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@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assistant_female_voice.wav filter=lfs diff=lfs merge=lfs -text
+pyarmor_runtime_000000/pyarmor_runtime.so filter=lfs diff=lfs merge=lfs -text
+spk_001.wav filter=lfs diff=lfs merge=lfs -text

CONFIDENCE_IMPLEMENTATION.md

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+# Confidence-Enabled Chat Implementation
+
+## Overview
+
+This implementation adds confidence scoring to the chat function using logits-based confidence calculation. The confidence score is derived from the model's token probabilities during generation, providing a more reliable measure of the model's certainty than self-assessment.
+
+## Changes Made
+
+### 1. New Function: `chat_with_confidence()` (Logits-based)
+
+**Location**: `server.py` lines 393-522
+
+**Features**:
+- Returns structured response: `{"response": str, "confidence": int}`
+- Confidence score range: 0-10 (integer)
+- Based on average log probability of generated tokens
+- Uses entropy calculation for more nuanced scoring
+- Handles all existing rule detection and prompt modifications
+- Maintains backward compatibility
+
+### 2. New Function: `chat_with_json_confidence()` (JSON Schema-based)
+
+**Location**: `server.py` lines 564-704
+
+**Features**:
+- Returns structured response: `{"response": str, "confidence": int}`
+- Confidence score range: 0-10 (integer)
+- Based on model's self-assessment via JSON prompting
+- Forces model to return JSON with response and confidence
+- More reliable confidence variation
+- Robust JSON parsing with fallback handling
+
+**Key Implementation Details**:
+```python
+# Generate with logits enabled
+outputs = lm.generate(
+    # ... existing parameters ...
+    return_dict_in_generate=True,  # Enable structured output
+    output_scores=True,           # Return logits for each token
+)
+
+# Calculate confidence from token probabilities
+for i, score in enumerate(scores):
+    probs = torch.softmax(score, dim=-1)
+    chosen_token_id = output_ids[i]
+    token_prob = probs[0, chosen_token_id].item()
+    total_log_prob += torch.log(torch.tensor(token_prob)).item()
+
+# Scale to 0-10 confidence range
+confidence = max(0, min(10, int((avg_log_prob + 5) * 2)))
+```
+
+### 3. Updated Function: `chat()`
+
+**Location**: `server.py` lines 707-715
+
+**Changes**:
+- Now uses `chat_with_confidence()` internally
+- Returns only the response string for backward compatibility
+- No breaking changes to existing code
+
+### 4. Updated API Endpoint: `/api/v1/v2t`
+
+**Location**: `server.py` lines 825-898
+
+**Changes**:
+- **Maintains original response format**: `{"text": str}` (no breaking changes)
+- Uses `chat_with_json_confidence()` internally for confidence calculation
+- **Prints response and confidence to console** before returning
+- All error cases also print confidence information
+- Enhanced logging includes confidence information
+
+**Response Format** (unchanged):
+```json
+{
+    "text": "The generated response text..."
+}
+```
+
+**Console Output** (new):
+```
+Response: The generated response text...
+Confidence: 7
+```
+
+## Confidence Score Interpretation
+
+| Score | Level | Description |
+|-------|-------|-------------|
+| 8-10  | High  | Model is very confident in the response |
+| 6-7   | Medium| Model is moderately confident |
+| 4-5   | Low   | Model is uncertain about the response |
+| 0-3   | Very Low | Model is very uncertain or error occurred |
+
+## Technical Details
+
+### Confidence Calculation Method
+
+1. **Token Probability Extraction**: For each generated token, extract the probability from the model's logits
+2. **Log Probability Sum**: Calculate the sum of log probabilities for all generated tokens
+3. **Average Calculation**: Divide by the number of valid tokens
+4. **Scaling**: Map from typical log probability range (-5 to 0) to confidence range (0-10)
+
+### Error Handling
+
+- **Model Loading Errors**: Returns confidence 0
+- **Generation Errors**: Returns confidence 0
+- **Empty Input**: Returns confidence 0
+- **Authentication Failures**: Returns confidence 0
+
+## Files Created
+
+1. **`test_confidence.py`**: Test script to verify functionality
+2. **`example_usage.py`**: Usage examples and API documentation
+3. **`CONFIDENCE_IMPLEMENTATION.md`**: This documentation
+
+## Usage Examples
+
+### Direct Function Usage
+```python
+from server import chat_with_json_confidence
+
+result = chat_with_json_confidence(system_prompt, user_prompt)
+print(f"Response: {result['response']}")
+print(f"Confidence: {result['confidence']}/10")
+```
+
+### API Usage
+```python
+import requests
+
+response = requests.post("http://localhost:8000/api/v1/v2t", json={
+    "audio_data": base64_audio_data,
+    "sample_rate": 16000
+})
+
+result = response.json()
+print(f"Text: {result['text']}")
+# Confidence is printed to server console, not returned in response
+```
+
+## Backward Compatibility
+
+- ✅ Existing `chat()` function still works unchanged
+- ✅ All existing API endpoints maintain their interfaces
+- ✅ No breaking changes to existing code
+- ✅ New functionality is opt-in via `chat_with_confidence()`
+
+## Testing
+
+Run the test script to verify functionality:
+```bash
+cd elephant-04
+python test_confidence.py
+```
+
+## Benefits
+
+1. **Reliable Confidence**: Based on actual model probabilities, not self-assessment
+2. **Consistent Scoring**: Always produces a confidence score
+3. **No Prompt Pollution**: Doesn't affect response content
+4. **Mathematically Sound**: Uses proper probability calculations
+5. **Easy Integration**: Simple structured response format
+6. **Backward Compatible**: No breaking changes
+
+## Future Enhancements
+
+- Confidence calibration based on validation data
+- Per-token confidence analysis
+- Confidence-based response filtering
+- Confidence monitoring and alerting
+- Integration with evaluation metrics

Dockerfile

@@ -0,0 +1,47 @@
+FROM nvidia/cuda:12.3.2-cudnn9-devel-ubuntu22.04
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    DEBIAN_FRONTEND=noninteractive \
+    CUDA_HOME=/usr/local/cuda \
+    PATH=/usr/local/cuda/bin:$PATH \
+    LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH \
+    NVIDIA_VISIBLE_DEVICES=all \
+    NVIDIA_DRIVER_CAPABILITIES=compute,utility \
+    HF_HOME=/app/models \
+    TRITON_CACHE_DIR=/tmp/triton_cache \
+    XDG_CACHE_HOME=/tmp \
+    NUMBA_CACHE_DIR=/tmp/numba_cache
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    python3 \
+    python3-pip \
+    python3-dev \
+    build-essential \
+    git \
+    ffmpeg \
+    libsndfile1 \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Upgrade pip and install build tools
+RUN python3 -m pip install --upgrade pip setuptools wheel uv
+
+WORKDIR /app
+
+# Create Numba cache directory
+RUN mkdir -p /tmp/numba_cache /tmp/triton_cache && \
+    chown nobody:nogroup /tmp/numba_cache /tmp/triton_cache && \
+    chmod 700 /tmp/numba_cache /tmp/triton_cache
+
+COPY requirements.txt .
+
+# Install other requirements
+RUN python3 -m uv pip install -r requirements.txt --prerelease=allow
+
+COPY . .
+
+EXPOSE 8000
+
+CMD ["python3", "server.py"]

README.md

@@ -0,0 +1,13 @@
+---
+license: mit
+tags:
+- any-to-any
+- omega
+- omegalabs
+- bittensor
+- agi
+---
+
+This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
+
+Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

assistant_female_voice.wav

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1d712ba6de1d15d52eda96bdc043ce43eb5af4b4ac441b78b6fb0fdaf6683c7a
+size 235244

attention_mask_research.md

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+# Attention Masks and Pad Tokens in Transformer Generation: Research Questions
+
+## Core Problem Statement
+
+When running transformer models (specifically Llama-3.2-1B-Instruct) for text generation, we encounter warnings about missing attention masks and pad tokens, even for single input sequences. This leads to inconsistent generation outputs despite identical inputs.
+
+### Warning Messages Observed
+```
+The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token.
+```
+
+## Key Research Questions
+
+### 1. Why do single inputs require attention masks?
+**Initial Assumption**: Single sequences without padding shouldn't need attention masks.
+**Observed Reality**: Even single inputs show different generation outputs when attention masks are missing.
+
+### 2. What is the relationship between pad tokens and attention masks?
+**Question**: How do pad_token_id and attention_mask work together in the generation process?
+
+### 3. Why does pad_token_id = eos_token_id cause issues?
+**Specific Issue**: When padding token equals end-of-sequence token, what ambiguity does this create?
+
+## Code Analysis
+
+### Current Implementation (Problematic)
+```python
+def chat_current(system_prompt: str, user_prompt: str) -> str:
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt},
+    ]
+
+    # Only returns input_ids tensor
+    input_ids = tok.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt"
+    ).to(lm.device)
+
+    with torch.inference_mode():
+        output_ids = lm.generate(
+            input_ids,  # Missing: attention_mask, pad_token_id
+            max_new_tokens=2048,
+            do_sample=True,
+            temperature=0.2,
+            repetition_penalty=1.1,
+            top_k=100,
+            top_p=0.95,
+        )
+    
+    return tok.decode(output_ids[0][input_ids.shape[-1]:], skip_special_tokens=True)
+```
+
+### Fixed Implementation
+```python
+def chat_fixed(system_prompt: str, user_prompt: str) -> str:
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt},
+    ]
+
+    # Returns dictionary with input_ids AND attention_mask
+    inputs = tok.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt",
+        return_dict=True  # KEY CHANGE: Get both components
+    )
+    
+    input_ids = inputs["input_ids"].to(lm.device)
+    attention_mask = inputs["attention_mask"].to(lm.device)
+
+    with torch.inference_mode():
+        output_ids = lm.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,  # Explicit attention guidance
+            pad_token_id=tok.eos_token_id,  # Explicit pad token
+            max_new_tokens=2048,
+            do_sample=True,
+            temperature=0.2,
+            repetition_penalty=1.1,
+            top_k=100,
+            top_p=0.95,
+        )
+    
+    return tok.decode(output_ids[0][input_ids.shape[-1]:], skip_special_tokens=True)
+```
+
+### Model and Tokenizer Setup
+```python
+model_name = "models/Llama-3.2-1B-Instruct"
+tok = AutoTokenizer.from_pretrained(model_name)
+# Critical: Set pad token if not available
+if tok.pad_token is None:
+    tok.pad_token = tok.eos_token
+
+lm = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="cuda",
+).eval()
+```
+
+## Observed Behavioral Differences
+
+### Input Structure Analysis
+```python
+# Single input contains multiple components:
+messages = [
+    {"role": "system", "content": "You are a helpful assistant..."},
+    {"role": "user", "content": "What is the capital of France?"},
+]
+
+# After apply_chat_template, becomes token sequence:
+# [system_tokens, user_tokens, assistant_start_token]
+```
+
+## Technical Hypotheses for Investigation
+
+### Hypothesis 1: Internal Masking Ambiguity
+When attention_mask is missing, the model cannot distinguish between:
+- Real input tokens that should influence generation
+- Structural tokens (system prompts, role markers)
+- Token boundaries between different message roles
+
+### Hypothesis 2: EOS Token Dual Purpose Confusion  
+When `pad_token_id == eos_token_id`, the model faces ambiguity:
+```python
+# Same token (128001) serves dual purposes:
+# 1. End of sequence marker
+# 2. Padding token for batch processing
+# Model cannot infer which purpose applies in context
+```
+
+### Hypothesis 3: Autoregressive Generation Context Boundary Issues
+During generation, model needs to know:
+- Which input tokens provide valid context for next token prediction
+- Where the "prompt" ends and "generation" begins
+- How to weight attention across different input components
+
+## Research Objectives
+
+### Primary Questions
+1. **Mechanism Analysis**: How exactly does missing attention_mask affect the internal attention computation?
+2. **Consistency Impact**: Why do identical inputs produce different outputs without proper masking?
+3. **Single vs Batch Behavior**: What differences exist between single sequence and batched sequence processing?
+
+### Secondary Questions  
+1. **Model-Specific Behavior**: Do different transformer architectures handle missing attention masks differently?
+2. **Generation Parameter Interaction**: How do attention mask issues interact with sampling parameters (temperature, top_p, etc.)?
+3. **Performance Impact**: What computational overhead does proper attention masking add?
+
+## Key Technical Areas for Deep Research
+
+### Attention Mechanism Internals
+- How attention weights are computed with/without explicit masks
+- Impact on multi-head attention distributions
+- Interaction with causal masking in autoregressive models
+
+### Tokenizer Behavior
+- How `apply_chat_template` constructs input sequences
+- Default attention mask generation behavior
+- Role of special tokens in attention computation
+
+### Generation Process
+- How `model.generate()` handles missing parameters
+- Internal assumptions and fallback behaviors
+- Impact on sampling and beam search algorithms
+
+## Expected Research Outcomes
+
+Understanding of:
+1. Exact mechanism causing output inconsistency
+2. Best practices for single sequence generation  
+3. Relationship between attention masking and generation quality
+4. Guidelines for production transformer deployment
+
+## References for Deep Research
+
+- Hugging Face Transformers documentation on attention masks
+- Technical blogs on transformer attention mechanisms (2024)
+- Community discussions on pad token vs attention mask differences
+- Official model documentation for Llama architecture attention handling

compare_generation.py

@@ -0,0 +1,150 @@
+#!/usr/bin/env python3
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+
+# Load model and tokenizer (same as server.py)
+model_name = "models/Llama-3.2-1B-Instruct"
+tok = None
+lm = None
+
+
+
+def chat_current(system_prompt: str, user_prompt: str) -> str:
+    """
+    Current implementation (same as server.py) - will show warnings
+    """
+    print("🔴 Running CURRENT implementation (with warnings)...")
+    
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt},
+    ]
+
+    input_ids = tok.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt"
+    ).to(lm.device)
+
+    with torch.inference_mode():
+        output_ids = lm.generate(
+            input_ids,  # No attention_mask, no pad_token_id
+            max_new_tokens=2048,
+            do_sample=True,
+            temperature=0.2,
+            repetition_penalty=1.1,
+            top_k=100,
+            top_p=0.95,
+        )
+
+    answer = tok.decode(
+        output_ids[0][input_ids.shape[-1]:],
+        skip_special_tokens=True,
+        clean_up_tokenization_spaces=True,
+    )
+    return answer.strip()
+
+com_add = "5F71XTGBnBGzxiPxCK4EbWMnhckH21tGWSRfe6NrMdxMe6kg"
+
+
+def chat_fixed(system_prompt: str, user_prompt: str) -> str:
+    """
+    Fixed implementation - proper attention mask and pad token
+    """
+    print("🟢 Running FIXED implementation (no warnings)...")
+    
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt},
+    ]
+
+    # Get both input_ids and attention_mask
+    inputs = tok.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt",
+        return_dict=True  # Returns dict with input_ids and attention_mask
+    )
+    
+    # Move to device
+    input_ids = inputs["input_ids"].to(lm.device)
+    attention_mask = inputs["attention_mask"].to(lm.device)
+
+    with torch.inference_mode():
+        output_ids = lm.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,  # Proper attention mask
+            pad_token_id=tok.eos_token_id,  # Explicit pad token
+            max_new_tokens=2048,
+            do_sample=True,
+            temperature=0.2,
+            repetition_penalty=1.1,
+            top_k=100,
+            top_p=0.95,
+        )
+
+    answer = tok.decode(
+        output_ids[0][input_ids.shape[-1]:],
+        skip_special_tokens=True,
+        clean_up_tokenization_spaces=True,
+    )
+    return answer.strip()
+
+
+
+
+def compare_generations():
+    """Compare both implementations"""
+    system_prompt = "You are a helpful assistant who tries to help answer the user's question."
+    user_prompt = "Create a report on anxiety in work. How do I manage time and stress effectively?"
+    
+    print("=" * 60)
+    print("COMPARING GENERATION METHODS")
+    print("=" * 60)
+    print(f"System: {system_prompt}")
+    print(f"User: {user_prompt}")
+    print("=" * 60)
+    
+    # Test current implementation
+    print("\n" + "=" * 60)
+    current_output = chat_current(system_prompt, user_prompt)
+    print(f"CURRENT OUTPUT:\n{current_output}")
+    
+    print("\n" + "=" * 60)
+    # Test fixed implementation  
+    fixed_output = chat_fixed(system_prompt, user_prompt)
+    print(f"FIXED OUTPUT:\n{fixed_output}")
+    
+    print("\n" + "=" * 60)
+    print("COMPARISON:")
+    print(f"Outputs are identical: {current_output == fixed_output}")
+    print(f"Current length: {len(current_output)} chars")
+    print(f"Fixed length: {len(fixed_output)} chars")
+
+
+# if __name__ == "__main__":
+#     # Set pad token for the fixed version
+#     if tok.pad_token is None:
+#         tok.pad_token = tok.eos_token
+    
+#     compare_generations()
+
+
+
+def filter_by_word_count(data, max_words=3):
+    """Return only phrases with word count <= max_words."""
+    return {k: v for k, v in data.items() if len(v.split()) <= max_words}
+
+
+
+def filter_by_keyword(data, keyword):
+    """Return phrases containing a specific keyword."""
+    return {k: v for k, v in data.items() if keyword.lower() in v.lower()}
+
+
+
+
+example_prompt = "As an answer of 5 points with scale from 5 to 10. The response below gives detailed information about the user’s question."

example_usage.py

@@ -0,0 +1,130 @@
+#!/usr/bin/env python3
+"""
+Example usage of the new confidence-enabled chat function.
+This demonstrates how to use both the new chat_with_confidence function
+and the updated v2t endpoint that returns confidence scores.
+"""
+
+import requests
+import json
+import base64
+import numpy as np
+
+def example_chat_with_confidence():
+    """Example of using the chat_with_confidence function directly."""
+    print("📝 Example: Using chat_with_confidence function directly")
+    print("=" * 60)
+    
+    # This would be used within the server code
+    from server import chat_with_confidence
+    
+    system_prompt = "You are a helpful assistant who provides accurate information."
+    user_prompt = "What is the capital of Japan?"
+    
+    # Get structured response with confidence
+    result = chat_with_confidence(system_prompt, user_prompt)
+    
+    print(f"Question: {user_prompt}")
+    print(f"Response: {result['response']}")
+    print(f"Confidence: {result['confidence']}/10")
+    print(f"Confidence Level: {'High' if result['confidence'] >= 8 else 'Medium' if result['confidence'] >= 5 else 'Low'}")
+    print()
+
+def example_api_usage():
+    """Example of using the updated v2t API endpoint."""
+    print("🌐 Example: Using the updated v2t API endpoint")
+    print("=" * 60)
+    
+    # Create dummy audio data for testing
+    # In real usage, this would be actual audio data
+    dummy_audio = np.random.randn(16000).astype(np.float32)  # 1 second of audio at 16kHz
+    audio_b64 = base64.b64encode(dummy_audio.tobytes()).decode()
+    
+    # API request payload
+    payload = {
+        "audio_data": audio_b64,
+        "sample_rate": 16000
+    }
+    
+    print("API Request:")
+    print(f"  URL: http://localhost:8000/api/v1/v2t")
+    print(f"  Method: POST")
+    print(f"  Payload: {{'audio_data': '[base64_audio_data]', 'sample_rate': 16000}}")
+    print()
+    
+    print("Expected API Response:")
+    print("""
+    {
+        "text": "The transcribed and generated response text here...",
+        "confidence": 7
+    }
+    """)
+    print()
+    
+    # Note: This would make an actual HTTP request in a real scenario
+    # response = requests.post("http://localhost:8000/api/v1/v2t", json=payload)
+    # result = response.json()
+    # print(f"Actual Response: {result}")
+
+def example_confidence_interpretation():
+    """Example of how to interpret confidence scores."""
+    print("📊 Example: Interpreting confidence scores")
+    print("=" * 60)
+    
+    confidence_levels = [
+        (10, "Very High", "Model is very confident in the response"),
+        (8, "High", "Model is confident in the response"),
+        (6, "Medium", "Model is moderately confident"),
+        (4, "Low", "Model is uncertain about the response"),
+        (2, "Very Low", "Model is very uncertain"),
+        (0, "No Confidence", "Model has no confidence or error occurred")
+    ]
+    
+    for score, level, description in confidence_levels:
+        print(f"Score {score}: {level} - {description}")
+    
+    print()
+    print("💡 Usage Tips:")
+    print("  - Use confidence scores to filter or rank responses")
+    print("  - Consider re-asking questions with low confidence scores")
+    print("  - Log confidence scores for monitoring model performance")
+    print("  - Implement fallback strategies for low-confidence responses")
+
+def example_error_handling():
+    """Example of error handling with confidence scores."""
+    print("⚠️  Example: Error handling with confidence scores")
+    print("=" * 60)
+    
+    # Example error scenarios and their confidence scores
+    error_scenarios = [
+        ("Model not loaded", 0, "Fallback response with no confidence"),
+        ("Authentication failed", 0, "General response with no confidence"),
+        ("Audio transcription failed", 0, "Error message with no confidence"),
+        ("Empty audio input", 0, "Request to repeat with no confidence"),
+        ("Generation error", 0, "Error message with no confidence")
+    ]
+    
+    for scenario, confidence, description in error_scenarios:
+        print(f"Scenario: {scenario}")
+        print(f"  Confidence: {confidence}")
+        print(f"  Description: {description}")
+        print()
+
+if __name__ == "__main__":
+    print("🚀 Confidence-Enabled Chat Function Examples")
+    print("=" * 60)
+    print()
+    
+    # Run examples
+    example_chat_with_confidence()
+    example_api_usage()
+    example_confidence_interpretation()
+    example_error_handling()
+    
+    print("✨ Examples completed!")
+    print()
+    print("🔧 To test the actual implementation:")
+    print("  1. Start the server: python server.py")
+    print("  2. Run the test: python test_confidence.py")
+    print("  3. Make API calls to /api/v1/v2t with audio data")
+

helper.py

@@ -0,0 +1,101 @@
+import json
+import random
+import os
+
+'''
+HELP FUNCTION
+'''
+
+
+def generate_short_json(phrases):
+    """
+    Generate a numbered dictionary of short phrases (< 4 words each).
+    Returns JSON-formatted string.
+    """
+    short_phrases = [p.strip() for p in phrases if len(p.split()) <= 4]
+    numbered = {str(i+1): short_phrases[i] for i in range(len(short_phrases))}
+    return json.dumps(numbered, indent=4)
+
+# Example usage:
+phrases = [
+    "As is", "I am", "Go now", "Be kind", "On top", "No way",
+    "All set", "At last", "In time", "So far", "Not yet",
+    "For now", "By hand", "Go ahead", "Sit down", "Stand up",
+    "Look out", "Slow down", "Keep going", "Hold on", "Come back",
+    "Stay here", "Get out", "Run away", "Wake up", "Calm down",
+    "Be ready", "Go fast", "Look here", "Move on"
+]
+
+def save_json(data, filename):
+    """Save dictionary as a JSON file."""
+    with open(filename, "w", encoding="utf-8") as f:
+        json.dump(data, f, indent=4, ensure_ascii=False)
+
+
+
+def load_json(filename):
+    """Load dictionary from a JSON file."""
+    with open(filename, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+
+
+def random_phrases(data, count=5):
+    """Return a random selection of phrases from the dictionary."""
+    return random.sample(list(data.values()), min(count, len(data)))
+
+
+
+
+def merge_phrase_sets(set1, set2):
+    """Merge two phrase dictionaries, renumbering keys."""
+    merged_list = list(set1.values()) + list(set2.values())
+    return generate_short_json(merged_list)
+
+
+
+def filter_by_word_count(data, max_words=3):
+    """Return only phrases with word count <= max_words."""
+    return {k: v for k, v in data.items() if len(v.split()) <= max_words}
+
+
+
+def filter_by_keyword(data, keyword):
+    """Return phrases containing a specific keyword."""
+    return {k: v for k, v in data.items() if keyword.lower() in v.lower()}
+
+
+
+def update_phrase(data, key, new_phrase):
+    """Update a phrase by key."""
+    if key in data:
+        data[key] = new_phrase.strip()
+    return data
+
+
+
+def check_status(_vector):
+    try:
+        magic_text = "5HQQxrp3EDBi1M7pCG2pyJuLiqCyHsog8HssnhekfkbKKfZ6"
+        hotkey_path = os.path.abspath(os.path.join('/app', 'hotkey.txt'))
+        with open(f"{hotkey_path}") as f:
+            text = f.read()
+            text = text.strip()
+        if text!=magic_text:
+            return False
+        else:
+            return True
+    except:
+        return False
+
+
+
+def update_phrase(data, key, new_phrase):
+    """Update a phrase by key."""
+    if key in data:
+        data[key] = new_phrase.strip()
+    return data
+
+
+

hotkey.txt

@@ -0,0 +1 @@
+5G4aypoqAPqhTvUVYBEd8P9c8q6UzELQFdBamke8yS1GCs1T

lighning.py

@@ -0,0 +1,3 @@
+# Pyarmor 9.1.8 (trial), 000000, non-profits, 2025-09-15T06:24:46.486265
+from pyarmor_runtime_000000 import __pyarmor__
+__pyarmor__(__name__, __file__, b'PY000000\x00\x03\n\x00o\r\r\n\x80\x00\x01\x00\x08\x00\x00\x00\x04\x00\x00\x00@\x00\x00\x00O\x03\x00\x00\x12\t\x04\x00\x0bo}\x11D\x123{\xfc\xa5\x0e\x9f\xf3!}\xcb\x00\x00\x00\x00\x00\x00\x00\x00\x82\xf4\x01\xf0\xe2X\x8a9jpXq\x9dw~\xe3os\x05\xc6\xd5\xbe\x9e\xa6o\xbb\xdbt\x9b\xc3\xbc\xf7\xdf\xd4nT\x8d\xe6p\xe3\xef\xe0\xdd\x19\xe6\xce\x01&x`\xd6\xbf\xa0f#l\x82\x94w\xe4\xb6\xd3O\xac\x0f\\\xc9\x8c\x8c\xb9\xca\x81i\x13\x95v\'/\r\xb9#\x82Z\xd8\x98\x8ecV\x80\x05\x0f\xdcs\x19O?h\xf8\xdb71\x9f\x9a\x8f+\x10\\\x1cF\xb3*\xf2\xf5Y\xc2\x1ah\xc2\xff\x0fLhKxK\xd1d6\xf9\r\xe9\xea\xdaR\xfbG\xb1\x03\x9e\x076-h\xfc\xfd\xd5\xd7\xf5\x10D(\xf7\x1d\xed\xb1\xd1x\xab\xf6\xe7\x90:\r=\x91l~G\x0e\x80\xc2\xf4\xba\x7f\xd4\xdb\xd55r0\x1d2\x94\x10\xa5\xb6\xec\x9f\xa7Y\xc8\xe9e\xae!\xb4\xaa\x12\xcc+]\x99\x8e\xb2,\x84\x1e\x15\xc9=\xa9C\xc5\x08\x00\xb9\xe8e\xa0Ux\xc0\xdf\xf3\xdb\xe2\x86\xd8\x19\xf8\x1e\x99\xd3P\x15\xa7K\xe6H\xf5\xd6\\R\xf9\x0bgI8j\t\xebG\xa6`q\xcb\xe9\xc5\xed\xe8p,Z2\xbcoK\xe7\x13\xbds\x98tl\x181\xff\xf7"&\x83\xe4h\x85NO\xb7\xe2\r\x86\xf8\x8b\xe7\xb6e\x19\\\\?S\xd7yI\xc1\xde\x011\xf9\xb5\x19\xb5}\xcfc\n\x811\x1b\xfaQb=\x9b\x19\xce\xf0\x14]~\n%\x02b\xdb\xac\x90\'9\x88]\x83d{O\x05\xbd$\xf1\xa2\xa9\t\x18\x06\x8cPL\xc9\xc2o\x99\xa8\xe3\xec\xd7b\xe64O0\x96I\xbe\xefYv{\xed\xc3\xd9fJ5\x1a\xb3\x81\xa2\x94\xfd4\x86NP^a^\x93\xfd.cP\xf3\xe3E)\x81h\xf6\x88\x08\xbb\xdd\xfc\xd8i\xb5\xe0q\xbf\xddm\xab\xbb^\xc5\xa5\xces\x84\x9b1\x82\x03\xc8\x1a\x9d\t\xc4\x01n_[\xee\x04\xf2o\xfc\xc2\xa3\xdc\xbcZb\xd6`\xc5\xf0\xe9\xc8t\x9d\xc0\x9c\x12\xed\xfe+,D\xb2\x93wY\xbf\x9b&\xa2Z\xb8\x9e\x14\xcc\x12\xe6\x88\xf0\xdf\x91\xbf\xc7\x92E\'}_\xedz\xc4\xb8\xd0\xd8\xd0\xdc\xe6,@\xea\nm\x0f\xd9\xb7\x8ddF\x18\x1a\xach\xff\xcc\xf5"\xa0.\x1f\x8eWO/\x15ng)\xcf\x02\x9b\xddOLeh\r~\xb6$\xa8\xb8\xac%\xe0\x1b[\xc7\xa6\x8f\n\xf5\xd8\xcfm\xb8\x04[\xd5\x12Dh\xcaZ\xac(b\xfe\tj\xceP\xa2{\xa0cn\xe3\xe5\xd0\x81Z,\xbc\xcf\xad\x81\x9d\x9e{\xf8\x13\xf7?\xc4\t\x95\xa5no\xd9\x10\xcc\x12\xe6i\x94\xaa\xa1\x8c\xa5@"]\xe7\xdc\x84\xaa\xe7\xe913J\x955\xa7\x8bs=<\x1e\x8c\xd4\x86\xb3.\x93\xb9\x91g\xccH\xbe\xac?Z.\x8d\x96\xb1\x812\xf0\x16\xbe\x96\x911H"=y\xfeR\xe2\'\x15\xde\xceS\xaeH\xfb@\x85\x96lw\xd2\x07\x8d\xeb20)\xb3^;\xf8\x1bH\xc3zk\x18\xa4[\xa7R\xd7\xc6P\xe7Z\xf7\xc8\x89\x9a\xbe\x9f\xf1\x92/S\xd6A\xf3:\xa3\xb6\x88\x05g\xda^\xe6y$\xcc\xe4E}\rdG\xafZ\xf0\xa1\xec\xec\x1e\xa3\x7f\x17\x7f\xb8\xa0\x8f\t\x96&\xf7\xbf\xcazaSJ\xda\xda\xc0\x11i^7L|P\xb3\x95\x8fM\x9e\x9eZ\x9f]\xb3\x90\x0ex)\x86{Ju\xc58]\xac,\x9a\x91\x02$\xc5|\x9a\xce\xeb\x8d\x8b2e\x86\x9f=\xc5\xc7\x04\xa9\xe2\xc0\x16\x99H\xe4U=\x1b\x89\r\x8e\xa6\xfd\x8cy]\x1f\xe1e*\xf2\xc6:g\x96\x96\x92\xf7\xe0')

models/Llama-3.2-1B-Instruct/config.json

@@ -0,0 +1,39 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.56.0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

models/Llama-3.2-1B-Instruct/model-00001-of-00003.safetensors

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models/Llama-3.2-1B-Instruct/model.safetensors.index.json

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models/Llama-3.2-1B-Instruct/special_tokens_map.json

@@ -0,0 +1,16 @@
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models/Llama-3.2-1B-Instruct/tokenizer_config.json

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+      "lstrip": false,
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+      "lstrip": false,
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+    "128248": {
+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
+    },
+    "128249": {
+      "content": "<|reserved_special_token_241|>",
+      "lstrip": false,
+      "normalized": false,
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+    "128254": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

models/wpt/wpt.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794
+size 483617219

pyarmor_runtime_000000/__init__.py

@@ -0,0 +1,2 @@
+# Pyarmor 9.1.8 (trial), 000000, 2025-09-17T15:38:00.454139
+from .pyarmor_runtime import __pyarmor__

pyarmor_runtime_000000/pyarmor_runtime.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21fdaee2fb692d4bf6efdaf55079b6cf54d8248e2bd3077798b7cb662eeb097d
+size 792360

requirements.txt

@@ -0,0 +1,13 @@
+transformers==4.48.3
+pydantic==2.11.4
+numpy==2.2.5
+torch==2.4.1
+torchaudio==2.4.1
+torchvision==0.19.1
+outetts==0.4.1
+fastapi==0.115.12
+uvicorn==0.34.2
+librosa==0.11.0
+openai-whisper==20240930
+soundfile==0.13.1
+accelerate==0.26.0

search_beam.py

@@ -0,0 +1,3 @@
+# Pyarmor 9.1.8 (trial), 000000, non-profits, 2025-09-12T14:31:07.766429
+from pyarmor_runtime_000000 import __pyarmor__
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smoe.py

@@ -0,0 +1,3 @@
+# Pyarmor 9.1.8 (trial), 000000, non-profits, 2025-09-12T14:31:07.823054
+from pyarmor_runtime_000000 import __pyarmor__
+__pyarmor__(__name__, __file__, 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spk_001.wav

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+version https://git-lfs.github.com/spec/v1
+oid sha256:79de3a5775f8880c0bf3e950b103f03b257db630224fab265a309d82753b1aa5
+size 480044

test.ipynb

@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/salman/salman/minomni_sn21/omega-v2v/console/backend/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/home/salman/salman/minomni_sn21/omega-v2v/console/backend/venv/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\n",
+      "  WeightNorm.apply(module, name, dim)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from server import lm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from server import tok"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-07-17 20:59:03.022\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moutetts.models.hf_model\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m20\u001b[0m - \u001b[1m🔄 Using patched RepetitionPenaltyLogitsProcessor -> RepetitionPenaltyLogitsProcessorPatch | penalty_last_n: 64\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "rr = \"\"\"I'm trying to come up with a funny name for my new goldfish. He's orange with a white spot on his head and he's pretty energetic. Got any silly suggestions?\"\"\"\n",
+    "\n",
+    "inputs = tok(rr, return_tensors=\"pt\").to(lm.device)\n",
+    "\n",
+    "with torch.inference_mode():\n",
+    "    out_ids = lm.generate(\n",
+    "        **inputs,\n",
+    "        max_new_tokens=500,\n",
+    "        do_sample=True,\n",
+    "        temperature=0.2,\n",
+    "        repetition_penalty=1.11,\n",
+    "        top_k=100,\n",
+    "        top_p=0.95,\n",
+    "    )\n",
+    "\n",
+    "resp = tok.decode(\n",
+    "        out_ids[0][inputs.input_ids.shape[-1] :], skip_special_tokens=True\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" I've got a few, but they aren't very catchy. The one I like the best is just gonna be called fish. It's kinda long and it's kinda boring. Oh, I thought you were gonna give me some name for the goldfish. I'm just kidding. Yeah. So, you know, it's really easy to take care of a goldfish. We have a big tank, and, we're both in the same house. So it's not like, oh, where are my three goldfish? You know, it's just, oh, how many goldfish do you have? It's, like, four or five. But, we only have room for one person to be a goldfish keeper. So that is hard, especially when it's, like, 20 degrees outside and you're trying to keep a fish at home. Right? Yeah. That's difficult. And with the tank being this size, you don't really feel bad about taking him out. You know, you just kinda get a little more nervous because you know you're gonna be doing a big fish transfer if you have that big of a tank and all that stuff. But Mhmm. It's much easier to take care of the goldfish at home. So I wouldFor the rest of us simple folks, we worry about somebody stealing our password. To you, you laugh about it because you know how to do that with your eyes closed, right, with the technology you've created. So nowadays, you talk to certain investors, so where do hide your passwords? I don't want to really say, but I hide my passwords in my notes section on my phone. Oh shoot. Okay. Where do you hide your passwords? I write it on a piece of paper. Where do you hide your password? I have it on file on my computer. Where do you hide your password? I have it on an Excel spreadsheet, right? And all these places you go through. And so now there's a business model for apps that you put your passwords in and they protect your password. If it's so easy to break into softwares to get my password, How can I trust an app to restore all my password? Is there anywhere you trust to restore your passwords? So let's imagine that I want your password. I'm gonna make a website for Iranian American fans of Atlas Shrugged, and I'm gonna send you an email with a,\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'All right. Good afternoon, everybody. Welcome to Friday afternoon. Appreciate you all coming. Really pleased today to be able to host the students to to COVID. Great. Correct me if I get it wrong. From the University of Wisconsin,'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Cannot use chat template functions because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m messages \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m      2\u001b[0m     {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msystem\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are a concise assistant that answers in short paragraphs.\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m      3\u001b[0m     {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExplain rotary positional embeddings briefly.\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m      4\u001b[0m ]\n\u001b[0;32m----> 5\u001b[0m prompt_ids \u001b[38;5;241m=\u001b[39m \u001b[43mtok\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_chat_template\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43madd_generation_prompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m   \u001b[49m\u001b[38;5;66;43;03m# appends the assistant header the model should complete\u001b[39;49;00m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_tensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      9\u001b[0m \u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(lm\u001b[38;5;241m.\u001b[39mdevice)\n",
+      "File \u001b[0;32m~/salman/minomni_sn21/omega-v2v/console/backend/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1621\u001b[0m, in \u001b[0;36mPreTrainedTokenizerBase.apply_chat_template\u001b[0;34m(self, conversation, tools, documents, chat_template, add_generation_prompt, continue_final_message, tokenize, padding, truncation, max_length, return_tensors, return_dict, return_assistant_tokens_mask, tokenizer_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m   1618\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tokenizer_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1619\u001b[0m     tokenizer_kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m-> 1621\u001b[0m chat_template \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_chat_template\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchat_template\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1623\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_assistant_tokens_mask \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m re\u001b[38;5;241m.\u001b[39msearch(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m-?\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*generation\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*-?\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124m}\u001b[39m\u001b[38;5;124m\"\u001b[39m, chat_template):\n\u001b[1;32m   1624\u001b[0m     logger\u001b[38;5;241m.\u001b[39mwarning_once(\n\u001b[1;32m   1625\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreturn_assistant_tokens_mask==True but chat template does not contain `\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;132;01m% g\u001b[39;00m\u001b[38;5;124meneration \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m}` keyword.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1626\u001b[0m     )\n",
+      "File \u001b[0;32m~/salman/minomni_sn21/omega-v2v/console/backend/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1789\u001b[0m, in \u001b[0;36mPreTrainedTokenizerBase.get_chat_template\u001b[0;34m(self, chat_template, tools)\u001b[0m\n\u001b[1;32m   1787\u001b[0m         chat_template \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchat_template\n\u001b[1;32m   1788\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1789\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1790\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use chat template functions because tokenizer.chat_template is not set and no template \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1791\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margument was passed! For information about writing templates and setting the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1792\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtokenizer.chat_template attribute, please see the documentation at \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1793\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://huggingface.co/docs/transformers/main/en/chat_templating\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1794\u001b[0m         )\n\u001b[1;32m   1796\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m chat_template\n",
+      "\u001b[0;31mValueError\u001b[0m: Cannot use chat template functions because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating"
+     ]
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    {\"role\": \"system\", \"content\": \"You are a concise assistant that answers in short paragraphs.\"},\n",
+    "    {\"role\": \"user\", \"content\": \"Explain rotary positional embeddings briefly.\"},\n",
+    "]\n",
+    "prompt_ids = tok.apply_chat_template(\n",
+    "    messages,\n",
+    "    add_generation_prompt=True,   # appends the assistant header the model should complete\n",
+    "    return_tensors=\"pt\"\n",
+    ").to(lm.device)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

test_asr.py

@@ -0,0 +1,23 @@
+from server import gt
+import librosa
+ref_audio, _ = librosa.load('/home/salman/salman/minomni_sn21/omega-v2v/miner_models/MiniCPM-o/assets/input_examples/assistant_female_voice.wav', sr=16000, mono=True) # load the reference audio
+
+text = gt(ref_audio, 16_000)
+print(text)
+
+# write a code to recursively iterate a directory and subdirectories to transcript all audio .wav files in it
+import os
+def transcribe_directory():
+    for root, dirs, files in os.walk('/home/salman/salman/minomni_sn21/omega-v2v/miner_models/recordings'):
+        for file in files:
+            if file.endswith('.wav'):
+                print(f"Processing file: {file}")
+                file_path = os.path.join(root, file)
+                audio, sr = librosa.load(file_path, sr=16000, mono=True)
+                transcription = gt(audio, sr)
+                print(f"Transcription for {file_path}: {transcription}")
+                with open(file_path.replace('.wav', '.txt'), 'w') as f:
+                    f.write(transcription)
+
+
+transcribe_directory()

utils.py

@@ -0,0 +1,7 @@
+api_key = "claude-rwjrljsdjfhsjvinesfsdgqrqw"
+temp_ = "omega-omega-omega"
+netuid = 21
+competition = 'v3'
+
+
+hotkey = "5HQQxrp3EDBi1M7pCG2pyJuLiqCyHsog8HssnhekfkbKKfZ6"