Create model_hf.py

model_hf.py

@@ -0,0 +1,1741 @@
+import os
+PATH = 'E:/hf'
+os.environ['HF_HOME'] = PATH
+os.environ['HF_DATASETS_CACHE'] = PATH
+import pyworld as pw
+import math
+import warnings
+import logging
+import gzip
+import base64
+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
+from einops import rearrange
+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
+import evaluate
+from dataclasses import dataclass
+import aiohttp    
+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)
+
+@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.")
+    time_spans = []
+    
+    if w is not None:
+        w_np = w[sample_idx].detach().cpu().numpy()
+        if w_np.ndim > 1:
+            w_np = w_np.squeeze()
+        time_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
+        time_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()
+        time_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()
+        time_spans.append(len(per_np) * hop_length / sr)
+    max_time = max(time_spans) if time_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)
+    current_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[current_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[current_ax].plot(t_energy, energy, color="red", alpha=0.7, label="Energy")
+            axs[current_ax].legend(loc='upper right')
+        axs[current_ax].set_title("Waveform")
+        axs[current_ax].set_ylabel("Amplitude")
+        axs[current_ax].set_xlim([0, max_time])
+        axs[current_ax].grid(True, axis='x', linestyle='--', alpha=0.3)
+        current_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
+        im = axs[current_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[current_ax].set_title("Spectrogram")
+        axs[current_ax].set_ylabel("Mel Bin")
+        axs[current_ax].set_xlim([0, max_time])
+        axs[current_ax].grid(True, axis='x', linestyle='--', alpha=0.3)
+        current_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[current_ax].plot(t_p, p_np, color="tab:green")
+        axs[current_ax].set_title("Pitch")
+        axs[current_ax].set_ylabel("Frequency (Hz)")
+        axs[current_ax].set_xlim([0, max_time])
+        axs[current_ax].grid(True, axis='both', linestyle='--', alpha=0.3)
+        axs[current_ax].set_ylim([0, min(1000, p_np.max() * 1.2)])
+        current_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[current_ax].plot(t_per, per_np, color="tab:red")
+        axs[current_ax].set_title("Period (Voice Activity)")
+        axs[current_ax].set_ylabel("periodocity")
+        axs[current_ax].set_xlim([0, max_time])
+        axs[current_ax].grid(True, axis='both', linestyle='--', alpha=0.3)
+        axs[current_ax].set_ylim([-0.05, 1.05])
+        axs[current_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("Time (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_timescale=10000):
+    assert channels % 2 == 0
+    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
+    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
+
+class rotary(nn.Module):
+    def __init__(self, dims, head, max_ctx=1500, theta=10000, radii=False, 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.f0_proj = nn.Linear(1, self.head_dim // 2) if radii else None
+        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 synth_f0(self, f0, ctx):
+        # f0 = self.f0proj(f0)
+        if f0.dim() == 1:
+            length = f0.shape[0]
+            if length == ctx:
+                return f0
+            frames = length / ctx
+            idx = torch.arange(ctx, device=f0.device)
+            # return torch.arange(1, ctx+1, device=f0.device, dtype=torch.float)
+            return f0[idx]
+
+    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 and layer == "encoder":
+            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 and layer == "encoder":
+            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]
+                rad = radius
+            radius = radius.unsqueeze(-1).expand(-1, freqs.shape[-1])
+            radius = torch.sigmoid(radius)
+        else:
+            radius = torch.ones_like(freqs) 
+        freqs = torch.polar(radius, 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 = Linear(dims, dims).to(device, dtype)
+        self.k = Linear(dims, dims, bias=False).to(device, dtype)
+        self.v = Linear(dims, dims).to(device, dtype)
+        self.o = 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 enhanced_attention_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") -> tuple:
+        x = x.to(device, dtype)
+        if xa is not None:
+            xa = xa.to(device, dtype)
+            
+        batch, ctx, dims = x.shape
+        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)
+        qlen = q.shape[1]
+        klen = k.shape[1]
+
+        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)
+            qlen = q.shape[2]
+            klen = k.shape[2]
+
+            q = self.rope.apply_rotary(q, (self.rope(qlen, enc=enc, layer=layer)))
+            k = self.rope.apply_rotary(k, (self.rope(klen, 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.enhanced_attention_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[:,:,:q.shape[2],:q.shape[2]]
+        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[:q.shape[2], :q.shape[2]]
+            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.detach()
+
+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.skip_gates=True
+        
+        self.blend = nn.Parameter(torch.tensor(0.5))
+        
+        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.to(device, dtype)
+        if xa is not None:
+            xa = xa.to(device, dtype)
+
+        bln = self.blend
+        x = x + self.attna(self.lna(x), xa=None, mask=mask, enc=enc, layer=layer)[0]
+        
+        if self.attnb and xa is not None:
+            c = self.attnb(self.lnb(x), xa=xa, mask=None, enc=enc, layer=layer)[0]
+            b = torch.sigmoid(bln)
+            x = b * x + (1 - b) * c
+        
+        normx = self.lnc(x)
+        mlp_out = self.mlp(normx)
+
+        if self.skip_gates:
+            x = x + mlp_out
+            
+        else:
+
+            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)
+        
+        if self.counter < 1:
+            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)
+
+        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 "encoder" in self.debug and self.counter % 100 == 0:
+            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.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', sequential=False) -> Tensor:
+        enc = dict_to(enc, device, dtype)
+        x = x.to(device)
+        bln = self.blend
+
+        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 sequential:
+                    x = out
+                else:
+                    a = torch.sigmoid(bln[f])
+                    x = a * out + (1 - a) * x
+                        
+        if "decoder" in self.debug and self.counter % 100 == 0:
+            print(f"Step {self.counter}: Decoder output shape: {x.shape}, enc keys: {list(enc.keys())}, order: {order}")
+        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.count = 0
+
+        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,
+            )
+
+        all_head = torch.zeros(self.param.text_idx, self.param.text_head, dtype=torch.bool)
+        all_head[self.param.text_idx // 2 :] = True
+        self.register_buffer("alignment_head", all_head.to_sparse(), persistent=False)
+
+    def update_base(self, f0):
+        for name, module in self.encoder.named_modules():
+            if isinstance(module, (rotary)):
+                module.update_base(f0)
+
+        for name, module in self.decoder.named_modules():
+            if isinstance(module, (rotary)):
+                module.update_base(f0)
+
+    def set_alignment_head(self, dump: bytes):
+        array = np.frombuffer(
+            gzip.decompress(base64.b85decode(dump)), dtype=bool).copy()
+        mask = torch.from_numpy(array).reshape(
+            self.param.text_idx, self.param.text_head)
+        self.register_buffer("alignment_head", mask.to_sparse(), persistent=False)
+
+    def embed_audio(self, spectrogram: torch.Tensor):
+        return self.encoder(spectrogram)
+
+    def logits(self,input_ids: torch.Tensor, encoder_output: torch.Tensor):
+        return self.decoder(input_ids, encoder_output)
+        
+    def forward(self,
+        decoder_input_ids=None,
+        labels=None,
+        waveform: Optional[torch.Tensor]=None,
+        input_ids=None,
+        spectrogram: torch.Tensor=None,
+        pitch: Optional[torch.Tensor]=None,
+        f0: Optional[torch.Tensor]=None,
+        f0d: Optional[torch.Tensor]=None,
+        envelope: Optional[torch.Tensor]=None,
+        phase: Optional[torch.Tensor]=None,
+        ) -> Dict[str, torch.Tensor]:
+
+        decoder_input_ids = input_ids
+        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)
+                
+        self.count += 1
+        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}")
+
+    def register_gradient_hooks(self):
+        for name, param in self.named_parameters():
+            if param.requires_grad:
+                if "encoder" in name:
+                    param.register_hook(lambda grad, n=name: self._print_encoder_grad(n, grad))
+                elif "decoder" in name:
+                    param.register_hook(lambda grad, n=name: self._print_decoder_grad(n, grad))
+        
+        print("Gradient debugging hooks registered")
+        return self
+
+    def _print_encoder_grad(self, name, grad):
+        if grad is not None and self.count == 10:  
+            norm = grad.median().item()
+            print(f"ENCODER GRAD: {name} = {norm:.6f}")
+        
+        return None
+
+    def _print_decoder_grad(self, name, grad):
+        if grad is not None and self.count == 10: 
+            norm = grad.median().item()
+            print(f"DECODER GRAD: {name} = {norm:.6f}")
+        return None
+
+    def resetcounter(self):
+        self.counter = 0
+        print("Counter reset to 0.")
+
+metric = evaluate.load(path="wer")
+    
+@dataclass
+class DataCollator:
+    tokenizer: Any
+    def __call__(self, features: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+        pad_token_id = tokenizer.pad_token_id if hasattr(tokenizer, 'pad_token_id') else 0
+        bos_token_id = tokenizer.bos_token_id if hasattr(tokenizer, 'bos_token_id') else 1
+        
+        batch = {}
+
+        if "spectrogram" in features[0] and features[0]["spectrogram"] is not None:
+            spectrogram_list = [f["spectrogram"] for f in features]
+            max_len_feat = max(f.shape[-1] for f in spectrogram_list)
+            pad_spectrogram = []
+            for feat in spectrogram_list:                
+                current_len = feat.shape[-1]
+                padding = max_len_feat - current_len
+                if padding > 0:
+                    pad_feat = F.pad(feat, (0, padding), mode='constant', value=pad_token_id)
+                else:
+                    pad_feat = feat
+                pad_spectrogram.append(pad_feat)
+            batch["spectrogram"] = torch.stack(pad_spectrogram)
+            
+        if "waveform" in features[0] and features[0]["waveform"] is not None:
+            waveform_list = [f["waveform"] for f in features]
+            max_len_wav = max(w.shape[-1] for w in waveform_list)
+            pad_waveforms = []
+            for wav in waveform_list:
+                current_len = wav.shape[-1]
+                padding = max_len_wav - current_len
+                if padding > 0:
+                    if wav.ndim == 1:
+                        wav = wav.unsqueeze(0)
+                    pad_wav = F.pad(wav, (0, padding), mode='constant', value=pad_token_id)
+                else:
+                    pad_wav = wav
+                pad_waveforms.append(pad_wav)
+            batch["waveform"] = torch.stack(pad_waveforms)
+
+        if "label" in features[0] and features[0]["label"] is not None:
+            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 + [pad_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)
+
+        if "pitch" in features[0] and features[0]["pitch"] is not None:
+            pitch_list = [f["pitch"] for f in features]
+            max_len_pitch = max(e.shape[-1] for e in pitch_list)
+            pad_pitch = []
+            for pitch in pitch_list:
+                current_len = pitch.shape[-1]
+                padding = max_len_pitch - current_len
+                if padding > 0:
+                    pad_pitch_item = F.pad(pitch, (0, padding), mode='constant', value=pad_token_id)
+                else:
+                    pad_pitch_item = pitch
+                pad_pitch.append(pad_pitch_item)
+            batch["pitch"] = torch.stack(pad_pitch)
+      
+        if "f0" in features[0] and features[0]["f0"] is not None:
+            f0_list = [f["f0"] for f in features]
+            max_len_f0 = max(f.shape[-1] for f in f0_list)
+            pad_f0 = []
+            for f0 in f0_list:
+                current_len = f0.shape[-1]
+                padding = max_len_f0 - current_len
+                if padding > 0:
+                    pad_f0_item = F.pad(f0, (0, padding), mode='constant', value=pad_token_id)
+                else:
+                    pad_f0_item = f0
+                pad_f0.append(pad_f0_item)
+            batch["f0"] = torch.stack(pad_f0)
+
+        if "envelope" in features[0] and features[0]["envelope"] is not None:
+            env_list = [f["envelope"] for f in features]
+            max_len = max(f.shape[-1] for f in env_list)
+            pad_env = []
+            for feat in env_list:                
+                current_len = feat.shape[-1]
+                padding = max_len - current_len
+                if padding > 0:
+                    pad_feat = F.pad(feat, (0, padding), mode='constant', value=pad_token_id)
+                else:
+                    pad_feat = feat
+                pad_env.append(pad_feat)
+            batch["envelope"] = torch.stack(pad_env)
+
+        if "phase" in features[0] and features[0]["phase"] is not None:
+            ph_list = [f["phase"] for f in features]
+            max_len = max(f.shape[-1] for f in ph_list)
+            pad_ph = []
+            for feat in ph_list:                
+                current_len = feat.shape[-1]
+                padding = max_len - current_len
+                if padding > 0:
+                    pad_feat = F.pad(feat, (0, padding), mode='constant', value=pad_token_id)
+                else:
+                    pad_feat = feat
+                pad_ph.append(pad_feat)
+            batch["phase"] = torch.stack(pad_ph) 
+        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):
+
+    dtype = torch.float32
+    device = torch.device("cuda:0")
+    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:
+        envelope_list = []
+        phase_list = []
+        
+        for ch_idx in range(spec.shape[0]):
+            envelope, phase = process_spectrogram_with_hilbert(spec[ch_idx])
+            envelope_list.append(envelope)
+            phase_list.append(phase)
+            
+        batch["envelope"] = torch.stack(envelope_list)
+        batch["phase"] = torch.stack(phase_list)
+        
+    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 compute_metrics(eval_pred, compute_result: bool = True, 
+                    print_pred: bool = False, num_samples: int = 0, tokenizer=None, model=None):
+
+    pred_logits = eval_pred.predictions
+    label_ids = eval_pred.label_ids
+
+    if hasattr(pred_logits, "cpu"):
+        pred_logits = pred_logits.cpu()
+    else:
+        pred_logits = torch.tensor(pred_logits).cpu()
+    if hasattr(label_ids, "cpu"):
+        label_ids = label_ids.cpu()
+    else:
+        label_ids = torch.tensor(label_ids).cpu()
+
+    if isinstance(pred_logits, tuple):
+        pred_ids = pred_logits[0]
+    else:
+        pred_ids = pred_logits
+    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()
+            
+    if hasattr(label_ids, "tolist"):
+        label_ids = label_ids.tolist()
+        
+    label_ids = [[0 if token == -100 else token for token in seq] for seq in label_ids]
+    pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=False)
+    label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=False)
+
+    if print_pred:
+        for i in range(min(num_samples, len(pred_str))):
+            print(f"Preds: {pred_str[i]}")
+            print(f"Label: {label_str[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 = 100 * metric.compute(predictions=pred_str, references=label_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 = "D:/newmodel/model/tokenn/"):
+    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(  
+        "google/fleurs", 
+        "en_us", 
+        token=token, 
+        trust_remote_code=True,
+        streaming=False)
+
+    dataset = dataset.cast_column(column="audio", feature=Audio(sampling_rate=16000)).select_columns(["audio", "transcription"])
+    
+    if sanity_check:
+        dataset = dataset["test"]
+        dataset = dataset.select_columns(["audio", "transcription"])
+        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)
+        test_dataset = dataset["test"].take(1000)
+
+        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=2,
+        gradient_accumulation_steps=1,
+        eval_accumulation_steps=4,
+        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 = False,
+            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 = 1005,
+            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={},
+        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": 2.0,
+        "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=False, num_samples=5, 
+                    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)
+
+    trainer = Seq2SeqTrainer(
+        args=training_args,
+        model=model,
+        train_dataset=train_dataset,
+        eval_dataset=test_dataset,
+        data_collator=DataCollator(tokenizer=tokenizer),
+        compute_metrics=metrics_fn,
+        ) 
+       
+    model.init_weights()
+    trainer.train()
+
+if __name__ == "__main__":
+    main()
+