Feedback: guides-titanet-speaker-identification

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/titanet-speaker-identification
Category: guides
Generated: 05/08/2025, 4:35:44 pm

---

Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:35:43 pm

Technical Documentation Analysis & Feedback

Overview

This documentation shows promise but needs significant improvements in structure, clarity, and user experience. The content is technically sound but poorly organized and lacks crucial information for successful implementation.

Critical Issues

1. Misleading Title and Structure

Problem: The document is titled as a “guide” but reads like a code dump with minimal explanation. Fix:

• Restructure as a proper step-by-step tutorial
• Add a clear table of contents
• Include an architecture overview diagram
• Separate conceptual explanations from code implementation

2. Dangerous Quickstart Section

Problem: The 200+ line “Quickstart” is overwhelming and contains hardcoded URLs that will expire. Fix:

# Replace this massive code block with:
# 1. A simple 10-line example showing the core concept
# 2. Link to full implementation below
# 3. Use placeholder URLs or explain how to get test data

# Example - Simple speaker identification
import assemblyai as aai
from nemo.collections.asr.models import EncDecSpeakerLabelModel

# Initialize models
aai.settings.api_key = "YOUR_API_KEY"
speaker_model = EncDecSpeakerLabelModel.from_pretrained("nvidia/speakerverification_en_titanet_large")

# Basic workflow
transcript = aai.Transcriber().transcribe("path/to/audio.wav")
embedding = speaker_model.get_embedding("path/to/audio.wav")
# ... identification logic

3. Missing Prerequisites and System Requirements

Add this section:

## Prerequisites

### System Requirements
- Python 3.8 or higher
- CUDA-capable GPU (recommended) or CPU with 8GB+ RAM
- 10GB+ free disk space for model downloads
- Linux/macOS (Windows support limited)

### Required Accounts
- [AssemblyAI API key](https://www.assemblyai.com/dashboard/signup) (free tier available)
- [Pinecone account](https://app.pinecone.io/) (free tier: 1M vectors)

### Audio Requirements
- WAV format (mono preferred, stereo will be converted)
- Sample rate: 16kHz recommended
- Minimum 3-5 seconds per speaker for fingerprinting

4. Installation Issues

Problem: Single pip install command is likely to fail and takes “several minutes” with no guidance. Fix:

## Installation

### Step 1: Create Virtual Environment
```bash
python -m venv speaker_id_env
source speaker_id_env/bin/activate  # Linux/macOS
# or
speaker_id_env\Scripts\activate     # Windows

Step 2: Install PyTorch (CUDA support)

# For CUDA 11.8
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
# For CPU only
pip install torch torchvision torchaudio

Step 3: Install Core Dependencies

pip install assemblyai pinecone-client pydub

Step 4: Install NeMo Toolkit

pip install nemo_toolkit[asr]

Expected install time: 10-15 minutes Total download size: ~3GB

### 5. **Poor Error Handling and Debugging**
**Problem**: No error handling, debugging tips, or troubleshooting section.
**Add**:
```markdown
## Troubleshooting

### Common Issues

#### CUDA Out of Memory
```python
# Add to your code:
import torch
if torch.cuda.is_available():
    torch.cuda.empty_cache()

Audio Format Errors

• Ensure files are in WAV format
• Check sample rate: ffmpeg -i input.wav
• Convert if needed: ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav

Pinecone Connection Issues

# Test connection:
try:
    pc.list_indexes()
    print("Pinecone connection successful")
except Exception as e:
    print(f"Pinecone error: {e}")

### 6. **Hardcoded Values and Magic Numbers**
**Problem**: Unexplained thresholds and hardcoded parameters.
**Fix**:
```python
# Replace magic numbers with explained constants
SPEAKER_SIMILARITY_THRESHOLD = 0.5  # Minimum confidence for speaker match
MIN_UTTERANCE_LENGTH_MS = 5000      # Minimum audio length for reliable identification
EMBEDDING_DIMENSION = 192           # TitaNet-Large embedding size
PINECONE_TOP_K = 3                 # Number of similar speakers to retrieve

7. Missing Usage Examples and Use Cases

Add practical examples:

## Use Cases

### 1. Meeting Transcription
- Identify recurring meeting participants
- Track speaker participation time
- Generate speaker-specific meeting notes

### 2. Podcast/Interview Processing
- Automatically label speakers in multi-episode series
- Create speaker-specific highlight reels
- Generate per-speaker transcripts

### 3. Call Center Analytics
- Identify customers vs agents
- Track agent performance
- Analyze conversation patterns

8. Security and API Key Management

Add security section:

## Security Best Practices

### Environment Variables
```bash
# .env file
ASSEMBLYAI_API_KEY=your_key_here
PINECONE_API_KEY=your_key_here

# In your code
import os
from dotenv import load_dotenv

load_dotenv()
aai.settings.api_key = os.getenv("ASSEMBLYAI_API_KEY")
pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))

Rate Limits

• AssemblyAI: 5 concurrent transcriptions (free tier)
• Pinecone: 100 requests/minute (free tier)

## Structural Improvements Needed

### 1. **Add Conceptual Overview**
```markdown
## How It Works

1. **Audio Preprocessing**: Convert audio to WAV, ensure mono channel
2. **Initial Diarization**: AssemblyAI separates speakers (Speaker A, B, C...)
3. **Embedding Generation**: TitaNet creates 192-dimension speaker fingerprints
4. **Speaker Matching**: Compare utterances against known speaker database
5. **Identity Assignment**: Replace generic labels with actual names

[Include architecture diagram here]

2. Break Down the Monolithic Functions

The current functions are too complex. Split them:

# Instead of one giant function, create focused utilities:
class SpeakerIdentifier:
    def __init__(self, pinecone_key, assemblyai_key):
        # initialization

    def add_speaker_fingerprint(self, name, audio_file):
        # focused fingerprint creation

    def identify_utterance(self, audio_segment):
        # single utterance identification

    def process_conversation(self, audio_file):
        # high-level workflow orchestration

3. Add Performance and Cost Information

## Performance & Costs

### Processing Time
- TitaNet embedding: ~2-3 seconds per utterance
- AssemblyAI transcription: ~15-30% of audio length
- Total: ~2-3x real-time for complete pipeline

### API Costs (approximate)
- AssemblyAI: $0.65/hour of audio
- Pinecone: Free up to 1M vectors
- Total: ~$1-2 per hour of processed audio

4. Add Data Management Section

## Managing Speaker Database

### Adding New Speakers
```python
# Best practices for speaker fingerprints:
# - Use 10-30 seconds of clear speech
# - Avoid background noise
# - Multiple samples improve accuracy

speaker_id.add_speaker_fingerprint(
    name="John Doe",
    audio_file="john_sample.wav",
    quality_check=True  #

---