Dpngtm
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wav2vec2-emotion-recognition

@@ -1,199 +1,120 @@
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 ---
+language: en
+tags:
+- audio
+- speech
+- emotion-recognition
+- wav2vec2
+datasets:
+- TESS
+- CREMA-D
+- SAVEE
+- RAVDESS
+license: mit
+metrics:
+- accuracy
+- f1
 ---
+# wav2vec2-emotion-recognition
+This model is fine-tuned on the Wav2Vec2 architecture for speech emotion recognition. It can classify speech into 8 different emotions with corresponding confidence scores.
+## Model Description
+- **Model Architecture:** Wav2Vec2 with sequence classification head
+- **Language:** English
+- **Task:** Speech Emotion Recognition
+- **Fine-tuned from:** facebook/wav2vec2-base
+- **Datasets:** Combined emotion datasets
+ - [TESS](https://www.kaggle.com/datasets/ejlok1/toronto-emotional-speech-set-tess)
+ - [CREMA-D](https://www.kaggle.com/datasets/ejlok1/cremad)
+ - [SAVEE](https://www.kaggle.com/datasets/barelydedicated/savee-database)
+ - [RAVDESS](https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio)
+## Performance Metrics
+- **Accuracy:** 79.57%
+- **F1 Score:** 79.43%
+## Supported Emotions
+- 😠 Angry
+- 😌 Calm
+- 🤢 Disgust
+- 😨 Fearful
+- 😊 Happy
+- 😐 Neutral
+- 😢 Sad
+- 😲 Surprised
 ## Training Details
+The model was trained with the following configuration:
+- **Epochs:** 15
+- **Batch Size:** 16
+- **Learning Rate:** 5e-5
+- **Optimizer:** AdamW
+- **Weight Decay:** 0.03
+- **Gradient Accumulation Steps:** 2
+- **Mixed Precision:** fp16
+For detailed training process, check out the [Fine-tuning Notebook](https://colab.research.google.com/drive/1VNhIjY7gW29d0uKGNDGN0eOp-pxr_pFL?usp=drive_link)
+## Limitations
+### Audio Requirements:
+- Sampling rate: 16kHz (will be automatically resampled)
+- Maximum duration: 1 minute
+- Clear speech with minimal background noise recommended
+### Performance Considerations:
+- Best results with clear speech audio
+- Performance may vary with different accents
+- Background noise can affect accuracy
+## Demo
+https://huggingface.co/spaces/Dpngtm/Audio-Emotion-Recognition
+## Contact
+* **GitHub**: [DGautam11](https://github.com/DGautam11)
+* **LinkedIn**: [Deepan Gautam](https://www.linkedin.com/in/deepan-gautam)
+* **Hugging Face**: [@Dpngtm](https://huggingface.co/Dpngtm)
+For issues and questions, feel free to:
+1. Open an issue on the [Model Repository](https://huggingface.co/Dpngtm/wav2vec2-emotion-recognition)
+2. Comment on the [Demo Space](https://huggingface.co/spaces/Dpngtm/Audio-Emotion-Recognition)
+## Usage
+```python
+from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2Processor
+import torch
+import torchaudio
+# Load model and processor
+model = Wav2Vec2ForSequenceClassification.from_pretrained("Dpngtm/wav2vec2-emotion-recognition")
+processor = Wav2Vec2Processor.from_pretrained("Dpngtm/wav2vec2-emotion-recognition")
+# Load and preprocess audio
+speech_array, sampling_rate = torchaudio.load("path_to_audio.wav")
+if sampling_rate != 16000:
+   resampler = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)
+   speech_array = resampler(speech_array)
+# Convert to mono if stereo
+if speech_array.shape[0] > 1:
+   speech_array = torch.mean(speech_array, dim=0, keepdim=True)
+speech_array = speech_array.squeeze().numpy()
+# Process through model
+inputs = processor(speech_array, sampling_rate=16000, return_tensors="pt", padding=True)
+with torch.no_grad():
+   outputs = model(**inputs)
+   predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+# Get predicted emotion
+emotion_labels = ["angry", "calm", "disgust", "fearful", "happy", "neutral", "sad", "surprised"]
+predicted_emotion = emotion_labels[predictions.argmax().item()]