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README.md

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+---
+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 Intel 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)*