Update README.md

README.md

@@ -3,197 +3,143 @@ library_name: transformers
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-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for wav2vec2-large-xlsr-persian-fine-tuned
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model is a fine-tuned version of `facebook/wav2vec2-large-xlsr-53` on Persian language data from the Mozilla Common Voice Dataset. The model is fine-tuned for automatic speech recognition (ASR) tasks.
 
 - **Developed by:** Alireza Dastmalchi Saei
-- **Funded by [optional]:** -
-- **Shared by [optional]:** -
+- **Funded by:** -
+- **Shared by:** -
 - **Model type:** wav2vec2
 - **Language(s) (NLP):** Persian
 - **License:** MIT
-- **Finetuned from model [optional]:** wav2vec2-large-xlsr-53
-
-### Model Sources [optional]
+- **Finetuned from model:** wav2vec2-large-xlsr-53
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Model Repository](https://huggingface.co/AlirezaSaei/wav2vec2-large-xlsr-persian-fine-tuned)
+- **Paper:** -
+- **Demo:** -
 
 ## 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. -->
+This model can be used directly for transcribing Persian speech to text but it needs to be further fine-tuned with data.
 
-[More Information Needed]
+### Downstream Use
 
-### 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]
+The model can be fine-tuned further for specific ASR tasks or integrated into larger speech-processing pipelines.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model is not suitable for languages other than Persian and may not perform well on noisy audio or speech with heavy accents not represented in the training data.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model is trained on a dataset that may not cover all variations of the Persian language, leading to potential biases in recognizing less represented dialects or accents.
 
 ### 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.
+Users should be aware of the biases, risks, and limitations. Further fine-tuning on diverse datasets is recommended to mitigate these biases.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import torch
+import torchaudio
 
-[More Information Needed]
+# Load processor and model
+processor = Wav2Vec2Processor.from_pretrained("AlirezaSaei/wav2vec2-large-xlsr-persian-fine-tuned")
+model = Wav2Vec2ForCTC.from_pretrained("AlirezaSaei/wav2vec2-large-xlsr-persian-fine-tuned")
 
-## Training Details
+# Load audio file
+audio_input, _ = torchaudio.load("path_to_audio.wav")
 
-### Training Data
+# Preprocess and predict
+inputs = processor(audio_input, sampling_rate=16000, return_tensors="pt", padding=True)
+logits = model(**inputs).logits
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = processor.batch_decode(predicted_ids)
 
-<!-- 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. -->
+print("Transcription:", transcription)
 
-[More Information Needed]
 
-### Training Procedure
+## Training Details
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### Training Data
 
-#### Preprocessing [optional]
+The model is fine-tuned on the Mozilla Common Voice Dataset. The training data includes Persian speech samples, with lengths filtered between 4 and 6 seconds for training and up to 15 seconds for testing.
 
-[More Information Needed]
+### Training Procedure
 
+The audio is resampled from 48000 Hz to 16000 Hz. The tokenizer, feature extractor, and processor are defined using the `Wav2Vec2CTCTokenizer`, `Wav2Vec2FeatureExtractor`, and `Wav2Vec2Processor` classes.
 
 #### 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 -->
+- **Training regime:** fp16 mixed precision
+- **Batch Size:** 12
+- **Num Epochs:** 5
+- **Learning Rate:** 1e-4
+- **Gradient Accumulation Steps:** 2
+- **Warmup Steps:** 1000
 
-#### Speeds, Sizes, Times [optional]
+### Speeds, Sizes, Times
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Training Files:** 2217
+- **Testing Files:** 5212
+- **Training Time (minutes):** 19.67
+- **Total Parameters:** 315,479,720
+- **Trainable Parameters:** 311,269,544
+- **WER:** 1.0
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+The model is evaluated on a subset of the Mozilla Common Voice Dataset.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Evaluation is disaggregated by different lengths of audio samples.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Word Error Rate (WER) is used as the evaluation metric. It measures the percentage of words that are incorrectly predicted.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The model achieves a WER of 1.0 on the test data.
 
 ## 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]
+- **Hardware Type:** Colab T4 GPU
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model uses the Wav2Vec2 architecture, which is designed for automatic speech recognition.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+Colab T4 GPU
 
 #### 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]
+Python Notebook (.ipynb)
 
 ## Model Card Contact
 
-[More Information Needed]
+For further information, contact me.