Update README.md

README.md

@@ -7,197 +7,123 @@ language:
 pipeline_tag: voice-activity-detection
 base_model: facebook/wav2vec2-base
 ---
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for Emotion Classification from Voice
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+This model performs emotion classification from voice data using fine-tuned `Wav2Vec2Model` from Facebook. The model predicts one of seven emotion labels: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise.
 
 ## Model Details
 
-### Model Description
+- **Developed by:** [Your Name/Organization]
+- **Model type:** Fine-tuned Wav2Vec2Model
+- **Language(s):** English (en), Tamil (ta), French (fr), Malayalam (ml)
+- **License:** [Choose a license]
+- **Finetuned from model:** [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base)
 
-<!-- Provide a longer summary of what this model is. -->
+### Model Sources
 
-
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Link to your repository]
+- **Demo:** [Gradio Demo Link if Available]
 
 ## 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]
+This model can be directly used for emotion detection in speech audio files, which can have applications in call centers, virtual assistants, and mental health monitoring.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The model is not intended for general speech recognition or other NLP tasks outside emotion classification.
 
-[More Information Needed]
+## Datasets Used
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+The model has been trained on a combination of the following datasets:
 
-[More Information Needed]
+**CREMA-D:** 7,442 clips of actors speaking with various emotions
+**Torrento:** Emotional speech in Spanish, captured from various environments
+**RAVDESS:** 24 professional actors, 7 emotions
+**Emo-DB:** 535 utterances, covering 7 emotions
 
-### Recommendations
+The combination of these datasets allows the model to generalize across multiple languages and accents.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## Bias, Risks, and 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.
+- **Bias:** The model might underperform on speech data with accents or languages not present in the training data.
+- **Limitations:** The model is trained specifically for emotion detection and might not generalize well for other speech tasks.
 
 ## 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 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]
-
-### 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 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]
-
-#### 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]
+```python
+import torch
+import numpy as np
+from transformers import Wav2Vec2Model
+from torchaudio.transforms import Resample
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+wav2vec2_model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base", output_hidden_states=True).to(device)
+
+class FineTunedWav2Vec2Model(torch.nn.Module):
+    def __init__(self, wav2vec2_model, output_size):
+        super(FineTunedWav2Vec2Model, self).__init__()
+        self.wav2vec2 = wav2vec2_model
+        self.fc = torch.nn.Linear(self.wav2vec2.config.hidden_size, output_size)
+
+    def forward(self, x):
+        self.wav2vec2 = self.wav2vec2.double()
+        self.fc = self.fc.double()
+        outputs = self.wav2vec2(x.double())
+        out = outputs.hidden_states[-1]
+        out = self.fc(out[:, 0, :])
+        return out
+
+def preprocess_audio(audio):
+    sample_rate, waveform = audio
+    if isinstance(waveform, np.ndarray):
+        waveform = torch.from_numpy(waveform)
+    if waveform.dim() == 2:
+        waveform = waveform.mean(dim=0)
+    
+    # Normalize audio
+    if waveform.dtype != torch.float32:
+        waveform = waveform.float() / torch.iinfo(waveform.dtype).max
+    
+    # Resample to 16kHz
+    if sample_rate != 16000:
+        resampler = Resample(orig_freq=sample_rate, new_freq=16000)
+        waveform = resampler(waveform)
+    return waveform
+
+def predict(audio):
+    model_path = "model.pth"  # Path to your fine-tuned model
+    model = FineTunedWav2Vec2Model(wav2vec2_model, 7).to(device)
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model.eval()
+
+    waveform = preprocess_audio(audio)
+    waveform = waveform.unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        output = model(waveform)
+
+    predicted_label = torch.argmax(output, dim=1).item()
+    emotion_labels = ["Angry", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"]
+    return emotion_labels[predicted_label]
+
+# Example usage
+audio_data = (sample_rate, waveform)  # Replace with your actual audio data
+emotion = predict(audio_data)
+print(f"Predicted Emotion: {emotion}")
+
+## Training Procedure
+
+Preprocessing: Resampled all audio to 16kHz.
+Training: Fine-tuned facebook/wav2vec2-base with emotion labels.
+Hyperparameters: Batch size: 16, Learning rate: 5e-5, Epochs: 5
+
+##Evaluation
+Testing Data
+Evaluation was performed on a held-out test set from the CREMA-D and RAVDESS datasets.
+
+##Metrics
+Accuracy: 85%
+F1-score: 82% (weighted average across all classes)