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--- |
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license: apache-2.0 |
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datasets: |
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- librispeech_asr |
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metrics: |
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- wer |
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pipeline_tag: automatic-speech-recognition |
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tags: |
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- automatic-speech-recognition |
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- int8 |
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- ONNX |
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- PostTrainingDynamic |
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- Intel® Neural Compressor |
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- neural-compressor |
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library_name: transformers |
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--- |
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## Model Details: INT8 Whisper large |
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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. |
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This int8 ONNX model is generated by [neural-compressor](https://github.com/intel/neural-compressor) and the fp32 model can be exported with below command: |
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```shell |
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optimum-cli export onnx --model openai/whisper-large whisper-large-with-past/ --task automatic-speech-recognition-with-past --opset 13 |
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``` |
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| Model Detail | Description | |
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| ----------- | ----------- | |
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| Model Authors - Company | Intel | |
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| Date | May 15, 2022 | |
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| Version | 1 | |
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| Type | Speech Recognition | |
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| Paper or Other Resources | - | |
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| License | Apache 2.0 | |
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| Questions or Comments | [Community Tab](https://huggingface.co/Intel/whisper-large-int8-dynamic/discussions)| |
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| Intended Use | Description | |
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| ----------- | ----------- | |
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| Primary intended uses | You can use the raw model for automatic speech recognition inference | |
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| Primary intended users | Anyone doing automatic speech recognition inference | |
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| 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.| |
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### How to use |
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Download the model by cloning the repository: |
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```shell |
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git clone https://huggingface.co/Intel/whisper-large-int8-dynamic |
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``` |
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Evaluate the model with below code: |
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```python |
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import os |
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from evaluate import load |
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from datasets import load_dataset |
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from transformers import WhisperForConditionalGeneration, WhisperProcessor, AutoConfig |
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model_name = 'openai/whisper-large' |
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model_path = 'whisper-large-int8-dynamic' |
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processor = WhisperProcessor.from_pretrained(model_name) |
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model = WhisperForConditionalGeneration.from_pretrained(model_name) |
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config = AutoConfig.from_pretrained(model_name) |
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wer = load("wer") |
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librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test") |
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from optimum.onnxruntime import ORTModelForSpeechSeq2Seq |
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from transformers import PretrainedConfig |
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model_config = PretrainedConfig.from_pretrained(model_name) |
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predictions = [] |
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references = [] |
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sessions = ORTModelForSpeechSeq2Seq.load_model( |
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os.path.join(model_path, 'encoder_model.onnx'), |
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os.path.join(model_path, 'decoder_model.onnx'), |
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os.path.join(model_path, 'decoder_with_past_model.onnx')) |
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model = ORTModelForSpeechSeq2Seq(sessions[0], sessions[1], model_config, model_path, sessions[2]) |
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for idx, batch in enumerate(librispeech_test_clean): |
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audio = batch["audio"] |
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input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features |
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reference = processor.tokenizer._normalize(batch['text']) |
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references.append(reference) |
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predicted_ids = model.generate(input_features)[0] |
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transcription = processor.decode(predicted_ids) |
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prediction = processor.tokenizer._normalize(transcription) |
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predictions.append(prediction) |
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wer_result = wer.compute(references=references, predictions=predictions) |
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print(f"Result wer: {wer_result * 100}") |
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accuracy = 1 - wer_result |
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print("Accuracy: %.5f" % accuracy) |
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``` |
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## Metrics (Model Performance): |
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| Model | Model Size (GB) | wer | |
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|---|:---:|:---:| |
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| FP32 |9.4|3.04| |
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| INT8 |2.4|2.89| |
