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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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-
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  ### Model Description
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  <!-- Provide a longer summary of what this model is. -->
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  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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-
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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-
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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  ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
 
 
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- [More Information Needed]
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  ## Citation [optional]
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  <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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  **BibTeX:**
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- [More Information Needed]
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- **APA:**
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  [More Information Needed]
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  <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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  ## More Information [optional]
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  ## Model Card Contact
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- [More Information Needed]
 
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  ---
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  library_name: transformers
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+ tags:
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+ - audio
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+ - automatic-speech-recognition
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+ license: mit
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+ language:
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+ - ar
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  ---
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+ # ArTST (ASR task)
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+ ArTST model finetuned for automatic speech recognition (speech-to-text) on MGB2.
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  ### Model Description
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  <!-- Provide a longer summary of what this model is. -->
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  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+ - **Developed by:** Speech Lab, MBZUAI
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+ - **Model type:** SpeechT5
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+ - **Language:** Arabic
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+ - **Finetuned from:** (ArTST pretrained)[https://github.com/mbzuai-nlp/ArTST]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## How to Get Started with the Model
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+ ```python
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+ import soundfile as sf
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+ from transformers import (
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+ SpeechT5Config,
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+ SpeechT5FeatureExtractor,
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+ SpeechT5ForSpeechToText,
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+ SpeechT5Processor,
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+ SpeechT5Tokenizer,
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+ )
 
 
 
 
 
 
 
 
 
 
 
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+ from custom_tokenizer import CustomTextTokenizer
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+ device = "cuda" if torch.cuda.is_available() else "cpu"
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+ tokenizer = SpeechT5Tokenizer.from_pretrained("mbzuai/artst_asr")
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+ processor = SpeechT5Processor.from_pretrained("mbzuai/artst_asr" , tokenizer=tokenizer)
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+ model = SpeechT5ForSpeechToText.from_pretrained("mbzuai/artst_asr").to(device)
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+ audio, sr = sf.read("audio.wav")
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+ inputs = processor(audio=audio, sampling_rate=sr, return_tensors="pt")
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+ predicted_ids = model.generate(**inputs.to(device), max_length=150)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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+ print(transcription[0])
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+ ```
 
 
 
 
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+ ### Model Sources [optional]
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+ - **Repository:** [github](https://github.com/mbzuai-nlp/ArTST)
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+ - **Paper :** [ArabicNLP2023](https://aclanthology.org/2023.arabicnlp-1.5/)
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+ <!-- - **Demo [optional]:** [More Information Needed] -->
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Citation [optional]
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  <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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  **BibTeX:**
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+ ```
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+ @inproceedings{toyin-etal-2023-artst,
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+ title = "{A}r{TST}: {A}rabic Text and Speech Transformer",
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+ author = "Toyin, Hawau and
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+ Djanibekov, Amirbek and
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+ Kulkarni, Ajinkya and
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+ Aldarmaki, Hanan",
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+ booktitle = "Proceedings of ArabicNLP 2023",
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+ month = dec,
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+ year = "2023",
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+ address = "Singapore (Hybrid)",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://aclanthology.org/2023.arabicnlp-1.5",
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+ doi = "10.18653/v1/2023.arabicnlp-1.5",
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+ pages = "41--51",
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+ }
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+ ```
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+ <!-- **APA:**
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  [More Information Needed]
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  <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+ <!-- [More Information Needed]
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  ## More Information [optional]
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  ## Model Card Contact
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+ [More Information Needed] -->