layer)
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class DiscriminatorP(nn.Module):
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
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super(DiscriminatorP, self).__init__()
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self.period = period
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self.use_spectral_norm = use_spectral_norm
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layers = [conv_layer]
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layers.append(nn.LeakyReLU(0.2, inplace=True))
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# Use spectral_norm for stability, or weight_norm for performance
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norm_f = spectral_norm if use_spectral_norm else weight_norm
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if use_instance_norm:
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layers.append(nn.InstanceNorm1d(out_channels))
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# We use 2D convs because we "fold" the 1D audio into 2D (Period x Time)
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self.convs = nn.ModuleList([
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norm_f(nn.Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(2, 0))),
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norm_f(nn.Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(2, 0))),
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norm_f(nn.Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(2, 0))),
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norm_f(nn.Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(2, 0))),
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norm_f(nn.Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
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])
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return nn.Sequential(*layers)
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class AttentionBlock(nn.Module):
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def __init__(self, channels):
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super(AttentionBlock, self).__init__()
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self.attention = nn.Sequential(
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nn.Conv1d(channels, channels // 4, kernel_size=1),
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nn.ReLU(inplace=True),
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nn.Conv1d(channels // 4, channels, kernel_size=1),
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nn.Sigmoid()
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)
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self.conv_post = norm_f(nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
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def forward(self, x):
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attention_weights = self.attention(x)
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return x * attention_weights
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fmap = []
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# 1d to 2d conversion: [B, C, T] -> [B, C, T/P, P]
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b, c, t = x.shape
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if t % self.period != 0: # Pad if not divisible by period
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n_pad = self.period - (t % self.period)
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x = F.pad(x, (0, n_pad), "reflect")
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t = t + n_pad
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x = x.view(b, c, t // self.period, self.period)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, 0.1)
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fmap.append(x) # Store feature map for Feature Matching Loss
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x = self.conv_post(x)
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fmap.append(x)
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# Flatten back to 1D for score
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x = torch.flatten(x, 1, -1)
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return x, fmap
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|
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class MultiPeriodDiscriminator(nn.Module):
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def __init__(self, periods=[2, 3, 5, 7, 11]):
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super(MultiPeriodDiscriminator, self).__init__()
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self.discriminators = nn.ModuleList([
|
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DiscriminatorP(p) for p in periods
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])
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|
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def forward(self, y, y_hat):
|
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y_d_rs = [] # Real scores
|
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y_d_gs = [] # Generated (Fake) scores
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fmap_rs = [] # Real feature maps
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fmap_gs = [] # Generated (Fake) feature maps
|
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|
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for i, d in enumerate(self.discriminators):
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y_d_r, fmap_r = d(y)
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y_d_g, fmap_g = d(y_hat)
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y_d_rs.append(y_d_r)
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fmap_rs.append(fmap_r)
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y_d_gs.append(y_d_g)
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fmap_gs.append(fmap_g)
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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|
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|
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# -------------------------------------------------------------------
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# 2. Multi-Scale Discriminator (MSD)
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# Captures structure at different audio resolutions (raw, x0.5, x0.25).
|
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# -------------------------------------------------------------------
|
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|
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class DiscriminatorS(nn.Module):
|
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def __init__(self, use_spectral_norm=False):
|
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super(DiscriminatorS, self).__init__()
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norm_f = spectral_norm if use_spectral_norm else weight_norm
|
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|
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# Standard 1D Convolutions with large receptive field
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self.convs = nn.ModuleList([
|
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norm_f(nn.Conv1d(1, 16, 15, 1, padding=7)),
|
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norm_f(nn.Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
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norm_f(nn.Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
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norm_f(nn.Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
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norm_f(nn.Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
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norm_f(nn.Conv1d(1024, 1024, 5, 1, padding=2)),
|
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])
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self.conv_post = norm_f(nn.Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
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for l in self.convs:
|
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x = l(x)
|
||||
x = F.leaky_relu(x, 0.1)
|
||||
fmap.append(x)
|
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x = self.conv_post(x)
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fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiScaleDiscriminator(nn.Module):
|
||||
def __init__(self):
|
||||
super(MultiScaleDiscriminator, self).__init__()
|
||||
# 3 Scales: Original, Downsampled x2, Downsampled x4
|
||||
self.discriminators = nn.ModuleList([
|
||||
DiscriminatorS(use_spectral_norm=True),
|
||||
DiscriminatorS(),
|
||||
DiscriminatorS(),
|
||||
])
|
||||
self.meanpools = nn.ModuleList([
|
||||
nn.AvgPool1d(4, 2, padding=2),
|
||||
nn.AvgPool1d(4, 2, padding=2)
|
||||
])
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
|
||||
for i, d in enumerate(self.discriminators):
|
||||
if i != 0:
|
||||
# Downsample input for subsequent discriminators
|
||||
y = self.meanpools[i-1](y)
|
||||
y_hat = self.meanpools[i-1](y_hat)
|
||||
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
|
||||
y_d_rs.append(y_d_r)
|
||||
fmap_rs.append(fmap_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# 3. Master Wrapper
|
||||
# Combines MPD and MSD into one class to fit your training script.
|
||||
# -------------------------------------------------------------------
|
||||
|
||||
class SISUDiscriminator(nn.Module):
|
||||
def __init__(self, base_channels=16):
|
||||
def __init__(self):
|
||||
super(SISUDiscriminator, self).__init__()
|
||||
layers = base_channels
|
||||
self.model = nn.Sequential(
|
||||
discriminator_block(1, layers, kernel_size=7, stride=1, spectral_norm=True, use_instance_norm=False),
|
||||
discriminator_block(layers, layers * 2, kernel_size=5, stride=2, spectral_norm=True, use_instance_norm=True),
|
||||
discriminator_block(layers * 2, layers * 4, kernel_size=5, stride=1, dilation=2, spectral_norm=True, use_instance_norm=True),
|
||||
AttentionBlock(layers * 4),
|
||||
discriminator_block(layers * 4, layers * 8, kernel_size=5, stride=1, dilation=4, spectral_norm=True, use_instance_norm=True),
|
||||
discriminator_block(layers * 8, layers * 4, kernel_size=5, stride=2, spectral_norm=True, use_instance_norm=True),
|
||||
discriminator_block(layers * 4, layers * 2, kernel_size=3, stride=1, spectral_norm=True, use_instance_norm=True),
|
||||
discriminator_block(layers * 2, layers, kernel_size=3, stride=1, spectral_norm=True, use_instance_norm=True),
|
||||
discriminator_block(layers, 1, kernel_size=3, stride=1, spectral_norm=False, use_instance_norm=False)
|
||||
self.mpd = MultiPeriodDiscriminator()
|
||||
self.msd = MultiScaleDiscriminator()
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
# Return format:
|
||||
# scores_real, scores_fake, features_real, features_fake
|
||||
|
||||
# Run Multi-Period
|
||||
mpd_y_d_rs, mpd_y_d_gs, mpd_fmap_rs, mpd_fmap_gs = self.mpd(y, y_hat)
|
||||
|
||||
# Run Multi-Scale
|
||||
msd_y_d_rs, msd_y_d_gs, msd_fmap_rs, msd_fmap_gs = self.msd(y, y_hat)
|
||||
|
||||
# Combine all results
|
||||
return (
|
||||
mpd_y_d_rs + msd_y_d_rs, # All real scores
|
||||
mpd_y_d_gs + msd_y_d_gs, # All fake scores
|
||||
mpd_fmap_rs + msd_fmap_rs, # All real feature maps
|
||||
mpd_fmap_gs + msd_fmap_gs # All fake feature maps
|
||||
)
|
||||
|
||||
self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.model(x)
|
||||
x = self.global_avg_pool(x)
|
||||
x = x.view(x.size(0), -1)
|
||||
return x
|
||||
|
||||
@@ -1,28 +0,0 @@
|
||||
import json
|
||||
|
||||
filepath = "my_data.json"
|
||||
|
||||
def write_data(filepath, data):
|
||||
try:
|
||||
with open(filepath, 'w') as f:
|
||||
json.dump(data, f, indent=4) # Use indent for pretty formatting
|
||||
print(f"Data written to '{filepath}'")
|
||||
except Exception as e:
|
||||
print(f"Error writing to file: {e}")
|
||||
|
||||
|
||||
def read_data(filepath):
|
||||
try:
|
||||
with open(filepath, 'r') as f:
|
||||
data = json.load(f)
|
||||
print(f"Data read from '{filepath}'")
|
||||
return data
|
||||
except FileNotFoundError:
|
||||
print(f"File not found: {filepath}")
|
||||
return None
|
||||
except json.JSONDecodeError:
|
||||
print(f"Error decoding JSON from file: {filepath}")
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"Error reading from file: {e}")
|
||||
return None
|
||||
106
generator.py
106
generator.py
@@ -1,42 +1,45 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn.utils.parametrizations import weight_norm
|
||||
|
||||
def GeneratorBlock(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
|
||||
padding = (kernel_size - 1) // 2 * dilation
|
||||
|
||||
def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
|
||||
return nn.Sequential(
|
||||
nn.Conv1d(
|
||||
|
||||
weight_norm(nn.Conv1d(
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
dilation=dilation,
|
||||
padding=(kernel_size // 2) * dilation
|
||||
),
|
||||
nn.InstanceNorm1d(out_channels),
|
||||
nn.PReLU()
|
||||
padding=padding
|
||||
)),
|
||||
nn.PReLU(num_parameters=1, init=0.1),
|
||||
)
|
||||
|
||||
|
||||
class AttentionBlock(nn.Module):
|
||||
"""
|
||||
Simple Channel Attention Block. Learns to weight channels based on their importance.
|
||||
"""
|
||||
def __init__(self, channels):
|
||||
super(AttentionBlock, self).__init__()
|
||||
self.attention = nn.Sequential(
|
||||
nn.Conv1d(channels, channels // 4, kernel_size=1),
|
||||
weight_norm(nn.Conv1d(channels, channels // 4, kernel_size=1)),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv1d(channels // 4, channels, kernel_size=1),
|
||||
nn.Sigmoid()
|
||||
weight_norm(nn.Conv1d(channels // 4, channels, kernel_size=1)),
|
||||
nn.Sigmoid(),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
attention_weights = self.attention(x)
|
||||
return x * attention_weights
|
||||
return x + (x * attention_weights)
|
||||
|
||||
|
||||
class ResidualInResidualBlock(nn.Module):
|
||||
def __init__(self, channels, num_convs=3):
|
||||
super(ResidualInResidualBlock, self).__init__()
|
||||
|
||||
self.conv_layers = nn.Sequential(
|
||||
*[conv_block(channels, channels) for _ in range(num_convs)]
|
||||
*[GeneratorBlock(channels, channels) for _ in range(num_convs)]
|
||||
)
|
||||
|
||||
self.attention = AttentionBlock(channels)
|
||||
@@ -47,28 +50,77 @@ class ResidualInResidualBlock(nn.Module):
|
||||
x = self.attention(x)
|
||||
return x + residual
|
||||
|
||||
def UpsampleBlock(in_channels, out_channels, scale_factor=2):
|
||||
return nn.Sequential(
|
||||
nn.Upsample(scale_factor=scale_factor, mode='nearest'),
|
||||
weight_norm(nn.Conv1d(
|
||||
in_channels=in_channels,
|
||||
out_channels=out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1
|
||||
)),
|
||||
nn.PReLU(num_parameters=1, init=0.1)
|
||||
)
|
||||
|
||||
class SISUGenerator(nn.Module):
|
||||
def __init__(self, channels=16, num_rirb=4, alpha=1.0):
|
||||
def __init__(self, channels=32, num_rirb=4):
|
||||
super(SISUGenerator, self).__init__()
|
||||
self.alpha = alpha
|
||||
|
||||
self.conv1 = nn.Sequential(
|
||||
nn.Conv1d(1, channels, kernel_size=7, padding=3),
|
||||
nn.InstanceNorm1d(channels),
|
||||
nn.PReLU(),
|
||||
self.first_conv = GeneratorBlock(1, channels)
|
||||
|
||||
self.downsample = GeneratorBlock(channels, channels * 2, stride=2)
|
||||
self.downsample_attn = AttentionBlock(channels * 2)
|
||||
self.downsample_2 = GeneratorBlock(channels * 2, channels * 4, stride=2)
|
||||
self.downsample_2_attn = AttentionBlock(channels * 4)
|
||||
|
||||
self.rirb = nn.Sequential(
|
||||
*[ResidualInResidualBlock(channels * 4) for _ in range(num_rirb)]
|
||||
)
|
||||
|
||||
self.rir_blocks = nn.Sequential(
|
||||
*[ResidualInResidualBlock(channels) for _ in range(num_rirb)]
|
||||
self.upsample = UpsampleBlock(channels * 4, channels * 2)
|
||||
self.upsample_attn = AttentionBlock(channels * 2)
|
||||
self.compress_1 = GeneratorBlock(channels * 4, channels * 2)
|
||||
|
||||
self.upsample_2 = UpsampleBlock(channels * 2, channels)
|
||||
self.upsample_2_attn = AttentionBlock(channels)
|
||||
self.compress_2 = GeneratorBlock(channels * 2, channels)
|
||||
|
||||
self.final_conv = nn.Sequential(
|
||||
weight_norm(nn.Conv1d(channels, 1, kernel_size=7, padding=3)),
|
||||
nn.Tanh()
|
||||
)
|
||||
|
||||
self.final_layer = nn.Conv1d(channels, 1, kernel_size=3, padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
residual_input = x
|
||||
x = self.conv1(x)
|
||||
x_rirb_out = self.rir_blocks(x)
|
||||
learned_residual = self.final_layer(x_rirb_out)
|
||||
output = residual_input + self.alpha * learned_residual
|
||||
|
||||
# Encoding
|
||||
x1 = self.first_conv(x)
|
||||
|
||||
x2 = self.downsample(x1)
|
||||
x2 = self.downsample_attn(x2)
|
||||
|
||||
x3 = self.downsample_2(x2)
|
||||
x3 = self.downsample_2_attn(x3)
|
||||
|
||||
# Bottleneck (Deep Residual processing)
|
||||
x_rirb = self.rirb(x3)
|
||||
|
||||
# Decoding with Skip Connections
|
||||
up1 = self.upsample(x_rirb)
|
||||
up1 = self.upsample_attn(up1)
|
||||
|
||||
cat1 = torch.cat((up1, x2), dim=1)
|
||||
comp1 = self.compress_1(cat1)
|
||||
|
||||
up2 = self.upsample_2(comp1)
|
||||
up2 = self.upsample_2_attn(up2)
|
||||
|
||||
cat2 = torch.cat((up2, x1), dim=1)
|
||||
comp2 = self.compress_2(cat2)
|
||||
|
||||
learned_residual = self.final_conv(comp2)
|
||||
|
||||
output = residual_input + learned_residual
|
||||
return output
|
||||
|
||||
@@ -1,12 +0,0 @@
|
||||
filelock==3.16.1
|
||||
fsspec==2024.10.0
|
||||
Jinja2==3.1.4
|
||||
MarkupSafe==2.1.5
|
||||
mpmath==1.3.0
|
||||
networkx==3.4.2
|
||||
numpy==2.2.3
|
||||
pillow==11.0.0
|
||||
setuptools==70.2.0
|
||||
sympy==1.13.3
|
||||
tqdm==4.67.1
|
||||
typing_extensions==4.12.2
|
||||
377
training.py
377
training.py
@@ -1,194 +1,247 @@
|
||||
import argparse
|
||||
import datetime
|
||||
import os
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import tqdm
|
||||
from accelerate import Accelerator
|
||||
from torch.utils.data import DataLoader, DistributedSampler
|
||||
|
||||
import argparse
|
||||
|
||||
import math
|
||||
|
||||
import os
|
||||
|
||||
from torch.utils.data import random_split
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
import AudioUtils
|
||||
from data import AudioDataset
|
||||
from generator import SISUGenerator
|
||||
from discriminator import SISUDiscriminator
|
||||
from generator import SISUGenerator
|
||||
from utils.TrainingTools import discriminator_train, generator_train
|
||||
|
||||
from training_utils import discriminator_train, generator_train
|
||||
import file_utils as Data
|
||||
|
||||
import torchaudio.transforms as T
|
||||
|
||||
# Init script argument parser
|
||||
parser = argparse.ArgumentParser(description="Training script")
|
||||
parser.add_argument("--generator", type=str, default=None,
|
||||
help="Path to the generator model file")
|
||||
parser.add_argument("--discriminator", type=str, default=None,
|
||||
help="Path to the discriminator model file")
|
||||
parser.add_argument("--device", type=str, default="cpu", help="Select device")
|
||||
parser.add_argument("--epoch", type=int, default=0, help="Current epoch for model versioning")
|
||||
parser.add_argument("--debug", action="store_true", help="Print debug logs")
|
||||
parser.add_argument("--continue_training", action="store_true", help="Continue training using temp_generator and temp_discriminator models")
|
||||
|
||||
# ---------------------------
|
||||
# Argument parsing
|
||||
# ---------------------------
|
||||
parser = argparse.ArgumentParser(description="Training script (safer defaults)")
|
||||
parser.add_argument("--resume", action="store_true", help="Resume training")
|
||||
parser.add_argument(
|
||||
"--epochs", type=int, default=5000, help="Number of training epochs"
|
||||
)
|
||||
parser.add_argument("--batch_size", type=int, default=8, help="Batch size")
|
||||
parser.add_argument("--num_workers", type=int, default=4, help="DataLoader num_workers") # Increased workers slightly
|
||||
parser.add_argument("--debug", action="store_true", help="Print debug logs")
|
||||
parser.add_argument(
|
||||
"--no_pin_memory", action="store_true", help="Disable pin_memory even on CUDA"
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
device = torch.device(args.device if torch.cuda.is_available() else "cpu")
|
||||
print(f"Using device: {device}")
|
||||
# ---------------------------
|
||||
# Init accelerator
|
||||
# ---------------------------
|
||||
|
||||
# Parameters
|
||||
sample_rate = 44100
|
||||
n_fft = 2048
|
||||
hop_length = 256
|
||||
win_length = n_fft
|
||||
n_mels = 128
|
||||
n_mfcc = 20 # If using MFCC
|
||||
|
||||
mfcc_transform = T.MFCC(
|
||||
sample_rate,
|
||||
n_mfcc,
|
||||
melkwargs = {'n_fft': n_fft, 'hop_length': hop_length}
|
||||
).to(device)
|
||||
|
||||
mel_transform = T.MelSpectrogram(
|
||||
sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length,
|
||||
win_length=win_length, n_mels=n_mels, power=1.0 # Magnitude Mel
|
||||
).to(device)
|
||||
|
||||
stft_transform = T.Spectrogram(
|
||||
n_fft=n_fft, win_length=win_length, hop_length=hop_length
|
||||
).to(device)
|
||||
|
||||
debug = args.debug
|
||||
|
||||
# Initialize dataset and dataloader
|
||||
dataset_dir = './dataset/good'
|
||||
dataset = AudioDataset(dataset_dir, device)
|
||||
models_dir = "models"
|
||||
os.makedirs(models_dir, exist_ok=True)
|
||||
audio_output_dir = "output"
|
||||
os.makedirs(audio_output_dir, exist_ok=True)
|
||||
|
||||
# ========= SINGLE =========
|
||||
|
||||
train_data_loader = DataLoader(dataset, batch_size=64, shuffle=True)
|
||||
|
||||
|
||||
# ========= MODELS =========
|
||||
accelerator = Accelerator(mixed_precision="bf16")
|
||||
|
||||
# ---------------------------
|
||||
# Models
|
||||
# ---------------------------
|
||||
generator = SISUGenerator()
|
||||
# Note: SISUDiscriminator is now an Ensemble (MPD + MSD)
|
||||
discriminator = SISUDiscriminator()
|
||||
|
||||
epoch: int = args.epoch
|
||||
epoch_from_file = Data.read_data(f"{models_dir}/epoch_data.json")
|
||||
accelerator.print("🔨 | Compiling models...")
|
||||
|
||||
if args.continue_training:
|
||||
generator.load_state_dict(torch.load(f"{models_dir}/temp_generator.pt", map_location=device, weights_only=True))
|
||||
discriminator.load_state_dict(torch.load(f"{models_dir}/temp_generator.pt", map_location=device, weights_only=True))
|
||||
epoch = epoch_from_file["epoch"] + 1
|
||||
else:
|
||||
if args.generator is not None:
|
||||
generator.load_state_dict(torch.load(args.generator, map_location=device, weights_only=True))
|
||||
if args.discriminator is not None:
|
||||
discriminator.load_state_dict(torch.load(args.discriminator, map_location=device, weights_only=True))
|
||||
# Torch compile is great, but if you hit errors with the new List/Tuple outputs
|
||||
# of the discriminator, you might need to disable it for D.
|
||||
generator = torch.compile(generator)
|
||||
discriminator = torch.compile(discriminator)
|
||||
|
||||
generator = generator.to(device)
|
||||
discriminator = discriminator.to(device)
|
||||
accelerator.print("✅ | Compiling done!")
|
||||
|
||||
# Loss
|
||||
criterion_g = nn.BCEWithLogitsLoss()
|
||||
criterion_d = nn.BCEWithLogitsLoss()
|
||||
# ---------------------------
|
||||
# Dataset / DataLoader
|
||||
# ---------------------------
|
||||
accelerator.print("📊 | Fetching dataset...")
|
||||
dataset = AudioDataset("./dataset", 8192)
|
||||
|
||||
# Optimizers
|
||||
optimizer_g = optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
|
||||
optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0001, betas=(0.5, 0.999))
|
||||
sampler = DistributedSampler(dataset) if accelerator.num_processes > 1 else None
|
||||
pin_memory = torch.cuda.is_available() and not args.no_pin_memory
|
||||
|
||||
# Scheduler
|
||||
scheduler_g = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_g, mode='min', factor=0.5, patience=5)
|
||||
scheduler_d = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_d, mode='min', factor=0.5, patience=5)
|
||||
train_loader = DataLoader(
|
||||
dataset,
|
||||
sampler=sampler,
|
||||
batch_size=args.batch_size,
|
||||
shuffle=(sampler is None),
|
||||
num_workers=args.num_workers,
|
||||
pin_memory=pin_memory,
|
||||
persistent_workers=pin_memory,
|
||||
)
|
||||
|
||||
def start_training():
|
||||
generator_epochs = 5000
|
||||
for generator_epoch in range(generator_epochs):
|
||||
low_quality_audio = (torch.empty((1)), 1)
|
||||
high_quality_audio = (torch.empty((1)), 1)
|
||||
ai_enhanced_audio = (torch.empty((1)), 1)
|
||||
if not train_loader or not train_loader.batch_size or train_loader.batch_size == 0:
|
||||
accelerator.print("🪹 | There is no data to train with! Exiting...")
|
||||
exit()
|
||||
|
||||
times_correct = 0
|
||||
loader_batch_size = train_loader.batch_size
|
||||
|
||||
# ========= TRAINING =========
|
||||
for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Training epoch {generator_epoch+1}/{generator_epochs}, Current epoch {epoch+1}"):
|
||||
# for high_quality_clip, low_quality_clip in train_data_loader:
|
||||
high_quality_sample = (high_quality_clip[0], high_quality_clip[1])
|
||||
low_quality_sample = (low_quality_clip[0], low_quality_clip[1])
|
||||
accelerator.print("✅ | Dataset fetched!")
|
||||
|
||||
# ========= LABELS =========
|
||||
batch_size = high_quality_clip[0].size(0)
|
||||
real_labels = torch.ones(batch_size, 1).to(device)
|
||||
fake_labels = torch.zeros(batch_size, 1).to(device)
|
||||
# ---------------------------
|
||||
# Losses / Optimizers / Scalers
|
||||
# ---------------------------
|
||||
|
||||
# ========= DISCRIMINATOR =========
|
||||
discriminator.train()
|
||||
d_loss = discriminator_train(
|
||||
high_quality_sample,
|
||||
low_quality_sample,
|
||||
real_labels,
|
||||
fake_labels,
|
||||
discriminator,
|
||||
generator,
|
||||
criterion_d,
|
||||
optimizer_d
|
||||
)
|
||||
optimizer_g = optim.AdamW(
|
||||
generator.parameters(), lr=0.0003, betas=(0.5, 0.999), weight_decay=0.0001
|
||||
)
|
||||
optimizer_d = optim.AdamW(
|
||||
discriminator.parameters(), lr=0.0003, betas=(0.5, 0.999), weight_decay=0.0001
|
||||
)
|
||||
|
||||
# ========= GENERATOR =========
|
||||
generator.train()
|
||||
generator_output, combined_loss, adversarial_loss, mel_l1_tensor, log_stft_l1_tensor, mfcc_l_tensor = generator_train(
|
||||
low_quality_sample,
|
||||
high_quality_sample,
|
||||
real_labels,
|
||||
generator,
|
||||
discriminator,
|
||||
criterion_d,
|
||||
optimizer_g,
|
||||
device,
|
||||
mel_transform,
|
||||
stft_transform,
|
||||
mfcc_transform
|
||||
)
|
||||
scheduler_g = torch.optim.lr_scheduler.ReduceLROnPlateau(
|
||||
optimizer_g, mode="min", factor=0.5, patience=5
|
||||
)
|
||||
scheduler_d = torch.optim.lr_scheduler.ReduceLROnPlateau(
|
||||
optimizer_d, mode="min", factor=0.5, patience=5
|
||||
)
|
||||
|
||||
if debug:
|
||||
print(f"D_LOSS: {d_loss.item():.4f}, COMBINED_LOSS: {combined_loss.item():.4f}, ADVERSARIAL_LOSS: {adversarial_loss.item():.4f}, MEL_L1_LOSS: {mel_l1_tensor.item():.4f}, LOG_STFT_L1_LOSS: {log_stft_l1_tensor.item():.4f}, MFCC_LOSS: {mfcc_l_tensor.item():.4f}")
|
||||
scheduler_d.step(d_loss.detach())
|
||||
scheduler_g.step(adversarial_loss.detach())
|
||||
# ---------------------------
|
||||
# Prepare accelerator
|
||||
# ---------------------------
|
||||
|
||||
# ========= SAVE LATEST AUDIO =========
|
||||
high_quality_audio = (high_quality_clip[0][0], high_quality_clip[1][0])
|
||||
low_quality_audio = (low_quality_clip[0][0], low_quality_clip[1][0])
|
||||
ai_enhanced_audio = (generator_output[0], high_quality_clip[1][0])
|
||||
generator, discriminator, optimizer_g, optimizer_d, train_loader = accelerator.prepare(
|
||||
generator, discriminator, optimizer_g, optimizer_d, train_loader
|
||||
)
|
||||
|
||||
new_epoch = generator_epoch+epoch
|
||||
|
||||
if generator_epoch % 25 == 0:
|
||||
print(f"Saved epoch {new_epoch}!")
|
||||
torchaudio.save(f"{audio_output_dir}/epoch-{new_epoch}-audio-crap.wav", low_quality_audio[0].cpu().detach(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
|
||||
torchaudio.save(f"{audio_output_dir}/epoch-{new_epoch}-audio-ai.wav", ai_enhanced_audio[0].cpu().detach(), ai_enhanced_audio[1])
|
||||
torchaudio.save(f"{audio_output_dir}/epoch-{new_epoch}-audio-orig.wav", high_quality_audio[0].cpu().detach(), high_quality_audio[1])
|
||||
|
||||
#if debug:
|
||||
# print(generator.state_dict().keys())
|
||||
# print(discriminator.state_dict().keys())
|
||||
torch.save(discriminator.state_dict(), f"{models_dir}/temp_discriminator.pt")
|
||||
torch.save(generator.state_dict(), f"{models_dir}/temp_generator.pt")
|
||||
Data.write_data(f"{models_dir}/epoch_data.json", {"epoch": new_epoch})
|
||||
# ---------------------------
|
||||
# Checkpoint helpers
|
||||
# ---------------------------
|
||||
models_dir = "./models"
|
||||
os.makedirs(models_dir, exist_ok=True)
|
||||
|
||||
|
||||
torch.save(discriminator, "models/epoch-5000-discriminator.pt")
|
||||
torch.save(generator, "models/epoch-5000-generator.pt")
|
||||
print("Training complete!")
|
||||
def save_ckpt(path, epoch, loss=None, is_best=False):
|
||||
accelerator.wait_for_everyone()
|
||||
if accelerator.is_main_process:
|
||||
state = {
|
||||
"epoch": epoch,
|
||||
"G": accelerator.unwrap_model(generator).state_dict(),
|
||||
"D": accelerator.unwrap_model(discriminator).state_dict(),
|
||||
"optG": optimizer_g.state_dict(),
|
||||
"optD": optimizer_d.state_dict(),
|
||||
"schedG": scheduler_g.state_dict(),
|
||||
"schedD": scheduler_d.state_dict()
|
||||
}
|
||||
|
||||
start_training()
|
||||
accelerator.save(state, os.path.join(models_dir, "last.pt"))
|
||||
|
||||
if is_best:
|
||||
accelerator.save(state, os.path.join(models_dir, "best.pt"))
|
||||
accelerator.print(f"🌟 | New best model saved with G Loss: {loss:.4f}")
|
||||
|
||||
|
||||
start_epoch = 0
|
||||
if args.resume:
|
||||
ckpt_path = os.path.join(models_dir, "last.pt")
|
||||
if os.path.exists(ckpt_path):
|
||||
ckpt = torch.load(ckpt_path)
|
||||
|
||||
accelerator.unwrap_model(generator).load_state_dict(ckpt["G"])
|
||||
accelerator.unwrap_model(discriminator).load_state_dict(ckpt["D"])
|
||||
optimizer_g.load_state_dict(ckpt["optG"])
|
||||
optimizer_d.load_state_dict(ckpt["optD"])
|
||||
scheduler_g.load_state_dict(ckpt["schedG"])
|
||||
scheduler_d.load_state_dict(ckpt["schedD"])
|
||||
|
||||
start_epoch = ckpt.get("epoch", 1)
|
||||
accelerator.print(f"🔁 | Resumed from epoch {start_epoch}!")
|
||||
else:
|
||||
accelerator.print("⚠️ | Resume requested but no checkpoint found. Starting fresh.")
|
||||
|
||||
|
||||
accelerator.print("🏋️ | Started training...")
|
||||
|
||||
try:
|
||||
for epoch in range(start_epoch, args.epochs):
|
||||
generator.train()
|
||||
discriminator.train()
|
||||
|
||||
discriminator_time = 0
|
||||
generator_time = 0
|
||||
|
||||
running_d, running_g, steps = 0.0, 0.0, 0
|
||||
|
||||
progress_bar = tqdm.tqdm(train_loader, desc=f"Epoch {epoch} | D {discriminator_time}μs | G {generator_time}μs")
|
||||
|
||||
for i, (
|
||||
(high_quality, low_quality),
|
||||
(high_sample_rate, low_sample_rate),
|
||||
) in enumerate(progress_bar):
|
||||
with accelerator.autocast():
|
||||
generator_output = generator(low_quality)
|
||||
|
||||
# --- Discriminator ---
|
||||
d_time = datetime.datetime.now()
|
||||
optimizer_d.zero_grad(set_to_none=True)
|
||||
with accelerator.autocast():
|
||||
d_loss = discriminator_train(
|
||||
high_quality,
|
||||
discriminator,
|
||||
generator_output.detach()
|
||||
)
|
||||
|
||||
accelerator.backward(d_loss)
|
||||
optimizer_d.step()
|
||||
discriminator_time = (datetime.datetime.now() - d_time).microseconds
|
||||
|
||||
# --- Generator ---
|
||||
g_time = datetime.datetime.now()
|
||||
optimizer_g.zero_grad(set_to_none=True)
|
||||
with accelerator.autocast():
|
||||
g_total, g_adv = generator_train(
|
||||
low_quality,
|
||||
high_quality,
|
||||
generator,
|
||||
discriminator,
|
||||
generator_output
|
||||
)
|
||||
|
||||
accelerator.backward(g_total)
|
||||
torch.nn.utils.clip_grad_norm_(generator.parameters(), 1)
|
||||
optimizer_g.step()
|
||||
generator_time = (datetime.datetime.now() - g_time).microseconds
|
||||
|
||||
d_val = accelerator.gather(d_loss.detach()).mean()
|
||||
g_val = accelerator.gather(g_total.detach()).mean()
|
||||
|
||||
if torch.isfinite(d_val):
|
||||
running_d += d_val.item()
|
||||
else:
|
||||
accelerator.print(
|
||||
f"🫥 | NaN in discriminator loss at step {i}, skipping update."
|
||||
)
|
||||
|
||||
if torch.isfinite(g_val):
|
||||
running_g += g_val.item()
|
||||
else:
|
||||
accelerator.print(
|
||||
f"🫥 | NaN in generator loss at step {i}, skipping update."
|
||||
)
|
||||
|
||||
steps += 1
|
||||
progress_bar.set_description(f"Epoch {epoch} | D {discriminator_time}μs | G {generator_time}μs")
|
||||
|
||||
if steps == 0:
|
||||
accelerator.print("🪹 | No steps in epoch (empty dataloader?). Exiting.")
|
||||
break
|
||||
|
||||
mean_d = running_d / steps
|
||||
mean_g = running_g / steps
|
||||
|
||||
scheduler_d.step(mean_d)
|
||||
scheduler_g.step(mean_g)
|
||||
|
||||
save_ckpt(os.path.join(models_dir, "last.pt"), epoch)
|
||||
accelerator.print(f"🤝 | Epoch {epoch} done | D {mean_d:.4f} | G {mean_g:.4f}")
|
||||
|
||||
except Exception:
|
||||
try:
|
||||
save_ckpt(os.path.join(models_dir, "crash_last.pt"), epoch)
|
||||
accelerator.print(f"💾 | Saved crash checkpoint for epoch {epoch}")
|
||||
except Exception as e:
|
||||
accelerator.print("😬 | Failed saving crash checkpoint:", e)
|
||||
raise
|
||||
|
||||
accelerator.print("🏁 | Training finished.")
|
||||
|
||||
@@ -1,144 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
|
||||
import torchaudio
|
||||
import torchaudio.transforms as T
|
||||
|
||||
def gpu_mfcc_loss(mfcc_transform, y_true, y_pred):
|
||||
mfccs_true = mfcc_transform(y_true)
|
||||
mfccs_pred = mfcc_transform(y_pred)
|
||||
|
||||
min_len = min(mfccs_true.shape[2], mfccs_pred.shape[2])
|
||||
mfccs_true = mfccs_true[:, :, :min_len]
|
||||
mfccs_pred = mfccs_pred[:, :, :min_len]
|
||||
|
||||
loss = torch.mean((mfccs_true - mfccs_pred)**2)
|
||||
return loss
|
||||
|
||||
def mel_spectrogram_l1_loss(mel_transform: T.MelSpectrogram, y_true: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:
|
||||
mel_spec_true = mel_transform(y_true)
|
||||
mel_spec_pred = mel_transform(y_pred)
|
||||
|
||||
# Ensure same time dimension length (due to potential framing differences)
|
||||
min_len = min(mel_spec_true.shape[-1], mel_spec_pred.shape[-1])
|
||||
mel_spec_true = mel_spec_true[..., :min_len]
|
||||
mel_spec_pred = mel_spec_pred[..., :min_len]
|
||||
|
||||
# L1 Loss (Mean Absolute Error)
|
||||
loss = torch.mean(torch.abs(mel_spec_true - mel_spec_pred))
|
||||
return loss
|
||||
|
||||
def mel_spectrogram_l2_loss(mel_transform: T.MelSpectrogram, y_true: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:
|
||||
mel_spec_true = mel_transform(y_true)
|
||||
mel_spec_pred = mel_transform(y_pred)
|
||||
|
||||
min_len = min(mel_spec_true.shape[-1], mel_spec_pred.shape[-1])
|
||||
mel_spec_true = mel_spec_true[..., :min_len]
|
||||
mel_spec_pred = mel_spec_pred[..., :min_len]
|
||||
|
||||
loss = torch.mean((mel_spec_true - mel_spec_pred)**2)
|
||||
return loss
|
||||
|
||||
def log_stft_magnitude_loss(stft_transform: T.Spectrogram, y_true: torch.Tensor, y_pred: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
|
||||
stft_mag_true = stft_transform(y_true)
|
||||
stft_mag_pred = stft_transform(y_pred)
|
||||
|
||||
min_len = min(stft_mag_true.shape[-1], stft_mag_pred.shape[-1])
|
||||
stft_mag_true = stft_mag_true[..., :min_len]
|
||||
stft_mag_pred = stft_mag_pred[..., :min_len]
|
||||
|
||||
loss = torch.mean(torch.abs(torch.log(stft_mag_true + eps) - torch.log(stft_mag_pred + eps)))
|
||||
return loss
|
||||
|
||||
def spectral_convergence_loss(stft_transform: T.Spectrogram, y_true: torch.Tensor, y_pred: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
|
||||
stft_mag_true = stft_transform(y_true)
|
||||
stft_mag_pred = stft_transform(y_pred)
|
||||
|
||||
min_len = min(stft_mag_true.shape[-1], stft_mag_pred.shape[-1])
|
||||
stft_mag_true = stft_mag_true[..., :min_len]
|
||||
stft_mag_pred = stft_mag_pred[..., :min_len]
|
||||
|
||||
norm_true = torch.linalg.norm(stft_mag_true, ord='fro', dim=(-2, -1))
|
||||
norm_diff = torch.linalg.norm(stft_mag_true - stft_mag_pred, ord='fro', dim=(-2, -1))
|
||||
|
||||
loss = torch.mean(norm_diff / (norm_true + eps))
|
||||
return loss
|
||||
|
||||
def discriminator_train(high_quality, low_quality, real_labels, fake_labels, discriminator, generator, criterion, optimizer):
|
||||
optimizer.zero_grad()
|
||||
|
||||
# Forward pass for real samples
|
||||
discriminator_decision_from_real = discriminator(high_quality[0])
|
||||
d_loss_real = criterion(discriminator_decision_from_real, real_labels)
|
||||
|
||||
with torch.no_grad():
|
||||
generator_output = generator(low_quality[0])
|
||||
discriminator_decision_from_fake = discriminator(generator_output)
|
||||
d_loss_fake = criterion(discriminator_decision_from_fake, fake_labels.expand_as(discriminator_decision_from_fake))
|
||||
|
||||
d_loss = (d_loss_real + d_loss_fake) / 2.0
|
||||
|
||||
d_loss.backward()
|
||||
# Optional: Gradient Clipping (can be helpful)
|
||||
# nn.utils.clip_grad_norm_(discriminator.parameters(), max_norm=1.0) # Gradient Clipping
|
||||
optimizer.step()
|
||||
|
||||
return d_loss
|
||||
|
||||
def generator_train(
|
||||
low_quality,
|
||||
high_quality,
|
||||
real_labels,
|
||||
generator,
|
||||
discriminator,
|
||||
adv_criterion,
|
||||
g_optimizer,
|
||||
device,
|
||||
mel_transform: T.MelSpectrogram,
|
||||
stft_transform: T.Spectrogram,
|
||||
mfcc_transform: T.MFCC,
|
||||
lambda_adv: float = 1.0,
|
||||
lambda_mel_l1: float = 10.0,
|
||||
lambda_log_stft: float = 1.0,
|
||||
lambda_mfcc: float = 1.0
|
||||
):
|
||||
g_optimizer.zero_grad()
|
||||
|
||||
generator_output = generator(low_quality[0])
|
||||
|
||||
discriminator_decision = discriminator(generator_output)
|
||||
adversarial_loss = adv_criterion(discriminator_decision, real_labels.expand_as(discriminator_decision))
|
||||
|
||||
mel_l1 = 0.0
|
||||
log_stft_l1 = 0.0
|
||||
mfcc_l = 0.0
|
||||
|
||||
# Calculate Mel L1 Loss if weight is positive
|
||||
if lambda_mel_l1 > 0:
|
||||
mel_l1 = mel_spectrogram_l1_loss(mel_transform, high_quality[0], generator_output)
|
||||
|
||||
# Calculate Log STFT L1 Loss if weight is positive
|
||||
if lambda_log_stft > 0:
|
||||
log_stft_l1 = log_stft_magnitude_loss(stft_transform, high_quality[0], generator_output)
|
||||
|
||||
# Calculate MFCC Loss if weight is positive
|
||||
if lambda_mfcc > 0:
|
||||
mfcc_l = gpu_mfcc_loss(mfcc_transform, high_quality[0], generator_output)
|
||||
|
||||
mel_l1_tensor = torch.tensor(mel_l1, device=device) if isinstance(mel_l1, float) else mel_l1
|
||||
log_stft_l1_tensor = torch.tensor(log_stft_l1, device=device) if isinstance(log_stft_l1, float) else log_stft_l1
|
||||
mfcc_l_tensor = torch.tensor(mfcc_l, device=device) if isinstance(mfcc_l, float) else mfcc_l
|
||||
|
||||
combined_loss = (lambda_adv * adversarial_loss) + \
|
||||
(lambda_mel_l1 * mel_l1_tensor) + \
|
||||
(lambda_log_stft * log_stft_l1_tensor) + \
|
||||
(lambda_mfcc * mfcc_l_tensor)
|
||||
|
||||
combined_loss.backward()
|
||||
# Optional: Gradient Clipping
|
||||
# nn.utils.clip_grad_norm_(generator.parameters(), max_norm=1.0)
|
||||
g_optimizer.step()
|
||||
|
||||
# 6. Return values for logging
|
||||
return generator_output, combined_loss, adversarial_loss, mel_l1_tensor, log_stft_l1_tensor, mfcc_l_tensor
|
||||
68
utils/MultiResolutionSTFTLoss.py
Normal file
68
utils/MultiResolutionSTFTLoss.py
Normal file
@@ -0,0 +1,68 @@
|
||||
from typing import Dict, List
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio.transforms as T
|
||||
|
||||
|
||||
class MultiResolutionSTFTLoss(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
fft_sizes: List[int] = [512, 1024, 2048, 4096, 8192],
|
||||
hop_sizes: List[int] = [64, 128, 256, 512, 1024],
|
||||
win_lengths: List[int] = [256, 512, 1024, 2048, 4096],
|
||||
eps: float = 1e-7,
|
||||
center: bool = True
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.eps = eps
|
||||
self.n_resolutions = len(fft_sizes)
|
||||
|
||||
self.stft_transforms = nn.ModuleList()
|
||||
for i, (n_fft, hop_len, win_len) in enumerate(zip(fft_sizes, hop_sizes, win_lengths)):
|
||||
stft = T.Spectrogram(
|
||||
n_fft=n_fft,
|
||||
hop_length=hop_len,
|
||||
win_length=win_len,
|
||||
window_fn=torch.hann_window,
|
||||
power=None,
|
||||
center=center,
|
||||
pad_mode="reflect",
|
||||
normalized=False,
|
||||
)
|
||||
self.stft_transforms.append(stft)
|
||||
|
||||
def forward(
|
||||
self, y_true: torch.Tensor, y_pred: torch.Tensor
|
||||
) -> Dict[str, torch.Tensor]:
|
||||
if y_true.dim() == 3 and y_true.size(1) == 1:
|
||||
y_true = y_true.squeeze(1)
|
||||
if y_pred.dim() == 3 and y_pred.size(1) == 1:
|
||||
y_pred = y_pred.squeeze(1)
|
||||
|
||||
sc_loss = 0.0
|
||||
mag_loss = 0.0
|
||||
|
||||
for stft in self.stft_transforms:
|
||||
stft.window = stft.window.to(y_true.device)
|
||||
stft_true = stft(y_true)
|
||||
stft_pred = stft(y_pred)
|
||||
|
||||
stft_mag_true = torch.abs(stft_true)
|
||||
stft_mag_pred = torch.abs(stft_pred)
|
||||
|
||||
norm_true = torch.linalg.norm(stft_mag_true, dim=(-2, -1))
|
||||
norm_diff = torch.linalg.norm(stft_mag_true - stft_mag_pred, dim=(-2, -1))
|
||||
sc_loss += torch.mean(norm_diff / (norm_true + self.eps))
|
||||
|
||||
log_mag_pred = torch.log(stft_mag_pred + self.eps)
|
||||
log_mag_true = torch.log(stft_mag_true + self.eps)
|
||||
mag_loss += F.l1_loss(log_mag_pred, log_mag_true)
|
||||
|
||||
sc_loss /= self.n_resolutions
|
||||
mag_loss /= self.n_resolutions
|
||||
total_loss = sc_loss + mag_loss
|
||||
|
||||
return {"total": total_loss, "sc": sc_loss, "mag": mag_loss}
|
||||
93
utils/TrainingTools.py
Normal file
93
utils/TrainingTools.py
Normal file
@@ -0,0 +1,93 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from utils.MultiResolutionSTFTLoss import MultiResolutionSTFTLoss
|
||||
|
||||
# Keep STFT settings as is
|
||||
stft_loss_fn = MultiResolutionSTFTLoss(
|
||||
fft_sizes=[512, 1024, 2048],
|
||||
hop_sizes=[64, 128, 256],
|
||||
win_lengths=[256, 512, 1024]
|
||||
)
|
||||
|
||||
|
||||
def feature_matching_loss(fmap_r, fmap_g):
|
||||
"""
|
||||
Computes L1 distance between real and fake feature maps.
|
||||
"""
|
||||
loss = 0
|
||||
for dr, dg in zip(fmap_r, fmap_g):
|
||||
for rl, gl in zip(dr, dg):
|
||||
rl = rl.detach()
|
||||
loss += torch.mean(torch.abs(rl - gl))
|
||||
|
||||
return loss * 2
|
||||
|
||||
|
||||
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
||||
"""
|
||||
Least Squares GAN Loss (LSGAN) for the Discriminator.
|
||||
Objective: Real -> 1, Fake -> 0
|
||||
"""
|
||||
loss = 0
|
||||
r_losses = []
|
||||
g_losses = []
|
||||
|
||||
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
||||
r_loss = torch.mean((dr - 1) ** 2)
|
||||
g_loss = torch.mean(dg ** 2)
|
||||
|
||||
loss += (r_loss + g_loss)
|
||||
r_losses.append(r_loss.item())
|
||||
g_losses.append(g_loss.item())
|
||||
|
||||
return loss, r_losses, g_losses
|
||||
|
||||
|
||||
def generator_adv_loss(disc_generated_outputs):
|
||||
"""
|
||||
Least Squares GAN Loss for the Generator.
|
||||
Objective: Fake -> 1 (Fool the discriminator)
|
||||
"""
|
||||
loss = 0
|
||||
for dg in zip(disc_generated_outputs):
|
||||
dg = dg[0] # Unpack tuple
|
||||
loss += torch.mean((dg - 1) ** 2)
|
||||
return loss
|
||||
|
||||
|
||||
def discriminator_train(
|
||||
high_quality,
|
||||
discriminator,
|
||||
generator_output
|
||||
):
|
||||
y_d_rs, y_d_gs, _, _ = discriminator(high_quality, generator_output.detach())
|
||||
|
||||
d_loss, _, _ = discriminator_loss(y_d_rs, y_d_gs)
|
||||
|
||||
return d_loss
|
||||
|
||||
|
||||
def generator_train(
|
||||
low_quality,
|
||||
high_quality,
|
||||
generator,
|
||||
discriminator,
|
||||
generator_output
|
||||
):
|
||||
y_d_rs, y_d_gs, fmap_rs, fmap_gs = discriminator(high_quality, generator_output)
|
||||
|
||||
loss_gen_adv = generator_adv_loss(y_d_gs)
|
||||
|
||||
loss_fm = feature_matching_loss(fmap_rs, fmap_gs)
|
||||
|
||||
stft_loss = stft_loss_fn(high_quality, generator_output)["total"]
|
||||
|
||||
lambda_stft = 45.0
|
||||
lambda_fm = 2.0
|
||||
lambda_adv = 1.0
|
||||
|
||||
combined_loss = (lambda_stft * stft_loss) + \
|
||||
(lambda_fm * loss_fm) + \
|
||||
(lambda_adv * loss_gen_adv)
|
||||
|
||||
return combined_loss, loss_gen_adv
|
||||
0
utils/__init__.py
Normal file
0
utils/__init__.py
Normal file
Reference in New Issue
Block a user