artificialguybr
/

whisper-small-pt-cv13

@@ -12,202 +12,78 @@ model-index:
       split: test
     metrics:
     - type: wer
-      value: 10.33
       name: WER
 ---
 # Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### 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.
-[More Information Needed]
 ## Training Details
-### Training Data
-<!-- This should link to a Data 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. -->
-[More Information Needed]
 ### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
 #### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Data Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

       split: test
     metrics:
     - type: wer
+      value: 10.30
       name: WER
 ---
 # Model Card for Model ID
+This is a finetune for Whisper Small. A finetune to achieve better results on Whisper Small for Portuguese.
+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.
+Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356)
+by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper).
 ## Model Details
+Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model.
+It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision.
+The models were trained on either English-only data or multilingual data. The English-only models were trained
+on the task of speech recognition. The multilingual models were trained on both speech recognition and speech
+translation. For speech recognition, the model predicts transcriptions in the *same* language as the audio.
+For speech translation, the model predicts transcriptions to a *different* language to the audio.
+Whisper checkpoints come in five configurations of varying model sizes.
+The smallest four are trained on either English-only or multilingual data.
+The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints
+are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper).
+- **Developed by:** [ArtificialGuyBr](https://twitter.com/@artificialguybr)
+- **Shared by:** [ArtificialGuyBr](https://twitter.com/@artificialguybr)
 ## Uses
+This repository contains a fine-tuned version of the Whisper ASR (Automatic Speech Recognition) system developed by OpenAI. The model has been specifically fine-tuned to improve performance in portuguese language.
 ### Out-of-Scope Use
+While this model is powerful and versatile, it's important to understand its limitations and inappropriate uses:
+1. **Misuse and Malicious Use**: This model should not be used for any illegal activities, including but not limited to eavesdropping, illegal surveillance, or any other form of privacy invasion. It's also not intended for the creation or spread of misinformation, hate speech, or harmful content.
+2. **Non-Portuguese Languages**: While this model has been fine-tuned for Portuguese, it may not perform well with other languages. It's not recommended for transcribing multilingual content where languages other than Portuguese are spoken.
+3. **Low-Quality Audio**: The model's performance can be significantly affected by the quality of the input audio. It may not work well with low-quality audio, background noise, or speakers who are far away from the microphone.
 ## Training Details
 ### Training Procedure
+Trained using the code from HF Whisper Event.
 #### Training Hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 64
+- eval_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 5000
 ## Evaluation
+Wer on CV13.0: 10.3
+- **Hardware Type:** 1XA100 80GB
+- **Hours used:** 8 Hours.
+- **Cloud Provider:** [Redmond.ai](https://redmond.ai)