Delete model_hf.py

model_hf.py

@@ -1,1625 +0,0 @@
-import os
-import pyworld as pw
-import math
-import warnings
-import logging
-import torch
-import torchaudio
-import torch.nn.functional as F
-import torch.nn.init as init
-from torch import nn, Tensor
-import numpy as np
-import matplotlib.pyplot as plt
-from typing import Optional, Dict, Union, List, Tuple, Any
-from functools import partial
-from datetime import datetime
-from datasets import load_dataset, Audio
-from transformers.trainer_seq2seq import Seq2SeqTrainer
-from transformers.training_args_seq2seq import Seq2SeqTrainingArguments
-import transformers
-from dataclasses import dataclass
-from opimizer import MaxFactor
-torch.backends.cudnn.allow_tf32 = True
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.set_float32_matmul_precision('high')
-transformers.utils.logging.set_verbosity_error()
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-dtype = torch.float32
-
-warnings.filterwarnings("ignore")
-logging.basicConfig(level=logging.ERROR)
-
-PATH = 'E:/hf'
-os.environ['HF_HOME'] = PATH
-os.environ['HF_DATASETS_CACHE'] = PATH
-os.environ['TORCH_HOME'] = PATH
-os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
-os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
-
-@dataclass
-class Dimensions:
-    vocab: int
-    text_ctx: int
-    text_dims: int
-    text_head: int
-    text_idx: int
-    mels: int
-    aud_ctx: int
-    aud_dims: int
-    aud_head: int
-    aud_idx: int
-    act: str
-    debug: List[str]
-    cross_attn: bool
-    features: List[str]
-
-def plot_waveform(x=None, w=None, p=None, per=None, sample_idx=0, sr=16000, hop_length=160, 
-                                 title="", markers=None, marker_labels=None, 
-                                 show_voiced_regions=True, show_energy=False):
-    num_plots = sum([x is not None, w is not None, p is not None, per is not None])
-    if num_plots == 0:
-        raise ValueError("No data to plot. Please provide at least one input tensor.")
-    t_spans = []
-    
-    if w is not None:
-        w_np = w[sample_idx].detach().cpu().numpy()
-        if w_np.ndim > 1:
-            w_np = w_np.squeeze()
-        t_spans.append(len(w_np) / sr)
-    if x is not None:
-        x_np = x[sample_idx].detach().cpu().numpy()
-        if x_np.shape[0] < x_np.shape[1]:
-            x_np = x_np.T
-        t_spans.append(x_np.shape[0] * hop_length / sr)
-    if p is not None:
-        p_np = p[sample_idx].detach().cpu().numpy()
-        if p_np.ndim > 1:
-            p_np = p_np.squeeze()
-        t_spans.append(len(p_np) * hop_length / sr)
-    if per is not None:
-        per_np = per[sample_idx].detach().cpu().numpy()
-        if per_np.ndim > 1:
-            per_np = per_np.squeeze()
-        t_spans.append(len(per_np) * hop_length / sr)
-    max_t = max(t_spans) if t_spans else 0
-    fig, axs = plt.subplots(num_plots, 1, figsize=(14, 4*num_plots), sharex=True)
-    if num_plots == 1:
-        axs = [axs]
-    if show_voiced_regions and per is not None:
-        per_np = per[sample_idx].detach().cpu().numpy()
-        if per_np.ndim > 1:
-            per_np = per_np.squeeze()
-        t_per = np.arange(len(per_np)) * hop_length / sr
-        threshold = 0.5
-        for ax in axs:
-            for i in range(len(per_np)-1):
-                if per_np[i] > threshold:
-                    ax.axvspan(t_per[i], t_per[i+1], color='lightblue', alpha=0.2, zorder=0)
-    cu_ax = 0
-    if w is not None:
-        w_np = w[sample_idx].detach().cpu().numpy()
-        if w_np.ndim > 1:
-            w_np = w_np.squeeze()
-        t = np.arange(len(w_np)) / sr
-        axs[cu_ax].plot(t, w_np, color="tab:blue")
-        
-        if show_energy:
-            frame_length = hop_length
-            hop_length_energy = hop_length // 2
-            energy = []
-            for i in range(0, len(w_np)-frame_length, hop_length_energy):
-                frame = w_np[i:i+frame_length]
-                energy.append(np.sqrt(np.mean(frame**2)))
-            energy = np.array(energy)
-            energy = energy / np.max(energy) * 0.8 * max(abs(w_np.min()), abs(w_np.max()))  
-            t_energy = np.arange(len(energy)) * hop_length_energy / sr
-            axs[cu_ax].plot(t_energy, energy, color="red", alpha=0.7, label="Energy")
-            axs[cu_ax].legend(loc='upper right')
-        axs[cu_ax].set_title("Waveform")
-        axs[cu_ax].set_ylabel("Amplitude")
-        axs[cu_ax].set_xlim([0, max_t])
-        axs[cu_ax].grid(True, axis='x', linestyle='--', alpha=0.3)
-        cu_ax += 1
-    
-    if x is not None:
-        x_np = x[sample_idx].detach().cpu().numpy()
-        if x_np.shape[0] < x_np.shape[1]:
-            x_np = x_np.T
-        axs[cu_ax].imshow(x_np.T, aspect="auto", origin="lower", cmap="magma", 
-                                   extent=[0, x_np.shape[0]*hop_length/sr, 0, x_np.shape[1]])
-        axs[cu_ax].set_title("Spectrogram")
-        axs[cu_ax].set_ylabel("Mel Bin")
-        axs[cu_ax].set_xlim([0, max_t])
-        axs[cu_ax].grid(True, axis='x', linestyle='--', alpha=0.3)
-        cu_ax += 1
-    
-    if p is not None:
-        p_np = p[sample_idx].detach().cpu().numpy()
-        if p_np.ndim > 1:
-            p_np = p_np.squeeze()
-        t_p = np.arange(len(p_np)) * hop_length / sr
-        axs[cu_ax].plot(t_p, p_np, color="tab:green")
-        axs[cu_ax].set_title("Pitch")
-        axs[cu_ax].set_ylabel("Frequency (Hz)")
-        axs[cu_ax].set_xlim([0, max_t])
-        axs[cu_ax].grid(True, axis='both', linestyle='--', alpha=0.3)
-        axs[cu_ax].set_ylim([0, min(1000, p_np.max() * 1.2)])
-        cu_ax += 1
-    
-    if per is not None:
-        per_np = per[sample_idx].detach().cpu().numpy()
-        if per_np.ndim > 1:
-            per_np = per_np.squeeze()
-        t_per = np.arange(len(per_np)) * hop_length / sr
-        axs[cu_ax].plot(t_per, per_np, color="tab:red")
-        axs[cu_ax].set_title("Period (Voice Activity)")
-        axs[cu_ax].set_ylabel("periodocity")
-        axs[cu_ax].set_xlim([0, max_t])
-        axs[cu_ax].grid(True, axis='both', linestyle='--', alpha=0.3)
-        axs[cu_ax].set_ylim([-0.05, 1.05])
-        axs[cu_ax].axhline(y=0.5, color='k', linestyle='--', alpha=0.3)
-    
-    if markers is not None:
-        for i, t in enumerate(markers):
-            label = marker_labels[i] if marker_labels and i < len(marker_labels) else None
-            for ax in axs:
-                ax.axvline(x=t, color='k', linestyle='-', alpha=0.7, label=label if i == 0 else None)
-        if marker_labels:
-            axs[0].legend(loc='upper right', fontsize='small')
-    axs[-1].set_xlabel("t (s)")
-    fig.suptitle(title, fontsize=16)
-    plt.tight_layout(rect=[0, 0, 1, 0.97])
-    plt.show()
-    return fig
-
-def dict_to(d, device, dtype=dtype):
-    """Because PyTorch should have this built-in but doesn't"""
-    return {k: v.to(device, dtype) if isinstance(v, torch.Tensor) else v 
-            for k, v in d.items()}
-    
-def exists(v):
-    return v is not None
-
-def default(v, b):
-    return v if exists(v) else b
-
-class Conv1d(nn.Conv1d):
-    def _conv_forward(
-        self, x: Tensor, weight: Tensor, bias) -> Tensor:
-        return super()._conv_forward(x, weight.to(x.device, x.dtype), None if bias is None else bias.to(x.device, x.dtype))
-
-class Conv2d(nn.Conv2d):
-    def _conv_forward(
-        self, x: Tensor, weight: Tensor, bias) -> Tensor:
-        return super()._conv_forward(x, weight.to(x.device, x.dtype), None if bias is None else bias.to(x.device, x.dtype))
-
-class Linear(nn.Module):
-    def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None:
-        super(Linear, self).__init__()
-        self.linear = nn.Linear(in_features, out_features, bias=bias)
-        init.xavier_uniform_(self.linear.weight)
-        if bias:
-            init.zeros_(self.linear.bias)
-    def forward(self, x: Tensor) -> Tensor:
-        return self.linear(x)
-    
-class RMSNorm(nn.Module):
-    def __init__(self, dims: Union[int, Tensor, List, Tuple], 
-                 eps = 1e-8, elementwise_affine = True):
-        super(RMSNorm, self).__init__()
-        if isinstance(dims, int):
-            self.normalized_shape = (dims,)
-        else:
-            self.normalized_shape = tuple(dims)
-        self.eps = eps
-        self.elementwise_affine = elementwise_affine
-        if self.elementwise_affine:
-            self.weight = nn.Parameter(torch.empty(self.normalized_shape))
-            init.ones_(self.weight)  
-        else:
-            self.register_parameter("weight", None)
-    def forward(self, x):
-        return F.rms_norm(x, self.normalized_shape, self.weight, self.eps)
-    
-def LayerNorm(x: Tensor, normalized_shape: Union[int, Tensor, List, Tuple],
-               weight: Optional[Tensor] = None, bias: Optional[Tensor] = None,
-               eps: float = 1e-5) -> Tensor:
-    return F.layer_norm(x, normalized_shape, weight, bias, eps)
-
-def get_device():
-    return torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-def get_dtype():
-    return torch.float32 if torch.cuda.is_available() else torch.float64
-
-def tox():
-    return {"device": get_device(), "dtype": get_dtype()}
-
-def sinusoids(length, channels, max_tscale=10000):
-    assert channels % 2 == 0
-    log_tscale_increment = np.log(max_tscale) / (channels // 2 - 1)
-    inv_tscales = torch.exp(-log_tscale_increment * torch.arange(channels // 2))
-    scaled_t = torch.arange(length)[:, np.newaxis] * inv_tscales[np.newaxis, :]
-    return torch.cat([torch.sin(scaled_t), torch.cos(scaled_t)], dim=1)
-
-class rotary(nn.Module):
-    def __init__(self, dims, head, max_ctx=1500, theta=10000, radii=True, debug: List[str] = [], use_pbias=False):
-        super(rotary, self).__init__()
-
-        self.use_pbias = use_pbias
-        self.dims = dims
-        self.head = head
-        self.head_dim = dims // head
-        self.radii = radii
-        self.dim = self.head_dim
-        self.debug = debug
-        self.counter = 0
-        self.last_theta = None
-
-        self.bias = nn.Parameter(torch.zeros(max_ctx, dims // 2))
-        self.theta = nn.Parameter(torch.tensor(theta, device=device, dtype=dtype), requires_grad=True)
-
-    def theta_freqs(self, theta):
-        freq = (theta / 220.0) * 700 * (torch.pow(10, torch.linspace(0, 2595 * torch.log10(torch.tensor(1 + 8000/700)), self.dim // 2, device=device, dtype=dtype) / 2595) - 1) / 1000
-        freqs = nn.Parameter(torch.tensor(freq, device=device, dtype=dtype), requires_grad=True)        
-        return freqs
-
-    def inverse_mel_scale_scalar(mel_freq: float) -> float:
-        return 700.0 * (math.exp(mel_freq / 1127.0) - 1.0)
-
-    def inverse_mel_scale(mel_freq: Tensor) -> Tensor:
-        return 700.0 * ((mel_freq / 1127.0).exp() - 1.0)
-
-    def mel_scale_scalar(freq: float) -> float:
-        return 1127.0 * math.log(1.0 + freq / 700.0)
-
-    def mel_scale(freq: Tensor) -> Tensor:
-        return 1127.0 * (1.0 + freq / 700.0).log()
-
-    def return_f0(self, f0=None):
-        if f0 is not None:
-            self.f0 = f0
-            self.update_base(f0)
-            return f0.squeeze(0).to(device, dtype)
-        elif hasattr(self, 'f0') and self.f0 is not None:
-            return self.f0.squeeze(0).to(device, dtype)
-        return None
-
-    def get_pitch_bias(self, f0):
-        if f0 is None:
-            return None
-        f0_flat = f0.squeeze().float()
-        f0_norm = (f0_flat - f0_flat.mean()) / (f0_flat.std() + 1e-8)
-        f0_sim = torch.exp(-torch.cdist(f0_norm.unsqueeze(1), 
-                                    f0_norm.unsqueeze(1)))
-        return f0_sim.unsqueeze(0).unsqueeze(0)
-
-    def f0proj(self, f0):
-        if f0.ndim == 3:
-            f0 = f0.squeeze(0)
-        self.f0_proj = nn.Linear(1, self.head_dim // 2, device=device, dtype=dtype)
-        f0 = f0.to(device, dtype)
-        f0 = self.f0_proj(f0.unsqueeze(-1))
-        if f0.ndim == 3:
-            f0 = f0.squeeze(0)
-        return f0.to(device=device, dtype=dtype)
-
-    def align_f0(self, ctx, f0):
-        f0 = self.f0proj(f0)
-        if f0.dim() == 3:
-            batch, length, dims = f0.shape
-            if length == ctx:
-                return f0
-            frames = length / ctx
-            idx = torch.arange(ctx, device=f0.device)
-            idx = (idx * frames).long().clamp(0, length - 1)
-            return f0[:, idx, :]
-        if f0.dim() == 1:
-            length = f0.shape[0]
-            if length == ctx:
-                return f0
-            frames = length / ctx
-            idx = torch.arange(ctx, device=f0.device)
-            idx = (idx * frames).long().clamp(0, length - 1)
-            return f0[idx]
-        else:
-            length, dims = f0.shape
-            if length == ctx:
-                return f0
-            frames = length / ctx
-            idx = torch.arange(ctx, device=f0.device)
-            idx = (idx * frames).long().clamp(0, length - 1)
-            return f0[idx, :]
-
-    def forward(self, x=None, enc=None, layer=None, feature_type="audio") -> Tensor:
-        f0 = enc.get("f0") if enc is not None else None 
-        if isinstance(x, int):
-            ctx = x
-        elif isinstance(x, torch.Tensor) and x.ndim == 2:
-            batch, ctx = x.shape
-        elif isinstance(x, torch.Tensor) and x.ndim == 3:
-            batch, ctx, dims = x.shape
-        else:
-            batch, head, ctx, head_dim = x.shape
-        t = torch.arange(ctx, device=device, dtype=dtype)
-
-        if f0 is not None and f0.dim() == 2:
-            if f0.shape[0] == 1: 
-                f0 = f0.squeeze(0)  
-            else:
-                f0 = f0.view(-1)        
-
-        if f0 is not None:
-            f0_mean = f0.mean()
-            theta = f0_mean + self.theta
-        else:
-            theta = self.theta 
-
-        freqs = self.theta_freqs(theta)
-
-        freqs = t[:, None] * freqs[None, :]
-
-        if self.radii and f0 is not None:
-            radius = f0.to(device, dtype)
-            L = radius.shape[0]
-            if L != ctx:
-                F = L / ctx
-                idx = torch.arange(ctx, device=f0.device)
-                idx = (idx * F).long().clamp(0, L - 1)
-                radius = radius[idx]
-            freqs = torch.polar(radius.unsqueeze(-1).expand_as(freqs), freqs)
-        else:
-            freqs = torch.polar(torch.ones_like(freqs), freqs)
-
-        if "radius" in self.debug and self.counter % 100 == 0:
-            theta_value = theta.item() if isinstance(theta, torch.Tensor) else theta
-            print(f"  [{layer}] [Radius] {radius.shape} {radius.mean():.2f} [Theta] {theta_value:.2f} [f0] {f0.shape if f0 is not None else None} [Freqs] {freqs.shape} {freqs.mean():.2f} [ctx] {ctx}")
-        
-        if "theta" in self.debug and self.counter % 100 == 0:
-            if self.last_theta is None or abs(self.last_theta - theta.item()) > 1.0:
-                self.last_theta = theta.item()
-                print(f"[Theta] {self.last_theta:.2f}")
-
-        self.counter += 1
-        return freqs.unsqueeze(0)
-
-    @staticmethod
-    def apply_rotary(x, freqs):
-        x1 = x[..., :freqs.shape[-1]*2]
-        x2 = x[..., freqs.shape[-1]*2:]
-        orig_shape = x1.shape
-        if x1.ndim == 2:
-            x1 = x1.unsqueeze(0)
-        x1 = x1.float().reshape(*x1.shape[:-1], -1, 2).contiguous()
-        x1 = torch.view_as_complex(x1) * freqs
-        x1 = torch.view_as_real(x1).flatten(-2)
-        x1 = x1.view(orig_shape)
-        return torch.cat([x1.type_as(x), x2], dim=-1)
-
-class MultiheadA(nn.Module):
-    _seen = set()  
-    rbf = False
-    def __init__(self, dims: int, head: int, rotary_emb: bool = True, 
-                 zero_val: float = 1e-4, minz: float = 1e-6, maxz: float = 1e-3, debug: List[str] = [], optim_attn=False):
-        super(MultiheadA, self).__init__()
-
-        self.dims = dims
-        self.head = head
-        self.head_dim = dims // head
-        self.debug = debug
-        self.counter = 0
-
-        self.q = nn.Linear(dims, dims).to(device, dtype)
-        self.k = nn.Linear(dims, dims, bias=False).to(device, dtype)
-        self.v = nn.Linear(dims, dims).to(device, dtype)
-        self.o = nn.Linear(dims, dims).to(device, dtype)
-
-        self.pad_token = 0
-        self.rotary_emb = rotary_emb
-        self.minz = minz
-        self.maxz = maxz
-        self.zero_val = zero_val
-        self.optim_attn = optim_attn        
-        self.fzero = nn.Parameter(torch.tensor(zero_val, device=device, dtype=dtype), requires_grad=False)
-        
-        if rotary_emb:
-            self.rope = rotary(
-                dims=dims,
-                head=head,
-                debug=debug,
-                radii=True,
-                )
-        else:
-            self.rope = None
-
-    def cos_sim(self, q: Tensor, k: Tensor, v: Tensor, mask) -> Tensor:
-        q_norm = torch.nn.functional.normalize(q, dim=-1, eps=1e-12)
-        k_norm = torch.nn.functional.normalize(k, dim=-1, eps=1e-12)
-        qk_cosine = torch.matmul(q_norm, k_norm.transpose(-1, -2))
-        qk_cosine = qk_cosine + mask
-        weights = F.softmax(qk_cosine, dim=-1)
-        out = torch.matmul(weights, v)
-        return out
-
-    def rbf_scores(self, q, k, rbf_sigma=1.0, rbf_ratio=0.0):
-        scale = (self.dims // self.head) ** -0.25
-        dot_scores = torch.matmul(q, k.transpose(-1, -2)) * scale
-        if rbf_ratio <= 0.0:
-            return dot_scores
-        q_norm = q.pow(2).sum(dim=-1, keepdim=True)
-        k_norm = k.pow(2).sum(dim=-1, keepdim=True)
-        qk = torch.matmul(q, k.transpose(-1, -2))
-        dist_sq = q_norm + k_norm.transpose(-1, -2) - 2 * qk
-        rbf_scores = torch.exp(-dist_sq / (2 * rbf_sigma**2))
-        return (1 - rbf_ratio) * dot_scores + rbf_ratio * rbf_scores
-          
-    def forward(self, x: Tensor, xa: Tensor = None, mask: Tensor = None, enc = None, layer = None, feature_type="audio", need_weights=True) -> tuple:
-
-        x = x.to(device, dtype)
-        if xa is not None:
-            xa = xa.to(device, dtype)
-        scale = (self.dims // self.head) ** -0.25
-        
-        z = default(xa, x).to(device, dtype)
-        q = self.q(x)
-        k = self.k(z)
-        v = self.v(z)
-
-        if self.rotary_emb:   
-            q = q.view(*q.shape[:2], self.head, -1).permute(0, 2, 1, 3)
-            k = k.view(*k.shape[:2], self.head, -1).permute(0, 2, 1, 3)
-            v = v.view(*v.shape[:2], self.head, -1).permute(0, 2, 1, 3)
-            q2 = q.shape[2]
-            k2 = k.shape[2]
-
-            q = self.rope.apply_rotary(q, (self.rope(q2, enc=enc, layer=layer)))
-            k = self.rope.apply_rotary(k, (self.rope(k2, enc=enc, layer=layer)))
-        else:
-            q = q.view(*q.shape[:2], self.head, -1).permute(0, 2, 1, 3)
-            k = k.view(*k.shape[:2], self.head, -1).permute(0, 2, 1, 3)
-            v = v.view(*v.shape[:2], self.head, -1).permute(0, 2, 1, 3)
-            batch, head, ctx, head_dim = q.shape
-        
-        if self.rbf:
-            qk = self.rbf_scores(q * scale, k * scale, rbf_sigma=1.0, rbf_ratio=0.3)
-        
-        qk = (q * scale) @ (k * scale).transpose(-1, -2)
-        if self.rope.use_pbias:
-            f0 = enc.get("f0", None) if enc is not None else None
-            pbias = self.rope.use_pbias(f0)
-            if pbias is not None:
-                qk = qk + pbias[:,:,:q2,:q2]
-        token_ids = k[:, :, :, 0]
-        zscale = torch.ones_like(token_ids)
-        fzero = torch.clamp(F.softplus(self.fzero), self.minz, self.maxz)
-        zscale[token_ids.float() == self.pad_token] = fzero
-        
-        if mask is not None:
-            mask = mask[:q2, :q2]
-            qk = qk + mask.unsqueeze(0).unsqueeze(0) * zscale.unsqueeze(-2).expand(qk.shape)
-        qk = qk * zscale.unsqueeze(-2)
-        w = F.softmax(qk, dim=-1).to(q.dtype)
-        wv = (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2)
-        
-        if "multihead" in self.debug and self.counter % 100 == 0:
-            print(f"MHA: q={q.shape}, k={k.shape}, v={v.shape} - {qk.shape}, wv shape: {wv.shape}")
-        self.counter += 1        
-        return self.o(wv), qk
-
-class t_gate(nn.Module):
-    def __init__(self, dims, num_types=4):
-        super().__init__()
-        self.gate_projections = nn.ModuleList([
-            nn.Sequential(Linear(dims, 1), nn.Sigmoid())
-            for _ in range(num_types)])
-        self.type_classifier = nn.Sequential(
-            Linear(dims, num_types),
-            nn.Softmax(dim=-1))
-    def forward(self, x):
-        type_probs = self.type_classifier(x)
-        gates = torch.stack([gate(x) for gate in self.gate_projections], dim=-1)
-        comb_gate = torch.sum(gates * type_probs.unsqueeze(2), dim=-1)
-        return comb_gate
-
-class m_gate(nn.Module):
-    def __init__(self, dims, mem_size=64):
-        super().__init__()
-        self.m_key = nn.Parameter(torch.randn(mem_size, dims))
-        self.m_val = nn.Parameter(torch.randn(mem_size, 1))
-        self.gate_proj = nn.Sequential(Linear(dims, dims//2), nn.SiLU(), Linear(dims//2, 1))
-        
-    def forward(self, x):
-        d_gate = torch.sigmoid(self.gate_proj(x))
-        attention = torch.matmul(x, self.m_key.transpose(0, 1))
-        attention = F.softmax(attention / math.sqrt(x.shape[-1]), dim=-1)
-        m_gate = torch.matmul(attention, self.m_val)
-        m_gate = torch.sigmoid(m_gate)
-        return 0.5 * (d_gate + m_gate)
-
-class c_gate(nn.Module):
-    def __init__(self, dims):
-        super().__init__()
-        self.s_gate = nn.Sequential(Linear(dims, 1), nn.Sigmoid())
-        self.w_gate = nn.Sequential(Linear(dims, 1), nn.Sigmoid())
-        self.p_gate = nn.Sequential(Linear(dims, 1), nn.Sigmoid())
-        self.e_gate = nn.Sequential(Linear(dims, 1), nn.Sigmoid())
-        self.ph_gate = nn.Sequential(Linear(dims, 1), nn.Sigmoid())
-        self.integ = Linear(dims*5, dims)
-        
-    def forward(self, x, features):
-        s_feat = features.get("spectrogram", x)
-        w_feat = features.get("waveform", x)
-        p_feat = features.get("pitch", x)
-        e_feat = features.get("envelope", x)
-        ph_feat = features.get("phase", x)
-        s = self.s_gate(x) * s_feat
-        w = self.w_gate(x) * w_feat
-        p = self.p_gate(x) * p_feat
-        e = self.e_gate(x) * e_feat
-        ph = self.ph_gate(x) * ph_feat
-        comb = torch.cat([s, w, p, e, ph], dim=-1)
-        return self.integ(comb)
-
-class Residual(nn.Module):
-    _seen = set()  
-    def __init__(self, ctx, dims, head, act, cross_attn=True, debug: List[str] = [], 
-                 tgate=True, mgate=False, cgate=False, mem_size=512, features=None):
-        super().__init__()
-        
-        self.dims = dims
-        self.head = head
-        self.ctx = ctx
-        self.head_dim = dims // head
-        self.cross_attn = cross_attn
-        self.features = features
-        self.debug = debug
-        self.counter = 0
-        self.dropout = 0.01
-       
-        self.t_gate = tgate
-        self.m_gate = mgate
-        self.c_gate = cgate
-        self.do_blend = "no_blend" not in self.debug
-        self.blend = nn.Parameter(torch.tensor(0.5)) 
-        self.skip_gates = True if "skip_gates" in self.debug else False
-            
-        act_map = {"gelu": nn.GELU(), "relu": nn.ReLU(), "sigmoid": nn.Sigmoid(), 
-                  "tanh": nn.Tanh(), "swish": nn.SiLU(), "tanhshrink": nn.Tanhshrink(), 
-                  "softplus": nn.Softplus(), "softshrink": nn.Softshrink(), 
-                  "leaky_relu": nn.LeakyReLU(), "elu": nn.ELU()}
-        act_fn = act_map.get(act, nn.GELU())
-
-        self.attna = MultiheadA(dims, head, rotary_emb=True, debug=debug)
-        self.attnb = (MultiheadA(dims, head, rotary_emb=True, debug=debug) if cross_attn else None)
-        
-        mlp = dims * 4
-        self.mlp = nn.Sequential(Linear(dims, mlp), act_fn, Linear(mlp, dims))
-        
-        self.t_gate = t_gate(dims=dims, num_types=4) if t_gate else None
-        self.m_gate = m_gate(dims=dims, mem_size=mem_size) if m_gate else None
-        self.c_gate = c_gate(dims=dims) if cgate else None
-        
-        self.lna = RMSNorm(dims)
-        self.lnb = RMSNorm(dims) if cross_attn else None
-        self.lnc = RMSNorm(dims)
-
-        if not any([t_gate, m_gate, c_gate]):
-            self.mlp_gate = nn.Sequential(Linear(dims, 1), nn.Sigmoid())
-
-    def forward(self, x, xa=None, mask=None, enc=None, layer=None, feature_type="audio") -> Tensor:
-
-        x = x + self.attna(self.lna(x), xa=None, mask=mask, enc=enc, layer=layer)[0]
-        xb = x
-        if self.attnb and xa is not None:
-            x = x + self.attnb(self.lnb(x), xa=xa, mask=None, enc=enc, layer=layer)[0]
-            
-            if self.do_blend:
-                b = torch.sigmoid(self.blend)
-                x = b * xb + (1 - b) * x
-        
-        if self.skip_gates:
-            x = x + self.mlp(self.lnc(x))
-        else:
-            normx = self.lnc(x)
-            mlp_out = self.mlp(normx)
-
-            if self.t_gate:
-                gate = self.t_gate(normx)
-                x = x + gate * mlp_out
-                
-            elif self.m_gate:
-                gate = self.m_gate(normx)
-                x = x + gate * mlp_out
-            
-            elif self.c_gate:
-                gate_output = self.c_gate(normx, self.features)
-                x = x + gate_output
-
-            else:
-                if hasattr(self, 'mlp_gate'):
-                    mlp_gate = self.mlp_gate(normx)
-                    x = x + mlp_gate * mlp_out
-                else:
-                    x = x + mlp_out
-                
-        if "residual" in self.debug and self.counter % 100 == 0:
-            print(f"Step {self.counter}: Residual block output shape: {x.shape}, xa shape: {xa.shape if xa is not None else None}")        
-            if self.t_gate:
-                print(f"Step {self.counter}: Using t_gate: {self.t_gate}")
-            elif self.m_gate:
-                print(f"Step {self.counter}: Using m_gate: {self.m_gate}")
-            elif self.c_gate:
-                print(f"Step {self.counter}: Using c_gate: {self.c_gate}")
-            else:
-                print(f"Step {self.counter}: Using MLP gate: {self.mlp_gate if hasattr(self, 'mlp_gate') else None}")
-        self.counter += 1      
-        return x
-
-class FEncoder(nn.Module):
-    def __init__(self, input_dims, dims, head, layer, kernel_size, act, stride=1, use_rope=False, spec_shape=None):
-        super().__init__()
-        
-        self.head = head
-        self.head_dim = dims // head  
-        self.dropout = 0.01 
-        self.use_rope = use_rope
-        self.dims = dims
-        
-        act_map = {"gelu": nn.GELU(), "relu": nn.ReLU(), "sigmoid": nn.Sigmoid(), "tanh": nn.Tanh(), "swish": nn.SiLU(), "tanhshrink": nn.Tanhshrink(), "softplus": nn.Softplus(), "softshrink": nn.Softshrink(), "leaky_relu": nn.LeakyReLU(), "elu": nn.ELU()}
-        act_fn = act_map.get(act, nn.GELU())
-        
-        self.encoder = nn.Sequential(
-            Conv1d(input_dims, dims, kernel_size=kernel_size, stride=stride, padding=kernel_size//2), act_fn,
-            Conv1d(dims, dims, kernel_size=5, padding=2), act_fn,
-            Conv1d(dims, dims, kernel_size=3, padding=1, groups=dims), act_fn)
-        
-        if use_rope:
-            if spec_shape is not None:
-                self.rope = rotary(
-                    dims=self.head_dim,
-                    use_2d_axial=True,
-                    spec_shape=spec_shape, debug=[])
-            else:
-                self.rope = rotary(
-                    dims=self.head_dim,
-                    use_2d_axial=False, debug=[])
-        else:
-            self.rope = None
-            self.positional = lambda length: sinusoids(length, dims)
-            
-        self.norm = RMSNorm(dims)
-        self._norm = RMSNorm(dims)
-
-    def apply_rope_to_features(self, x, layer=None, feature_type="audio"):
-        if feature_type in ["envelope", "phase"]:
-            feature_type = "spectrogram"
-        batch, ctx, dims = x.shape
-        x = x.view(batch, ctx, self.head, self.head_dim).permute(0, 2, 1, 3)
-        if feature_type == "spectrogram" and hasattr(self.rope, 'use_2d_axial') and self.rope.use_2d_axial:
-            rope_freqs = self.rope(ctx, layer=layer, input_type="spectrogram")
-        else:
-            rope_freqs = self.rope(ctx, layer=layer, input_type="audio")
-        x = self.rope.apply_rotary(x, rope_freqs)
-        x = x.permute(0, 2, 1, 3).contiguous().view(batch, ctx, dims)
-        return x
-
-    def forward(self, x, enc=None, layer=None, feature_type="audio"):
-        x = self.encoder(x).permute(0, 2, 1)
-        if self.use_rope:
-            x = self.apply_rope_to_features(x, layer=layer, feature_type=feature_type)
-        else:
-            x = x + self.positional(x.shape[1]).to(x.device, x.dtype)
-        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
-        x = self._norm(x)
-        return x
-
-class WEncoder(nn.Module):
-    def __init__(self, input_dims, dims, head, layer, kernel_size, act, use_rope=False):
-        super().__init__()
-        
-        self.head = head
-        self.head_dim = dims // head
-        self.dropout = 0.01
-        self.use_rope = use_rope
-        self.dims = dims
-        
-        act_map = {"gelu": nn.GELU(), "relu": nn.ReLU(), "sigmoid": nn.Sigmoid(), "tanh": nn.Tanh(), "swish": nn.SiLU(), "tanhshrink": nn.Tanhshrink(), "softplus": nn.Softplus(), "softshrink": nn.Softshrink(), "leaky_relu": nn.LeakyReLU(), "elu": nn.ELU()}
-        act_fn = act_map.get(act, nn.GELU())
-        
-        self.downsample = nn.Sequential(
-            Conv1d(input_dims, dims//8, kernel_size=15, stride=8, padding=7), act_fn,
-            Conv1d(dims//8, dims//4, kernel_size=7, stride=4, padding=3), act_fn,
-            Conv1d(dims//4, dims, kernel_size=9, stride=5, padding=4), act_fn)
-        
-        self.encoder = nn.Sequential(
-            Conv1d(dims, dims, kernel_size=3, padding=1, groups=dims//8),  act_fn,
-            Conv1d(dims, dims, kernel_size=1), act_fn)
-        if use_rope:
-            self.rope = rotary(
-                dims=self.head_dim,
-                use_2d_axial=False,
-                theta=50.0, debug=[])
-        else:
-            self.rope = None
-            self.positional = lambda length: sinusoids(length, dims)
-        self.norm = RMSNorm(dims)
-
-    def apply_rope_to_features(self, x, layer=None):
-        if not self.use_rope or self.rope is None:
-            return x
-        batch, ctx, dims = x.shape
-        x = x.view(batch, ctx, self.head, self.head_dim).permute(0, 2, 1, 3)
-        rope_freqs = self.rope(ctx, layer=layer, input_type="waveform")
-        x = self.rope.apply_rotary(x, rope_freqs)
-        x = x.permute(0, 2, 1, 3).contiguous().view(batch, ctx, dims)
-        return x
-        
-    def forward(self, x, enc=None, layer=None, feature_type="waveform"):
-        x = self.downsample(x)
-        x = self.encoder(x)
-        x = x.permute(0, 2, 1)
-        if self.use_rope:
-            x = self.apply_rope_to_features(x, layer=layer)
-        else:
-            x = x + self.positional(x.shape[1]).to(x.device, x.dtype)
-        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
-        return self.norm(x)
-
-class PEncoder(nn.Module):
-    def __init__(self, input_dims, dims, head, layer, kernel_size, act, use_rope=False):
-        super().__init__()
-        
-        self.head = head
-        self.head_dim = dims // head
-        self.dropout = 0.01
-        self.use_rope = use_rope
-        self.dims = dims
-        
-        act_map = {"gelu": nn.GELU(), "relu": nn.ReLU(), "sigmoid": nn.Sigmoid(), "tanh": nn.Tanh(), "swish": nn.SiLU(), "tanhshrink": nn.Tanhshrink(), "softplus": nn.Softplus(), "softshrink": nn.Softshrink(), "leaky_relu": nn.LeakyReLU(), "elu": nn.ELU()}
-        act_fn = act_map.get(act, nn.GELU())
-        
-        self.encoder = nn.Sequential(
-            Conv1d(input_dims, dims//4, kernel_size=7, stride=8, padding=3), act_fn,
-            Conv1d(dims//4, dims//2, kernel_size=5, stride=4, padding=2), act_fn,
-            Conv1d(dims//2, dims, kernel_size=5, stride=5, padding=2), act_fn)
-        
-        if use_rope:
-            self.rope = rotary(
-                dims=self.head_dim,
-                use_2d_axial=False,
-                theta=100.0, debug=[])
-        else:
-            self.rope = None
-            self.positional = lambda length: sinusoids(length, dims)
-        self.norm = RMSNorm(dims)
-
-    def apply_rope_to_features(self, x, layer=None):
-        if not self.use_rope or self.rope is None:
-            return x
-        batch, ctx, dims = x.shape
-        x = x.view(batch, ctx, self.head, self.head_dim).permute(0, 2, 1, 3)
-        rope_freqs = self.rope(ctx, layer=layer, input_type="pitch")
-        x = self.rope.apply_rotary(x, rope_freqs)
-        x = x.permute(0, 2, 1, 3).contiguous().view(batch, ctx, dims)
-        return x
-        
-    def forward(self, x, enc=None, layer=None, feature_type="pitch"):
-        x = self.encoder(x).permute(0, 2, 1)
-        if self.use_rope:
-            x = self.apply_rope_to_features(x, layer=layer)
-        else:
-            x = x + self.positional(x.shape[1]).to(x.device, x.dtype)
-        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
-        x = self.norm(x)
-        return x
-
-class AudioEncoder(nn.Module):
-    _seen = set()  
-    def __init__(self, mels: int, ctx: int, dims: int, head: int, layer: int, debug: List[str], features: List[str], act: str = "gelu"):
-        super(AudioEncoder, self).__init__()
-
-        self.dims = dims
-        self.head = head
-        self.ctx = ctx
-        self.head_dim = dims // head
-        self.debug = debug
-        self.counter = 0
-        self.features = features
-        self.dropout = 0.01
-
-        act_map = {"gelu": nn.GELU(), "relu": nn.ReLU(), "sigmoid": nn.Sigmoid(), "tanh": nn.Tanh(), "swish": nn.SiLU(),"tanhshrink": nn.Tanhshrink(), "softplus": nn.Softplus(), "softshrink": nn.Softshrink(), "leaky_relu": nn.LeakyReLU(), "elu": nn.ELU()}
-        act_fn = act_map.get(act, nn.GELU())
-        
-        if features == ["spectrogram", "waveform", "pitch"]:
-            cgate=True
-        else:
-            cgate = False
-            
-        self.blocks = nn.ModuleDict({
-
-            "spectrogram": nn.ModuleList(
-            [FEncoder(input_dims=mels, dims=dims, head=head, layer=layer, kernel_size=3, act=act_fn)] + 
-            [Residual(ctx=ctx, dims=dims, head=head, act=act, debug=debug, features=features, cgate=cgate) for _ in range(layer)] 
-            if "spectrogram" in features else None), 
-
-            "waveform": nn.ModuleList(
-            [WEncoder(input_dims=1, dims=dims, head=head, layer=layer, kernel_size=11, act=act_fn)] +
-            [Residual(ctx=ctx, dims=dims, head=head, act=act, debug=debug, features=features, cgate=cgate) for _ in range(layer)] 
-            if "waveform" in features else None),
-
-            "pitch": nn.ModuleList(
-            [FEncoder(input_dims=1, dims=dims, head=head, layer=layer, kernel_size=9, act=act, stride=2)] +
-            [Residual(ctx=ctx, dims=dims, head=head, act=act, debug=debug, features=features, cgate=cgate) for _ in range(layer)] 
-            if "pitch" in features else None),
-
-            "envelope": nn.ModuleList(
-            [FEncoder(input_dims=mels, dims=dims, head=head, layer=layer, kernel_size=3, act=act_fn)] + 
-            [Residual(ctx=ctx, dims=dims, head=head, act=act, debug=debug, features=features, cgate=cgate) for _ in range(layer)] 
-            if "envelope" in features else None),
-
-            "phase": nn.ModuleList(
-            [FEncoder(input_dims=mels, dims=dims, head=head, layer=layer, kernel_size=3, act=act_fn)] + 
-            [Residual(ctx=ctx, dims=dims, head=head, act=act, debug=debug, features=features, cgate=cgate) for _ in range(layer)] 
-            if "phase" in features else None),
-            })
-
-    def forward(self, enc, layer="encoder"):
-        enc = dict_to(enc, device, dtype)
-        out = {}
-        out.update(enc)
-
-        for f in self.features:
-            if f in enc and f in self.blocks:
-                x = enc[f]
-                for block in self.blocks[f]:
-                    x = block(x, enc=enc, layer=layer)
-                out[f] = x
-
-        if self.counter < 1 and "encoder" in self.debug:      
-            s = enc.get("spectrogram")
-            w = enc.get("waveform")
-            p = default(enc.get("pitch"), enc.get("f0"))
-            plot_waveform(x=s, w=w, p=p, hop_length=128)
-            shapes = {k: v.shape for k, v in enc.items()}
-            print(f"Step {self.counter}: mode: {list(enc.keys()) }: shapes: {shapes}")
-        self.counter += 1
-        return out
-
-class TextDecoder(nn.Module):
-    def __init__(self, vocab: int, ctx: int, dims: int, head: int, layer: int, cross_attn: bool, 
-                debug: List[str], features: List[str]): 
-        super(TextDecoder, self).__init__()
-
-        self.ctx = ctx     
-        self.dims = dims
-        self.head = head
-        self.head_dim = dims // head
-        self.debug = debug
-        self.counter = 0
-        self.dropout = 0.01
-        self.features = features
-        self.do_blend = "no_blend" not in self.debug
-        self.sequential = "sequential" in self.debug
-
-        self.token = nn.Embedding(num_embeddings=vocab, embedding_dim=dims)
-        with torch.no_grad():
-            self.token.weight[0].zero_()
-        self.positional = nn.Parameter(data=torch.empty(ctx, dims), requires_grad=True)
-        
-        self.block = nn.ModuleList([
-            Residual(ctx=ctx, dims=dims, head=head, act="gelu", cross_attn=cross_attn, debug=debug, features=features)
-            for _ in range(layer)])
-        
-        self.blocks = nn.ModuleDict({
-        f: nn.ModuleList([Residual(ctx=ctx, dims=dims, head=head, act="gelu", cross_attn=cross_attn, debug=debug, features=features)
-            for _ in range(layer)]) for f in features})
-        
-        self.blend = nn.ParameterDict({f: nn.Parameter(torch.tensor(0.5)) for f in features})
-        self.ln_dec = RMSNorm(dims)
-        
-        mask = torch.tril(torch.ones(ctx, ctx), diagonal=0)        
-        self.register_buffer("mask", mask, persistent=False)
-
-    def forward(self, x, enc, order=None, layer='decoder') -> Tensor:
-
-        if order is None:
-            order = self.features
-        
-        mask = self.mask[:x.shape[1], :x.shape[1]]
-        x = self.token(x) + self.positional[:x.shape[1]]
-        x = F.dropout(x, p=self.dropout, training=self.training)
-        
-        for block in self.block:
-            x = block(x, xa=None, mask=mask, enc=None, layer=layer)
-
-        for f in order:
-            if f in enc:
-                xa = enc[f]
-                for block in self.blocks[f]:
-                    out = block(x=x, xa=xa, mask=None, enc=None, layer=layer)
-
-                if self.sequential:
-                    x = out
-                else:
-                    a = torch.sigmoid(self.blend[f])
-                    x = a * out + (1 - a) * x
-                        
-        if self.counter < 1 and "decoder" in self.debug:
-            shapes = {k: v.shape for k, v in enc.items()}
-            print(f"Step {self.counter}: Decoder output shape: {x.shape}, enc keys: {list(enc.keys())}, order: {order}: shapes: {shapes}")
-        self.counter += 1  
-
-        x = self.ln_dec(x)   
-        return x @ torch.transpose(self.token.weight.to(dtype), 0, 1).float()
-
-class Echo(nn.Module):
-    def __init__(self, param: Dimensions):
-        super().__init__()
-        self.param = param
-
-        self.encoder = AudioEncoder(
-            mels=param.mels,
-            ctx=param.aud_ctx,
-            dims=param.aud_dims,
-            head=param.aud_head,
-            layer=param.aud_idx,
-            act=param.act,
-            debug=param.debug,
-            features=param.features,
-            )
-        
-        self.decoder = TextDecoder(
-            vocab=param.vocab,
-            ctx=param.text_ctx,
-            dims=param.text_dims,
-            head=param.text_head,
-            layer=param.text_idx,
-            cross_attn=param.cross_attn,
-            debug=param.debug,
-            features=param.features,
-            )
-        
-    def forward(self,
-        labels=None,
-        input_ids=None,
-        waveform: Optional[torch.Tensor]=None,
-        spectrogram: torch.Tensor=None,
-        pitch: Optional[torch.Tensor]=None,
-        f0: Optional[torch.Tensor]=None,
-        envelope: Optional[torch.Tensor]=None,
-        phase: Optional[torch.Tensor]=None,
-        ) -> Dict[str, torch.Tensor]:
-
-        encoder_inputs = {}
-        if spectrogram is not None:
-            encoder_inputs["spectrogram"] = spectrogram
-        if waveform is not None:
-            encoder_inputs["waveform"] = waveform
-        if pitch is not None:
-            encoder_inputs["pitch"] = pitch
-        if envelope is not None:
-            encoder_inputs["envelope"] = envelope
-        if phase is not None:
-            encoder_inputs["phase"] = phase
-        if f0 is not None:
-            encoder_inputs["f0"] = f0
-
-        encoder_outputs = self.encoder(encoder_inputs)
-        logits = self.decoder(input_ids, encoder_outputs)
-
-        loss = None
-        if labels is not None:
-            loss = F.cross_entropy(
-                logits.view(-1, logits.shape[-1]), labels.view(-1), ignore_index=0)
-                
-        return {"logits": logits, "loss": loss} 
-
-    @property
-    def device(self):
-        return next(self.parameters()).device
-    @property
-    def dtype(self):
-        return next(self.parameters()).dtype
-
-    def _init_weights(self, module):
-        std = 0.02
-        self.init_counts = {
-            "Linear": 0, "Conv1d": 0, "LayerNorm": 0, "RMSNorm": 0,
-            "Conv2d": 0, "SEBlock": 0, "TextDecoder": 0, "AudioEncoder": 0, 
-            "Residual": 0, "MultiheadA": 0, "MultiheadB - Cross Attention": 0, 
-            "MultiheadC": 0, "MultiheadD": 0, "FEncoder": 0,
-            "WEncoder": 0, "PEncoder": 0}
-
-        for name, module in self.named_modules():
-            if isinstance(module, RMSNorm):
-                nn.init.ones_(module.weight)
-                self.init_counts["RMSNorm"] += 1
-            elif isinstance(module, nn.Linear):
-                if module.weight is not None:
-                    nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    nn.init.zeros_(module.bias)
-                self.init_counts["Linear"] += 1
-            elif isinstance(module, Conv1d):
-                nn.init.normal_(module.weight, mean=0.0, std=std)
-                if module.bias is not None:
-                    nn.init.zeros_(module.bias)
-                self.init_counts["Conv1d"] += 1
-            elif isinstance(module, Conv2d):
-                nn.init.normal_(module.weight, mean=0.0, std=std)
-                if module.bias is not None:
-                    nn.init.zeros_(module.bias)
-                self.init_counts["Conv2d"] += 1
-            elif isinstance(module, MultiheadA):
-
-                self.init_counts["MultiheadA"] += 1
-            elif isinstance(module, TextDecoder):
-                self.init_counts["TextDecoder"] += 1
-            elif isinstance(module, AudioEncoder):
-                self.init_counts["AudioEncoder"] += 1
-            elif isinstance(module, Residual):
-                self.init_counts["Residual"] += 1
-    
-    def init_weights(self):
-        print("Initializing model weights...")
-        self.apply(self._init_weights)
-        print("Initialization summary:")
-        for module_type, count in self.init_counts.items():
-            if count > 0:
-                print(f"{module_type}: {count}")
-
-@dataclass
-class DataCollator:
-    tokenizer: Any
-
-    def __call__(self, features: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
-        all_keys = set()
-        for f in features:
-            all_keys.update(f.keys())
-        batch = {}
-        pad_token_id = getattr(self.tokenizer, 'pad_token_id', 0)
-        bos_token_id = getattr(self.tokenizer, 'bos_token_id', 1)
-        eos_token_id = getattr(self.tokenizer, 'eos_token_id', 2)
-
-        for key in all_keys:
-            if key == "label":
-                labels_list = [f["label"] for f in features]
-                max_len = max(len(l) for l in labels_list)
-                all_ids, all_labels = [], []
-                for label in labels_list:
-                    label_list = label.tolist() if isinstance(label, torch.Tensor) else label
-                    decoder_input = [bos_token_id] + label_list
-                    label_eos = label_list + [eos_token_id]
-                    input_len = max_len + 1 - len(decoder_input)
-                    label_len = max_len + 1 - len(label_eos)
-                    padded_input = decoder_input + [pad_token_id] * input_len
-                    padded_labels = label_eos + [pad_token_id] * label_len
-                    all_ids.append(padded_input)
-                    all_labels.append(padded_labels)
-                batch["input_ids"] = torch.tensor(all_ids, dtype=torch.long)
-                batch["labels"] = torch.tensor(all_labels, dtype=torch.long)
-            elif key in ["spectrogram", "waveform", "pitch", "f0", "envelope", "phase"]:
-                items = [f[key] for f in features if key in f]
-                max_len = max(item.shape[-1] for item in items)
-                padded = []
-                for item in items:
-                    pad_width = max_len - item.shape[-1]
-                    if pad_width > 0:
-                        pad_item = F.pad(item, (0, pad_width), mode='constant', value=pad_token_id)
-                    else:
-                        pad_item = item
-                    padded.append(pad_item)
-                batch[key] = torch.stack(padded)
-                if key == "spectrogram":
-                    batch["spectrogram"] = batch[key]
-        return batch
-
-def hilbert_transform(x):
-    N = x.shape[-1]
-    xf = torch.fft.rfft(x)
-    h = torch.zeros(N // 2 + 1, device=x.device, dtype=x.dtype)
-    if N % 2 == 0:
-        h[0] = h[N//2] = 1
-        h[1:N//2] = 2
-    else:
-        h[0] = 1
-        h[1:(N+1)//2] = 2
-    return torch.fft.irfft(xf * h, n=N)
-
-def analytic_signal(x):
-    return x + 1j * hilbert_transform(x)
-
-def hilbert_transform_2d(x, dim=-1):
-    N = x.shape[dim]
-    if dim == -1 or dim == len(x.shape) - 1:
-        xf = torch.fft.rfft(x)
-    else:
-        xf = torch.fft.rfft(x, dim=dim)
-    h_shape = [1] * len(x.shape)
-    h_shape[dim] = N // 2 + 1
-    h = torch.zeros(h_shape, device=x.device, dtype=x.dtype)
-    if dim == -1 or dim == len(x.shape) - 1:
-        if N % 2 == 0:
-            h[..., 0] = h[..., -1] = 1
-            h[..., 1:-1] = 2
-        else:
-            h[..., 0] = 1
-            h[..., 1:] = 2
-    else:
-        pass
-    return torch.fft.irfft(xf * h, n=N, dim=dim)
-
-def hilbert_transform_true_2d(x):
-    xf = torch.fft.rfft2(x)
-    h1, h2 = torch.meshgrid(
-        torch.fft.rfftfreq(x.shape[-2]) * 2 - 1,
-        torch.fft.rfftfreq(x.shape[-1]) * 2 - 1,
-        indexing='ij')
-    h = -1j / (math.pi * (h1 + 1j*h2))
-    h[0, 0] = 0 
-    return torch.fft.irfft2(xf * h.to(x.device))
-
-def process_spectrogram_with_hilbert(spec):
-    analytic = spec + 1j * hilbert_transform(spec)
-    envelope = torch.abs(analytic)
-    phase = torch.angle(analytic)
-    return envelope, phase
-        
-def load_wave(wave_data, sample_rate):
-    if isinstance(wave_data, str):
-        waveform, sr = torchaudio.load(uri=wave_data, normalize=False)
-    elif isinstance(wave_data, dict):
-        waveform = torch.tensor(data=wave_data["array"]).float()
-        sr = wave_data["sampling_rate"]
-    else:
-        raise TypeError("Invalid wave_data format.")
-    
-    if waveform.dim() == 1:
-        waveform = waveform.unsqueeze(0)
-    
-    if sr != sample_rate:
-        original_length = waveform.shape[1]
-        target_length = int(original_length * (sample_rate / sr))
-        
-        resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=sample_rate)
-        waveform = resampler(waveform)
-        
-    return waveform.flatten()
-
-def extract_features(batch, tokenizer, spectrogram, waveforms, pitch, frequency=False,
-                     hop_length=128, fmin=0, fmax=8000, n_mels=128, n_fft=1024, sampling_rate=16000,
-                     pad_mode="constant", center=True, power=2.0, window_fn=torch.hann_window, mel_scale="htk", 
-                     norm=None, normalized=False, downsamples=False, period=False, hilbert=False):
-
-    audio = batch["audio"]
-    sampling_rate = audio["sampling_rate"]
-    sr = audio["sampling_rate"]
-    wav = load_wave(wave_data=audio, sample_rate=sr)
-
-    if spectrogram:
-        transform = torchaudio.transforms.MelSpectrogram(
-            f_max=fmax,
-            f_min=fmin,
-            n_mels=n_mels,
-            sample_rate=sr,
-            n_fft=n_fft,
-            hop_length=hop_length,
-            norm=norm,
-            normalized=normalized,
-            power=power,
-            center=center, 
-            mel_scale=mel_scale,
-            window_fn=window_fn,
-            pad_mode=pad_mode)
-        
-        mel_spectrogram = transform(wav)      
-        log_mel = torch.clamp(mel_spectrogram, min=1e-10).log10()
-        log_mel = torch.maximum(log_mel, log_mel.max() - 8.0)
-        spec = (log_mel + 4.0) / 4.0
-        spec = torch.tensor(spec)
-        batch["spectrogram"] = spec
-        
-    if hilbert:
-        batch["envelope"] = process_spectrogram_with_hilbert(spec)[0]
-        batch["phase"] = process_spectrogram_with_hilbert(spec)[1]
-    
-    wav_1d = wav.unsqueeze(0)
-    
-    if waveforms:
-        batch["waveform"] = wav_1d
-            
-    if pitch:
-        wav_np = wav.numpy().astype(np.float64)  
-        f0, t = pw.dio(wav_np, sampling_rate, 
-                    frame_period=hop_length/sampling_rate*1000)
-        f0 = pw.stonemask(wav_np, f0, t, sampling_rate)
-        f0 = torch.from_numpy(f0)
-        batch["pitch"] = f0.unsqueeze(0)
-        
-    if frequency:
-        wav_np = wav.numpy().astype(np.float64)  
-        f0, t = pw.dio(wav_np, sampling_rate, frame_period=hop_length/sampling_rate*1000)
-        f0 = pw.stonemask(wav_np, f0, t, sampling_rate)
-        f0 = torch.from_numpy(f0)  
-        batch["f0"] = f0
-                  
-    if spectrogram and waveforms and pitch:
-        spec_mean = batch["spectrogram"].mean()
-        spec_std = batch["spectrogram"].std() + 1e-6
-        batch["spectrogram"] = (batch["spectrogram"] - spec_mean) / spec_std
-        
-        wav_mean = batch["waveform"].mean()
-        wav_std = batch["waveform"].std() + 1e-6
-        batch["waveform"] = (batch["waveform"] - wav_mean) / wav_std
-        
-        if batch["pitch"].max() > 1.0:
-            pitch_min = 50.0
-            pitch_max = 500.0
-            batch["pitch"] = (batch["pitch"] - pitch_min) / (pitch_max - pitch_min)
-            
-    batch["label"] = tokenizer.encode(batch["transcription"], add_special_tokens=False)
-    return batch
-
-def levenshtein(reference_words, hypothesis_words):
-
-    m, n = len(reference_words), len(hypothesis_words)
-    dist_matrix = [[0 for _ in range(n+1)] for _ in range(m+1)]
-    
-    for i in range(m+1):
-        dist_matrix[i][0] = i
-    for j in range(n+1):
-        dist_matrix[0][j] = j
-    
-    for i in range(1, m+1):
-        for j in range(1, n+1):
-            if reference_words[i-1] == hypothesis_words[j-1]:
-                dist_matrix[i][j] = dist_matrix[i-1][j-1]
-            else:
-                substitution = dist_matrix[i-1][j-1] + 1
-                insertion = dist_matrix[i][j-1] + 1
-                deletion = dist_matrix[i-1][j] + 1
-                dist_matrix[i][j] = min(substitution, insertion, deletion)
-    
-    return dist_matrix[m][n]
-
-def wer_batch(references, hypotheses):
-
-    total_errors = 0
-    total_words = 0
-    for ref, hyp in zip(references, hypotheses):
-        ref_words = ref.lower().split()
-        errors = levenshtein(ref_words, hyp.lower().split()) 
-        total_errors += errors
-        total_words += len(ref_words)
-    return (total_errors / total_words) * 100 if total_words > 0 else 0.0
-
-def compute_metrics(pred, compute_result: bool = True, print_pred: bool = False, num_samples: int = 0, tokenizer = None, model = None):
-
-    pred_ids = pred.predictions
-    label_ids = pred.label_ids
-
-    if isinstance(pred_ids, tuple):
-        pred_ids = pred_ids[0]
-    else:
-        pred_ids = pred_ids
-    if hasattr(pred_ids, "ndim") and pred_ids.ndim == 3:
-        if not isinstance(pred_ids, torch.Tensor):
-            pred_ids = torch.tensor(pred_ids)
-        pred_ids = pred_ids.argmax(dim=-1)
-
-    pred_ids = pred_ids.tolist()
-    label_ids = label_ids.tolist()
-
-    pad_token_id = tokenizer.pad_token_id if hasattr(tokenizer, 'pad_token_id') else 0
-    label_ids = [[pad_token_id if token == -100 else token for token in seq] for seq in label_ids]
-
-    if print_pred:
-        pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=False)
-        label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=False)
-        for i in range(min(num_samples, len(pred_str))):
-            print(f"Preds: {pred_str[i]}")
-            print(f"Label: {label_str[i]}")
-            print(f"Preds: {pred_ids[i]}")
-            print(f"Label: {label_ids[i]}")
-            print("--------------------------------")  
-
-    pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
-    label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)
-    wer = wer_batch(label_str, pred_str)
-
-    if model is None:
-        global global_model
-        if 'global_model' in globals():
-            model = global_model
-    
-    if model is not None:
-        trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) / 1_000_000
-        if trainable_params > 0:
-            efficiency_score = (100 - wer) / trainable_params
-        else:
-            print("Warning: Zero trainable parameters detected")
-            efficiency_score = 0.0
-    else:
-        print("Warning: Model not available for parameter counting")
-        trainable_params = 0.0
-        efficiency_score = 0.0
-    
-    if hasattr(wer, "item"):
-        wer = wer.item()
-    
-    metrics = {
-        "wer": float(wer),
-        "trainable_params_M": float(trainable_params),
-        "efficiency_score": float(efficiency_score),
-    }
-    return metrics
-
-logger = logging.getLogger(__name__)
-
-def create_model(param: Dimensions) -> Echo:
-    model = Echo(param).to('cuda')
-    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
-    total_params = sum(p.numel() for p in model.parameters())
-    logger.info(f"Trainable parameters: {trainable_params:,}")
-    logger.info(f"Total parameters: {total_params:,}")
-    print(f"Trainable parameters: {trainable_params:,}")
-    print(f"Total parameters: {total_params:,}")
-    
-    return model
-
-def setup_tokenizer(token: str, local_tokenizer_path: str = "./"):
-    from tokenizers import Tokenizer
-    tokenizer = Tokenizer.from_file(f"{local_tokenizer_path}/tokenizer.json")
-    orig_encode = tokenizer.encode
-    def enc(text, add_special_tokens=True):
-        ids = orig_encode(text).ids
-        if not add_special_tokens:
-            sp_ids = [tokenizer.token_to_id(t) for t in ["<PAD>", "<BOS>", "<EOS>"]]
-            ids = [id for id in ids if id not in sp_ids]
-        return ids
-
-    def bdec(ids_list, skip_special_tokens=True):
-        results = []
-        for ids in ids_list:
-            if skip_special_tokens:
-                ids = [id for id in ids if id not in [0, 1, 2]]
-            results.append(tokenizer.decode(ids))
-        return results
-
-    def save_pretrained(save_dir):
-        os.makedirs(save_dir, exist_ok=True)
-        tokenizer.save(f"{save_dir}/tokenizer.json")
-    tokenizer.encode = enc
-    tokenizer.batch_decode = bdec
-    tokenizer.save_pretrained = save_pretrained
-    tokenizer.pad_token_id = 0
-    tokenizer.bos_token_id = 1
-    tokenizer.eos_token_id = 2
-    return tokenizer
-
-def prepare_datasets(tokenizer, token: str, sanity_check: bool = False, dataset_config: Optional[Dict] = None) -> Tuple[any, any]:
-    if dataset_config is None:
-        dataset_config = {
-            "spectrogram": True,
-            "waveforms": True,
-            "pitch": True,
-            "frequency": True,
-            "downsamples": True,
-            "hop_length": 128,
-            "fmin": 50,
-            "fmax": 2000,
-            "n_mels": 128,
-            "n_fft": 1024,
-            "sampling_rate": 16000,
-        }
-
-    dataset = load_dataset(  
-        "mozilla-foundation/common_voice_17_0",
-        "en",
-        token=token, 
-        trust_remote_code=True,
-        streaming=True)
-
-    dataset = dataset.rename_column("sentence", "transcription")
-    dataset = dataset.cast_column(column="audio", feature=Audio(sampling_rate=16000)).select_columns(["audio", "transcription"])
-    
-    if sanity_check:
-        dataset = dataset["test"].take(10)
-        prepare_fn = partial(extract_features, tokenizer=tokenizer, **dataset_config)
-        dataset = dataset.map(function=prepare_fn, remove_columns=["audio", "transcription"]).with_format(type="torch")
-        train_dataset = dataset
-        test_dataset = dataset
-    else:
-
-        def filter_func(x):
-            return (0 < len(x["transcription"]) < 512 and
-                   len(x["audio"]["array"]) > 0 and
-                   len(x["audio"]["array"]) < 1500 * 160)
-        
-        dataset = dataset.filter(filter_func)
-        prepare_fn = partial(extract_features, tokenizer=tokenizer, **dataset_config)
-        train_dataset = dataset["train"].take(10000).shuffle()
-        test_dataset = dataset["test"].take(1000).shuffle()
-
-        train_dataset = train_dataset.map(
-            function=prepare_fn, 
-            remove_columns=["audio", "transcription"]
-        ).with_format(type="torch")
-        
-        test_dataset = test_dataset.map(
-            function=prepare_fn, 
-            remove_columns=["audio", "transcription"]
-        ).with_format(type="torch")
-        
-    return train_dataset, test_dataset
-
-def get_training_args(
-    log_dir: str,
-    batch_eval_metrics: bool = False,
-    max_steps: int = 10,
-    save_steps: int = 1000,
-    eval_steps: int = 1,
-    warmup_steps: int = 0,
-    num_train_epochs: int = 1,
-    logging_steps: int = 1,
-    eval_on_start: bool = False,
-    learning_rate: float = 1e-4,
-    weight_decay: float = 0.01,
-    max_grad_norm: float = 1.0,
-) -> Seq2SeqTrainingArguments:
-
-    return Seq2SeqTrainingArguments(
-        output_dir=log_dir,
-        per_device_train_batch_size=1,
-        per_device_eval_batch_size=1,
-        gradient_accumulation_steps=1,
-        eval_accumulation_steps=1,
-        eval_strategy="steps",
-        save_strategy="no",
-        max_steps=max_steps,
-        save_steps=save_steps,
-        eval_steps=eval_steps,
-        warmup_steps=warmup_steps,
-        num_train_epochs=num_train_epochs,
-        logging_steps=logging_steps,
-        logging_dir=log_dir,
-        logging_strategy="steps",
-        report_to=["tensorboard"],
-        push_to_hub=False,
-        disable_tqdm=False,
-        save_total_limit=1,
-        label_names=["labels"],
-        optim="adamw_torch",
-        adam_beta1=0.9,
-        adam_beta2=0.999,
-        adam_epsilon=1e-8,
-        lr_scheduler_type="cosine",
-        learning_rate=learning_rate,
-        weight_decay=weight_decay,
-        save_safetensors=False,
-        eval_on_start=eval_on_start,
-        batch_eval_metrics=batch_eval_metrics,
-        max_grad_norm=max_grad_norm,       
-    )
-
-def main():
-     
-    token = ""
-    log_dir = os.path.join('./output/logs', datetime.now().strftime(format='%m-%d_%H_%M_%S'))
-    os.makedirs(name=log_dir, exist_ok=True)
-    tokenizer = setup_tokenizer(token)
-
-    def sanity(sanity: bool):
-
-        if sanity:
-            training_args = get_training_args(
-            log_dir,
-            batch_eval_metrics = False,
-            max_steps = 10,
-            save_steps = 0,
-            eval_steps = 1,
-            warmup_steps = 0,
-            logging_steps = 1,
-            eval_on_start = True,
-            learning_rate = 5e-6,
-            weight_decay = 0.01,
-            max_grad_norm = 0.6,            
-            )
-        else:
-            training_args = get_training_args(
-            log_dir,
-            batch_eval_metrics = False,
-            max_steps = 10000,   
-            save_steps = 10000,
-            eval_steps = 1000,   
-            warmup_steps = 1000,
-            logging_steps = 100,
-            eval_on_start = False,
-            learning_rate = 2.5e-4,
-            weight_decay = 0.01,
-            max_grad_norm = 0.6,            
-            )
-
-        return training_args
-        
-    param = Dimensions(
-        mels=128,
-        aud_ctx=1500,
-        aud_head=4,
-        aud_dims=512,
-        aud_idx=4,
-        vocab=40000,
-        text_ctx=512,
-        text_head=4,
-        text_dims=512,
-        text_idx=4,
-        act="swish",
-        debug={"encoder"},
-        cross_attn=True,
-        features = ["spectrogram"],
-        )
-
-    sanity_check = False
-
-    training_args = sanity(sanity_check)
-    dataset_config = {
-        "spectrogram": True,
-        "waveforms": False,
-        "pitch": False,
-        "downsamples": False,
-        "frequency": True,
-        "hilbert": False,
-        "hop_length": 128,
-        "fmin": 150,
-        "fmax": 2000,
-        "n_mels": 128,
-        "n_fft": 1024,
-        "sampling_rate": 16000,
-        "pad_mode": "constant",
-        "center": True, 
-        "power": 1.0,
-        "window_fn": torch.hann_window,
-        "mel_scale": "htk",
-        "norm": None,
-        "normalized": False}
-    
-    model = create_model(param)
-    
-    global global_model
-    global_model = model
-    
-    metrics_fn = partial(compute_metrics, print_pred=True, num_samples=1, 
-                    tokenizer=tokenizer, model=model)
-    
-    print(f"{'Sanity check' if sanity_check else 'Training'} mode")
-    train_dataset, test_dataset = prepare_datasets(
-        tokenizer=tokenizer,
-        token=token,
-        sanity_check=sanity_check,
-        dataset_config=dataset_config)
-
-    # optimizer = MaxFactor(model.parameters(), lr=0.025, beta2_decay=-0.8, eps=(1e-10, 1e-7), d=1.0, 
-    # weight_decay=0.025, gamma=0.99, max=False)
-
-    # optimizer = torch.optim.AdamW(model.parameters(), lr=0.00025, eps=1e-8, weight_decay=0.025, betas=(0.9, 0.999), 
-    # amsgrad=False, foreach=False, fused=False, capturable=False, differentiable=False, maximize=False)
-    
-    # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=training_args.max_steps, eta_min=0.0, last_epoch=-1)
-
-    trainer = Seq2SeqTrainer(
-        args=training_args,
-        model=model,
-        train_dataset=train_dataset,
-        eval_dataset=test_dataset,
-        data_collator=DataCollator(tokenizer=tokenizer),
-        compute_metrics=metrics_fn,
-            # optimizers=(optimizer, scheduler)    
-        ) 
-       
-    model.init_weights()
-    trainer.train()
-
-if __name__ == "__main__":
-    main()
-