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e618d88 | 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 | import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from datasets import load_dataset
from transformers import AutoTokenizer
from tqdm import tqdm
import math
import speech_recognition as sr
import pyttsx3
class FullChatDataset(Dataset):
def __init__(self, dataset_names=["blended_skill_talk", "conv_ai_2", "social_i_qa"], max_length=128):
self.datasets = []
for name in dataset_names:
try:
dataset = load_dataset(name, split="train")
self.datasets.append(dataset)
except Exception as e:
print(f"Failed to load dataset {name}: {e}")
self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
self.tokenizer.add_special_tokens({'pad_token': '[PAD]'})
self.max_length = max_length
def __len__(self):
return sum(len(d) for d in self.datasets)
def __getitem__(self, idx):
for dataset in self.datasets:
if idx < len(dataset):
item = dataset[idx]
break
idx -= len(dataset)
if 'dialog' in item:
dialog = item['dialog']
elif 'messages' in item:
dialog = [msg['text'] for msg in item['messages']]
else:
dialog = [v for k, v in item.items() if isinstance(v, str)]
context = " [SEP] ".join(dialog[:-1])
response = dialog[-1]
inputs = self.tokenizer(
context,
text_pair=response,
max_length=self.max_length,
padding='max_length',
truncation=True,
return_tensors="pt"
)
return {
'input_ids': inputs['input_ids'].flatten(),
'attention_mask': inputs['attention_mask'].flatten(),
'labels': inputs['input_ids'].flatten()
}
class SimpleTransformerModel(nn.Module):
def __init__(self, vocab_size, d_model=256, nhead=4, num_layers=3):
super().__init__()
self.embedding = nn.Embedding(vocab_size, d_model)
self.pos_encoder = PositionalEncoding(d_model)
encoder_layer = nn.TransformerEncoderLayer(d_model, nhead)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
self.fc = nn.Linear(d_model, vocab_size)
def forward(self, x, mask=None):
x = self.embedding(x)
x = self.pos_encoder(x)
x = self.transformer(x, mask)
return self.fc(x)
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=500):
super().__init__()
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
pe = torch.zeros(max_len, d_model)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
def forward(self, x):
return x + self.pe[:x.size(1)]
class VoiceInterface:
def __init__(self):
self.recognizer = sr.Recognizer()
self.engine = pyttsx3.init()
def listen(self):
with sr.Microphone() as source:
print("Listening...")
audio = self.recognizer.listen(source)
try:
text = self.recognizer.recognize_google(audio)
print(f"You said: {text}")
return text
except Exception as e:
print(f"Error recognizing speech: {e}")
return None
def speak(self, text):
print(f"Bot: {text}")
self.engine.say(text)
self.engine.runAndWait()
def train_model(model, dataloader, epochs=3, lr=3e-4):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
criterion = nn.CrossEntropyLoss(ignore_index=0)
optimizer = optim.Adam(model.parameters(), lr=lr)
for epoch in range(epochs):
model.train()
total_loss = 0
pbar = tqdm(dataloader, desc=f"Epoch {epoch+1}/{epochs}")
for batch in pbar:
inputs = batch['input_ids'].to(device)
masks = batch['attention_mask'].to(device)
labels = batch['labels'].to(device)
optimizer.zero_grad()
outputs = model(inputs, masks)
loss = criterion(outputs.view(-1, outputs.size(-1)), labels.view(-1))
loss.backward()
optimizer.step()
total_loss += loss.item()
pbar.set_postfix({'loss': loss.item()})
print(f"Epoch {epoch+1} - Avg loss: {total_loss/len(dataloader):.4f}")
def generate_response(model, tokenizer, prompt, max_length=50, voice_interface=None):
device = next(model.parameters()).device
model.eval()
inputs = tokenizer(
prompt,
return_tensors="pt",
max_length=128,
truncation=True,
padding='max_length'
).to(device)
with torch.no_grad():
outputs = model.generate(
input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
max_length=max_length,
do_sample=True,
top_k=50,
top_p=0.95,
temperature=0.7
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
if voice_interface:
voice_interface.speak(response)
return response |