AHA_EXPORT_BUG_ANALYSIS.md

🎯 AHA Moment #20 - Parakeet-TDT 1.1B Export Bug Analysis

Date: 2025-11-10 Status: ✅ ROOT CAUSE IDENTIFIED & FIXED

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🔍 Problem Statement

sherpa-onnx (v1.12.15) failed to load the exported 1.1B model with error:

<blk> is not the last token!

Initial hypothesis was that tokens.txt format was incorrect, but deeper investigation revealed TWO critical metadata bugs in the export script.

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🎯 Root Cause Analysis

Bug #1: Incorrect vocab_size Metadata

What We Did Wrong:

# export_parakeet_tdt_1.1b.py line 86 (WRONG!)
vocab_size = len(asr_model.joint.vocabulary) + 1  # = 1024 + 1 = 1025

Why It's Wrong:

• The metadata vocab_size should represent vocabulary ONLY (excluding blank token)
• sherpa-onnx adds the blank token internally
• For TDT models, sherpa-onnx validates: joiner_output_dim - num_durations = vocab_size + 1

The Math:

Our model:
- Joint vocabulary: 1024 tokens
- Joiner output: 1030 dimensions
- TDT durations: 5 (0, 1, 2, 3, 4)

sherpa-onnx calculation:
- Expected vocab_size = (1030 - num_durations) - 1
- With num_durations=4: (1030 - 4) - 1 = 1025 ❌ (but we set 1025)
- With num_durations=5: (1030 - 5) - 1 = 1024 ✅

The issue: sherpa-onnx detected 4 durations instead of 5!
But even with 5, our vocab_size=1025 was still wrong!

Official 0.6B Model for Comparison:

• vocab_size (metadata): 1024 (NOT 1025!)
• tokens.txt lines: 1025 (1024 vocab + 1 blank)
• joiner output: 1030 (1024 + 1 blank + 5 durations)

Correct Formula:

vocab_size = len(asr_model.joint.vocabulary)  # Don't add 1!

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Bug #2: Incorrect feat_dim Metadata

What We Did Wrong:

# export_parakeet_tdt_1.1b.py line 120 (WRONG!)
"feat_dim": 128,  # Copied from 0.6B model

Why It's Wrong:

• The 1.1B model uses 80 mel filterbank features, not 128
• This was discovered during Rust testing when encoder expected [batch, 80, time] inputs
• We incorrectly assumed it matched the 0.6B model's 128 features

Official Specs:

• 0.6B model: 128 mel features
• 1.1B model: 80 mel features ← Critical difference!

Correct Value:

"feat_dim": 80,  # 1.1B uses 80 mel features!

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🔬 Investigation Process

1. Initial Error

/project/sherpa-onnx/csrc/offline-recognizer-transducer-nemo-impl.h:PostInit:180
<blk> is not the last token!

2. Token File Analysis

• Checked tokens.txt format: ✅ CORRECT (1025 lines, at end)
• Compared with official 0.6B: Same format ✅
• tokens.txt was NOT the problem!

3. ONNX Model Inspection

# Joiner output dimensions
model.graph.output[0].shape: [batch, time, 1, 1030]
                                              ^^^^
# But vocabulary only has 1024 tokens + blank = 1025
# Missing 5 dimensions = TDT duration outputs!

4. TDT Model Discovery

• Researched TDT (Token-and-Duration Transducer) architecture
• Found: Model config shows num_extra_outputs: 5
• Found: Loss config shows durations: [0, 1, 2, 3, 4]
• Insight: Joiner outputs BOTH tokens AND durations!

5. sherpa-onnx Compatibility Research

• Discovered sherpa-onnx added TDT support in v1.11.5 (May 2025)
• Confirmed TDT support in v1.12.15 (our version) ✅
• Found bug fix for TDT decoding in v1.12.14 (PR #2606)

6. Official Model Comparison

Downloaded official 0.6B model metadata:

vocab_size: 1024  ← AHA! Not 1025!
feat_dim: 128

Our 1.1B metadata (WRONG):

vocab_size: 1025  ← Should be 1024!
feat_dim: 128     ← Should be 80!

7. Debug Output Analysis

sherpa-onnx debug output revealed:

TDT model. vocab_size: 1026, num_durations: 4

But our metadata said vocab_size=1025 → MISMATCH!

Also: sherpa-onnx detected only 4 durations, but model has 5 durations!

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✅ Solution

Fixed Export Script

File: /opt/swictation/scripts/export_parakeet_tdt_1.1b.py

Change 1 (Line 87):

# BEFORE:
vocab_size = len(asr_model.joint.vocabulary) + 1

# AFTER:
# CRITICAL: vocab_size should NOT include blank token (sherpa-onnx adds it internally)
vocab_size = len(asr_model.joint.vocabulary)

Change 2 (Line 122):

# BEFORE:
"feat_dim": 128,  # Mel filterbank features

# AFTER:
"feat_dim": 80,  # CRITICAL: 1.1B uses 80 mel features, not 128!

Re-Export Process

docker run --rm \
  -v /opt/swictation:/workspace \
  -w /workspace/models/parakeet-tdt-1.1b \
  nvcr.io/nvidia/nemo:25.07 \
  bash -c "pip install onnxruntime && python3 /workspace/scripts/export_parakeet_tdt_1.1b.py"

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📊 Comparison: 0.6B vs 1.1B

Feature	0.6B	1.1B
Vocabulary Size	1024	1024
Mel Features	128	80
Encoder Output Dim	128	1024
Decoder Hidden	640	640
TDT Durations	5 (0-4)	5 (0-4)
tokens.txt Lines	1025	1025
Joiner Output	1030	1030
vocab_size (metadata)	1024	1024 ✅
feat_dim (metadata)	128	80 ✅

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🎓 Key Learnings

1. Metadata vs File Format:

   • vocab_size metadata = vocabulary only (no blank)
   • tokens.txt file = vocabulary + blank token
   • These are different!

2. TDT Model Architecture:

   • Joiner outputs = vocab + blank + duration outputs
   • For 1.1B: 1024 + 1 + 5 = 1030 total outputs
   • Duration outputs (0-4) allow frame skipping for faster inference

3. sherpa-onnx TDT Support:

   • Requires v1.11.5+ for TDT models
   • Validates: joiner_output_dim - num_durations = vocab_size + 1
   • Internally shifts blank token to position 0 (NeMo has it at end)

4. Model-Specific Parameters:

   • Don't assume all models from same family have same parameters!
   • 0.6B uses 128 mel features, 1.1B uses 80
   • Always check model config for exact specifications

5. Validation Strategy:

   • Use reference implementation (sherpa-onnx) to validate exports
   • Compare with known-working models (0.6B)
   • Check debug output for mismatch clues

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✅ Next Steps

1. Validation: Test re-exported model with sherpa-onnx validation script
2. Rust Integration: Test with Rust recognizer once validation passes
3. Documentation: Update README with correct metadata requirements
4. Testing: Add automated tests to catch metadata bugs in future exports

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📝 References

• sherpa-onnx TDT support: https://github.com/k2-fsa/sherpa-onnx/issues/2183
• Official 0.6B model: https://huggingface.co/csukuangfj/sherpa-onnx-nemo-parakeet-tdt-0.6b-v2-int8
• NeMo 1.1B model: https://huggingface.co/nvidia/parakeet-tdt-1.1b
• TDT paper: Token-and-Duration Transducer for fast inference

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Status: 🔄 Re-exporting with corrected metadata...