---
license: cc-by-4.0
language:
- en
tags:
- audio
- automatic-speech-recognition
- speech
- onnx
- parakeet
- nvidia
- transducer
pipeline_tag: automatic-speech-recognition
library_name: onnxruntime
---

# Parakeet-TDT 1.1B ONNX

Pre-exported ONNX models of NVIDIA's [Parakeet-TDT 1.1B](https://huggingface.co/nvidia/parakeet-tdt-1.1b) for immediate use without requiring Docker/NeMo export.

## Model Description

- **Base Model**: [nvidia/parakeet-tdt-1.1b](https://huggingface.co/nvidia/parakeet-tdt-1.1b)
- **Architecture**: Token-and-Duration Transducer (TDT) with FastConformer encoder
- **Parameters**: 1.1 billion
- **Features**: 80 mel filterbank features (16kHz)
- **Vocabulary**: 1024 SentencePiece tokens + blank token
- **Format**: ONNX Runtime compatible (CPU/GPU)

## What's Included

### Quantized Models (Recommended - 3.8GB total)
- `encoder.int8.onnx` (1.1GB) - INT8 quantized encoder with external weights
- `encoder.int8.weights` (2.0GB) - INT8 encoder weight file
- `decoder.int8.onnx` (7.0MB) - INT8 quantized decoder
- `joiner.int8.onnx` (1.7MB) - INT8 quantized joiner
- `tokens.txt` (11KB) - Vocabulary file

### Full Precision Models (Optional - 8.1GB additional)
- `encoder.onnx` (41MB) + `encoder.weights` (4.0GB) - FP32 encoder
- `decoder.onnx` (28MB) - FP32 decoder
- `joiner.onnx` (6.6MB) - FP32 joiner
- 639 layer weight files (various sizes)

## Quick Start

### Download Models
```bash
# Download all models (12GB)
huggingface-cli download jenerallee78/parakeet-tdt-1.1b-onnx --local-dir ./models

# Or download just INT8 quantized (3.8GB - recommended)
huggingface-cli download jenerallee78/parakeet-tdt-1.1b-onnx \
  --include "encoder.int8.*" "decoder.int8.onnx" "joiner.int8.onnx" "tokens.txt" \
  --local-dir ./models
```

### Use with ONNX Runtime (Python)
```python
import onnxruntime as ort
import numpy as np

# Load models
encoder_session = ort.InferenceSession("models/encoder.int8.onnx")
decoder_session = ort.InferenceSession("models/decoder.int8.onnx")  
joiner_session = ort.InferenceSession("models/joiner.int8.onnx")

# Load vocabulary
with open("models/tokens.txt") as f:
    vocab = [line.split()[0] for line in f]

# Inference (simplified example)
# ... (add mel feature extraction)
encoder_out = encoder_session.run(None, {"audio_signal": mel_features})[0]
# ... (add decoding loop with decoder and joiner)
```

### Use with Rust
See the [Swictation STT implementation](https://github.com/robertelee78/swictation) for a complete Rust example using these models.

## Verification

These models have been verified to produce **identical outputs** to the original NeMo export:
- ✅ Correct transcriptions on test audio
- ✅ MD5 checksums match original export
- ✅ Metadata verified (`feat_dim: 80`, `vocab_size: 1024`)
- ✅ Tested with both short (6s) and long (84s) audio

### Test Results
- Short audio: 100% accuracy - "hello world testing one two three"
- Long audio: 95% accuracy - Complex technical article (375 tokens from 8410 frames)

## Export Details

**Export Script**: Available in [Swictation repository](https://github.com/robertelee78/swictation/blob/main/scripts/export_parakeet_tdt_1.1b.py)

**Export Method**:
```bash
docker run --rm -v $(pwd):/workspace -w /workspace \
  nvcr.io/nvidia/nemo:25.07 \
  bash -c "pip install onnxruntime && python3 export_parakeet_tdt_1.1b.py"
```

## Performance

- **WER**: 1.39% (LibriSpeech test-clean, from base model)
- **Speed**: 64% faster than RNNT baseline
- **Inference**: Real-time on modern CPUs, faster on GPU
- **Blank Rate**: 28.6% (excellent for continuous speech)

## Technical Details

### Model Architecture
- **Encoder**: 42-layer FastConformer (1024-dim)
- **Decoder**: 2-layer LSTM (640-dim hidden)
- **Joiner**: Feed-forward network
- **Subsampling**: 8x (8410 mel frames → 1051 encoder frames)

### Metadata
```json
{
  "vocab_size": 1024,
  "normalize_type": "per_feature",
  "pred_rnn_layers": 2,
  "pred_hidden": 640,
  "subsampling_factor": 8,
  "model_type": "EncDecRNNTBPEModel",
  "feat_dim": 80
}
```

## License

Same as the original NVIDIA Parakeet-TDT 1.1B model (CC-BY-4.0).

## Citation

```bibtex
@misc{parakeet-tdt-1.1b-onnx,
  author = {Robert Lee},
  title = {Parakeet-TDT 1.1B ONNX Export},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/jenerallee78/parakeet-tdt-1.1b-onnx}},
  note = {Verified ONNX export of NVIDIA's Parakeet-TDT 1.1B model}
}
```

Original model:
```bibtex
@misc{nvidia-parakeet-tdt,
  author = {NVIDIA},
  title = {Parakeet-TDT 1.1B},
  year = {2024},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/nvidia/parakeet-tdt-1.1b}}
}
```

## Credits

- **Base Model**: NVIDIA Research
- **ONNX Export**: [Swictation Project](https://github.com/robertelee78/swictation)
- **Verification**: Extensive testing with Rust ONNX Runtime implementation