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---
license: apache-2.0
datasets:
- librispeech_asr
metrics:
- wer
pipeline_tag: automatic-speech-recognition
tags:
- automatic-speech-recognition
- ONNX
- PostTrainingStatic
- Intel® Neural Compressor
- neural-compressor
library_name: transformers
---
## INT4 Whisper tiny ONNX Model
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning.
This INT4 ONNX model is generated by [neural-compressor](https://github.com/intel/neural-compressor).
| Model Detail | Description |
| ----------- | ----------- |
| Model Authors - Company | Intel |
| Date | October 8, 2023 |
| Version | 1 |
| Type | Speech Recognition |
| Paper or Other Resources | - |
| License | Apache 2.0 |
| Questions or Comments | [Community Tab](https://huggingface.co/Intel/whisper-tiny-onnx-int4/discussions)|
| Intended Use | Description |
| ----------- | ----------- |
| Primary intended uses | You can use the raw model for automatic speech recognition inference |
| Primary intended users | Anyone doing automatic speech recognition inference |
| Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.|
### Export to ONNX Model
The FP32 model is exported with openai/whisper-tiny:
```shell
optimum-cli export onnx --model openai/whisper-tiny whisper-tiny-with-past/ --task automatic-speech-recognition-with-past --opset 13
```
### Install ONNX Runtime
Install `onnxruntime>=1.16.0` to support [`MatMulFpQ4`](https://github.com/microsoft/onnxruntime/blob/v1.16.0/docs/ContribOperators.md#com.microsoft.MatMulFpQ4) operator.
### Run Quantization
Run INT4 weight-only quantization with [Intel® Neural Compressor](https://github.com/intel/neural-compressor/tree/master).
The weight-only quantization cofiguration is as below:
| dtype | group_size | scheme | algorithm |
| :----- | :---------- | :------ | :--------- |
| INT4 | 32 | asym | RTN |
We provide the key code below. For the complete script, please refer to [whisper example](https://github.com/intel/intel-extension-for-transformers/tree/main/examples/huggingface/onnxruntime/speech-recognition/quantization).
```python
from neural_compressor import quantization, PostTrainingQuantConfig
from neural_compressor.utils.constant import FP32
model_list = ['encoder_model.onnx', 'decoder_model.onnx', 'decoder_with_past_model.onnx']
for model in model_list:
config = PostTrainingQuantConfig(
approach="weight_only",
calibration_sampling_size=[8],
op_type_dict={".*": {"weight": {"bits": 4,
"algorithm": ["RTN"],
"scheme": ["asym"],
"group_size": 32}}},
op_name_dict={'/proj_out/MatMul': FP32},) # fallback last matmul in decoder to FP32
q_model = quantization.fit(
os.path.join("/path/to/whisper-tiny", model), # FP32 model path
config,
calib_dataloader=dataloader)
q_model.save(os.path.join("/path/to/whisper-tiny-onnx-int4", model)) # INT4 model path
```
### Evaluation
**Operator Statistics**
Below shows the operator statistics in the INT4 ONNX model:
|Model| Op Type | Total | INT4 weight | FP32 weight |
|:-------:|:-------:|:-------:|:-------:|:-------:|
|encoder_model| MatMul | 32 | 24 | 8 |
|decoder_model| MatMul | 57 | 40 | 17 |
|decoder_with_past_model| MatMul | 49 | 32 | 17 |
**Evaluation of wer**
Evaluate the model on `librispeech_asr` dataset with below code:
```python
import os
from evaluate import load
from datasets import load_dataset
from transformers import WhisperForConditionalGeneration, WhisperProcessor, AutoConfig
model_name = 'openai/whisper-tiny'
model_path = 'whisper-tiny-onnx-int4'
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
wer = load("wer")
librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test")
from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from transformers import PretrainedConfig
model_config = PretrainedConfig.from_pretrained(model_name)
predictions = []
references = []
sessions = ORTModelForSpeechSeq2Seq.load_model(
os.path.join(model_path, 'encoder_model.onnx'),
os.path.join(model_path, 'decoder_model.onnx'),
os.path.join(model_path, 'decoder_with_past_model.onnx'))
model = ORTModelForSpeechSeq2Seq(sessions[0], sessions[1], model_config, model_path, sessions[2])
for idx, batch in enumerate(librispeech_test_clean):
audio = batch["audio"]
input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
reference = processor.tokenizer._normalize(batch['text'])
references.append(reference)
predicted_ids = model.generate(input_features)[0]
transcription = processor.decode(predicted_ids)
prediction = processor.tokenizer._normalize(transcription)
predictions.append(prediction)
wer_result = wer.compute(references=references, predictions=predictions)
print(f"Result wer: {wer_result * 100}")
```
## Metrics (Model Performance):
| Model | Model Size (GB) | wer |
|---|:---:|:---:|
| FP32 |0.36|7.56|
| INT8 |0.32|9.94|
|