File size: 6,703 Bytes
d0469c4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 |
"""
Router Model Architecture for Smart ASR Routing.
Regression-based approach: predicts WER for each backend model.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass
from typing import Optional, Dict
from transformers import PreTrainedModel, PretrainedConfig, WhisperModel, WhisperFeatureExtractor
from transformers.modeling_outputs import ModelOutput
class AttentionPooling(nn.Module):
"""Learnable attention pooling for variable-length sequences."""
def __init__(self, input_dim: int):
super().__init__()
self.attention = nn.Sequential(
nn.Linear(input_dim, 1),
nn.Tanh()
)
def forward(self, x: torch.Tensor, mask: torch.Tensor = None) -> torch.Tensor:
"""
Args:
x: [Batch, Time, Dim]
mask: [Batch, Time] (1 for valid, 0 for pad)
Returns:
pooled: [Batch, Dim]
"""
scores = self.attention(x) # [Batch, Time, 1]
if mask is not None:
scores = scores.masked_fill(mask.unsqueeze(-1) == 0, -1e9)
weights = F.softmax(scores, dim=1) # [Batch, Time, 1]
return torch.sum(x * weights, dim=1) # [Batch, Dim]
class ASRRouterConfig(PretrainedConfig):
"""Configuration for ASRRouter model."""
model_type = "asr_router"
def __init__(
self,
input_dim: int = 384, # whisper-tiny encoder dim
hidden_dim: int = 128,
intermediate_dim: int = 64,
dropout: float = 0.1, # Lower dropout for regression
num_models: int = 3, # Number of backends to predict scores for
**kwargs
):
super().__init__(**kwargs)
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.intermediate_dim = intermediate_dim
self.dropout = dropout
self.num_models = num_models
@dataclass
class RouterOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
pred_wers: torch.FloatTensor = None # Predicted WER for each model
class ASRRouterModel(PreTrainedModel):
"""
Regression Router.
Input: 384-dimensional Whisper encoder embeddings
Output: Estimated WER (0.0+, unbounded) for each backend model.
Uses Softplus activation to ensure non-negative outputs while allowing WER > 1.0.
"""
config_class = ASRRouterConfig
MODEL_ID_MAP = {0: "kyutai", 1: "granite", 2: "tiny_audio"}
def __init__(self, config: ASRRouterConfig):
super().__init__(config)
self.network = nn.Sequential(
nn.Linear(config.input_dim, config.hidden_dim),
nn.GELU(),
nn.LayerNorm(config.hidden_dim), # Better for batch_size=1
nn.Dropout(config.dropout),
nn.Linear(config.hidden_dim, config.intermediate_dim),
nn.GELU(),
nn.LayerNorm(config.intermediate_dim),
nn.Linear(config.intermediate_dim, config.num_models)
)
self.post_init()
def forward(
self,
embeddings: torch.Tensor,
labels: Optional[torch.Tensor] = None, # Actual WERs from ground truth
) -> RouterOutput:
# Softplus for unbounded positive WER (WER can exceed 1.0)
pred_wers = F.softplus(self.network(embeddings))
loss = None
if labels is not None:
loss = F.mse_loss(pred_wers, labels)
return RouterOutput(loss=loss, pred_wers=pred_wers)
def predict_proba(self, embeddings: torch.Tensor) -> torch.Tensor:
"""Get predicted WERs for each model."""
with torch.no_grad():
return F.softplus(self.network(embeddings))
class RouterWithFeatureExtractor:
"""
Production-ready router with attention pooling and memory optimizations.
"""
def __init__(self, router: ASRRouterModel, device: str = "cpu"):
self.device = device
self.router = router.to(device)
self.router.eval()
# Attention pooling for variable-length sequences
self.attention_pooling = AttentionPooling(input_dim=384).to(device)
self.attention_pooling.eval()
# Memory Optimization: Load full model, extract encoder, delete rest
print("Loading Whisper Encoder...")
full_whisper = WhisperModel.from_pretrained("openai/whisper-tiny")
self.whisper_encoder = full_whisper.encoder.to(device)
self.whisper_encoder.eval()
del full_whisper.decoder
del full_whisper
torch.cuda.empty_cache() if torch.cuda.is_available() else None
self.feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-tiny")
def extract_features(self, waveform: torch.Tensor) -> torch.Tensor:
"""Extract embeddings using Attention Pooling for variable lengths."""
if waveform.dim() == 1:
waveform = waveform.unsqueeze(0)
# Convert batch tensor to list of 1D numpy arrays (required by WhisperFeatureExtractor)
audio_np = [w.cpu().numpy() for w in waveform]
inputs = self.feature_extractor(
audio_np,
sampling_rate=16000,
return_tensors="pt",
return_attention_mask=True
)
input_features = inputs.input_features.to(self.device)
attention_mask = inputs.attention_mask.to(self.device)
with torch.no_grad():
last_hidden_state = self.whisper_encoder(input_features).last_hidden_state
# Resize mask to match encoder output temporal dimension
mask_resized = F.interpolate(
attention_mask.unsqueeze(1).float(),
size=last_hidden_state.shape[1],
mode='nearest'
).squeeze(1)
# Attention Pooling
return self.attention_pooling(last_hidden_state, mask_resized)
def predict(self, waveform: torch.Tensor) -> Dict:
"""Select the model with the lowest predicted WER."""
embeddings = self.extract_features(waveform)
with torch.no_grad():
output = self.router(embeddings)
pred_wers = output.pred_wers[0].cpu().numpy()
scores = {
"kyutai": float(pred_wers[0]),
"granite": float(pred_wers[1]),
"tiny_audio": float(pred_wers[2])
}
best_model = min(scores.items(), key=lambda x: x[1])
return {
"selected_model": best_model[0],
"predicted_wers": scores,
"confidence": max(0.0, 1.0 - best_model[1]) # Clamp since WER can exceed 1.0
}
# Register for auto classes
ASRRouterConfig.register_for_auto_class()
ASRRouterModel.register_for_auto_class("AutoModel")
|