---
tags:
- whisper
- speech
- audio
- litert
- tflite
- edge
- on-device
license: mit
base_model: openai/whisper-tiny
pipeline_tag: automatic-speech-recognition
---

# whisper-tiny - LiteRT

This is a [LiteRT](https://ai.google.dev/edge/litert) (formerly TensorFlow Lite) conversion of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) for efficient on-device inference.

## Model Details

| Property | Value |
|----------|-------|
| **Original Model** | [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) |
| **Format** | LiteRT (.tflite) |
| **File Size** | 31.4 MB |
| **Task** | Speech Recognition (Encoder Only) |
| **Max Sequence Length** | 3000 |
| **Output Dimension** | 384 |
| **Pooling Mode** | N/A (Encoder output) |

## Performance

Benchmarked on AMD CPU (WSL2):

| Metric | Value |
|--------|-------|
| **Inference Latency** | 144.7 ms |
| **Throughput** | 6.9/sec |
| **Cosine Similarity vs Original** | 1.0000 ✅ |

## Quick Start

```python
import numpy as np
from ai_edge_litert.interpreter import Interpreter
from transformers import WhisperProcessor
import librosa

# Load model
interpreter = Interpreter(model_path="openai_whisper-tiny_encoder.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Load processor
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")

def encode_audio(audio_path: str) -> np.ndarray:
    """Extract encoder features from audio file."""
    audio, sr = librosa.load(audio_path, sr=16000)
    input_features = processor(audio, sampling_rate=16000, return_tensors="np").input_features

    interpreter.set_tensor(input_details[0]["index"], input_features.astype(np.float32))
    interpreter.invoke()

    return interpreter.get_tensor(output_details[0]["index"])

# Example
# features = encode_audio("audio.wav")
```

**Note**: This is the encoder-only model. For full ASR, you need the decoder as well.

## Files

- `openai_whisper-tiny_encoder.tflite` - The LiteRT model file

## Conversion Details

- **Conversion Tool**: [ai-edge-torch](https://github.com/google-ai-edge/ai-edge-torch)
- **Conversion Date**: 2026-01-12
- **Source Framework**: PyTorch → LiteRT
- **Validation**: Cosine similarity 1.0000 vs original

## Intended Use

- **Mobile Applications**: On-device semantic search, RAG systems
- **Edge Devices**: IoT, embedded systems, Raspberry Pi
- **Offline Processing**: Privacy-preserving inference
- **Low-latency Applications**: Real-time processing

## Limitations

- Fixed sequence length (3000 tokens)
- CPU inference (GPU delegate requires setup)
- Tokenizer loaded separately from original model
- Float32 precision

## License

This model inherits the license from the original:
- **License**: MIT ([source](https://huggingface.co/openai/whisper-tiny))

## Citation

```bibtex
@misc{radford2022whisper,
    title={Robust Speech Recognition via Large-Scale Weak Supervision},
    author={Alec Radford and Jong Wook Kim and others},
    year={2022},
    eprint={2212.04356},
    archivePrefix={arXiv},
}
```

## Acknowledgments

- Original model by [openai](https://huggingface.co/openai)
- Conversion using [ai-edge-torch](https://github.com/google-ai-edge/ai-edge-torch)

---

*Converted by [Bombek1](https://huggingface.co/Bombek1)*