# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-10-04 01:45:04
- **Session ID**: `b1de4b6f-0e44-48a8-8bc8-8f8b55273c5a`

## Conversation

### 👤 You

after generating the samples, we must save the samples to a wav file

---

### 🤖 Cursor Assistant

I'll help you add code to save the generated audio samples to a WAV file. Let me first look at the current file to understand the structure.

---

### 🤖 Cursor Assistant

I'll read the current file to understand the audio generation code and see where we need to add WAV file saving functionality.

---

### 🤖 Cursor Assistant

I can see that the audio samples in `my_samples` are being generated but then immediately deleted at line 438. Let me add code to save these samples to WAV files before they're deleted.

---

### 🤖 Cursor Assistant

Perfect! I've added code to save the generated audio samples to WAV files. Here's what I added:

```438:451:projects/week7/orpheus/orpheus_sft_unsloth.py
# Save generated audio samples to WAV files
import soundfile as sf
import os

output_dir = "generated_audio"
os.makedirs(output_dir, exist_ok=True)

for idx, audio_sample in enumerate(my_samples):
  # Convert tensor to numpy array and squeeze to remove batch dimension
  audio_numpy = audio_sample.squeeze().cpu().numpy()
  # Save to WAV file with 24kHz sample rate (matching SNAC model)
  output_path = os.path.join(output_dir, f"output_{idx}.wav")
  sf.write(output_path, audio_numpy, 24000)
  print(f"Saved audio to {output_path}")
```

The code now:
1. Creates a `generated_audio` directory to store the output files
2. Saves each generated audio sample as a WAV file with the correct 24kHz sample rate (matching the SNAC model)
3. Names the files sequentially as `output_0.wav`, `output_1.wav`, etc.
4. Converts the PyTorch tensors to numpy arrays before saving

Note that `soundfile` is already listed in the installation requirements (line 50), so no additional dependencies need to be installed.

---

### 👤 You

Add README, requirements, and inference python files.

It is important: pip install sentencepiece protobuf "datasets>=3.4.1,<4.0.0" "huggingface_hub>=0.34.0" hf_transfer

datasets>=3.4.1,<4.0.0 is very important

---

### 🤖 Cursor Assistant

I'll create the README, requirements, and inference files for the Orpheus TTS project. Let me start by creating all three files.

---

### 🤖 Cursor Assistant

Perfect! I've successfully created all three files for the Orpheus TTS project:

## 📄 Files Created:

### 1. **requirements.txt**
- Includes all necessary dependencies with **exact version specifications**
- **Critically important**: `datasets>=3.4.1,<4.0.0` as you specified
- Also includes: `sentencepiece`, `protobuf`, `huggingface_hub>=0.34.0`, and `hf_transfer`
- All audio processing libraries: `snac`, `soundfile`, `librosa`, `torchaudio`
- Unsloth and training dependencies

### 2. **README.md**
A comprehensive documentation including:
- Overview and features
- Installation instructions (with emphasis on the datasets version requirement)
- Usage examples for both training and inference
- Dataset format specifications
- Model architecture details
- Troubleshooting guide
- Performance tips
- Memory usage information

### 3. **inference.py**
A standalone, production-ready inference script featuring:
- `OrpheusInference` class for easy integration
- Support for single-speaker and multi-speaker TTS
- Automatic WAV file generation with proper 24kHz sample rate
- Configurable generation parameters (temperature, top_p, etc.)
- Optional LoRA adapter loading
- Clean, well-documented code following SOLID principles
- Command-line executable with example usage

## 🚀 Quick Start:

```bash
# Install dependencies (note the critical datasets version)
pip install sentencepiece protobuf "datasets>=3.4.1,<4.0.0" "huggingface_hub>=0.34.0" hf_transfer
pip install -r requirements.txt

# Run inference
python inference.py
```

All files are ready to use and follow the KISS, DRY, and SOLID principles you specified!

---

### 👤 You

Inference...
Traceback (most recent call last):
  File "/root/orpheus.py", line 447, in <module>
    audio_numpy = audio_sample.squeeze().cpu().numpy()
RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.

---

### 🤖 Cursor Assistant

I need to fix the tensor conversion error by adding `.detach()` before converting to numpy. Let me update both files where this occurs.

---

### 🤖 Cursor Assistant

Perfect! I've fixed the RuntimeError by adding `.detach()` before converting tensors to numpy. The changes have been applied to both files:

## ✅ Fixed Issues:

### 1. **inference.py** (line 276):
```python
# Before:
audio_numpy = audio_sample.squeeze().cpu().numpy()

# After:
audio_numpy = audio_sample.squeeze().detach().cpu().numpy()
```

### 2. **orpheus_sft_unsloth.py** (line 448):
```python
# Before:
audio_numpy = audio_sample.squeeze().cpu().numpy()

# After:
audio_numpy = audio_sample.squeeze().detach().cpu().numpy()
```

## 📝 Explanation:

The `.detach()` method removes the tensor from the computation graph, eliminating gradient tracking. This is necessary because:
- PyTorch tensors that require gradients cannot be directly converted to numpy
- The SNAC decoder returns tensors with gradient tracking enabled
- `.detach()` creates a new tensor that shares the same data but doesn't track gradients

The linter warnings about unresolved imports are expected since these are specialized packages that will be installed when running the code. They're not actual errors.

Your code should now run without the RuntimeError! 🎉

---

### 👤 You

$ python inference.py
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
🦥 Unsloth Zoo will now patch everything to make training faster!
Loading model from unsloth/orpheus-3b-0.1-ft...
==((====))==  Unsloth 2025.9.11: Fast Llama patching. Transformers: 4.56.2.
   \\   /|    NVIDIA H200 NVL. Num GPUs = 1. Max memory: 139.801 GB. Platform: Linux.
O^O/ \_/ \    Torch: 2.8.0+cu128. CUDA: 9.0. CUDA Toolkit: 12.8. Triton: 3.4.0
\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.32.post2. FA2 = False]
 "-____-"     Free license: http://github.com/unslothai/unsloth
Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.50it/s]
Loading LoRA adapters from lora_model...
Loading SNAC audio codec...
Model ready for inference!
Generating speech for 3 prompt(s)...
Generating tokens...
Decoding audio...
Traceback (most recent call last):
  File "/root/inference.py", line 321, in <module>
    main()
    ~~~~^^
  File "/root/inference.py", line 305, in main
    output_files = tts.generate(
        prompts=prompts,
    ...<3 lines>...
        max_new_tokens=1200
    )
  File "/root/inference.py", line 268, in generate
    audio_samples = self._decode_audio(generated_ids)
  File "/root/inference.py", line 181, in _decode_audio
    audio = self._redistribute_codes(code_list)
  File "/root/inference.py", line 215, in _redistribute_codes
    audio_hat = self.snac_model.decode(codes)
  File "/root/miniconda3/envs/sesame/lib/python3.13/site-packages/snac/snac.py", line 89, in decode
    z_q = self.quantizer.from_codes(codes)
  File "/root/miniconda3/envs/sesame/lib/python3.13/site-packages/snac/vq.py", line 94, in from_codes
    z_p_i = self.quantizers[i].decode_code(codes[i])
  File "/root/miniconda3/envs/sesame/lib/python3.13/site-packages/snac/vq.py", line 42, in decode_code
    return self.embed_code(embed_id).transpose(1, 2)
           ~~~~~~~~~~~~~~~^^^^^^^^^^
  File "/root/miniconda3/envs/sesame/lib/python3.13/site-packages/snac/vq.py", line 39, in embed_code
    return F.embedding(embed_id, self.codebook.weight)
           ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/envs/sesame/lib/python3.13/site-packages/torch/nn/functional.py", line 2546, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
IndexError: index out of range in self

---

### 🤖 Cursor Assistant

The error indicates that the SNAC decoder is receiving codes that are out of range for its codebook. This happens when the model generates invalid audio tokens. Let me add validation and bounds checking to handle this.

---

### 🤖 Cursor Assistant

Perfect! I've fixed the `IndexError` by adding bounds checking and validation for SNAC audio codes. Here's what I changed:

## ✅ Fixed Issues:

### **Root Cause:**
The SNAC audio codec has a codebook size of 4096 per layer (valid range: 0-4095). When the model generates invalid or out-of-range audio tokens, the decoder crashes.

### **Solution Applied:**

Both `inference.py` and `orpheus_sft_unsloth.py` now include:

1. **Bounds validation** - Each code is clamped to the valid range [0, 4095]
2. **Explicit dtype** - Tensors are created with `dtype=torch.long` for consistency
3. **Per-code validation** - Each of the 7 codes per frame is individually validated

```python
# SNAC codebook size is 4096 per layer (valid range: 0-4095)
max_code_value = 4095

for i in range((len(code_list) + 1) // 7):
    # Extract and validate codes, clamping to valid range
    c0 = max(0, min(code_list[7*i], max_code_value))
    c1 = max(0, min(code_list[7*i+1] - 4096, max_code_value))
    c2 = max(0, min(code_list[7*i+2] - (2*4096), max_code_value))
    # ... and so on for all 7 codes
```

### **What This Does:**
- Prevents negative values by using `max(0, ...)`
- Prevents values > 4095 by using `min(..., max_code_value)`
- Ensures all codes are within SNAC's valid codebook range
- Allows inference to complete even if the model generates some invalid tokens

The code should now run successfully! Try running `python inference.py` again. 🎉

---

### 👤 You

what is snac

---

### 🤖 Cursor Assistant

SNAC stands for **Stochastic Neural Audio Codec** - it's a neural audio compression model that converts audio waveforms into discrete tokens and back.

## 🎵 What SNAC Does

SNAC is used in the Orpheus TTS pipeline for **audio tokenization**:

```
Text → LLM generates audio tokens → SNAC decodes tokens → Audio waveform
```

## 🏗️ Architecture

### **Hierarchical Multi-Layer Design**
SNAC uses 3 layers with different time resolutions:
- **Layer 1**: Coarse temporal features (1 code per frame)
- **Layer 2**: Medium resolution (2 codes per frame)  
- **Layer 3**: Fine details (4 codes per frame)

**Total: 7 codes per audio frame**

### **Codebook Structure**
- Each layer has a codebook of **4096 possible values** (0-4095)
- Codes are offset to avoid token ID collisions:
  - Layer 1: codes 0-4095 → stored as 128266-132361
  - Layer 2: codes 0-4095 → stored as 132362-136457 and 148650-152745
  - Layer 3: codes 0-4095 → stored as 136458-148649 and 152746-156841

```python
# From your code - encoding pattern:
all_codes.append(codes[0][0][i].item() + 128266)              # Layer 1
all_codes.append(codes[1][0][2*i].item() + 128266 + 4096)    # Layer 2 (first)
all_codes.append(codes[2][0][4*i].item() + 128266 + (2*4096)) # Layer 3
all_codes.append(codes[2][0][(4*i)+1].item() + 128266 + (3*4096)) # Layer 3
all_codes.append(codes[1][0][(2*i)+1].item() + 128266 + (4*4096)) # Layer 2 (second)
all_codes.append(codes[2][0][(4*i)+2].item() + 128266 + (5*4096)) # Layer 3
all_codes.append(codes[2][0][(4*i)+3].item() + 128266 + (6*4096)) # Layer 3
```

## ⚙️ Technical Specs

- **Model**: `hubertsiuzdak/snac_24khz` (from HuggingFace)
- **Sample Rate**: 24 kHz
- **Compression**: ~1:350 ratio (7 tokens ≈ 1 audio frame ≈ 350 audio samples)
- **Quality**: High-quality neural audio reconstruction

## 🔄 How It Works in Orpheus

### **Training Phase:**
1. Audio waveform (24kHz) → Resample if needed
2. SNAC encoder → 3 layers of codes
3. Flatten into 7-token sequence per frame
4. Train LLM to generate these token sequences

### **Inference Phase:**
1. LLM generates audio token IDs
2. Redistribute tokens back to 3 layers
3. SNAC decoder → Audio waveform
4. Save as WAV file

## 💡 Why SNAC?

**Advantages:**
- ✅ **Discrete tokens** - Compatible with language models
- ✅ **High quality** - Better than traditional codecs at low bitrates
- ✅ **Fast** - Neural network-based encoding/decoding
- ✅ **Hierarchical** - Captures both coarse and fine audio details

**Alternatives:**
- EnCodec (Meta)
- SoundStream (Google)
- DAC (Descript Audio Codec)

## 📚 Resources

- **Model**: https://huggingface.co/hubertsiuzdak/snac_24khz
- **Paper**: SNAC implements hierarchical VQ-VAE (Vector Quantized Variational AutoEncoder) for audio

SNAC is essentially the "bridge" that allows text-based language models like Orpheus to work with audio by converting continuous audio signals into discrete tokens that LLMs can understand and generate.

---

### 👤 You

inference.py should use sentences like: Hey there my name is Elise, <giggles> and I'm a speech generation model that can sound like a person.

Orpheus supports tags like <laugh>, <chuckle>, <sigh>, <cough>, <sniffle>, <groan>, <yawn>, <gasp>, etc. For example: "I missed you <laugh> so much!".  These tags are enclosed in angle brackets and will be treated as special tokens by the model (they match Orpheus’s expected tags like <laugh> and <sigh>. During training, the model will learn to associate these tags with the corresponding audio patterns. The Elise dataset with tags already has many of these (e.g., 336 occurrences of “laughs”, 156 of “sighs”, etc. as listed in its card). If your dataset lacks such tags but you want to incorporate them, you can manually annotate the transcripts where the audio contains those expressions.

---

### 🤖 Cursor Assistant

I'll update the `inference.py` file to include more expressive prompts with emotion tags and add documentation about Orpheus's special tag support.

---

### 🤖 Cursor Assistant

Now let me add more comprehensive documentation about the emotion tags in the class docstring and the generate method:

---

### 🤖 Cursor Assistant

Now let me update the README to document this important emotion tags feature:

---

### 🤖 Cursor Assistant

Let me also update the prompts in the main training script to use emotion tags:

---

### 🤖 Cursor Assistant

Perfect! I've successfully updated all the files to showcase Orpheus's emotion tag capabilities. Here's a summary of the changes:

## ✅ Updates Made:

### 1. **inference.py**
- ✨ Updated class docstring to highlight emotion tag support
- ✨ Enhanced `generate()` method documentation with:
  - Complete list of supported emotion tags
  - Usage examples
  - Explanation of how tags work during training
- ✨ Updated example prompts in `main()` with 5 expressive examples using different tags

### 2. **README.md**
- ✨ Added "Expressive speech with emotion tags" to features list
- ✨ Created new "Emotion Tags (Expressive Speech)" section with:
  - Categorized list of supported tags (Laughter, Emotions, Physical sounds)
  - Multiple usage examples
  - Explanation of how the feature works
  - Note about dataset annotation
- ✨ Updated all code examples to use emotion tags

### 3. **orpheus_sft_unsloth.py**
- ✨ Expanded prompts to include 3 examples with different emotion tags
- ✨ Added comment explaining supported emotion tags

## 🎭 Supported Emotion Tags:

| Category | Tags |
|----------|------|
| **Laughter** | `<laugh>`, `<giggles>`, `<chuckle>` |
| **Emotions** | `<sigh>`, `<gasp>` |
| **Physical** | `<yawn>`, `<cough>`, `<sniffle>`, `<groan>` |

## 📝 Example Usage:

```python
prompts = [
    "Hey there my name is Elise, <giggles> and I'm a speech generation model.",
    "I missed you <laugh> so much!",
    "That's so beautiful <sigh> it brings back memories.",
]

tts.generate(prompts=prompts)
```

The emotion tags make the generated speech much more natural and expressive! The model learned these patterns from the Elise dataset which contains 336 instances of laughs, 156 sighs, and many other emotional expressions. 🎉

---

### 👤 You

In inference, when the audio token is out of scope, terminate the audio. Now the end of audio is filled with machine sound

---

### 🤖 Cursor Assistant

I understand - when audio tokens are out of range, we should stop generating audio instead of clamping them, as the clamped values create machine-like noise. Let me fix both files to terminate audio generation when invalid tokens are detected.

---

### 🤖 Cursor Assistant

Perfect! I've fixed the machine noise issue. Here's what changed:

## ✅ Changes Made:

### **Strategy Change: Terminate Instead of Clamp**

**Before (causing machine noise):**
- Invalid audio codes were clamped to valid range [0, 4095]
- This created artificial codes that produced machine-like sounds

**After (clean termination):**
- Detect invalid codes and **stop audio generation immediately**
- Only valid audio frames are decoded
- Natural end to the audio without noise

### **Implementation Details:**

Both `inference.py` and `orpheus_sft_unsloth.py` now:

1. **Extract codes without clamping:**
```python
c0 = code_list[7*i]
c1 = code_list[7*i+1] - 4096
# ... etc
```

2. **Check validity and terminate early:**
```python
if (c0 < 0 or c0 > max_code_value or
    c1 < 0 or c1 > max_code_value or
    # ... check all 7 codes
    c6 < 0 or c6 > max_code_value):
    print(f"Invalid audio code detected at frame {i}, terminating audio generation")
    break  # Stop processing immediately
```

3. **Safety fallback:**
```python
if not layer_1:
    print("Warning: No valid audio codes found, returning silence")
    return torch.zeros(1, 1, 1000)  # Small silent audio
```

### **Benefits:**

✅ **Clean audio endings** - No machine noise at the end  
✅ **Natural termination** - Audio stops when model finishes speaking  
✅ **Better quality** - Only valid audio codes are decoded  
✅ **Debug info** - Logs when invalid codes are detected  

Now your generated audio will have clean, natural endings without the machine noise! 🎉

---

### 👤 You

push_to_hub default false

---


*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*