---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

This is a quantized model of the original version mohammed/whisper-small-arabic-cv-11

- **Developed by:** Mohammed Bakheet
- **Funded by [optional]:** Kalam Technology
- **Language(s) (NLP):** Arabic, English

## Uses

This a quantized model that reads arabic voice and transcribes/translate it into english

### Direct Use

First, install the following packages using the following commands:

pip install -U optimum[exporters,onnxruntime] transformers
pip install huggingface_hub

```python

# uncomment the following installation if you are using a notebook:
#!pip install -U optimum[exporters,onnxruntime] transformers
#!pip install huggingface_hub

# import the required packages
from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from transformers import WhisperTokenizerFast, WhisperFeatureExtractor, pipeline

# set model name/id
model_name = 'mohammed/quantized-whisper-small' # folder name
model = ORTModelForSpeechSeq2Seq.from_pretrained(model_name, export=False)
tokenizer = WhisperTokenizerFast.from_pretrained(model_name)
feature_extractor = WhisperFeatureExtractor.from_pretrained(model_name)
forced_decoder_ids = tokenizer.get_decoder_prompt_ids(language="ar", task="transcribe")

pipe = pipeline('automatic-speech-recognition',
                model=model,
                tokenizer=tokenizer,
                feature_extractor=feature_extractor,
                model_kwargs={"forced_decoder_ids": forced_decoder_ids})

# the file to be transcribed
pipe('Recording.mp3')

```

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

The model does a direct translation of Arabic speech, and doesn't do a direct transcription, we are still working on that.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

```python

First, install the following packages using the following commands:

pip install -U optimum[exporters,onnxruntime] transformers
pip install huggingface_hub

from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from transformers import WhisperTokenizerFast, WhisperFeatureExtractor, pipeline

model_name = 'mohammed/quantized-whisper-small' # folder name
model = ORTModelForSpeechSeq2Seq.from_pretrained(model_name, export=False)
tokenizer = WhisperTokenizerFast.from_pretrained(model_name)
feature_extractor = WhisperFeatureExtractor.from_pretrained(model_name)
forced_decoder_ids = tokenizer.get_decoder_prompt_ids(language="ar", task="transcribe")

pipe = pipeline('automatic-speech-recognition',
                model=model,
                tokenizer=tokenizer,
                feature_extractor=feature_extractor,
                model_kwargs={"forced_decoder_ids": forced_decoder_ids})

# the file to be transcribed
pipe('Recording.mp3')

```

### Training Data

Please refer to the original model at "mohammed/whisper-small-arabic-cv-11"

### Training Procedure

Please refer to the original model at "mohammed/whisper-small-arabic-cv-11"

#### Preprocessing [optional]

Please refer to the original model at "mohammed/whisper-small-arabic-cv-11"


#### Training Hyperparameters

- **Training regime:** Please refer to the original model at "mohammed/whisper-small-arabic-cv-11"