Update README.md

README.md

@@ -1,199 +1,180 @@
+
 ---
 library_name: transformers
-tags: []
+datasets:
+- kresnik/zeroth_korean
+language:
+- ko
+metrics:
+- cer
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for wav2vec2-base-korean
 
 ## 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's wav2vec2-base model, adapted for Korean language recognition using the Zeroth-Korean dataset. The model has been trained to transcribe Korean speech into text, specifically utilizing the unique jamo characters of the Korean language.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [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]
+- **Developed by:** [jeonghyeon Park, Jaeyoung Kim]
+- **Model type:** Speech-to-Text
+- **Language(s) (NLP):** Korean
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** facebook/wav2vec2-base
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [github.com/KkonJJ/wav2vec2-base-korean]
 
 ## 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]
+The model can be directly used for transcribing Korean speech to text without additional fine-tuning. It is particularly useful for applications requiring accurate Korean language recognition such as voice assistants, transcription services, and language learning tools.
 
 ### 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]
+This model can be integrated into larger systems that require speech recognition capabilities, such as automated customer service, voice-controlled applications, and more.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is not suitable for recognizing languages other than Korean or for tasks that require understanding context beyond the transcription of spoken Korean.
 
 ## 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.
+Users should be aware of the limitations of the model, including potential biases in the training data which may affect the accuracy for certain dialects or speakers. It is recommended to evaluate the model's performance on a representative sample of the intended application domain.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+To get started with the model, use the code below:
+
+```python
+!pip install transformers[torch] accelerate -U
+!pip install datasets torchaudio -U
+!pip install jiwer jamo
+!pip install tensorboard
+
+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import torchaudio
+from jamo import h2j, j2hcj
+
+model_name = "Kkonjeong/wav2vec2-base-korean"
+model = Wav2Vec2ForCTC.from_pretrained(model_name)
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+
+model.to("cuda")
+model.eval()
+
+def load_and_preprocess_audio(file_path):
+    speech_array, sampling_rate = torchaudio.load(file_path)
+    if sampling_rate != 16000:
+        resampler = torchaudio.transforms.Resample(sampling_rate, 16000)
+        speech_array = resampler(speech_array)
+    input_values = processor(speech_array.squeeze().numpy(), sampling_rate=16000).input_values[0]
+    return input_values
+
+def predict(file_path):
+    input_values = load_and_preprocess_audio(file_path)
+    input_values = torch.tensor(input_values).unsqueeze(0).to("cuda")
+    with torch.no_grad():
+        logits = model(input_values).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)[0]
+    return transcription
+
+audio_file_path = "your_audio_file.wav"
+transcription = predict(audio_file_path)
+print("Transcription:", transcription)
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- 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. -->
-
-[More Information Needed]
+The model was trained using the Zeroth-Korean dataset, a collection of Korean speech data. This dataset includes audio recordings and their corresponding transcriptions.
 
 ### 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]
+#### Preprocessing
 
+Special characters were removed from the transcriptions, and the text was converted to jamo characters to better align with the Korean language's phonetic structure.
 
 #### 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]
+- **Training regime:** Mixed precision (fp16)
+- **Batch size:** 32
+- **Learning rate:** 1e-4
+- **Number of epochs:** 10
 
 ## 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]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+The model was evaluated using the test split of the Zeroth-Korean dataset.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+The primary evaluation metric used was the Character Error Rate (CER), which measures the percentage of characters that are incorrect in the transcription compared to the reference text.
 
 ### Results
 
-[More Information Needed]
+- **Final CER:** 0.073
 
 #### Summary
 
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The model achieved a CER of 7.3%, indicating good performance on the Zeroth-Korean dataset.
 
 ## 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).
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
 
-- **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:** NVIDIA A100
+- **Hours used:** Approximately  8hours
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model architecture is based on wav2vec2.0, designed to convert audio input into text output by modeling the phonetic structure of speech.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- **GPUs:** NVIDIA A100
 
 #### Software
 
-[More Information Needed]
+- **Framework:** PyTorch
+- **Libraries:** Transformers, Datasets, Torchaudio, Jiwer, Jamo
 
-## 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]
+```bibtex
+@misc{your_bibtex_key,
+  author = {Your Name},
+  title = {wav2vec2-base-korean},
+  year = {2024},
+  publisher = {Hugging Face},
+  note = {https://huggingface.co/Kkonjeong/wav2vec2-base-korean}
+}
+```
 
 **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]
+Your Name. (2024). wav2vec2-base-korean. Hugging Face. https://huggingface.co/Kkonjeong/wav2vec2-base-korean
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+[Your Name]
 
 ## Model Card Contact
 
-[More Information Needed]
+For more information, contact [shshjhjh4455@gmail.com, kbs00717@gmail.com].