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 *.zip filter=lfs diff=lfs merge=lfs -text
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+brontes_00840000 filter=lfs diff=lfs merge=lfs -text

brontes_00840000

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dabee31e427555b53757c8626a6687740e6f8682a8d0461eb238e6d0d8a219ce
+size 864451583

config.yaml

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+# Configuration for TransAudio model with ~30M parameters for 44.1kHz audio
+# 
+# This configuration uses STFT preprocessing with U-Net architecture for audio processing
+# STFT parameters are optimized for 44.1kHz audio to balance frequency and temporal resolution
+# UNet parameters are tuned to achieve approximately 30M total parameters
+
+model:
+  # STFT Configuration for 44.1kHz audio processing
+  stft_config:
+    n_fft: 16              # FFT size - provides good frequency resolution for 44.1kHz
+    hop_length: 8          # Hop size - 1/4 of n_fft for good temporal resolution
+    win_length: 16         # Window length - same as n_fft for standard hann window
+    
+  # UNet Configuration targeting ~30M parameters
+  unet_config:
+    in_ch: null              # Will be automatically calculated as n_audio_channels
+    out_ch: null             # Will be automatically calculated as n_audio_channels
+    base_ch: 64              # Base channel count to keep parameter count in check
+    depth: 6                 # Depth of U-Net (with downsampling/upsampling)
+    ch_mults: [2, 3, 4, 6, 6, 8]
+    k: 8                     # Kernel size for convolutions
+    decoder_k: null
+    stride: 4
+    norm: 'weight'               # Normalization type: group norm for stability
+    act: 'snake'
+    separable: false         # Use standard convolutions rather than depthwise separable
+    use_deconv: true         # Use transposed convolutions for upsampling
+    bottleneck_dilations: [1, 2, 4, 8]  # Dilated convolutions in bottleneck
+    learnable_alpha: false    # Learnable residual scaling parameter
+    alpha_init: 1.0          # Initial value for residual scaling
+    use_lstm_bottleneck: true
+    lstm_layers: 2
+    skip_layer_indexes: [-1, -2]
+    skip_residual_scales: [1.0, 0.1]
+    # iSTFT output head configuration (iSTFTNet-style synthesis)
+    use_istft_head: false     # Enable iSTFT output head instead of direct waveform
+    istft_n_fft: 32         # FFT size for iSTFT synthesis
+    istft_hop_length: 16     # Hop length for iSTFT
+    istft_win_length: null    # Window length (null = same as n_fft)
+    phase_eps: 1.0e-8         # Epsilon for safe atan2 phase recovery
+    
+    
+# Dataset configuration
+dataset:
+  sample_rate: 48000         # Target sample rate for audio
+  chunk_size: 16384          # Audio chunk size in samples (about 1.5 seconds at 44.1kHz)
+  mono: true                 # Convert to mono
+  normalize: true            # Normalize to [-1, 1]
+  file_extensions: [".wav", ".mp3", ".flac", ".aac", ".m4a", ".ogg"]
+  cache_dir: "./audio_cache" # Directory to cache resampled files
+  min_samples: 12000         # Minimum number of samples required for a file to be included
+
+# Training Configuration
+training:
+  # Basic training parameters
+  batch_size: 16
+  num_epochs: 9999
+  learning_rate: 0.0001      # Keep at 1e-4 as in original
+  discriminator_lr_multiplier: 1.0 # Discriminator LR is 4x of G.
+  lr_warmup_steps: 2000      # Linear learning rate warmup over 7500 steps
+  adam_b1: 0.8               # Adam beta1, slightly higher for stability
+  adam_b2: 0.99              # Adam beta2
+  lr_decay: 0.999            # Learning rate decay per epoch
+  seed: 1234                 # Random seed for reproducibility
+  fp16_run: false            # Use mixed precision training (FP16)
+  bf16_run: true             # Use BF16 training (mutually exclusive with fp16_run)
+  gradient_clip: 1.5         # Default gradient clipping value (0.0 to disable)
+  generator_gradient_clip: 1.0      # Generator gradient clip (defaults to gradient_clip if not set)
+  discriminator_gradient_clip: 4.0  # Discriminator gradient clip (defaults to gradient_clip if not set)
+  disc_loss_type: "hinge"
+
+
+  # Adversarial training parameters
+  use_adversarial: true       # Enable adversarial training
+  pretrain_steps: 10000      # Number of steps to pretrain generator before adversarial training
+  pretrain_reset: true       # Reset generator optimizer when switching to adversarial training
+  use_se_blocks: false        # Enable Squeeze-and-Excitation blocks in discriminators
+  enable_mpd: true             # Enable Multi-Period Discriminator
+  enable_msd: false             # Enable Multi-Scale Discriminator
+  enable_mbsd: true            # Enable Multi-Band Spectral Discriminators
+  feature_matching_weight: 1.5 # Weight for feature matching loss
+  disc_instance_noise_std: 0 # Gaussian noise std added to D inputs (prevents D overpowering, 0 to disable)
+  gen_s_weight: 1.0          # Weight for multi-scale generator loss
+  gen_f_weight: 1.0          # Weight for multi-period generator loss
+  disc_loss_weight: 1.0      # Weight for discriminator loss
+  
+  # MultiBandSpec Discriminator parameters (part of unified Discriminator)
+  mbsd_window_lengths: [2048, 1024, 512]  # Window lengths for each MBSD instance
+  mbsd_hop_factor: 0.25                    # Hop factor as fraction of window length
+
+
+  # Audio loss parameters (adapted for 44.1kHz)
+  sampling_rate: 48000        # Updated to 44.1kHz
+  n_fft: 2048                # Increased for 44.1kHz audio
+  win_size: 2048             # Window size matching n_fft
+  hop_size: 512              # Hop size - 1/4 of window size
+  num_mels: 80               # Number of mel bands
+  fmin: 0.0                  # Minimum frequency for mel
+  fmax_for_loss: 22050.0     # Maximum frequency for loss (half of sample rate)
+
+  # Mel loss weight
+  mel_loss_weight: 15.0
+
+  # Multi-scale mel loss parameters
+  use_multi_scale_mel_loss: true
+  multi_scale_mel_win_lengths: [512, 1024, 2048]
+  multi_scale_mel_n_mels:      [40,   80,   128]
+  multi_scale_mel_hop_divisor: 4
+  multi_scale_mel_loss_mode: "charbonnier"
+  multi_scale_mel_log_eps: 0.00001
+  multi_scale_mel_l2_weight: 1.0
+  multi_scale_mel_charbonnier_eps: 0.000001
+  multi_scale_mel_f_min: 0.0
+  multi_scale_mel_f_max: null
+  multi_scale_mel_power: 1.0
+  multi_scale_mel_scale: "htk"
+  multi_scale_mel_norm: null
+  multi_scale_mel_clamp_min: null
+
+  # MR-STFT loss parameters (updated for 44.1kHz)
+  use_mr_stft_loss: false
+  mr_stft_n_ffts: [1024, 512, 256, 128]  # Updated for 44.1kHz
+  mr_stft_hop_sizes: [256, 128, 64, 32]   # Updated for 44.1kHz
+  mr_stft_win_sizes: [1024, 512, 256, 128] # Updated for 44.1kHz
+  mr_stft_use_charbonnier: true
+  mr_stft_charbonnier_eps: 0.000001
+  mr_stft_loss_weight: 1.0
+
+  # Waveform-domain loss parameters
+  use_waveform_loss: false               # Enable direct waveform loss
+  waveform_loss_type: "mae"     # Loss type: "mse", "mae", or "charbonnier"
+  waveform_loss_weight: 1.0             # Weight for waveform loss
+  waveform_loss_charbonnier_eps: 0.000001  # Epsilon for Charbonnier loss
+
+  # Pitch loss parameters
+  use_pitch_loss: true
+  pitch_loss_use_activation_loss: false
+  pitch_loss_act_weight: 0.1
+  pitch_loss_use_charbonnier: false
+  pitch_loss_charbonnier_eps: 0.000001
+  pitch_loss_tau: 0.7
+  pitch_loss_wmin: 0.15
+  pitch_loss_conf_clip_min: 0.05
+  pitch_loss_conf_clip_max: 0.95
+  pitch_loss_vuv_thresh: 0.5
+  pitch_loss_weight: 2.0
+  pitch_loss_model: "mir-1k_g7"
+  pitch_loss_step_size: 20.0
+
+
+
+  # Loss configuration (specific to STFT domain processing)
+  loss:
+    # Add different loss functions as needed
+    mse_weight: 1.0
+    recon_weight: 2.0
+    log_mag_weight: 1.0
+    cos_phase_weight: 0.5
+    sin_phase_weight: 0.5
+    # Could add other losses like:
+    # stft_loss_weight: 0.5
+    # perceptual_loss_weight: 0.1
+
+  # Data loading
+  num_workers: 4
+  shuffle: true
+  pin_memory: true
+
+  # Checkpointing
+  checkpoint_interval: 20000  # Save checkpoint every N steps
+  validation_interval: 5000  # Run validation every N steps
+  save_best_only: true       # Only save checkpoints when validation loss improves
+
+  # Logging
+  log_interval: 100          # Log training progress every N steps
+  tensorboard_log_dir: "./logs/transaudio_44khz"
+
+  # Early stopping
+  early_stopping_patience: 10  # Stop if validation loss doesn't improve for N validations
+  early_stopping_min_delta: 0.001  # Minimum change to qualify as improvement
+
+# Hardware configuration
+hardware:
+  num_gpus: 1                # Number of GPUs to use (0 for CPU only)
+  cuda_visible_devices: "0"  # Which GPUs to use (comma separated)
+
+# Paths
+paths:
+  audio_dir: "./audio_files"      # Directory containing training audio
+  checkpoint_dir: "./checkpoints/transaudio_44khz" # Directory to save checkpoints
+  output_dir: "./outputs"         # Directory for output files
+  log_dir: "./logs/transaudio_44khz"               # Directory for logs
+
+# Validation configuration
+validation:
+  batch_size: 2               # Reduced for 44.1kHz audio processing
+  num_workers: 2
+
+# Inference configuration
+inference:
+  chunk_size: 16384          # Increased for 44.1kHz audio
+  overlap: 8192              # Overlap between chunks for seamless reconstruction (1/8 of chunk)
+  batch_size: 1              # Usually 1 for inference
+
+# Architecture notes:
+# Input channels after STFT: 3 * (2048//2 + 1) = 3 * 1025 = 3075
+# With depth=5 and base_ch=96, the channel progression is:
+# Encoder: 3075 -> 96 -> 192 -> 384 -> 768 -> 1536 -> 3072
+# Bottleneck: 3072 -> 3072 with dilated convolutions
+# Decoder: 3072 -> 1536 -> 768 -> 384 -> 192 -> 96 -> 3075
+# This configuration should provide approximately 30M parameters while being more manageable

torchscript/exported_cpu.pt

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torchscript/exported_gpu.pt

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